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1. Prevalence and outcome of primary postpartum haemorrhage in a tertiary care hospital: a hospital record based study

Author: S Ramani and B Vijaya
Subjects: postpartum hemorrhage, maternal mortality, uterine atony, risk factor, hysterectomy, Gynecology and obstetrics, RG1-991
Abstract: Background: Postpartum haemorrhage (PPH) is one of the well known reasons for maternal morbidity and mortality worldwide. Primary PPH occurs within 24 hours postpartum and secondary PPH between 24 hours and six weeks postpartum. The most common obstetric haemorrhage is primary PPH. Objectives: This study was aimed to estimate the prevalence and outcome of PPH in a tertiary care hospital. Methodology: The present study was a retrospective study that included 225 mothers diagnosed with PPH. Data was collected from medical records available in the hospital. Prevalence and outcome of postpartum hemorrhage was the primary outcome, and the obstetric score, mode of delivery, and etiology were considered as secondary outcomes. Results: The prevalence of postpartum hemorrhage was 0.73%. The etiologies were found to be atonic uterus (79.11%), traumatic (19.56%), and tissue/retained bits (1.33%), and the risk factors were induced labor 16%, followed by prolonged labor 11.1%, preeclampsia 4.4%. 91.11% of patients were treated medically, and 8.88% were treated surgically. The percentage of patients who were multigravida and underwent surgical management was 65%. 221 (98.22%) participants survived, and four (1.78%) participants died. The cause of death was shock (75.00%) and cardiac arrest (25%). Conclusion: In this study, a prevalence of 0.73% was noted for primary PPH. Uterine atony was the most common etiology causing PPH. The most frequently encountered risk factors were induced labor. The majority of the patients underwent conservative management. Shock was the common cause of death due to PPH.
Published: 2023
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2. National statutory reporting: not even ticking the boxes? The quality of ‘Learning from Deaths’ reporting in quality accounts within the NHS in England 2017–2020

Author: Cecilia Vindrola-Padros, S Ramani Moonesinghe, Zoe Brummell, Dorit Braun, and Zainab Hussein
Subjects: Medicine (General), R5-920
Abstract: Introduction Regulation through statutory reporting is used in healthcare internationally to improve accountability, quality of care and patient safety. Since 2017, within the National Health Service (NHS) in England, NHS Secondary Care Trusts (NSCTs) are legally required to report annually both quantitative and qualitative information related to patient deaths within their care within their publicly available Quality Accounts as part of a countrywide patient safety programme: The Learning from Deaths (LfDs) programme.Method All LfDs reports published between 2017 (programme inception) and 2020 were reviewed and evaluated through a critical realist lens, quantitatively reported using descriptive statistics and qualitatively using reflexive thematic analysis.Results In 2017/2018, 44% of NSCTs reported all six statutory elements of the LfDs reporting regulations, in 2019/2020 35% of NSCTs were reporting this information. A small number of NSCTs did not report any parts of the LfDs regulatory requirements between 2017 and 2020. Multiple qualitative themes arose from this study suggesting problematic engagement with the LfDs programme, erroneous reporting accuracy and errors in written communication.Conclusions The LfDs programme has, to some extent, reduced variation and improved consistency to the way that NSCTs identify, report and investigate deaths. However, 3 years into the LfDs programme, the majority of NSCTs are not reporting as required by law. This makes the validity of National statutory reporting in Quality Accounts within the NHS in England questionable as a regulatory process.
Published: 2023
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3. Is anybody ‘Learning’ from deaths? Sequential content and reflexive thematic analysis of national statutory reporting within the NHS in England 2017–2020

Author: Cecilia Vindrola-Padros, S Ramani Moonesinghe, Zoe Brummell, Dorit Braun, and Zainab Hussein
Subjects: Medicine (General), R5-920
Abstract: Introduction The imperative to learn when a patient dies due to problems in care is absolute. In 2017, the Learning from Deaths (LfDs) framework, a countrywide patient safety programme, was launched in the National Health Service (NHS) in England. NHS Secondary Care Trusts (NSCTs) are legally required to publish quantitative and qualitative information relating to deaths due to problems in care within their organisation, including any learning derived from these deaths.Method All LfDs report from 2017 to 2020 were reviewed and evaluated, quantitatively and qualitatively using sequential content and reflexive thematic analysis, through a critical realist lens to understand what we can learn from LfDs reporting and the mechanisms enabling or preventing engagement with the LfDs programme.Results The majority of NSCTs have identified learning, actions and, to a lesser degree, assessed the impact of these actions. The most frequent learning relates to missed/delayed/uncoordinated care and communication/cultural issues. System issues and lack of resources feature infrequently. There is significant variation among NSCTs as to what ‘learning’ in this context actually means and a lack of oversight combining patient safety initiatives.Discussion Engagement of NSCTs with the LfDs programme varies significantly. Learning as a result of the LfDs programme is occurring. The ability, significance or value of this learning in preventing future patient deaths remains unclear. Consensus about what constitutes effective learning with regard to patient safety needs to be defined and agreed on.
Published: 2023
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4. Understanding recruitment to a perioperative randomised controlled trial: protocol for a mixed-methods substudy nested within a feasibility trial of octreotide infusion during liver transplantation

Author: Duncan Wagstaff, S Ramani Moonesinghe, Jez Fabes, Michael Spiro, Edgar Brodkin, Ee-Neng Loh, and Vivienne Hannon
Subjects: Medicine
Abstract: Introduction Recruitment to perioperative randomised controlled trials is known to be challenging. Qualitative methods offer insight into barriers and enablers to participation. This is a substudy within a feasibility randomised controlled trial of octreotide infusion during liver transplantation at two National Health Service hospitals, which will evaluate patient and staff experiences of trial processes. By sharing formative understanding from these methods with the trials team we aim to improve staff–patient interactions and hence recruitment rates.Methods and analysis This prospective mixed-methods study will comprise two workstreams. First, after consent to the randomised controlled trial is sought, all patients will be invited to complete a questionnaire to explore their perceptions of the information given to them and motivating factors that influenced their decision to consent or not. Questionnaires will be analysed using descriptive statistics and framework analysis.If the recruitment:approach ratio drops below a predetermined ratio or if there are any specific recruitment concerns from the trials team, a second workstream involving mixed-methods fieldwork will be implemented. This will involve audiorecording of recruitment consultations and a follow-up semistructured interview to explore patients’ perception of their decision-making regarding recruitment. Semistructured interviews will also be conducted with the recruitment team to establish their views about the trial, barriers to recruitment and ways to overcome them. Recruitment consultations will be analysed using Q-QAT methodology and interviews will be analysed using framework analysis. Findings from both workstreams will be formatively fed back to the trials team to enable iterative improvement to recruitment processes.Ethics and dissemination Approval has been granted by Greater Manchester West Research Ethics Committee (ref 20/NW/0071), the Health Research Authority and the local Research and Development offices. A manuscript detailing the summative findings will be submitted to peer-reviewed journals.Trial registration number NCT04941911.
Published: 2022
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5. Implementation of corticosteroids in treatment of COVID-19 in the ISARIC WHO Clinical Characterisation Protocol UK: prospective, cohort study

Author: Fiina Närhi, BMedSci, S Ramani Moonesinghe, ProfMD[Res], Susan D Shenkin, PhD, Thomas M Drake, MBChB, Rachel H Mulholland, MSci, Cara Donegan, BSc, Jake Dunning, PhD, Cameron J Fairfield, MBChB, Michelle Girvan, BSc, Hayley E Hardwick, Antonia Ho, PhD, Gary Leeming, BA, Jonathan S Nguyen-Van-Tam, ProfPhD, Riinu Pius, PhD, Clark D Russell, MBChB, Catherine A Shaw, PhD, Rebecca G Spencer, LLM, Lance Turtle, PhD, Peter J M Openshaw, ProfPhD, J Kenneth Baillie, ProfPhD, Ewen M Harrison, ProfPhD, Malcolm G Semple, ProfPhD, Annemarie B Docherty, PhD, J Kenneth Baillie, Malcolm G Semple, Peter JM Openshaw, Gail Carson, Beatrice Alex, Petros Andrikopoulos, Benjamin Bach, Wendy S Barclay, Debby Bogaert, Meera Chand, Kanta Chechi, Graham S Cooke, Ana da Silva Filipe, Thushan de Silva, Annemarie B Docherty, Gon¸alo dos Santos Correia, Marc-Emmanuel Dumas, Jake Dunning, Tom Fletcher, Christopher A Green, William Greenhalf, Julian Griffin, Rishi K Gupta, Ewen M Harrison, Julian A Hiscox, Antonia YW Ho, Peter W Horby, Samreen Ijaz, Say Khoo, Paul Klenerman, Andrew Law, Matthew Lewis, Sonia Liggi, Wei Shen Lim, Lynn Maslen, Alexander J Mentzer, Laura Merson, Alison M Meynert, Shona C Moore, Mahdad Noursadeghi, Michael Olanipekun, Anthonia Osagie, Massimo Palmarini, Carlo Palmieri, William A Paxton, Georgios Pollakis, Nicholas Price, Andrew Rambaut, David L Robertson, Clark D Russell, Vanessa Sancho-Shimizu, Caroline Sands, Janet T Scott, Louise Sigfrid, Tom Solomon, Shiranee Sriskandan, David Stuart, Charlotte Summers, Olivia V Swann, Zoltan Takats, Panteleimon Takis, Richard S Tedder, AA Roger Thompson, Emma C Thomson, Ryan S Thwaites, Lance CW Turtle, Maria Zambon, Hayley Hardwick, Chloe Donohue, Fiona Griffiths, Wilna Oosthuyzen, Cara Donegan, Rebecca G Spencer, Lisa Norman, Riinu Pius, Thomas M Drake, Cameron J Fairfield, Stephen R Knight, Kenneth A Mclean, Derek Murphy, Catherine A Shaw, Jo Dalton, Michelle Girvan, Egle Saviciute, Stephanie Roberts, Janet Harrison, Laura Marsh, Marie Connor, Sophie Halpin, Clare Jackson, Carrol Gamble, Daniel Plotkin, James Lee, Gary Leeming, Murray Wham, Sara Clohisey, Ross Hendry, James Scott-Brown, Victoria Shaw, Sarah E McDonald, Seán Keating, Katie A. Ahmed, Jane A Armstrong, Milton Ashworth, Innocent G Asiimwe, Siddharth Bakshi, Samantha L Barlow, Laura Booth, Benjamin Brennan, Katie Bullock, Benjamin WA Catterall, Jordan J Clark, Emily A Clarke, Sarah Cole, Louise Cooper, Helen Cox, Christopher Davis, Oslem Dincarslan, Chris Dunn, Philip Dyer, Angela Elliott, Anthony Evans, Lorna Finch, Lewis WS Fisher, Terry Foster, Isabel Garcia-Dorival, Philip Gunning, Catherine Hartley, Rebecca L Jensen, Christopher B Jones, Trevor R Jones, Shadia Khandaker, Katharine King, Robyn T. Kiy, Chrysa Koukorava, Annette Lake, Suzannah Lant, Diane Latawiec, Lara Lavelle-Langham, Daniella Lefteri, Lauren Lett, Lucia A Livoti, Maria Mancini, Sarah McDonald, Laurence McEvoy, John McLauchlan, Soeren Metelmann, Nahida S Miah, Joanna Middleton, Joyce Mitchell, Ellen G Murphy, Rebekah Penrice-Randal, Jack Pilgrim, Tessa Prince, Will Reynolds, P. Matthew Ridley, Debby Sales, Victoria E Shaw, Rebecca K Shears, Benjamin Small, Krishanthi S Subramaniam, Agnieska Szemiel, Aislynn Taggart, Jolanta Tanianis-Hughes, Jordan Thomas, Erwan Trochu, Libby van Tonder, Eve Wilcock, J. Eunice Zhang, Lisa Flaherty, Nicole Maziere, Emily Cass, Alejandra Doce Carracedo, Nicola Carlucci, Anthony Holmes, Hannah Massey, Lee Murphy, Sarah McCafferty, Richard Clark, Angie Fawkes, Kirstie Morrice, Alan Maclean, Nicola Wrobel, Lorna Donelly, Audrey Coutts, Katarzyna Hafezi, Louise MacGillivray, Tammy Gilchrist, Kayode Adeniji, Daniel Agranoff, Ken Agwuh, Dhiraj Ail, Erin L. Aldera, Ana Alegria, Sam Allen, Brian Angus, Abdul Ashish, Dougal Atkinson, Shahedal Bari, Gavin Barlow, Stella Barnass, Nicholas Barrett, Christopher Bassford, Sneha Basude, David Baxter, Michael Beadsworth, Jolanta Bernatoniene, John Berridge, Colin Berry, Nicola Best, Pieter Bothma, David Chadwick, Robin Brittain-Long, Naomi Bulteel, Tom Burden, Andrew Burtenshaw, Vikki Caruth, Duncan Chambler, Nigel Chee, Jenny Child, Srikanth Chukkambotla, Tom Clark, Paul Collini, Catherine Cosgrove, Jason Cupitt, Maria-Teresa Cutino-Moguel, Paul Dark, Chris Dawson, Samir Dervisevic, Phil Donnison, Sam Douthwaite, Andrew Drummond, Ingrid DuRand, Ahilanadan Dushianthan, Tristan Dyer, Cariad Evans, Chi Eziefula, Chrisopher Fegan, Adam Finn, Duncan Fullerton, Sanjeev Garg, Atul Garg, Effrossyni Gkrania-Klotsas, Jo Godden, Arthur Goldsmith, Clive Graham, Elaine Hardy, Stuart Hartshorn, Daniel Harvey, Peter Havalda, Daniel B Hawcutt, Maria Hobrok, Luke Hodgson, Anil Hormis, Michael Jacobs, Susan Jain, Paul Jennings, Agilan Kaliappan, Vidya Kasipandian, Stephen Kegg, Michael Kelsey, Jason Kendall, Caroline Kerrison, Ian Kerslake, Oliver Koch, Gouri Koduri, George Koshy, Shondipon Laha, Steven Laird, Susan Larkin, Tamas Leiner, Patrick Lillie, James Limb, Vanessa Linnett, Jeff Little, Mark Lyttle, Michael MacMahon, Emily MacNaughton, Ravish Mankregod, Huw Masson, Elijah Matovu, Katherine McCullough, Ruth McEwen, Manjula Meda, Gary Mills, Jane Minton, Mariyam Mirfenderesky, Kavya Mohandas, Quen Mok, James Moon, Elinoor Moore, Patrick Morgan, Craig Morris, Katherine Mortimore, Samuel Moses, Mbiye Mpenge, Rohinton Mulla, Michael Murphy, Megan Nagel, Thapas Nagarajan, Mark Nelson, Lillian Norris, Matthew K. O'Shea, Igor Otahal, Marlies Ostermann, Mark Pais, Selva Panchatsharam, Danai Papakonstantinou, Hassan Paraiso, Brij Patel, Natalie Pattison, Justin Pepperell, Mark Peters, Mandeep Phull, Stefania Pintus, Jagtur Singh Pooni, Tim Planche, Frank Post, David Price, Rachel Prout, Nikolas Rae, Henrik Reschreiter, Tim Reynolds, Neil Richardson, Mark Roberts, Devender Roberts, Alistair Rose, Guy Rousseau, Bobby Ruge, Brendan Ryan, Taranprit Saluja, Matthias Schmid, Aarti Shah, Prad Shanmuga, Anil Sharma, Anna Shawcross, Jeremy Sizer, Manu Shankar-Hari, Richard Smith, Catherine Snelson, Nick Spittle, Nikki Staines, Tom Stambach, Richard Stewart, Pradeep Subudhi, Tamas Szakmany, Kate Tatham, Jo Thomas, Chris Thompson, Robert Thompson, Ascanio Tridente, Darell Tupper-Carey, Mary Twagira, Nick Vallotton, Rama Vancheeswaran, Lisa Vincent-Smith, Shico Visuvanathan, Alan Vuylsteke, Sam Waddy, Rachel Wake, Andrew Walden, Ingeborg Welters, Tony Whitehouse, Paul Whittaker, Ashley Whittington, Padmasayee Papineni, Meme Wijesinghe, Martin Williams, Lawrence Wilson, Sarah Sarah, Stephen Winchester, Martin Wiselka, Adam Wolverson, Daniel G Wootton, Andrew Workman, Bryan Yates, and Peter Young
Subjects: Computer applications to medicine. Medical informatics, R858-859.7
Abstract: Summary: Background: Dexamethasone was the first intervention proven to reduce mortality in patients with COVID-19 being treated in hospital. We aimed to evaluate the adoption of corticosteroids in the treatment of COVID-19 in the UK after the RECOVERY trial publication on June 16, 2020, and to identify discrepancies in care. Methods: We did an audit of clinical implementation of corticosteroids in a prospective, observational, cohort study in 237 UK acute care hospitals between March 16, 2020, and April 14, 2021, restricted to patients aged 18 years or older with proven or high likelihood of COVID-19, who received supplementary oxygen. The primary outcome was administration of dexamethasone, prednisolone, hydrocortisone, or methylprednisolone. This study is registered with ISRCTN, ISRCTN66726260. Findings: Between June 17, 2020, and April 14, 2021, 47 795 (75·2%) of 63 525 of patients on supplementary oxygen received corticosteroids, higher among patients requiring critical care than in those who received ward care (11 185 [86·6%] of 12 909 vs 36 415 [72·4%] of 50 278). Patients 50 years or older were significantly less likely to receive corticosteroids than those younger than 50 years (adjusted odds ratio 0·79 [95% CI 0·70–0·89], p=0·0001, for 70–79 years; 0·52 [0·46–0·58], p80 years), independent of patient demographics and illness severity. 84 (54·2%) of 155 pregnant women received corticosteroids. Rates of corticosteroid administration increased from 27·5% in the week before June 16, 2020, to 75–80% in January, 2021. Interpretation: Implementation of corticosteroids into clinical practice in the UK for patients with COVID-19 has been successful, but not universal. Patients older than 70 years, independent of illness severity, chronic neurological disease, and dementia, were less likely to receive corticosteroids than those who were younger, as were pregnant women. This could reflect appropriate clinical decision making, but the possibility of inequitable access to life-saving care should be considered. Funding: UK National Institute for Health Research and UK Medical Research Council.
Published: 2022
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6. NHS ‘Learning from Deaths’ reports: a qualitative and quantitative document analysis of the first year of a countrywide patient safety programme

Author: Cecilia Vindrola-Padros, S Ramani Moonesinghe, Zoe Brummell, and Dorit Braun
Subjects: Medicine
Abstract: Objectives To review how National Health Service (NHS) Secondary Care Trusts (NSCTs) are using the Learning from Deaths (LfDs) programme to learn from and prevent, potentially preventable deaths.Introduction Potentially preventable deaths occur worldwide within healthcare organisations. In England, inconsistencies in how NSCTs reviewed, investigated and shared LfDs, resulted in the introduction of national guidance on ‘LfDs’ in 2017. This guidance provides a ‘framework for identifying, reporting, investigating and LfDs’. Amendments to NHS Quality Account regulations, legally require NSCTs in England to report quantitative and qualitative information relating to patient deaths annually. The programme intended NSCTs would share this learning and take measurable action to prevent future deaths.Method We undertook qualitative and quantitative secondary data, document analysis of all NSCTs LfDs reports within their 2017/2018 Quality Accounts (n=222).Results All statutory elements of LfDs reporting were reported by 98 out of 222 (44%) NSCTs. The percentage of deaths judged more likely than not due to problems in healthcare was between 0% and 13%. The majority of NSCTs (89%) reported lessons learnt; the most common learning theme was poor communication. 106 out of 222 NSCTs (48%) have shared or plan to share the learning within their own organisation. The majority of NSCTs (86%) reported actions taken and 47% discussed or had a plan for assessment of impact. 37 out of 222 NSCTs (17%) mentioned involvement of bereaved families.Conclusions The wide variation in reporting demonstrates that some NSCTs have engaged fully with LfDs, while other NSCTs appear to have disengaged with the programme. This may reveal a disparity in organisational learning and patient safety culture which could result in inequity for bereaved families. Many themes identified from the LfDs reports have previously been identified by national and international reports and inquiries.
Published: 2021
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7. Developing and validating subjective and objective risk-assessment measures for predicting mortality after major surgery: An international prospective cohort study.

Author: Danny J N Wong, Steve Harris, Arun Sahni, James R Bedford, Laura Cortes, Richard Shawyer, Andrew M Wilson, Helen A Lindsay, Doug Campbell, Scott Popham, Lisa M Barneto, Paul S Myles, SNAP-2: EPICCS collaborators, and S Ramani Moonesinghe
Subjects: Medicine
Abstract: BackgroundPreoperative risk prediction is important for guiding clinical decision-making and resource allocation. Clinicians frequently rely solely on their own clinical judgement for risk prediction rather than objective measures. We aimed to compare the accuracy of freely available objective surgical risk tools with subjective clinical assessment in predicting 30-day mortality.Methods and findingsWe conducted a prospective observational study in 274 hospitals in the United Kingdom (UK), Australia, and New Zealand. For 1 week in 2017, prospective risk, surgical, and outcome data were collected on all adults aged 18 years and over undergoing surgery requiring at least a 1-night stay in hospital. Recruitment bias was avoided through an ethical waiver to patient consent; a mixture of rural, urban, district, and university hospitals participated. We compared subjective assessment with 3 previously published, open-access objective risk tools for predicting 30-day mortality: the Portsmouth-Physiology and Operative Severity Score for the enUmeration of Mortality (P-POSSUM), Surgical Risk Scale (SRS), and Surgical Outcome Risk Tool (SORT). We then developed a logistic regression model combining subjective assessment and the best objective tool and compared its performance to each constituent method alone. We included 22,631 patients in the study: 52.8% were female, median age was 62 years (interquartile range [IQR] 46 to 73 years), median postoperative length of stay was 3 days (IQR 1 to 6), and inpatient 30-day mortality was 1.4%. Clinicians used subjective assessment alone in 88.7% of cases. All methods overpredicted risk, but visual inspection of plots showed the SORT to have the best calibration. The SORT demonstrated the best discrimination of the objective tools (SORT Area Under Receiver Operating Characteristic curve [AUROC] = 0.90, 95% confidence interval [CI]: 0.88-0.92; P-POSSUM = 0.89, 95% CI 0.88-0.91; SRS = 0.85, 95% CI 0.82-0.87). Subjective assessment demonstrated good discrimination (AUROC = 0.89, 95% CI: 0.86-0.91) that was not different from the SORT (p = 0.309). Combining subjective assessment and the SORT improved discrimination (bootstrap optimism-corrected AUROC = 0.92, 95% CI: 0.90-0.94) and demonstrated continuous Net Reclassification Improvement (NRI = 0.13, 95% CI: 0.06-0.20, p < 0.001) compared with subjective assessment alone. Decision-curve analysis (DCA) confirmed the superiority of the SORT over other previously published models, and the SORT-clinical judgement model again performed best overall. Our study is limited by the low mortality rate, by the lack of blinding in the 'subjective' risk assessments, and because we only compared the performance of clinical risk scores as opposed to other prediction tools such as exercise testing or frailty assessment.ConclusionsIn this study, we observed that the combination of subjective assessment with a parsimonious risk model improved perioperative risk estimation. This may be of value in helping clinicians allocate finite resources such as critical care and to support patient involvement in clinical decision-making.
Published: 2020
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8. The incidence of postoperative vasopressor usage: protocol for a prospective international observational cohort study (SQUEEZE)

Author: Creagh-Brown, Ben, Wunsch, Hannah, Martin, Peter, Harlet, Pierre, Forni, Lui, Moonesinghe, S. Ramani, and Jammer, Ib
Published: 2023
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9. Systematic reviews and consensus definitions for the Standardised Endpoints in Perioperative Medicine (StEP) initiative: mortality, morbidity, and organ failure

Author: Myles, Paul, Gan, T.J., Kurz, Andrea, Peyton, Phil, Sessler, Dan, Tramèr, Martin, Cyna, Alan, De Oliveira, Gildasio S., Jr., Wu, Christopher, Jensen, Mark, Kehlet, Henrik, Botti, Mari, Boney, Oliver, Haller, Guy, Grocott, Mike, Cook, Tim, Fleisher, Lee, Neuman, Mark, Story, David, Gruen, Russell, Bampoe, Sam, Evered, Lis, Scott, David, Silbert, Brendan, van Dijk, Diederik, Kalkman, Cor, Chan, Matthew, Grocott, Hilary, Eckenhoff, Rod, Rasmussen, Lars, Eriksson, Lars, Beattie, Scott, Wijeysundera, Duminda, Landoni, Giovanni, Leslie, Kate, Biccard, Bruce, Howell, Simon, Nagele, Peter, Richards, Toby, Lamy, Andre, Lalu, Manoj, Pearse, Rupert, Mythen, Monty, Canet, Jaume, Moller, Ann, Gin, Tony, Schultz, Marcus, Pelosi, Paolo, Gabreu, Marcelo, Futier, Emmanuel, Creagh-Brown, Ben, Abbot, Tom, Klein, Andy, Corcoran, Tomas, Cooper, D. Jamie, Dieleman, Stefan, Diouf, Elisabeth, McIlroy, David, Bellomo, Rinaldo, Shaw, Andrew, Prowle, John, Karkouti, Keyvan, Billings, Josh, Mazer, David, Jayarajah, Mohindas, Murphy, Michael, Bartoszko, Justyna, Sneyd, Rob, Morris, Steve, George, Ron, Moonesinghe, Ramani, Shulman, Mark, Lane-Fall, Meghan, Nilsson, Ulrica, Stevenson, Nathalie, van Klei, Wilton, Cabrini, Luca, Miller, Tim, Jackson, Sandy, Buggy, Donal, Short, Tim, Riedel, Bernhard, Gottumukkala, Vijay, Pace, Nathan, Alkhaffaf, Bilal, Johnson, Mark, Jackson, Alexander I.R., Pearse, Rupert M., Cooper, D. James, van Klei, Wilton A., Miller, Timothy E., Moonesinghe, S. Ramani, Myles, Paul S., and Grocott, Michael P.W.
Published: 2023
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10. Highlighting uncertainty in clinical risk prediction using a model of emergency laparotomy mortality risk

Author: Mathiszig-Lee, Jakob F., Catling, Finneas J. R., Moonesinghe, S. Ramani, and Brett, Stephen J.
Published: 2022
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11. The Perioperative Quality Improvement Programme (PQIP patient study): protocol for a UK multicentre, prospective cohort study to measure quality of care and outcomes after major surgery

Author: Moonesinghe, S. Ramani, McGuckin, Dermot, Martin, Peter, Bedford, James, Wagstaff, Duncan, Gilhooly, David, Santos, Cristel, Wilson, Jonathan, Dorey, Jenny, Leeman, Irene, Smith, Helena, Vindrola-Padros, Cecilia, Edwards, Kylie, Singleton, Georgina, Swart, Michael, Baumber, Rachel, Sahni, Arun, Warnakulasuriya, Samantha, Vohra, Ravi, Ellicott, Helen, Bougeard, Anne-Marie, Chazapis, Maria, Ignacka, Aleksandra, Cripps, Martin, Brent, Alexandra, Drake, Sharon, Goodwin, James, Martinez, Dorian, Williams, Karen, Singh, Pritam, Bedford, Matthew, Vallance, Abigail E., Samuel, Katie, Lourtie, Jose, Olive, Dominic, Taylor, Christine, Tucker, Olga, Aresu, Giuseppe, Swift, Andrew, Fulop, Naomi, and Grocott, Mike
Published: 2022
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12. Implementation of corticosteroids in treatment of COVID-19 in the ISARIC WHO Clinical Characterisation Protocol UK: prospective, cohort study

Author: Baillie, J Kenneth, Semple, Malcolm G, Openshaw, Peter JM, Carson, Gail, Alex, Beatrice, Andrikopoulos, Petros, Bach, Benjamin, Barclay, Wendy S, Bogaert, Debby, Chand, Meera, Chechi, Kanta, Cooke, Graham S, da Silva Filipe, Ana, de Silva, Thushan, Docherty, Annemarie B, dos Santos Correia, Gon¸alo, Dumas, Marc-Emmanuel, Dunning, Jake, Fletcher, Tom, Green, Christopher A, Greenhalf, William, Griffin, Julian, Gupta, Rishi K, Harrison, Ewen M, Hiscox, Julian A, Ho, Antonia YW, Horby, Peter W, Ijaz, Samreen, Khoo, Say, Klenerman, Paul, Law, Andrew, Lewis, Matthew, Liggi, Sonia, Lim, Wei Shen, Maslen, Lynn, Mentzer, Alexander J, Merson, Laura, Meynert, Alison M, Moore, Shona C, Noursadeghi, Mahdad, Olanipekun, Michael, Osagie, Anthonia, Palmarini, Massimo, Palmieri, Carlo, Paxton, William A, Pollakis, Georgios, Price, Nicholas, Rambaut, Andrew, Robertson, David L, Russell, Clark D, Sancho-Shimizu, Vanessa, Sands, Caroline, Scott, Janet T, Sigfrid, Louise, Solomon, Tom, Sriskandan, Shiranee, Stuart, David, Summers, Charlotte, Swann, Olivia V, Takats, Zoltan, Takis, Panteleimon, Tedder, Richard S, Thompson, AA Roger, Thomson, Emma C, Thwaites, Ryan S, Turtle, Lance CW, Zambon, Maria, Hardwick, Hayley, Donohue, Chloe, Griffiths, Fiona, Oosthuyzen, Wilna, Donegan, Cara, Spencer, Rebecca G, Norman, Lisa, Pius, Riinu, Drake, Thomas M, Fairfield, Cameron J, Knight, Stephen R, Mclean, Kenneth A, Murphy, Derek, Shaw, Catherine A, Dalton, Jo, Girvan, Michelle, Saviciute, Egle, Roberts, Stephanie, Harrison, Janet, Marsh, Laura, Connor, Marie, Halpin, Sophie, Jackson, Clare, Gamble, Carrol, Plotkin, Daniel, Lee, James, Leeming, Gary, Wham, Murray, Clohisey, Sara, Hendry, Ross, Scott-Brown, James, Shaw, Victoria, McDonald, Sarah E, Keating, Seán, Ahmed, Katie A., Armstrong, Jane A, Ashworth, Milton, Asiimwe, Innocent G, Bakshi, Siddharth, Barlow, Samantha L, Booth, Laura, Brennan, Benjamin, Bullock, Katie, Catterall, Benjamin WA, Clark, Jordan J, Clarke, Emily A, Cole, Sarah, Cooper, Louise, Cox, Helen, Davis, Christopher, Dincarslan, Oslem, Dunn, Chris, Dyer, Philip, Elliott, Angela, Evans, Anthony, Finch, Lorna, Fisher, Lewis WS, Foster, Terry, Garcia-Dorival, Isabel, Gunning, Philip, Hartley, Catherine, Jensen, Rebecca L, Jones, Christopher B, Jones, Trevor R, Khandaker, Shadia, King, Katharine, Kiy, Robyn T., Koukorava, Chrysa, Lake, Annette, Lant, Suzannah, Latawiec, Diane, Lavelle-Langham, Lara, Lefteri, Daniella, Lett, Lauren, Livoti, Lucia A, Mancini, Maria, McDonald, Sarah, McEvoy, Laurence, McLauchlan, John, Metelmann, Soeren, Miah, Nahida S, Middleton, Joanna, Mitchell, Joyce, Murphy, Ellen G, Penrice-Randal, Rebekah, Pilgrim, Jack, Prince, Tessa, Reynolds, Will, Ridley, P. Matthew, Sales, Debby, Shaw, Victoria E, Shears, Rebecca K, Small, Benjamin, Subramaniam, Krishanthi S, Szemiel, Agnieska, Taggart, Aislynn, Tanianis-Hughes, Jolanta, Thomas, Jordan, Trochu, Erwan, van Tonder, Libby, Wilcock, Eve, Zhang, J. Eunice, Flaherty, Lisa, Maziere, Nicole, Cass, Emily, Doce Carracedo, Alejandra, Carlucci, Nicola, Holmes, Anthony, Massey, Hannah, Murphy, Lee, McCafferty, Sarah, Clark, Richard, Fawkes, Angie, Morrice, Kirstie, Maclean, Alan, Wrobel, Nicola, Donelly, Lorna, Coutts, Audrey, Hafezi, Katarzyna, MacGillivray, Louise, Gilchrist, Tammy, Adeniji, Kayode, Agranoff, Daniel, Agwuh, Ken, Ail, Dhiraj, Aldera, Erin L., Alegria, Ana, Allen, Sam, Angus, Brian, Ashish, Abdul, Atkinson, Dougal, Bari, Shahedal, Barlow, Gavin, Barnass, Stella, Barrett, Nicholas, Bassford, Christopher, Basude, Sneha, Baxter, David, Beadsworth, Michael, Bernatoniene, Jolanta, Berridge, John, Berry, Colin, Best, Nicola, Bothma, Pieter, Chadwick, David, Brittain-Long, Robin, Bulteel, Naomi, Burden, Tom, Burtenshaw, Andrew, Caruth, Vikki, Chambler, Duncan, Chee, Nigel, Child, Jenny, Chukkambotla, Srikanth, Clark, Tom, Collini, Paul, Cosgrove, Catherine, Cupitt, Jason, Cutino-Moguel, Maria-Teresa, Dark, Paul, Dawson, Chris, Dervisevic, Samir, Donnison, Phil, Douthwaite, Sam, Drummond, Andrew, DuRand, Ingrid, Dushianthan, Ahilanadan, Dyer, Tristan, Evans, Cariad, Eziefula, Chi, Fegan, Chrisopher, Finn, Adam, Fullerton, Duncan, Garg, Sanjeev, Garg, Atul, Gkrania-Klotsas, Effrossyni, Godden, Jo, Goldsmith, Arthur, Graham, Clive, Hardy, Elaine, Hartshorn, Stuart, Harvey, Daniel, Havalda, Peter, Hawcutt, Daniel B, Hobrok, Maria, Hodgson, Luke, Hormis, Anil, Jacobs, Michael, Jain, Susan, Jennings, Paul, Kaliappan, Agilan, Kasipandian, Vidya, Kegg, Stephen, Kelsey, Michael, Kendall, Jason, Kerrison, Caroline, Kerslake, Ian, Koch, Oliver, Koduri, Gouri, Koshy, George, Laha, Shondipon, Laird, Steven, Larkin, Susan, Leiner, Tamas, Lillie, Patrick, Limb, James, Linnett, Vanessa, Little, Jeff, Lyttle, Mark, MacMahon, Michael, MacNaughton, Emily, Mankregod, Ravish, Masson, Huw, Matovu, Elijah, McCullough, Katherine, McEwen, Ruth, Meda, Manjula, Mills, Gary, Minton, Jane, Mirfenderesky, Mariyam, Mohandas, Kavya, Mok, Quen, Moon, James, Moore, Elinoor, Morgan, Patrick, Morris, Craig, Mortimore, Katherine, Moses, Samuel, Mpenge, Mbiye, Mulla, Rohinton, Murphy, Michael, Nagel, Megan, Nagarajan, Thapas, Nelson, Mark, Norris, Lillian, O'Shea, Matthew K., Otahal, Igor, Ostermann, Marlies, Pais, Mark, Panchatsharam, Selva, Papakonstantinou, Danai, Paraiso, Hassan, Patel, Brij, Pattison, Natalie, Pepperell, Justin, Peters, Mark, Phull, Mandeep, Pintus, Stefania, Singh Pooni, Jagtur, Planche, Tim, Post, Frank, Price, David, Prout, Rachel, Rae, Nikolas, Reschreiter, Henrik, Reynolds, Tim, Richardson, Neil, Roberts, Mark, Roberts, Devender, Rose, Alistair, Rousseau, Guy, Ruge, Bobby, Ryan, Brendan, Saluja, Taranprit, Schmid, Matthias, Shah, Aarti, Shanmuga, Prad, Sharma, Anil, Shawcross, Anna, Sizer, Jeremy, Shankar-Hari, Manu, Smith, Richard, Snelson, Catherine, Spittle, Nick, Staines, Nikki, Stambach, Tom, Stewart, Richard, Subudhi, Pradeep, Szakmany, Tamas, Tatham, Kate, Thomas, Jo, Thompson, Chris, Thompson, Robert, Tridente, Ascanio, Tupper-Carey, Darell, Twagira, Mary, Vallotton, Nick, Vancheeswaran, Rama, Vincent-Smith, Lisa, Visuvanathan, Shico, Vuylsteke, Alan, Waddy, Sam, Wake, Rachel, Walden, Andrew, Welters, Ingeborg, Whitehouse, Tony, Whittaker, Paul, Whittington, Ashley, Papineni, Padmasayee, Wijesinghe, Meme, Williams, Martin, Wilson, Lawrence, Sarah, Sarah, Winchester, Stephen, Wiselka, Martin, Wolverson, Adam, Wootton, Daniel G, Workman, Andrew, Yates, Bryan, Young, Peter, Närhi, Fiina, Moonesinghe, S Ramani, Shenkin, Susan D, Mulholland, Rachel H, Hardwick, Hayley E, Ho, Antonia, Nguyen-Van-Tam, Jonathan S, Turtle, Lance, and Openshaw, Peter J M
Published: 2022
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13. Validation of the days alive and out of hospital outcome measure after emergency laparotomy: a retrospective cohort study

Author: Spurling, Leigh-James, Moonesinghe, S. Ramani, and Oliver, C. Matthew
Published: 2022
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14. Quality metrics for emergency abdominal surgery in children: a systematic review

Author: Sogbodjor, Lisa A., Singleton, Georgina, Davenport, Mark, Walker, Suellen, and Moonesinghe, S. Ramani
Published: 2022
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15. Seasonality in surgical outcome data: a systematic review and narrative synthesis

Author: Spencer, Emma, Berry, Michael, Martin, Peter, Rojas-Garcia, Antonio, and Moonesinghe, S. Ramani
Published: 2022
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16. Protocolised care pathways: a double‐edged sword?

Author: Rintoul, Edward, Moonesinghe, S. Ramani, and Bashford, Tom
Subjects: *FEMORAL fractures, *COST effectiveness, *PERIOPERATIVE care, *SURGERY, *FEMUR neck, *OPERATING room nursing, *GERIATRICIANS, *TREATMENT delay (Medicine)
Abstract: The article explores the use of protocolised care pathways in emergency surgery in the UK, specifically for hip fracture and emergency laparotomy patients. These pathways aim to enhance care and outcomes for these patients, with support from national quality improvement programs and financial incentives. However, the article raises concerns about potential drawbacks, such as delays or disparities in care for other patient groups, as well as the impact on resources and workforce allocation. It emphasizes the need for further research and qualitative exploration to fully comprehend the effects of these pathways on diverse patient populations. The text also emphasizes the importance of considering individual clinical judgment and innovation alongside standardization, as well as understanding system-level resource constraints and potential unintended consequences. It mentions the "Getting It Right First Time" program as an opportunity to improve care quality through a systems-based approach. The article acknowledges the resource limitations of the NHS and calls for a nuanced understanding of the effects of care pathways on different patient groups. [Extracted from the article]
Published: 2024
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17. Core Outcome Measures for Perioperative and Anaesthetic Care (COMPAC): a modified Delphi process to develop a core outcome set for trials in perioperative care and anaesthesia

Author: Bartoszko, J., Beattie, W.S., Bellomo, R., Buggy, D., Cabrini, L., Canet, J., Cook, T., Cooper, D.J., Corcoran, T., Devereaux, P.J., Eckenhoff, R., Evered, L., Gan, T.J., Gin, T., Grocott, H., Haller, G., Howell, S., Jayarajah, M., Kalkman, C., Karkouti, K., Kavanagh, B., Klein, A., Landoni, G., Leslie, K., McIlroy, D.R., Mazer, D., Moller, A., Mythen, M., Neuman, M., Pearse, R., Peyton, P., Prowle, J., Richards, T., Scott, D.A., Sessler, D., Shaw, A., Short, T., Shulman, M., Silbert, B., Singer, M., Sneyd, J.R., Story, D., van Dijk, D., van Klei, W., Boney, Oliver, Moonesinghe, S. Ramani, Myles, Paul S., and Grocott, Michael P.W.
Published: 2022
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18. The Anesthesiologist as Public Health Physician

Author: Moonesinghe, S. Ramani
Published: 2023
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19. Revitalising audit and feedback by understanding and responding to local contexts

Author: Wagstaff, Duncan, Vindrola, Cecilia, Fulop, Naomi, and Moonesinghe, S Ramani
Published: 2020

20. Perioperative interventions for prevention of postoperative pulmonary complications : systematic review and meta-analysis

Author: Odor, Peter M, Bampoe, Sohail, Gilhooly, David, Creagh-Brown, Benedict, and Moonesinghe, S Ramani
Published: 2020

21. Socioeconomic deprivation and long-term outcomes after elective surgery: analysis of prospective data from two observational studies

Author: Wan, Yize I., McGuckin, Dermot, Fowler, Alexander J., Prowle, John R., Pearse, Rupert M., and Moonesinghe, S. Ramani
Published: 2021
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22. Causal inference in perioperative medicine observational research: part 1, a graphical introduction

Author: Krishnamoorthy, Vijay, Wong, Danny J.N., Wilson, Matt, Raghunathan, Karthik, Ohnuma, Tetsu, McLean, Duncan, Moonesinghe, S. Ramani, and Harris, Steve K.
Published: 2020
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23. Preoperative chronic beta-blocker prescription in elderly patients as a risk factor for postoperative mortality stratified by preoperative blood pressure: a cohort study

Author: Venkatesan, Sudhir, Jørgensen, Mads Emil, Manning, Helen J., Andersson, Charlotte, Mozid, Abdul M., Coburn, Mark, Moonesinghe, S. Ramani, Foex, Pierre, Mythen, Monty, Grocott, Michael P.W., Hardman, Jonathan G., Myles, Puja R., and Sanders, Robert D.
Published: 2019
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24. Cancelled operations: a 7-day cohort study of planned adult inpatient surgery in 245 UK National Health Service hospitals

Author: Moonesinghe, S. Ramani, Wong, Danny J.N., Harris, Steve K., Bedford, James, Boney, Oliver, Chazapis, Maria, Drake, Sharon, Farmer, Laura, Gilhooly, David, Goodwin, James, Lourtie, Jose, Wagstaff, Duncan, Grocott, Mike, Sneyd, Robert, Batchelor, Anna, Brett, Stephen, Plowright, Catherine, Shrestha, Suman, Shawyer, Richard, Ahmed, Shafi, Khondoker, Mizan, Nathanson, Mike, Cripps, Paul, Research & Audit Federation of Trainees (RAFT), Anaesthetic Audit and Research Matrix of Yorkshire (AARMY), Intensive Care & Anaesthesia Research Network of North East Trainees (INCARNNET), Merseyside Anaesthetic Group for Improving Quality (MAGIQ), Midlands East Research by Critical Care and Anaesthetic Trainees (MERCAT), North West Research and Audit Group (NWRAG), Oxford Critical Care Anaesthetic Research Enterprise (OxCCARE), Pan-London Peri-operative Audit & Research Network (PLAN), Scottish East Quality Improvement & Research Academy (SEQuoIA), SESSA Quality Improvement and Research Network (SQUARES), Severn Trainees Anaesthetic Research (STAR), South West Anaesthetic Research Matrix (SWARM), South Yorkshire Hospitals Audit and Research Collaboration (SHARC), Southcoast Peri-operative Audit and Research Collaboration (SPARC), Welsh Anaesthetic Audit Research & Engagement Network (WAAREN), West Midlands Trainee Research Anaesthesia & Intensive Care Network (WMTRAIN), Rawat, Shilpa, Sathe, Sonia, Campbell, Alison, Anandarajah, Janakan, Ball, Nicola, Bandara, Lalindra, Barnes, Dennis, Chapman, Catherine, Connor, Lynda, Cook, Amanda, Ebejer, Amanda, Evans, Samantha, Gallagher, Maire, Goff, Sarah, Halfacree, Irina, Harford, Rachel, Harris, Catherine, Jackson, Rebecca, James, Kathryn, Jones, Claire, Jones, Sharon, Malinovszky, Kathy, Mungai, Serah, Nageswaran, Hari, Perumal, Anand, Pudge, Harriet, Quinn, Leanne, Sheppard, Thomas, Smith, Trudy, Spargo, James, Storton, Sharon, Thomas, Caradog, Thomas, Charlotte, Tyrrell, Samuel, Vale, Owen, Williams, Catrin, Williams, Marie, Williams, Tom, Winfield-Young, Lewys, Worrell, Helen, Moloney, Dermot, Chuni, Chandini, Comara, Jasna, Conway, Brian, Eagle, Zara, Foster, Greg, Platt, Chris, Thorne, Alison, Hee, Wendy Lum, Armstrong, Lisa, Brennan, Albert, Burns, Justine, Busby, Chantal, Couch-Upite, Mechele, Dooks, Emma, Dunlop, Sally, Eijk, Anya, Govindaraju, Ramana, Gregory, Tamsin, Grover, Alka, Hairsine, Brigid, Hennell, Sarah, Hill, Philippa, Hollins, Thomas, Hood, Julian, Horner, Lisa, Hothersall, Helen, Ingham, Rachel, Jackson, Richard, Kitching, Amy, Kurasz, Claire, Munsie, Sarah, Nemeth, Ildiko, Paley, Carole, Premraj, Prabhakaran, Redhead, Sue, Scriven, John, Shaw, Alison, Shenton, Liz, Shiva, Hemantha, Snell, Josie, Sootheran, Lucy, Stewart, Josephine, Swinton, Frank, Totten, Claire, Varghese, Liz, Vest, Sarah, Wetherell, Ben, Hamlyn, Vincent, Tozer, James, Alce, Timothy, Bopanna, Poonam, Draper, Kevin, Emmett, Lucy, Falkner, Phillippa, Fisher, Ellie, Griffiths, Cat, Gunter, Una, Harris, Rebecca, Hashmi, Saima, Jewitt, Helen, Lodhi, Sonal, Organ, Angie, Owen, Elana, Taylor, Frances, Tritean, Maria, Wassall, Richard, Woolley, Jade, MacGregor, Mark, Atkinson, Claire, Croft, Maria, Frost, Victoria, George, Richard, Gray, Catherine, Kuttler, Anja, Lalani, Christina, Peers, Beth, Sharpe, Lisa, Skeoch, Christopher, Tarft, Hayley, Tyson, Emma, Wrigley, Martha, Qadri, Shabir, Abbas, Tahir, Barot, Niraj, Cole, Steve, Ganesh, Ritesh, Groves, Christopher, Iles, Lindsey, Petkova, Hristina, Rau, Clovis, Roberts, Jack, Shakir, Nadeem, Chaurasia, Sunil Kumar, Brown, Julia, Cunningham, Mishell, Daniels, Allison, Gannon, Emily, Haines, Annette, Johnson, Nicola, Ogle, Paul, Perumal, Srinivasan, Zeidan, Lisa, Torrance, Hew, Anwar, Sibtain, Gooneratne, Mevan, Raj, Ashok, Abbott, Tom, Ashton, Laura, Brown, Katherine, Butterfield, Emma, Fenn, Jon, Forsyth-Jones, Tim, Garth, Thomas, Gleeson, Liam, Haines, Ryan, Jacobs, Benjamin, Jyothiraj, Haren, Madhi, Shareef, Marriot, Charlotte, Martin, Rebecca, McMillan, Timothy, Menon, Arun, Mohr, Otto Ernst, Muelmenstaedt, Maria, Naeem, Hanzla, Nagendram, Myura, O'Connor, Cormac, Pang, Ching, Plummer, Kimberley, Rob, Zak, Sellers, Dan, Shaw, Michael, Smith, Christopher, Taylor, Tom, Thorat, Priya, Van Rensberg, Gerhardus, Walker, Sophie, Waqas, Farkhunda, Wicks, Peter, Wirth, Daniel, Armanious, Samuel, Allen, Kellie, Bruni, Salvatore, Burijinti Chenna, Keshava Reddy, Kaliappan, Agilan, Ocampo, Madelaine, Percuin, Adrian, Riches, Joanne, Saclot, Louie, Sevillano, Annaliza, Vertue, Mark, Knowlden, Peter, David, Beena, Gajendragadkar, Pushpaj, Gladstone, George, Hammerbeck, Henry, Iaverdino, Marina, Lilley, Carina, Linton-Willoughby, Ben, Murrell, Daniel, Patro, Shweta, Richie-Mclean, Susanna, Turnbull, Marc, Vatsala, Padmanabhan, McCourt, Killian, Black, Nick, Coary, Ciara, McCarter, Jonathan, Murphy, Alexandra, Pugh, Richard, Littler, Chris, Clements, Stephan, Bairkdar, Linda, Bennett, Gillian, Bird, Tom, Bolger, Annette, Dhar, King, Ene, Raluca, George, David, Haron, Dhania, Hobson, Lucie, Iqbal, Saba, Irvine, Lizzie, Lewis, Sion, McCallum, Roisin, O'Donovan, Benjamin, Patrick, Christopher, Perman, Christopher, Rahman, Nowfal, Roberts, Iolo, Sutcliffe, William, Towers, Tammy, Waite, Alicia, Wright, Stella, Pilsbury, Jane, Allan, Michael, Allen, Cody, Carden, Nikkita, Cotterill, Amanda, Cullis, Kerry, Eden, Daniel, Farmer, Nicola, Galloway, Andrea, Geoghegan, James, Gilani, Ahmed, Hard, Kelly, Hasan, Khalid, Horner, Christopher, Iqbal, Virginia, Lowe, Julie, Mallavalli, Ravindra, McCooty, Shanteela, McKillop, Sian, Mellers, Diane, Mukherjee, Ronan, Nicholas, Michelle, O'Hara, Chloe, Osborne, Fiona, Plunkett, Emma, Sharih, Gauhar, Westwood, John, Whapples, Annabelle, Whitehouse, Diane, Williams, Lucinda, Yasin, Bilal, Cupitt, Jason, Brown, Janette, Stoddard, Emma, Balasubramaniam, Madhu, Athanasopoulos, Paul, Bhatti, Mohammad, Blood, Laura, Buckley, Hugo, Chadwick, Julie, Cocks, Shirley, Fradkin, Deborah, Hetherington, Emily, Hill, Rebecca, Hull, Rob, Loftus, Alison, McKenna, Emma, Mcmanoman, Lucy, Milian, Lauren, Mistry, Raksha, Murthen, Lisa, Parkinson, Dave, Rhead, Kat, Sandbach, Peter, Walker, Ciara, Wheatley, Emma, Spencer, Robert, Alderson, Stephen, Bull, Christopher, Butterworth, Rachel, Cousins, Simon, Goellner, Sarah, Greasley, Mark, Lyle, Fiona, Munro, Michael, Northey, Martin, White, Stuart, Harrison, Alex, Whitney, Paul-Simon, Drake, Jeremy, Babla, Rohan, Bharti, Ash, Cole, Stephen, Crotty, Stephen, Farrow, Amy, Golhar, Anil, Indoe, Peter, Jackson, Rosada, Latif, Mariam, Leong, Melvin, Lewith, Henry, Maton-Howarth, Matthew, Millar, Victoria, Millette, Ben, Oliver, Hannah, Ralhan, Vandita, Rashid, Yasir, Rivero-Bosch, Maria, Rizvi, Sana, Saleh, Rosahn, Venkatesh, Harisg, Wilson, Kate, Nair, Ashok, Basvi, Precious, Bell, Gillian, Edwards, James, Lee, David, Luo, Guanmei, Mewies, Clare, Qureshi, Arif, Sohail, Behzad, Valap, Sachin, Hill, Stephen, Bhuwanee, Deepti, Chan, James, Dereix, John, George, Allen, Ghafoor, Tasmeen, Gold, Emma, Haigh, Daniel, Harding, David, Janardhana, Bhavia, Jayan, Jithu, King, James, Laloë, Pnt, Pearce, Lauren, Pruthi, Amit, Roscoe, Eleanor, Seharawat, Deepak, Stubbington, Mark, Vanjari, Vinayak, Venkataraman, Anantharaman, Yen, Serena, Patil, Vishal, Chan, Charles, Davenport, Garry, Featherstone, Peter, Fourie, Petrus, Hall, Christopher, Mahidik, Preeti, Masoni, Elisa, Ortu, Andrea, Patil, Anita, Pickett, Janet, Sharma, Sandeep, Goodwin, Naomi, Allen, Lucy, Anderson, Nathan, Bateson, Ethan, Bhatia, Sanchita, Churchill, Sara, Cohen, Leon, Colhoun, Patrick, Dunn, Charlotte, Durie, Carys, Elgarf, Sarah, Ferry, Jenny, Guilford, Jeremy, Halligan, Claire, Harrison, Jade, Hayes, Dominic, Holst, Ben, Hughes, Rhys, Hunt, Thomas, Jackson, John, Kohler, George, Kriger, Michael, Kulikouskaya, Svetlana, Kurani, Amit, Maden, Charlotte, Moore, Alex, Mounce, Adam, O'Donnell, Alice, Sparrow, Robert, Sutherland, Jayne, Hanison, James, Bansal, Sujesh, Greenwood, Nick, Alkholany, Mahmoud, Apple, Zoe, Asif, Anam, Bhardwaj, Neeraj, Braid, Karl, Coates, Rebecca, Czukowska, Liliana, Elliott, Lauren, Fang, Li, Greenlee, Hannah, Maria, Jashmin, Mostafa, Tarek, Patil, Amarjeet, Weigert, Andrea, Matson, Alexandra, Adamson, Christine, Barclay, Philip, Barker, Amy, Bautista, Carina, Biswas, Suman, Bull, Rhian, Carungcong, Jaime, Christoper, Fenner, Cifre, Elna, Clancy, Olivia, Friedman, Lauren, Frunza, Gabriella, Habib, Sadia, Keerthikumar, Hindusha, Kirwan, Ursula, Laycock, Helen, Leir, Sarah, Maduekwe, Victor, Martella, Carmela, Meritahti, Minna, Nundlall, Kribashnie, Oblak, Metod, Panesar, Kirat, Peters, Timothy, Pett, Eleanor, Routley, Catriona, Samani, Amee, Santhirapala, Vatsharlan, Sayan, Amrinda, Sisson, Alice, Svensson, Marie-Louise, Thankachen, Mini, Tilley, Bernadette, Topham, Charlotte, Visvendra, Sathya, Warrington, Anna, Wibberley, Helen, Spittle, Nick, Andrews, Mia, Beavis, Sarah, Bernotaitis, Daiva, Chinna, Shalini, Church, Helen, Edwards, Sian, Ford, Nicky, Hazeldine, Sarah, Hudson, James, Macey, Claire, Morgans, Matthew, Toms, Julie, Whileman, Amanda, Woodward, Jan, Wright, Stephanie, Nariani, Jaya, Clark, Sam, Duniec, Larysa, Slorach, Marc, Allan, Ashley, Burrill, Bryony, Fairless, Lynn, Francis, Nicholas, McAdam, Colin, Sheriff, Julie, McLoughlin, Thomas, Arshed, Shayan, Bird, Clare, Campbell, Debbie, Chinery, Elaine, Onya, Enoch, Perry, Ryan, Sabugueiro, Jose Miguel, Shatanda, Lydia, Sheik, Muzaffar, Jo simpson, Wong, Gabriela, Yun-Han, Chen, Wilson, Lawrence, Adshead, Duncan, Castillo, David, Faulkner, Maria, Gibson, Kim, Keogh, Tara, Michlig, Sam, Perritt, Elizabeth, Ridler, Simon, Tizzard, Kate, Troy, Anne, Rafi, Amir, Melsom, Helen, Atkinson, Vikki, Azad, Tariq, Bashliyski, Vladimir, Brown, Ellen, Clark, Sarah, Cowton, Sister Amanda, Deane, Jill, Dent, Jean, Duckett, Karin, Duncan, Louise, Hooper, Nicholas, Johnson, Tom, Kay, Andrea, Kelly, Donna, Kent, Melanie, Knight, Jo, McCarroll, Christina, Newby, Annie, Potts, Kathryn, Roe, James, Rollins, David, Sarwar, Asia, Schofield, Lewis, Skelton, Deborah, Somanath, Sameer, Taysum, Peter, Temple, Julie, Udall, William, Varin, Jane, Venyo, Lucy, Warin, Martin, Wood, Shelly, Dhileepan, Srinivasan, Saibaba, Ravishankar Jakkala, Stewart, Emma, Warwick, Eleanor, Pemberton, Omar Alex, Garmash, Maryna, Griffiths, Rhys, Hale, Suzi, Lahloub, Fatma, Lynch, Ceri, Majeed, Abbas, Patchell, Ifan, Quinton, Jennifer, Sedghi, Babak, Wallis, Stephanie, Mansoor, Sange, Belagodu, Zakaulla, Dastur, Shirin, Fuller, Bridget, Hayes, Imogen, Keshkamat, Ashwini, Monantera, Georgia, Sandhar, Roxana, Sivakumaran, Shamini, Thomas, Megan, Torne, Manish, Turner, Sherma, Wakefield, Phillipa, Rogerson, David, Addleton, Catherine, Aju, Lisha, Appleby, Marie, Barnes, Elizabeth, Boateng, Trish, Carrick, Laura, Coulborn, Elaine, Darbyshire, Rebecca, Downes, Charlotte, Fitzgerald, Jodie, Hall, Linda, Harvey, Laura, Hassan, Amr, Holding, Kathleen, Hufton, Lianne, Humphries, Louise, Humphries, Ryan, Inman, Laurence, Issit, Nicole, Longhurst, Sarah, MacGregor, Melody, Mistry, Trusha, Mohamed, Mona, Otieno, Charlene, Read, David, Scott, Michelle, Singleton, Abbie, Smith, Jill, Sohal, Aariana, Tou, Samson, Unsworth, Vanessa, Wan, Richard, Worth, Christopher, Dobson, Richard, Buckley, Rose, Codling, Rachel, Hunter, Cain, Jackson, Jenny, Lowrie, Caroline, McMillan, Catherine, McNab, Raj, Richardson, Max, Sylvester, James, Watkin, Anna, Chambers, Jonathan, Bown, Rachel, Christie, Colin, Dukes, Stephanie, Horton, Sarah, Rees, Andrew, Williams, Patricia, Williams, Sarah, Pierson, Richard, Agarwal, Nipun, Allcock, Clare, Banga, Irmeet, Baron, Kirsty, Cole, Andrea, Dalay, Santinder, Gidda, Ranjit, Lo, Phillip, Merotra, Susan, Newton, Kathryn, Riddell, Zoe, Sonksen, Julian, Jamadarkhana, Sunil, Alcock, Chanice, Allen, Wei Lin, Baker, Pearl, Beadle, Hannah, Bramall, Jon, Chiravasuta, Radu, Chohan, Amina, Christofides, Angela, Cook, Angela, Cotta, Janet, Cruz, Carina, Gilmore, Jemma, Hills, Vicky, King, Kathryn, McClelland, Lucy, Nishimura, Sei, Nyathi, Rumyana, Panagoda, Prasan, Peacock, Louise, Shinn, Emma, Spencer, Sophie, Vianzon, Mayumi, Kapoor, Ritoo, Natarajan, Nagendra, Moore, Joanna, Amoakwa-Adu, Freda, Bahlool, Sinan, Cosier, Tracey, Driscoll, Sarah, Dulea, Claudia, Feneley, Andrew, Fletcher, Marcus, Hammad, Walid, Hazelton, Tracy, Hoyland, Kim, Katyayani, Bennur, Lloyd, Catherine, Moon, Angela, Musselwhite, Janine, Nair, Ganesh, O'Donnell, Joshua, Patankar, Pushkar, Redington, Katy, Sange, Aalia, Snazelle, Mark, Srbljak, Vanja, Stead, Richard, Tariq, Ismail, Thomas, Janine, Turney, Sharon, Chukkambotla, Srikanth, Mathew, Aji, Lie, Jason, Badsha, Zackriah, Bews, Sam, Bhandari, Kumud, Cartmell, Catherine, Cox, Greg, Desai, Hawa, Doran, Mark, Duberley, Stephen, Fitchett, Jill, Forrest, Diane, Freeman, David, Gani, Asif, Gedling, Catherine, Giles, Jessica, Goddard, Wendy, Gregson, Linda, Grimes, Yvonne, Hammond, Bev, Hartley, Janice, Hudson, Gemma, Ibrahim, Ramez, Iqbal, Nasreen, Jewers, Karen, Johnston, Brian, Kakkar, Abhishek, Koczka, Istvan, Lovell, Matt, Mahmoud, Mohamad, Melvin, Cathie, Milner, Matt, Moss, Nicky, Patel, Jeet, Perry, David, Pollard, Mike, Reed, Stephanie, Rennie, Gillian, Riley, Karen, Samuels, Sherrie, Simionescu, Ioana, Sissons, Lorna, Traynor, Stephen, Tucker, Daniel, Vetuz, Glenn, Whalley, Gillian, Marshall, Philippa, Arnold, Tim, Brookes, Jamie, Cowley, Anne, Delascasas, Ruth, Edmunds, Emma, Espitalier-Noel, Dominic, Jeevananthan, Rajeev, Law, Jenni, Mintrim, Kelly, Reid, Jack, Terry, Jackie, Verma, Manish, Zahra, Jean-Paul, Thorning, Geoff, Bailes, John, Dawson, Lindsay, Donovan, Clare, Gan, Jo Han, Jeyarajah, Kaya, Selman, Andrew, Woods, Justin, Csabi, Peter, Choo, Yin Yong, Das, Saheli, Denford, Clare, Desai, Mihir, Dimber, Rahul, Ellisy, Khaled, Griffiths, Ben, Lee, Sindy, Linnett, Vanessa, Daniels, Alex, Perkin, Sadie, Ritzema, Sister Jenny, Storey, Sister Bryony, Wood, Christine, Orme, Robert, Koh, Sock, Banks, Charis, Barker, Oliver, Beames, Susan, Brown, Aislinn, Corbett, Lucy, Dalton, James, Davis, Fiona, Edmunds, Matthew, Gatfield, Will, Gerrans, Edmund, Hickish, Tom, Hignell, Emily, Hiltout, Paula, Johnston, Helen, Miller, Sam, Mullender, Jo, O'Connell, Susan, Rosedale, Kath, Samuel, Katie, Vannahme, Milena, Baigel, Gary, Grayland, Sarah, Hawley, Victoria, McAffrey, Laura, Peggler, Suzannah, Stevens, Joe, Bidd, Heena, El-Boghdadly, Kariem, Zucco, Liana, Adelaja, Yemi, Bhattacharyya, Sanjoy, Bickmore, Elsie, Body, Suzanne, May-Ling Cheung, Jenny, Chowdhury, Priaykam, Cuddihy, Joshua, Ekwere, Ndi, John-Baptiste, Ange Lise, Kelly, Paul, Leslie, Daniel, Mon, Wint, Muzengi, Natasha, Ong, Cheng, Pattni, Nisha, Potter, Thomas, Stanton, Elena, Zaky, Maryam, Partridge, Richard, Bromhead, Helen, Evans, Peter, Griffin, Denise, Kinsella, J. Joseph, Knight, Alexander, Lewis, Rosemary Anna, Lightfoot, Heidi, Martin, Jane, Mupudzi, McDonald, Nahajski, Katherine, Oladele, Segun, Ottaway, Julia, Petley, Stephen, Sheldrake, Ian, Stewart, Alexander Michael, Stokes, Aaron, Trodd, Dawn, Varsani, Nimu, Walshe, Eleanor, Brown, Caroline Wrey, Huntley, Martin, Baldry, Natalie, Bennett, Caroline, Earl, Sophie, Guy, Joyce, Peat, Maggie, Stephenson, Lorraine, Tan, Hao Ern, Wills, Louise, Yare, Michelle, Yeung, Joyce, Antrolikar, Supriya, Atterbury, Katie, Baba, Fayaz, Bancroft, Hollie, Begum, Safia, Bellamy, Mary, Couper, Keith, Evans, Lucy, Flight, Rachel, Frankling, Catriona, Ganesh, Srikant, Gresty, Joanne, Joslyn, Ruth, Kadiri, Salma, Karimi, Aalisha Mariam, Kerr, Amy, Melody, Teresa, Moore, Faye, Morrish, Jennifer, O'Sullivan, Mary, Patel, Vanisha, Reeves, Eleanor, Robley, Richard, Sampson, Julia, Sangombe, Mirriam, Shabangu, Vusumuzi, Sheppard, Lucy, Stafford, Sam, Sutton, Peter, Taylor, Joanne, Turner, James, Velho, Rochelle, Webb, Joanne, Webber, Linda, Werpachowska, Ewa, Wilkinson-Guy, Lisa, Papageorgiou, Constandinos, Bisonoothan, Sohan, Casely, Emma, Casey, Carina, Kam, Elisa, Landers, Geraldine, Pearse, Laura, Raja, Meera, Solomon, Daniel, Stoyanov, Plamen, Talati, Chiraag, Arunachalam, Hari, Ashton, Victoria, Full, Jens, Green, Samira, Gratrix, Andrew, Abernethy, Caroline, Clarke, Abigail, Desai, Pallavbhai, Martinson, Victoria, Parekh, Ravi, Phillips, Mirain, Raina, Anju, Raut, Sarah, Smales, Chris, Smith, Neil, Ugochukwu, Ruth, Willis, Matt, Ignatova, Zhana, Gill, Stuart, Agarwal, Sunita, Barclay, Harry, Campbell, Brian, Collingborn, Maggie, Dagnan, Richard, Davies, David Steven, Dhaliwal, Randeep, Dharmarathna, Ishan, Doriaswami, Yuvraj, Evans, Hywel, Foly, Ahmed, Francis, John, Green, Tim, Hassanin, Ahmed, Havalda, Peter, Huda, Ahmad, Jones, Beryl, Khoju, Ramesh, Lackmann, Maria, Mensah, Kofi, Nwaefulu, Christopher, Schmidt, Ilona, Selvraj, Gopinath, Skarbit, Norbert, Suganthi, Obla, Zeber, Jacek, Nagaratnam, Vidhya, Mukherjee, Smitangshu, Kirby, Sue, Arnold, Glenn, Bendel, Daniel, Boynton, Claire, Carvell, James, Carver, Anthony, Clayton, Matt, Desai, Keyury, Fadhlillah, Fiqry, Georgiou, Christiana, Jayakumar, Vineetha, Jeanes, Lara, Jhugursing, Mhairi, Jose, Jasmine, Karsten, Emma, Kormendy, Flora, Lewis, Stephanie, Lloyd, Dafydd, Mautadin, Francisca, McCahill, Claire, McInerney, Susan, Morland, Laura, Morsman, Elaine, Nayee, Sunny, O'Callaghan, David, Pickford, Emily, Pratap, Bhavesh, Rao, Chandana, Shah, Alka, Smith, Stephen, Smyth, Claire, Souleimanova, Icel, Templeton, Maie, Upadhyaya, Kavita, Uren, Sophie, Voralia, Shree, Wilson, Rebecca, Kushakavsky, Vlad, Ang, Justin, Beeby, Debroah, Bell, Stephanie, Boys, Helen, Brixey, Sue, Chabo, Cathleen, Cubitt, Lynsey, Dawson, Maggie, Finch, Jenny, Lewis, Robert, Purewal, Bally, Pyae, Kaung, Ramsey, Judi, Scoones, Ben, Debreceni, Gabor, Tarin, Obaid, Zondo, Nicola, Bothma, Pieter, Carmichael, Jack, Ferreira, Teresa, Goffin, Kelly, Gurung, Sarada, Kundu, Ashish, MacLennan, Sarah, Murray, Theresa, Verma, Karan, Wilson, Graeme, Winthein, Aleinmar, Jakkampudi, Satyanarayan, Ahmed, Muneeba, Baines, Duncan, Beirnes, Charmaine, Braybrook, Chrissy, Fernandez, Olga, Gracey, Jayshree, Guha, Snehasish, Jain, Arihant, Krishnaiyan, Raghavendran, Moody, Thomas, Novaga, Jo, Tait, Frances, Tate, Jemma, Taylor, Jennifer Claire, Waldron, Molly, White, Sonia, Gupta, Anish, Botfield, Claire, Ahmed, Mohamed, Ashton, Lizzie, Bailey, Andrew Robert, Banugo, Pele, Birch, Sian, Boyle, Henry, Brown, Alice, Chen, Mark, Connolly, Lucy, Dalal, Viral, Edwards, Alexandra, Elnour, Shaima, Faccini, Timothy, Floyd, Theodore, Georgiou, Thomas, Gill, James, Hedley, Russell, Hopkins, Philip, Kumar, Roshmi, Michell, Alice, O'Shaughnessy, Rhiann Marie, O'Sullivan, Laura, Pai, Seema, Parsons, Benjamin, Peltola, Laura, Penhaligan, Beth, Place, Merate, Ramsden, Jonathan, Ratnayake, Gamunu, Shammas, Kathy, Watts, Ali, Williams, Thomas, Woodham, Valantine, Puranik, Sarang, Crooks, Jennifer, Dean, Ania, Honstvet, Chris, Hunter, Katherine, Steynberg, Sarah, Swain, Andrew, Laha, Shondipon, Bonner, Alex, Doyle, Donna, Fallon, Robert, Verlander, Mark, Williams, Alexandra, Yardley, Richard, Whiteley, Simon, Ahmed, Zaid, Child, Ossian Aukland, Black, Sheila, Calderwood, Jamie, Chapman, Andy, Collin, Robert, Dwornik, Kinga, Gallagher, Kate, Gardener, Michelle, Harrop, Gareth, Hart, Peter, Howcroft, Clare, Ilyas, Carl, Kempanna, Umakanth, Krishnamurthy, Gnanshree, Kumara, Paramesh, Loh, LI Lian, Morgan, James, Pipe, Jonathan, Sathiapillai, Anand, Stones, John, Wilby, Elizabeth, Shephard, Buzz, Agarwal, Manju, Bolonenkova, Tatyana, Dunlop, Lisa, Duraisamy, Karthick, Kelly, Jenna, Nurmi, Eveliina, Patel, Mitul, Perry, Jonathan, Prior, Charles, Reece-Anthony, Rosie, Rose, Bernd Oliver, Rowland, Katie, Van Ross, Jennifer, McNamara, Helen, Permall, Natasha, Robertson, Gillian, Siew, Sarah, Smith, Amy, Spearritt, Sue, Verma, Dipali, Williams, Rhys, Zaidi, Suhail, Clayton, Samantha, Goddin, James, Joy, Stuart, Malhotra, Vikram, Mindel, Samuel, Neilson, Zoe, Wijesuria, Shanelle Tharuka, Wilkins, Ryan, Mukherjee, Kirtida, Bradley, Andrew, Coetzee, Samantha, Crook, Alexandra, Emeakaroha, Eunice, Frenkiel, Marek, Hatton, Tom, Kamraj, Kootharajan, Long, Natalie, Manikundalam, Badrinath, Peiris, Muditha, Roche, Matthew, Sandhu, Banher, White, Nichola, Woods, Annette, Yarnold, Adam, Price, Nicolas, Ashe, Marion, Disney, Tom, Dixon, Caroline, Dodds, Emily, El-Sheika, Sarah, Hameed, Yasir, Jones, Rob, Kanal, Karan, Ranaweera, Sanjeewa, Ripoll, Simon, Russell, Nicki, Smith, Sally Anne, Tung, Swee Ang, Emerson, Bruce, Apps, Victoria, Austin, Mandy, Catchpole, Laura, Catley, Christine, Child, Jenny, Cranmer, Karen, Dawson, Lizzie, Farr, Beth, Gerrish, Helen, Gibson, Sian, Gunn, Jacqueline, Holland, Kim, James, Lorri, Jayasekera, Dushyanthi, Lester, Yvonne, Lyle, Amanda, McNeela, Fiona, Oborska, Aneta, Pennington, James, Perkins, Lauren, Robinson-Perrie, Hollie, Shillito, Lauren, Singizi, Bryan, Smolen, Susan, Topliffe, Joanne, Vowles, Ben, Whybro, Natalie, Wijunamai, Amon, Willsher, Lucy, Wootton, Joanne, Varma, Sandeep, Atkinson, Philip, Buckley, Sarah, Darian, Thelma, Dhamaratne, Sachini, Gould, Richard, Graham, Laura, Hirst, Charlotte, Peeling, Andrew, Prentice, Saxon, Rajasekar, Naresh, Rose, Alastair, Smith, Craig, Wilkinson, Kate, Stewart, Richard, Chevis, Chrissie, Ekpa, John, Greig, Nurse Sara, Hinch, Nurse Rebecca, Holder, Carole, Makris, Nikolaos, Moran, Nurse Louise, Padilla Harris, Nurse Cheryl, Scarletta, Nurse Diane, Smith, Jeanette, Sara-Beth Sutherland, Nurse, Turner, Joanne, Valentine, Peter, van Manen, Amelia, Walia, Nurse Sonia, White, Nurse Charlotte, Wilson, Georgina, Wren, Nurse Lynn, Wright, Nurse Francesca, Faulds, Matthew, Agius, Claire-Marie, Ahmed, Raheel, Ahmed, Sameer, Alva, Arjun, Bird, Rachael, Buckley, David, Calder, Verity, Clark, Caroline, Craven, Emily, Cullen, Katherine, Davis, John, Diamond-Fox, Sadie, Doherty, Helen, Dutta, Bhaskar, Fortune, Jonathan, Gatehouse, Alexandra, Gibson, Jane, Hay, David, Hillier, Steve, Hindmarch, Paul, James, Michael, Keshvara, Kiran, Kotur, Karuna, Lawley, Peter, Lochanie, Nimali, Macbeth, Lisa, Madhu, Yamuna, Marcangelo, Sam, McShane, Lesley, Morgan, Amy, Mowat, Stephen, Nelson, Joshua, O'Leary, Denis, Partridge, Jennifer, Pearson, Janaki, Poulton, Tom, Prasad, Sneha, Protheroe, Kay, Qureshi, Adnaan, Rangaswamy, Girish, Richardson, Victoria, Scott, Carmen, Smallshaw, Karen, Soliman, Abdelrahman, Soulsby, Graham, Wahed, Anna, Walker, Iain, Welch, Sarah, Wilson, Jessica, Winkley, Elaine, Womack, Jonathan, Mitchell, Joellene, O'Brien, Peter, Callaghan, Debbie, Clarke, Ruth, Foggo, Graeme, Gilmour, Danielle, Hickey, Stephen, Hii, Stephanie, Livingstone, Susan, McAtear, Megan, Meehan, Sharon, Milne, Gemma, Small, James, Williams, Dewi, Jardine, Catherine, White, Graham, Wright, David, Hunter, Katie, Fleming, Gillian, Marc Hastie, P.A.-A., Hunter, Catherine, Lockhart, Jennifer, Reid, Kirsten, Robb, Hilary, Harten, Johann, Crooks, Barbara A., Ratnasabapathy, Urmila, Staber, Manfred, Pettigrew, Tom, Varveris, Daphne, Brett, Michael, Appleton, Richard, Briskoe, Michael, Brown, Graeme, Brown, Stewart, Christie, Fiona, Coady, Guy, Cowan, Richard, Drysdale, Alice, D'Sylva, Stewart, Edgar, Paul, Finnie, Graeme, Gardner, John, Gupta, Shub, Hall, Colin, Hamilton, Jane, Hunter, Laura, Ikram, Umairali, Imrie, Gregor, Kerr, Euan, Kinsella, Michael, Kommer, Maya, Lafferty, Brian, Lochrin, Christopher, Lovejoy, Tom, Loy, Maria, MacLean, Aillison, MacPherson, David, Marshall, Cara, Mawhinney, Ruth, McAteer, Claire, McCaul, Prof James, McCusker, Michael, McDowall, Katherine, McGovern, Christopher, Mckie, Stewart, Morrison, Melanie, Mulla, Suleman, Neale, Elizabeth, Niciu, Cristina, Ong, Su Ying, O'Sullivan, Finbar, Parcell, Sophie, Ramsay, Sarah, Robson, Rebecca, Roy, Nithin, Samuel, Jasmine, Scotland, Gemma, Sharma, Natasha, Slade, Kate, Soosay, Alvin, Soulsby, Charlotte, Steuart, Lois, Stevenson, Sara, Sweeney, Laura, Tan, Kerwei, Taylor, Calum, Th'ng, Francesca, Thompson, Caroline, Wardlow, James, Watson, Martin, Williams, Robin, Woodier, James, Young, Simon, Robinson, David, Howes, Jacqueline, Berwick, Scott, Boyle, Andrew, Brown, Neil, Celnik, Anna, Coleman, Alistair, Elwkhiee, Mohamed, Fernandes, Mario, Hanlon, Katie, MacLean, Sarah, Morgan, Rhian, Edgar, Jonathan, Chapman, Roddy, Razouk, Khaled, Atrah, Ali, Bhat, Sunny, Bramma, Yvonne, Campbell, Euan, Campbell, Rachel, Cormack, Anna, Dalchow, Stephan, Dodd, Ammy, Ferguson, Laura, Ferraioli, Donna, Hynd, Moyra, Janeczko, Adam, Jeffrey, Sally, Kinsey, Tony, Kummur, Yuvaraj, McIntosh, Karen, McKenna, John, Mclaughlin, Maria, Millington, Ryhs, Murphy, Emma, Parker, Kris, Reevell, Tom, Robertson, Fiona, Shankey-Smith, Will, Slorach, Colum, Szelei, Tamas, Warnock, Geoff, Watson, Stuart, Alston, Peter, Geddes, Murray, McLellan, Stuart, Allen, Eireann, Birnie, Karen, Brown, Kirsteen, Critchley, Julia, Cullen, Margaret, Eros, Alex, Forbes, Callum, French, Helen, Hall, David, Hetherington, Jonathan, Kellner, Maximiliane, Kelly, Keith, Khaled, Hiba, Mantle, Damien, McLenachan, John, Midgley, Susan, Nimmo, Susan, Ordys, Bart, Phelan, Hannah, Roddam, Philip, Shaw, Louise, Smith, Carolyn, Steel, Elizabeth, Stuart, Robert, Sturrock, John, Waite, Alasdair, Waterson, Fraser, Watson, Hannah, Wood, Elizabeth, Schraag, Stefan, Boyd, Elizabeth, Jain, Surabhi, Matthews, Elaine, Rappai, Geejo, Barr, Catriona, Stewart, Michael, Christie, Sharon, Smith, Tim, Arrow, Kate, Austin, Pauline, Chapman, Susie, Little, Jonathan, Malein, William, Sadler, Amy, Younger, Rhona, Reavley, Caroline, Allan, Katy, Ashok, Vignesh, Boroojeny, Ayda Borjian, Cutts, John-Paul, Faulds, Fiona, Fernie, Mark, Gadre, Amit, Gately, Tom, Haddon, Richard, Induruwage, Lalani, King, Siobhan, Liddle, Sian, Mallett, Paul, Mehotra, Saurabh, Ali, Inthekab Mohammed, Morris, Rachel, Nunn, David, Ferraro, Rocio Ochoa, Padhi, Pulak, Prusack, Ewa, Quak, Edmund, Singleton, Georgina, So, Kin, Stolady, Daniel, Welch, Drew, Whitear, Michael, Wilkinson, Kathy, Wu, James, Yusaf, Alex, Kakodkar, Prashant, Ghoneim, Hoda Abou, Balliston, Sara, Dudgeon, Lucy, Idris, Muna Elsheikh, Karattuparambil, Juneenath, Nanda, Lohita, Raithatha, Bhavesh, Saad, Mahmood, Spimpolo, Jenny, Titinchi, Linda, Wilson, Julie, Winstone, Ulrika, Martindale, Sarah, Cameron, Ewen, Fenner, Lynn, Hartford-Beynon, Jake, Mooney, Iain, Mundy, Charlotte, Rhidian, Rhys, Scutt, Sophie, Todd, Anna, Graham, Fiona, Ashton, Amy, Cooper, Theresa, Davidson, Katherine, Eapen, Suresh, Crowther, Hannah, Hindmoor, Claire, McSkeane, Anna, Mostafa, Mahamed, Mudimadagu, Gururaj, Rice, Polly, Wilson, Antoinette, Henry, Garry, Hollister, Nigel, Hunt, Jane, Isaac, Amanda, Morris, David, Paredes-Guerra, Antonio, Pass, Katherine, Paul, Martin, Rait, Kiran, Rousseau, Guy, Shacklock, Emma, Skinner, Amanda, Stancombe, Lucia, Thomson, Laura, Nutt, Christopher, Wright, Geoffrey, Holland, Johnny, O'Neill, Orla, Sengottaiyans, Arun, Mitra, Atideb, Garg, Sanjeev, Adams, Jonathan, Aiyedun, Temitope, Ali, Hani, Biuk, Sahar, Bond, Bryany, Cheeseman, Michelle, Clayton, Mark, Cottam, Lisa-Jayne, Dent, Kathy, Evans, Elizabeth, Evans, Sandra, Goodhand, Hannah, Green, Karen, Hatton, Jonathan, Hill, Joanne, Hutchinson, Dorothy, Jacob, Shibu, Joseph, Renjith, MacNally, Laura, Mann, Jason, McTaggart, Virginia, Newton, Maria, Pearson, Sandra, Rayet, Jaspreet, Saunders, Rebecca, Saxena, Sandeep, Venkatesh, Hemamangala, Whotton, Nikita, Ezihe-Ejiofor, Ada, Barot, Hemangini, Bridger, Hayley, Elhallous, Shady, Ghafoor, Rizana, Manhas, Vitul, Patil, Tanmay, Sadavarte, Nitin, Tharmalingam, Bhamini, Pritchard, David, Bage, Jean, Higgin, Sarah, Krishnaier, Asokan, Neilly, Esther, Rayappu, Sean, Siaw, Chye, Songaile, Ilma, Standen, Peter, Suresh, Narayanan, Weston, William, Williams, Lynne, Yuchen, Jiang, Alagar, Ravi, Taylor, Adrian, Matata, Claire, McCrossan, Roopa, Robertson, Andrew, Shekar, Priya, Stoker, Wendy, Turnbull, Elizabeth, Watkinson, Clare, Worthy, Stephen, Yates, Bryan, Ignatov, Dancho, Davies, Matthew, Abdu, Ayman, Banni, Donata, Bowyer, Helen, Brooks, David, Buhmann, Ulf, Christenssen, Victoria, Collins, James, D'Sa, Aaron, Forde, Steven, Groves, Alice, Hosahalli, Jyothi, Hunt, Lauren, Koshy, George, Lee, Robyn, Martin, Susan, McKnight, Angus, Murphy, Piers, Nash, Henry, Nkanang, Bassey, Oracki, Pamela, Pauley, Tara, Pope, Alan, Samanta, Romit, Sgardelis, Panagiotis, Stocks, Tom, Vaitheeswaran, Sivaprakash, Verma, Priya, Vrazhalska, Madlena Ivanova, Weisz, Mike, Moppett, Iain, Arunachalam, Subha, Begum, Zorba, Bhangu, Sonia, Bown, Gemma, Chana, Avninder, Chhaniyara, Puja, Constable, Natalie, Delme, Luff, Fenner, Helen, Griffith, Oliver, Hackett, Stephen, Hewson, David, Inkpin, Kristofor, Jones, Richard, Kirkdale, Richard, Lyons, Ian, Mandal, Arnab, Mehrotra, Prerna, Murphy, Martin, Pearce-Smith, Alison, Priestley, Martin, Pyke, Shirley, Rasoli, Sonia, Sharma, Suneal, Squires, Gemma, Tsim, Peter, Veitch, Jonathan, Bond-Smith, Giles, Kay, Olivia, Siviter, Richard, Allen, John, Baxter, Linden, Beer, Sally, Bernardo, Mariana, Coles, Holly, Cudlip, Jo, Devries, Talitha, Duggleby, Phil, Espinosa, Alexis, Fitzgibbon, Karina, Galente, Joao, Georgiou, Domonique, Ghosh, Soumi, Hingley, Nicholas, Kingan, Ryan, Levett, Chris, Martinez, Jose, Mattin, Alex, Moore, Anna, Morrish, Samuel, Nash, Amy, Patel, Tejuswi, Roe, Cieron, Thwaites, Victoria, Wali, Anuj, Watterson, Dafydd, Charlotte Yates, Anna Wilson, Gimenez, Marc, Boltres, Anita, McClean, Rebecca, Papadopoullos, Sam, Teh, Elena, Webb, Stephen, Brammar, Andrew, Humphreys, Joanne, Alibhai, Moiz, Alston, Daisy, Cohen, Jolyon, Coleman, Laura, Davies, Simon, Dervin, Dionne, Dooley, Rebecca, Drummond, Andrew, Findlay, Douglas, Fleming, Naomi, Hammoud, Yazzim, Hussain, Sanniah, Irwin, Katy, Javed, Asad, Kent, Linda, Lock, Lorraine, McSorland, Denise, Meadows, Alison, Mulligan, Paula, Nichols, Wendy, O'Callaghan, Maria, Ogor, Jonathan, Poole, Joanna, Ryder, Geoffrey, Shakoor, Akmal, Teo, Wei, Minto, Gary, Adshead, Stephen, Anderson, Helen, Carpenter, Adam, Clelland, Stuart, Datar, Priya, Elloway, Esme, Holl, Matt, Hulbert, Aidan, Humphreys, James, Hyams, Ben, McAleer, Sam, Newmarch, Mary, Notman, Holly, Patrick, Abigail, Perham, Anna, Pitman, Catherine, Reed, Fiona, Sampson, Paul, Smith, Hannah, Spinney, Sam, Toms, Nina, Warrener, Tim, Girgis, Michael, Barcraft-Barnes, Helena, Camsooksai, Julie, Colvin, Carolyn, Davies, Jack, Hamilton, Alex, Linton, Fiona, Nussbaum, Greg, Patch, Sarah, Simpson, Diane, Tbaily, Lee, Bain, James, A'Court, Alicja, Barr, Emma, Blethyn, Kate, Craig, Thomas, Cutler, Hugh, Davies, Jenny, Fry, Rebecca, Hawes, Elizabeth, Jenkins, Nicholas, Knight, Martin, Restall, David, Stubbs, Matthew, Sutherland, Angus, Swarbrick, Claire, Wilson, Robin, Winwright, Paul, Hamilton, Kevin, Adams, Anne, Allen, Orlanda, Boampomaa, Marilyn, Boniface, Anna-Marie, Coe, Robert, Daniel, Amelia, Finn, Joanne, Gill, Issac, Griffiths, Huw, Hackney, Joanna, Han, Ruth, Hare, Edward, Rintoul, Edward, Shpuza, Ervin, Smith, Ruth, Staines, Nikki, Suppiah, Puvan, White, Tracey, Pearson, Darcy, Cracknell, Ben, Farrell, Nadine, Hermanowski, Jane, Hodgson, Ruth, Hoskins, Toby, Jones, Rhiannon, Keable, Sam, Rosbergen, Melissa, Giles, Julian, Dawes, Thomas, Green, Ben Eden, Pottinger, Gail, Weller, Debbie, John, John, Burgess, Lisa, Denton, Jean, Done, Alaine, Edwards, Jayne, Garrett, Theresa, Gilla, Rajesh, Griffith-Norris, Samantha, Jones, Teresa, Linklater-Jones, Barbara, Nicholas, Claire, Owen, Sara, Perkins, Charlotte, Rowlands, Tessa, Steen, Julie, Turner, Sarah, Wales, Johanna, Woolley, Carol-Ann, Hormis, Anil, Collier, Dawn, Dixon, Kathryn, Gardner, Michael, Murray, Vicky, Soar, Harry, Walker, Rachel, Yaruk, Shifa, Yarwood, Daniel, Dill-Russell, Patrick, ruthy Arumugam, Bird, Georgina, Bleasby, Nichola, Chater-Lea, Peter, Craig, Emma, Davies, George, Foxton, Julie, Gilfillan, Helen, Jerram, Keelan, Kudsk-Iversen, Søren, Lee, Rebecca, Livesy, John, Mohabir, Amanda, Morris, Katherine, Raffles, Mike, Smith, Annika, Wang, Huiqi, Craig, James, Barratt, Nina, Bates, Andy, Bokor, Norbert, Cranshaw, Julius, Dominguez, Sergio, Fraine, Annette, Green, Richard, Molloy, Katie, Penny, Christina, Pitts, Sally, Purandare, Laura, Rabbi, Taslima, Serrano, Monica, Stoycheva, Tina, Vamplew, Luke, Williams, Steve, Berry, Julian, Adams, Benita, Bean, Sarah, Burt, Karen, Devooght-Johnson, Nicki, Hammonds, Fiona, Nicholls, Tom, Rowe, Michelle, Summers, Jessica, Ward, Olivia, Wroath, Belinda, Young, Gabbie, Matthews, Cathryn, Crosbie, Gearoid, Dunn, Alex, Eddy, Sara, Garcia, Lynda, Garwood, James, Green, Adam, Gupwell, Karin, Jones, Nicola, Marriott, Suzie, Masood, Mohammad, Mazzola, Francesca, McEvoy, Evanna, Ng, Yang, Park, Linda, Pauling, Stephanie, Potter, Alison, Renton, Caroline, Samways, Adam, Sibley, Pauline, Stead, Jenny, Thorpe, Kevin, Turner, Elizabeth, Walter, Michelle, Windsor, Kevin, Jha, Rajeev, Hooker, Nicolas, Kidel, Carlos, Abayalingam, Mayavan, Boulding, Richard, Coakley, Philip, Elahi, Noor, Helder, Filipe, Hilton, James, Joshi, Kunal, Kaur, Rupinder, Male, Victoria, Naji, Saqib, Pinto, Manuel, Rajendran, Yohinee, Save, Manjeet, Smith, Megan, Spence, Emily, Tang, Sophie, Urwin, Thomas, Wittenberg, Marc, Yan, Ruoling, Williams, Colin, Ardeshna, Arjun, Bamford, Peter, Hagyard, Sarah, Johnson, Patrick, McMonagle, Martina, Morris, Hayleigh, Murphy, Thomas, Papadopoulos, Stavros, Simpson, Emma, Smith-Williams, Jonathan, Vyapury, Vin, Juneja, Rohit, Black, Ethel, Eeles, Alex, Elliot, Justine, Jhanji, Shaman, Knoetze, Reynard, McCanny, Jamie, Oswald, Lauren, Roderick, Ellie, Gunning, Paul, Siddaiah, Narendra, Balachandar, Krishna, Bhimarasetty, Chandra, Bird, Laura, Blunt, Conny, Da Silva, Egidio, Darbyshire, Mark, Echaveznaguicni, Irene, Girgis, Youssef, Goodman, Martin, Gowda, Dinkar, Gowni, Rajashekar, Hayes, Melony, Jackson, Natalie, James, Anitha, Jayapal, Rekha, Jones, Claudette, Leung, Enid, Minhas, Simran, Mubarak, Mona, Muniyappa, Sudeshkumar, Nassa, Zehrin, Neal, Tom, Nyangoni, Kudakwashe, Panchakshariah, Suresh, Philpott, Bethany, Pinner, Craig, Pygall, Daniel, Rangaiah, Manamohan, Rao, Anil, Rea, William, Shellard, Richard, Shinner, Guy, Sutherland, Tony, Tharakan, Liza, Thomas, Sue, Travasso, Betty, Venkatesh, Pyda, Wortley, Dionne, Dickinson, Matthew, Benoliel, Sophie, Doyle, Nanci, Marriott, Cheryl, Martin, Sarah, Montague, Laura, Penhaligon, Kate, Saha, Rita, Santhirapala, Ramai, Suntharanathan, Lushani, Wakeford, Nichola, Webb, Carly, Winterbottom, Toby, Jordan, Lesley, Aldridge, Matt, Bolton-Hill, Clare, Cook, Prof Tim, Kangesan, Inthu, Kettley, Laura, Mew, Edward, Penketh, James, Quinn, Eleonore, Scholes, Sam, Uppugonduri, Sreekanth Rayalu, Allen, Lara, Lyndsay Bibb, Jill Brown, Busby, Charlotte, Katherine Cheshire, Davis, Matron Beryl, Denyer, Rebecca, Julie Edwards, Gardiner, Laura, Haque, Shamim, Icke, Julia, Jones, Katherine, Donna McIntosh, Mandy Oakley, Prasad, Linda, Victoria Roberts, Smallwood, Andrew, Tammy Smith, Stelfox, Lucy, Angela Stevens, Angela Willberry, Pratt, Oliver, Chincholkar, Mahindra, Darwin, Leanne, Dearden, Joy, Flowerdew, Adele, Harrison, Louise, Hartley, Robert, Ingham, Andrea, Jones, Ben, Lee, Stephanie, Longdon, Elizabeth, Marsden, Tracy, Meats, Kirsty, Michael, Angiy, Norwood-Green, Drew, O'Loughlin, Victoria, Owen, Holly, Patel, Manju, Perez, Jane, Whiston, Jessica, Wood, Andrea, Williams, Simon, Billingham, Chloe, Clarke, Caroline, Holmwood, Xantha, Knighton, Melba, Sands, Becky, Stevens, Paul, Strong-Sheldrake, Sophia, Taylor, Matthew, Rambhatla, Mrutyunjaya Rao, Brodier, Elizabeth, Choudhry, Ozerah, Choudhry, Usman, Fitzmaurice, Bethany, Hulme, Jonathan, Kannan, Santhana, Mak, Kay Anne, Namih, Miriam, Rajdev, Bharati, Rambhatla, Swetha, Reilley, Stuart, Rice, Rebecca, Sethia, Nalini, Smith, Katy, Wakhle, Rahul, Walmsley, Chris, Wrench, Ian, Agyemang, Michael, Bell, Elizabeth, Bird, Sarah, Briscoe, James, Cruickshanks, Andy, Drinkwater, Jake, Eggleston, Sam, Elms, Henry, Hammon, Laura, Harris, Meredith, Harrison, Christopher, Hirst, Claire, Housley, Kay, Kapoor, Krish, Kibutu, Faith, Kilner, Beverly, King, Emily, Kirk, Emily, Marshall, Esme, Mazzone, Paolo, Middle, Janet, Milner, Leanne, Mitchell, Adam, Morris, Becky, Mudd, Alexandra, Needham, Matthew, Nicholas, James, Penson, Rob, Perfitt, Joel, Pinkerton, Mark, Recto, Mayeth, Rehnstrom, Maria, Riley, Catherine, Ryan, Lorna, Smith, Jennie, Strong, Samantha, Thornley, Helen, Tookey, Bethany, West, Daniel, Whitaker, John, Willcock, Sarah, Willoughby, Shay, Wright, Jane, Jones, Paul, Abbott, Riquella, Bacon, Lisa, Godden, Christopher, Howe, David, Loughlin, Angela, Moore, Helen, Passey, Samuel, Rodgers, Rebekah, Foley, Paul, Hagan, Samantha, Marcus, Simon, Gardner, Irene, Haslam, Zena, Morrison, Moira, Obeysekera, Chai, Porter, Kim, Terrett, Helen, Henning, Jeremy, Baillie, Kirsty, Buchanan, Ella, Cain, Martyn, Colling, Kerry, Duarte, Raquel, Gibb, Chris, Hynd, Ryan, Irvine, Victoria, Lyons, Rob, Middleton, Karoline, Razaque, Usman, Sheikh, Faisal, Tabiner, Tariq, Frey, Christian, Brown, Carly, Clifford, Gayle, McKee, Madeleine, Stidolph, Beverley, White, Emert, Campbell, Bridget, Mason, Sophie, Parsons, Penny, Santana-Vaz, Natasha, Stagg, Camilla, Shah, Nirav, Barnes, Veronica, Bedoes, Penny, Clarke, Jonathan, Crichton, Robert, Das, Madhurima, Davies, Paul, Farrah, Helen, Hegarty, Aoife, Howlett, Luisa, Kleine-Brueggeney, Maren, Ma, Samson, Marsh, Veronica, McAndrew, Katherine, Mellinghoff, Johannes, Oscier, Christopher, Ryan, Christine, Smee, Elizabeth, Thurairasa, Dushanthi, Goel, Vandana, Adeney, Christopher, Behl, Sanjay, Chadwick, Karen, Coleman, Thomas, Davies, Claire, Dealing, Sharon, Elwood, Jamie, Fadden, Emma, Gillies, Stella, Kennedy, Tom, Miller, Thomas, Morton, Simon, Reilly, Rebecca, Smyth, Sue, Syratt, Thomas, Welfare, Emma, Thomas, Elizabeth, Awolesi, Jennifer, Bennett, Hannah, Bradshaw, Elizabeth, Crabtree, David, Duraman, Lauren, Hughes, Kerry, Kirk, Sarah, Leonard, Dee, Stoeter, Rachel, Parrington, Simon, Andorka, Matyas, Ang, Kingsin, Bailey, Linda, Brooks, Stephanie, Chiu, Oon, Collins, Sally, Jack, James, Knight, Harry, Lynn-Smith, Anastasia, Mackenzie, Matt, Maher, Sarah, Nimako, Louise, Sangaralingham, Siva, Sanusi, Seliat, Weller, Samantha, Willard, Elizabeth, Kulkarni, Anand, Carroll, Peter, Clark, Martyn, Coulding, Martina, Denman, Elizabeth, El Habishi, Mohyman, Haywood, Patrick, McConniffe, Jane, McCormick, Jacqueline, Melachuri, Krishnakar, Philip, Evelyn, Ridgway, Stephanie, Savill, Heather, Sharif, Kamal, Vere, Joanne, Harris, Stephen, Coombs, Alex, Darch, Flora, Doble, Patricia, Foot, Jayne, Gillett, Chris, Judd, Thomas, Moss, Alison, Pawley, Corinne, Tait, Moira, Whitcher, Alison, Wood, Natalie, Montgomery, Jane, De Selincourt, Gabrielle, Fitzell, Pauline, Flower, Katie, Horan, Kathleen, Hulance, Bianca, Mercer, Pauline, Shaw, Sarah, Webber, Robin, Kakkar, Manish, Joachim, Suganthi, Aggarwal, Seetal, Ali, Amr, Ang, Boon, Bhardwa, Vishnu, Deamer, Wendy, Filby, Lorna, Fletcher, Jo, Fombon, Felix, Gawley, Shannon, Gosavi, Chandrakant, Hindle, Julia, Lawrence, Tara, Maher, Robert, Moore, Samantha, Netherton, Kimberley, Orasanu, Teodora, Poimenidi, Evangelia, Potter, Louise, Ranasinghe, Niyesa, Ribeiro, Lester, Ruth, Thoy, Butler, Susie, Sutton, Tim, Thomas, Issy, Veluvolu, Divya, Walker, Thomas, Wariyapola, Charindri, Zaheer, Lail, Luoma, Valpuri, Ferguson, Catriona, Stewart, Adrienne, Bapat, Smita, Bose, Gahan, Bugo, John, Campbell, Marta, Cervi, Elizabeth, Ciechanowicz, Sarah, Coathup, Rachel, El-Wahab, Niveen, Frith, Claire, Gill, Preea, Gray, Martin, Luoma, Astri, Mehta, Sachin, Murali, Mayur, Mussad, Asya, Parker, Laura, Patch, Josh, Perella, Paolo, Robson, Emily, Setty, Thunga, Vieira, Filipe, Wall, Josh, Witby, Jon, Zghaibe, Wael, Zhang, Sheldon, Merron, Stephen, Abano, Nenette, Barnett, Loretta, Barry, Adrian, Birks, Pamela, Brade, Judith, Butler, Adrian, Carpio, Racquel, Colclough, Viv, Eaton, Katrina, Edwards, Carol, Emery, Lisa, Finney, Kay, Gallagher, Susan, Ghatge, Satyajeet, Grocott, Jeanette, Hall, Amanda, Hardwick, Alison, Harrison, Anne, Hendy, Susan, Hiden, Joanne, James, Alex, Jeganath, Vijay, Kumar, Rahul, Lord, Stephen, Lovatt, Rachel, Lyjko, Susan, Maguire, Holly, Marsden, Mia, Mehta, Venu, Mishra, Rajeev, Mittal, Rohit, Moores, Andrew, Nalwaya, Pramod, Omeara, Matthew, Orosz, Sandor, Patel, Nehal, Pattison, Nicola, Ponce, Ida, Puttapa, Ashok, Rajamanickam, Tamilselvan, Rao, Nagaraj, Remegoso, Alda, Rhodes, Rochelle, Rooney, Angela, Sadler, Emma, Singh, Permendra, Soon, Wai, Tomlins, Simon, Umbarje, Kanchan, Vallabhaneni, Murali, Varquez, Resti, Wain, Jill, White, Sorana, Willetts, Elizabeth, Williams, Benedict, Wood, Andrew, Washington, Stephen, Anipindi, Sujata, Aston, Jennifer, Bradley-Potts, Joanne, Butler, Ian, Cunningham, Jennifer, Duncan, Alastair, Garrard, Hywel, Haslam, Daniel, Kirk, Danielle, McCormick, Richard, McEvoy, Michael, Mistry, Sara, Ormandy, Danielle, Sharp, Tracy, Song, Andrew, Williamson, Georgina, Young, Francis, Tasker, Laura, Bailey, Elizabeth, Baker, Roisin, Bamford, Amy, Bergin, Colin, Blackburn, Julia, Bodo, Kinga, Carrera, Ronald, Cooper, Lauren, Despy, Liesl, Ellis, Karen, Foster, Morgan, Garner, Sharon, Harkett, Lyndon, Harkett, Samantha, Ip, Peter, Lay, Frances, Liu, Hon Sum, Mee, Laura, Nazir, Samir, Osbourne-Wylde, Jacob, Patel, Krupali, Pope, Martin, Reeves, Emma, Sahni, Melanie, Small, Charlotte, Spooner, Brendon, Spruce, Elaine, Whitehouse, Arlo, Wharton, Nicholas, Alexander, Rachel, Ashford, Amy, Aspinall, Rebecca, Bateman, Richard, Bell, Matthew, Bennett-Britton, Jennifer, Bewley, Jeremy, Blanshard, Hannah, Bourdeaux, Christopher, Brook, Peter, Cain, Helen, Cameron, Claire, Cassells, Naomi, Chauhan, Nilesh, Clarke, Adrian, Cockroft, Melanie, Cohen, Alan, Collins, Joanna, Cominos, Timothy, Craven, Rachael, Davies, Ian, Dolling, Sarah, Dowse, Claire, Driver, Kate, Duffen, Adam, Dunn, Sarah-Jane, Earnshaw, Charlotte, Forrest, Frances, Freshwater-Turner, Dan, Gardner, Issie, Gibbins, Matthew, Gibbison, Ben, Gleeson, Suzanne, Gould, Timothy, Govier, Matthew, Gupta, Benjamin, Hadfield, John, Harvey, Nicola, Henderson, Sophia, Herbert, Lara, Hillier, James, Hood, Elizabeth, Howes, Ben, Howes, Helen, Hughes, Sian, Jackson, Kathryn, Jacobs, Toby, Jenkins, Emma, Jenkins, Ian, Johnstone, Alistair, Jones, Lydia, Jones, Rebecca, Joshi, Natasha, Kamalanathan, Kajan, Kellie, Neil, Kidd, Lawrence, Kinsella, Mike (Stephen), Knight, Thomas, Leslie, Rebecca, Linter, Stephen, Lovell, Tim, Martin, Matthew, McHugh, Ursula, McIndoe, Andrew, McKendry, Rachel, Middleditch, Alexander, Miles, Edward, Milne, Lesley, Molyneux, Mat, Muchatuta, Neil, Murphy, Ruth, O'Higgins, Fran, Oglesby, Kieran, Pachucki, Marcin, Paoloni, Claudia, Patel, Nishita, Patton, Catherine (Katie), Paulich, Skylar, Pearson, Annabel, Pickering, Anthony, Platt, Martin, Pope, Charlie, Pryn, Steve, Ranjan, Amit, Rasburn, Neil, Ray, Andrew, Rivers, Jonathan, Rooney, Kieron, Ryder, Ian, Sanders, Sarah, Sasi-Kumar, Kavita, Scott, Liam, Scrutton, Mark, Searles, Chloe, Self, James, Shah, Sanjoy, Sheils, Mark, Shipway, Toby, Short, Heather, Simmonds, Lauren, Smith, Daniela, Soodan, Anjali, Steeds, Charlotte, Sweet, Katie, Syme, Phoebe, Thomas, Matthew J., Tomkins, Sally, Underwood, Susan, Wagstaff, Adrian, Watson, Paul, Webster, Denise, Welham, Katie, Whaley, Anne, Williams, Helen, Wilson, Hannah, Winton, Anoushka, Woodward, Thomas, Worthington, Jeanie, Younie, Stuart, Bradbury, Carol, Auldin, Mohammad, Bosworth, Kate, Choksey, Falguni, Dudkowsky, Bernice, Elton, John, Evans, Gail, Grove, Charlotte, Ingram, Clare, Kelly, Andrew, Lax, Peter, Lynes, Elena, Madden, George, Mistry, Vinesh, Molitor, Timothy, Morgan, Catherine, Morris, Natalie, Pallister, Emily, Patel, Pooja, Ragothaman, Vijay, Ramm, Katie, Robinson, Ben, Sharma, Meghna, Shuttleworth, Daniel, Vaidyanath, Chandrashekhar, Warren, Jen, Wong, Rachel, Lau, Gary, Conway, Hannah, Majeed, Adeel, Parmar, Jaina, Rana, Meenal, Yusuff, Hakeem, Rimmer, Corinne, Abdelrhamen, Wael, Allison, Kathryn, Burns, Karen, Cahill, Nichola, Clark, Jon, Donaldson, Hannah, Heseltine, Nick, Mackenzie, Nicola, McArthur, Carol, Moreland, Neil, Shohelly, Sharmin, Thatcher, Hilary, Tran, Steven, Walker, Catriona, Al-Rawi, Samar, Wakatsuki, Mai, Aarvold, Alice, Addy, Melissa, Ashfield, Georgina, Beauchamp, Nigel, Belcher, Alex, Bennett, Tom, Burgess, Victoria, Burton, Andy, Campbell, Matthew, Cruikshank, Ross, Cumpstey, Andy, Davies, Rhys, Ford, Chris, Ford, Renee, Hawkins, Lesley, Hendra, Tim, Huttley, Thomas, Jolly, Erica, Kalcheva, Nevena, Kennedy, Charlie, Lees, Jessica, Lowe, Richard, Martindale, Tim, McCormick, Sarah, McPhee, Hannah, Millinchamp, Fran, Muddanyake, Rahul, Osorio, Eduardo, Patel, Kiran, Pestell, Samuel, Phillips, Steve, Plumb, Jamie, Pratt, Thomas, Quartermain, Bert, Reeves, Rebecca, Robin, Sophie, Scully, Thecla, Sehgal, Anamika, Seifalan, Said, Shuttleworth, Suzanne, Siddique, Omar, Sleap, Esme, Smith, Victoria, Snooks, Richard, Solanki, Virginia, Timmons, Christina, Trimble, Alice, Tunney, Doug, Walker, Kat, Wee, Naomi, Wigley, James, Wilkins, Huw, Wong, Hanna, Wood, Laura, Yogya, Dinithi, Young, Ruth, Shanbhag, Sumant, Ameer, Farrukh, Finnity, Jonathan, Haddock, James, Hunt, Alex, Kuravi, Aditya, Lenehen, Fionnuala, McOwat, Lindsay, Naqvi, Asad, Richardson, Lisa, Siddons, Victoria, Charters, Seema, Jumani, Deepa, Bradley, Peter, Bubb, Laura, Chan, Rebekah, Connell, Lynne, Crone, Rachel, Davis, Hannah, Defeyter, Pascal, Evans, Alison, Howard, Ramdas, Prady, Helena, Rogers, Natalie, Roughley, Lindsay, Whittle, Helen, Zeinali, Daniel, Cherian, Vinanti, Latif, Muhammad Usman, Bajwa, Maqsood, Guy, Jonny, Guz, Krzysztos, Iftikhar, Nauman, Iwanicka, Beata, Khan, Mujeeb, Love, Anna-Marie, Montgomery, Celia, Mulvany, Stephen, O'Kane, Emma, O'Kane, Sinead, Siddiqui, Mansoor, Small, Cathal, Subhani, Shaik, Dana, Emily, Kennedy, Richard, Amey, Isobel, Ashton, Hilary, Baird, Yolanda, Bushell, Caroline, Chadbourn, Indra, Cohen, Merle, Finlay, Emma, Gilbert, Jeanette, Helm, David, Lewis, Carla, Macallan, Jenny, Margalef, Jordi, Martins, Nic, Metiu, Gabi, Moore, Sally, Rose, Hannah, Standley, Tom, Page, Valerie, Bakonyi, Tomas, Ben-Nathan, Simon, Claridge, Melanie, Evans, Jackie, Gardiner, Harriet, Kantsedikas, Ilya, Lachana, Ann, Makker, Ratna, Markwell, Karen, Merrill, Colin, Mukerjee, Prasun, O'Donohoe, Lynn, Oglesby, Fiona, Osbourne, Laura, Parker, Beena, Passaro, Emelia, Russell, Katherine, Shah, Sneh, Sundayi, Saul, Walker, Elaine, Zhao, Xiao, Hosdurga, Guru, Cotterill, Donna, Crowther, Nicola, Dymond, Harvey, Ratcliffe-Law, Thomas, Saunders, Glenn, Saunders, Thomas, Gopal, Vijayakumar, Ahmed, Sajjad, Ali, Abdalla, Crawford, Sarah, De'Ath, Clare, Humphreys, Sally, Swanevelder, Carin, Yap, Yeng, Silk, Jane, Adeniba, Rashidat, Armstrong, Richard, Egole, Osi, Fan, Karen, Gilbert, Kayleigh, Gould, Laura, Hickman, John, Long, Fei, Pahary, Sheik, Reichman, Michelle, Simpson, Kathryn, Singaravelu, Suresh, Ahmed, Dabeeruddeen, Brailsford, Fiona, Bridge, Laura, Collis, Lauren, Jacob, Reni, Jones, Cathy, Kirrage, Amanda, Lowe, Jessica, Mullen, Joanne, Prenter, Andrew, Sarao, Sarbpreet, Sharpley, David, Wankhade, Kavita, Persad, Cindy, Burtenshaw, Andrew, Blyth, Kate, Corcoran, Felicity, Davis, Caroline, Dolan, Rachel, Hussain, Nageena, Pearson, Emma, Pierson, Anna, Poyntz, Victoria, Thrush, Jessica, Wollaston, Alex, Wollaston, Julie, Clements, Paul, Biswas, Arindam, Clayton, Sarah, Clayton, Thomas, Cochrane, Naomi, Duell, Kirsty, Evans, James, Gaskell, Mark, Heaton, Dianne, Heaton, Linzi, Hill, Claire, Killen, Ruth, Latham, Sandra, McIntosh, Gemma, Moore, Chris, Parkinson, Val, Pemberton, Nicola, Robinson, Matthew, Sawyer, Alastair, Sykes, Isabelle, Taylor, Tracey, Tierney, Caroline, van de Snepscheut-Jones, Katja, Wade, Emily, Wahid, Mohammed, Wang, Henry, Williams, Claire, Wu, Dominic, Yadagiri, Manjula, Zabauski, Dmitry, Troth, Laura, Andrews, Eleanor, Bains, Ranjit, Beard, Laura, Birch, Janine, Britton, Rachael, Salmon, Mike, Vassell, Elizabeth, Kubisz-Pudelko, Agnieszka, Allen, Jamie, Allison, Joanna, Baird, Andrew, Gregory, Rob, Luckhurst, Hannah, Mairs, Rebecca, Marshall, Ben, Pippard, Lucy, Pitt-Kerby, Tressy, Savva, Andrew, Walkden, Graham, Weegenaar, Celly, Chandler, Ben, Kanakaraj, Muthuraj, Chamberlain, Andy, Dyer, Simon, Elliott, Kerry, Forshaw, Greg, Furness, Rosie, Gibson, Andy, Howe, Laura, Kent, Sarah, Rodgers, Alicia, Straughan, Ben, Temlett, Emma, Whitwell, John, Wong, D.J.N., Harris, S.K., and Moonesinghe, S.R.
Published: 2018
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25. Intrusion Detection in Computer Networks using Lazy Learning Algorithm

Author: Chellam, Aditya, L, Ramanathan, and S, Ramani
Published: 2018
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26. Patient-reported outcomes for ambulatory surgery

Author: Warnakulasuriya, Samantha R., Patel, Reshma C., Singleton, Georgina F., and Moonesinghe, S. Ramani
Published: 2020
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27. Systematic reviews and consensus definitions for the Standardised Endpoints in Perioperative Medicine (StEP) initiative: mortality, morbidity, and organ failure

Author: Alexander I.R. Jackson, Oliver Boney, Rupert M. Pearse, Andrea Kurz, D. James Cooper, Wilton A. van Klei, Luca Cabrini, Timothy E. Miller, S. Ramani Moonesinghe, Paul S. Myles, Michael P.W. Grocott, Paul Myles, T.J. Gan, Phil Peyton, Dan Sessler, Martin Tramèr, Alan Cyna, Gildasio S. De Oliveira, Christopher Wu, Mark Jensen, Henrik Kehlet, Mari Botti, Guy Haller, Mike Grocott, Tim Cook, Lee Fleisher, Mark Neuman, David Story, Russell Gruen, Sam Bampoe, Lis Evered, David Scott, Brendan Silbert, Diederik van Dijk, Cor Kalkman, Matthew Chan, Hilary Grocott, Rod Eckenhoff, Lars Rasmussen, Lars Eriksson, Scott Beattie, Duminda Wijeysundera, Giovanni Landoni, Kate Leslie, Bruce Biccard, Simon Howell, Peter Nagele, Toby Richards, Andre Lamy, Manoj Lalu, Rupert Pearse, Monty Mythen, Jaume Canet, Ann Moller, Tony Gin, Marcus Schultz, Paolo Pelosi, Marcelo Gabreu, Emmanuel Futier, Ben Creagh-Brown, Tom Abbot, Andy Klein, Tomas Corcoran, D. Jamie Cooper, Stefan Dieleman, Elisabeth Diouf, David McIlroy, Rinaldo Bellomo, Andrew Shaw, John Prowle, Keyvan Karkouti, Josh Billings, David Mazer, Mohindas Jayarajah, Michael Murphy, Justyna Bartoszko, Rob Sneyd, Steve Morris, Ron George, Ramani Moonesinghe, Mark Shulman, Meghan Lane-Fall, Ulrica Nilsson, Nathalie Stevenson, Wilton van Klei, Tim Miller, Sandy Jackson, Donal Buggy, Tim Short, Bernhard Riedel, Vijay Gottumukkala, Nathan Pace, Bilal Alkhaffaf, Mark Johnson, Intensive Care Medicine, ACS - Pulmonary hypertension & thrombosis, and AII - Inflammatory diseases
Subjects: surgery, Anesthesiology and Pain Medicine, consensus, morbidity, organ failure, anaesthesia, postoperative morbidity, perioperative outcomes, mortality
Abstract: Background: Mortality, morbidity, and organ failure are important and common serious harms after surgery. However, there are many candidate measures to describe these outcome domains. Definitions of these measures are highly variable, and validity is often unclear. As part of the International Standardised Endpoints in Perioperative Medicine (StEP) initiative, this study aimed to derive a set of standardised and valid measures of mortality, morbidity, and organ failure for use in perioperative clinical trials. Methods: Three domains of endpoints (mortality, morbidity, and organ failure) were explored through systematic literature review and a three-stage Delphi consensus process using methods consistently applied across the StEP initiative. Reliability, feasibility, and patient-centredness were assessed in round 3 of the consensus process. Results: A high level of consensus was achieved for two mortality time points, 30-day and 1-yr mortality, and these two measures are recommended. No organ failure endpoints achieved threshold criteria for consensus recommendation. The Clavien–Dindo classification of complications achieved threshold criteria for consensus in round 2 of the Delphi process but did not achieve the threshold criteria in round 3 where it scored equivalently to the Post Operative Morbidity Survey. Clavien–Dindo therefore received conditional endorsement as the most widely used measure. No composite measures of organ failure achieved an acceptable level of consensus. Conclusions: Both 30-day and 1-yr mortality measures are recommended. No measure is recommended for organ failure. One measure (Clavien–Dindo) is conditionally endorsed for postoperative morbidity, but our findings suggest that no single endpoint offers a reliable and valid measure to describe perioperative morbidity that is not dependent on the quality of deli-vered care. Further refinement of current measures, or development of novel measures, of postoperative morbidity might improve consensus in this area.
Published: 2023

28. Structural indicators of quality care for children undergoing emergency abdominal surgery.

Author: Selman, Andrew M. R., Sogbodjor, Lisa A., Williams, Karen, Davenport, Mark, and Moonesinghe, S. Ramani
Subjects: SURGICAL emergencies, ABDOMINAL surgery, CHILD care
Published: 2023
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29. Improvement Science in Anaesthesia

Author: Wagstaff, Duncan T., Bedford, James, and Moonesinghe, S. Ramani
Published: 2017
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30. Developing and validating subjective and objective risk-assessment measures for predicting mortality after major surgery: An international prospective cohort study

Author: Wong, Danny J. N., Harris, Steve, Sahni, Arun, Bedford, James R., Cortes, Laura, Shawyer, Richard, Wilson, Andrew M., Lindsay, Helen A., Campbell, Doug, Popham, Scott, Barneto, Lisa M., Myles, Paul S., Moonesinghe, S. Ramani, Gaynor-Brook, Louise, and Stone, Clare
Subjects: Surgery -- Complications and side effects -- Patient outcomes, Prognosis -- Methods -- Evaluation, Perioperative care -- Quality management, Postoperative complications -- Risk factors, Biological sciences
Abstract: Background Preoperative risk prediction is important for guiding clinical decision-making and resource allocation. Clinicians frequently rely solely on their own clinical judgement for risk prediction rather than objective measures. We aimed to compare the accuracy of freely available objective surgical risk tools with subjective clinical assessment in predicting 30-day mortality. Methods and findings We conducted a prospective observational study in 274 hospitals in the United Kingdom (UK), Australia, and New Zealand. For 1 week in 2017, prospective risk, surgical, and outcome data were collected on all adults aged 18 years and over undergoing surgery requiring at least a 1-night stay in hospital. Recruitment bias was avoided through an ethical waiver to patient consent; a mixture of rural, urban, district, and university hospitals participated. We compared subjective assessment with 3 previously published, open-access objective risk tools for predicting 30-day mortality: the Portsmouth-Physiology and Operative Severity Score for the enUmeration of Mortality (P-POSSUM), Surgical Risk Scale (SRS), and Surgical Outcome Risk Tool (SORT). We then developed a logistic regression model combining subjective assessment and the best objective tool and compared its performance to each constituent method alone. We included 22,631 patients in the study: 52.8% were female, median age was 62 years (interquartile range [IQR] 46 to 73 years), median postoperative length of stay was 3 days (IQR 1 to 6), and inpatient 30-day mortality was 1.4%. Clinicians used subjective assessment alone in 88.7% of cases. All methods overpredicted risk, but visual inspection of plots showed the SORT to have the best calibration. The SORT demonstrated the best discrimination of the objective tools (SORT Area Under Receiver Operating Characteristic curve [AUROC] = 0.90, 95% confidence interval [CI]: 0.88-0.92; P-POSSUM = 0.89, 95% CI 0.88-0.91; SRS = 0.85, 95% CI 0.82-0.87). Subjective assessment demonstrated good discrimination (AUROC = 0.89, 95% CI: 0.86-0.91) that was not different from the SORT (p = 0.309). Combining subjective assessment and the SORT improved discrimination (bootstrap optimism-corrected AUROC = 0.92, 95% CI: 0.90-0.94) and demonstrated continuous Net Reclassification Improvement (NRI = 0.13, 95% CI: 0.06-0.20, p < 0.001) compared with subjective assessment alone. Decision-curve analysis (DCA) confirmed the superiority of the SORT over other previously published models, and the SORT-clinical judgement model again performed best overall. Our study is limited by the low mortality rate, by the lack of blinding in the 'subjective' risk assessments, and because we only compared the performance of clinical risk scores as opposed to other prediction tools such as exercise testing or frailty assessment. Conclusions In this study, we observed that the combination of subjective assessment with a parsimonious risk model improved perioperative risk estimation. This may be of value in helping clinicians allocate finite resources such as critical care and to support patient involvement in clinical decision-making., Author(s): Danny J. N. Wong 1,2, Steve Harris 3, Arun Sahni 1,2, James R. Bedford 1,2, Laura Cortes 2, Richard Shawyer 4, Andrew M. Wilson 5, Helen A. Lindsay 5, [...]
Published: 2020
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31. Chapter 50 - Perioperative Quality Improvement Programme

Author: Singleton, Georgina F., Edwards, Kylie-Ellen, and Moonesinghe, S. Ramani
Published: 2023
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32. Chapter 7 - Large-Scale Audits: Using Citizen Science to Gather High-Quality “Big Data”

Author: Moonesinghe, S. Ramani
Published: 2023
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33. Predicting severe pain after major surgery: a secondary analysis of the Peri‐operative Quality Improvement Programme (PQIP) dataset.

Author: Armstrong, R. A., Fayaz, A., Manning, G. L. P., Moonesinghe, S. R., Oliver, C. M., Moonesinghe, S. Ramani, Wagstaff, Duncan, Bedford, James, Sahni, Arun, McGuckin, Dermot, Gilhooly, David, Santos, Cristel, Wilson, Jonathan, Martin, Peter, Singleton, Georgina, Edwards, Kylie, Vindrola‐Padros, Cecilia, Warnakulasuriya, Samantha, Dorey, Jenny, and Leemans, Irene
Subjects: SECONDARY analysis, TYPE 1 diabetes, POSTOPERATIVE pain, PSYCHOLOGICAL well-being, SURGERY, CANCER pain, ANALGESIA
Abstract: Summary: Acute postoperative pain is common, distressing and associated with increased morbidity. Targeted interventions can prevent its development. We aimed to develop and internally validate a predictive tool to pre‐emptively identify patients at risk of severe pain following major surgery. We analysed data from the UK Peri‐operative Quality Improvement Programme to develop and validate a logistic regression model to predict severe pain on the first postoperative day using pre‐operative variables. Secondary analyses included the use of peri‐operative variables. Data from 17,079 patients undergoing major surgery were included. Severe pain was reported by 3140 (18.4%) patients; this was more prevalent in females, patients with cancer or insulin‐dependent diabetes, current smokers and in those taking baseline opioids. Our final model included 25 pre‐operative predictors with an optimism‐corrected c‐statistic of 0.66 and good calibration (mean absolute error 0.005, p = 0.35). Decision‐curve analysis suggested an optimal cut‐off value of 20–30% predicted risk to identify high‐risk individuals. Potentially modifiable risk factors included smoking status and patient‐reported measures of psychological well‐being. Non‐modifiable factors included demographic and surgical factors. Discrimination was improved by the addition of intra‐operative variables (likelihood ratio χ2 496.5, p < 0.001) but not by the addition of baseline opioid data. On internal validation, our pre‐operative prediction model was well calibrated but discrimination was moderate. Performance was improved with the inclusion of peri‐operative covariates suggesting pre‐operative variables alone are not sufficient to adequately predict postoperative pain. [ABSTRACT FROM AUTHOR]
Published: 2023
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34. Distance travelled to hospital for emergency laparotomy and the effect of travel time on mortality: cohort study

Author: C.M. Oliver, Peter Martin, Tom Salih, Mike G. Bassett, S Ramani Moonesinghe, and Tom Poulton
Subjects: medicine.medical_specialty, business.industry, medicine.medical_treatment, Confounding, Health services research, 030208 emergency & critical care medicine, health policy, health services research, Hospital medicine, Continuous variable, Travel time, hospital medicine, surgery, 03 medical and health sciences, 0302 clinical medicine, Laparotomy, Emergency medicine, Medicine, 030212 general & internal medicine, business, Socioeconomic status, Cohort study, Original Research
Abstract: ObjectivesTo evaluate whether distance and estimated travel time to hospital for patients undergoing emergency laparotomy is associated with postoperative mortality.DesignNational cohort study using data from the National Emergency Laparotomy Audit.Setting171 National Health Service hospitals in England and Wales.Participants22 772 adult patients undergoing emergency surgery on the gastrointestinal tract between 2013 and 2016.Main outcome measuresMortality from any cause and in any place at 30 and 90 days after surgery.ResultsMedian on-road distance between home and hospital was 8.4 km (IQR 4.7–16.7 km) with a median estimated travel time of 16 min. Median time from hospital admission to operating theatre was 12.7 hours. Older patients live on average further from hospital and patients from areas of increased socioeconomic deprivation live on average less far away.We included estimated travel time as a continuous variable in multilevel logistic regression models adjusting for important confounders and found no evidence for an association with 30-day mortality (OR per 10 min of travel time=1.02, 95% CI 0.97 to 1.06, p=0.512) or 90-day mortality (OR 1.02, 95 % CI 0.97 to 1.06, p=0.472).The results were similar when we limited our analysis to the subgroup of 5386 patients undergoing the most urgent surgery. 30-day mortality: OR=1.02 (95% CI 0.95 to 1.10, p=0.574) and 90-day mortality: OR=1.01 (95% CI 0.94 to 1.08, p=0.858).ConclusionsIn the UK NHS, estimated travel time between home and hospital was not a primary determinant of short-term mortality following emergency gastrointestinal surgery.
Published: 2020

35. Women in anaesthesia, a special issue of the British Journal of Anaesthesia

Author: Christa Boer, S Ramani Moonesinghe, Jennifer M. Hunter, Hugh C. Hemmings, Cynthia A. Wong, IOO, Anesthesiology, ACS - Microcirculation, and ACS - Diabetes & metabolism
Subjects: medicine.medical_specialty, Biomedical Research, business.industry, media_common.quotation_subject, Sexism, MEDLINE, Cultural Diversity, Career Mobility, Leadership, Physicians, Women, Anesthesiology and Pain Medicine, Anesthesiology, Family medicine, Cultural diversity, Humans, Medicine, Female, business, Inclusion (education), Introductory Journal Article, Diversity (politics), media_common
Published: 2020

36. The Postoperative Pancreas Imaging

Author: Ajaykumar C. Morani, Ahmed Taher, Nisha S. Ramani, Bilal Mujtaba, and Asif Patel
Subjects: stricture, medicine.medical_specialty, lcsh:Internal medicine, business.industry, Mortality rate, medicine.medical_treatment, pancreatic cancer, medicine.disease, Surgery, Review article, medicine.anatomical_structure, pancreatic fistula, Pancreatic fistula, Intensive care, Pancreatic cancer, Pancreatectomy, postoperative complications, Medicine, lcsh:Diseases of the digestive system. Gastroenterology, Postsurgical complications, pancreatectomy, lcsh:RC799-869, business, Pancreas, lcsh:RC31-1245
Abstract: Abstract Recent advances in the surgical techniques and postoperative intensive care have led to a decrease in the mortality rates after major pancreatic procedures, which now ranges from 1 to 3%. However, the morbidity rates are still high, resulting in longer hospital stays and greater cost. Imaging plays a fundamental role in the postoperative assessment. Specially, multidetector computed tomography scans (MDCT) is the modality of choice in the postoperative settings. Early diagnosis of the postoperative complications and differentiating them from being normal or expected postoperative findings is crucial to offer the best possible care for patients and to decrease the morbidity and mortality associated with surgery. In this article, we will briefly review the normal pancreatic anatomy, discuss the main types of pancreatic surgeries, and illustrate the imaging findings during the early postoperative period and of the main postsurgical complications in both acute and chronic postoperative settings.
Published: 2020

37. Chapter 7 - Blockchain for digital rights management

Author: S, Ramani, Vishva E, Sri, Dua, Lakshit, Abrol, Arya, and Karuppiah, Marimuthu
Published: 2022
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38. Chapter 5 - Blockchain for intrusion detection systems

Author: Shetty, Tanmay, Negi, Saloni, Kulshrestha, Anushka, Choudhary, Shaifali, S, Ramani, and Karuppiah, Marimuthu
Published: 2022
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39. Chapter 1 - Blockchain architecture, taxonomy, challenges, and applications

Author: Kasi, Nisanth Reddy, S, Ramani, and Karuppiah, Marimuthu
Published: 2022
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40. Surgical Antibiotic Prophylaxis in an Era of Antibiotic Resistance: Common Resistant Bacteria and Wider Considerations for Practice

Author: Menz,Bradley D, Charani,Esmita, Gordon,David L, Leather,Andrew JM, Moonesinghe,S Ramani, and Phillips,Cameron J
Subjects: Infection and Drug Resistance
Abstract: Bradley D Menz,1 Esmita Charani,2,3 David L Gordon,4,5 Andrew JM Leather,6 S Ramani Moonesinghe,7,8 Cameron J Phillips1,4,9 1SA Pharmacy, Flinders Medical Centre, Southern Adelaide Local Health Network, Adelaide, South Australia, Australia; 2Division of Infectious Diseases and HIV Medicine, Department of Medicine, Groote Schuur Hospital, University of Cape Town, Cape Town, South Africa; 3National Institute for Health Research, Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance, Department of Medicine, Imperial College London, London, UK; 4Flinders Health & Medical Research Institute, College of Medicine and Public Health, Flinders University, Adelaide, South Australia, Australia; 5Division of Medicine, Flinders Medical Centre, Southern Adelaide Local Health Network, Adelaide, South Australia, Australia; 6Centre for Global Health and Health Partnerships, School of Population Health and Environmental Science, Kings College London, London, UK; 7Centre for Perioperative Medicine, UCL Division of Surgery and Interventional Science, London, UK; 8UCL Hospitals NIHR Biomedical Research Centre, London, UK; 9Clinical and Health Sciences, University of South Australia, Adelaide, South Australia, AustraliaCorrespondence: Cameron J PhillipsCollege of Medicine and Public Health, Flinders University, Adelaide, South Australia, 5001, AustraliaTel +61 8 8204 4400Email Cameron.Phillips@flinders.edu.auAbstract: The increasing incidence of antimicrobial resistance (AMR) presents a global crisis to healthcare, with longstanding antimicrobial agents becoming less effective at treating and preventing infection. In the surgical setting, antibiotic prophylaxis has long been established as routine standard of care to prevent surgical site infection (SSI), which remains one of the most common hospital-acquired infections. The growing incidence of AMR increases the risk of SSI complicated with resistant bacteria, resulting in poorer surgical outcomes (prolonged hospitalisation, extended durations of antibiotic therapy, higher rates of surgical revision and mortality). Despite these increasing challenges, more data are required on approaches at the institutional and patient level to optimise surgical antibiotic prophylaxis in the era of antibiotic resistance (AR). This review provides an overview of the common resistant bacteria encountered in the surgical setting and covers wider considerations for practice to optimise surgical antibiotic prophylaxis in the perioperative setting.Keywords: antibiotic resistance, perioperative care, surgical antibiotic prophylaxis, surgical site infection, antimicrobial stewardship
Published: 2021

41. Development and internal validation of a model for postoperative morbidity in adults undergoing major elective colorectal surgery: the peri‐operative quality improvement programme (PQIP) colorectal risk model.

Author: Bedford, J., Martin, P., Crowe, S., Wagstaff, D., Santos, C., Singleton, G., Baumber, R., Vindrola‐Padros, C., Vohra, R., Swart, M., Oliver, C. M., Dorey, J., Leeman, I., Moonesinghe, S. R., Moonesinghe, S. Ramani, Wagstaff, Duncan, Bedford, James, Sahni, Arun, McGuckin, Dermot, and Gilhooly, David
Abstract: Summary: Over 1.5 million major surgical procedures take place in the UK NHS each year and approximately 25% of patients develop at least one complication. The most widely used risk‐adjustment model for postoperative morbidity in the UK is the physiological and operative severity score for the enumeration of mortality and morbidity. However, this model was derived more than 30 years ago and now overestimates the risk of morbidity. In addition, contemporary definitions of some model predictors are markedly different compared with when the tool was developed. A second model used in clinical practice is the American College of Surgeons National Surgical Quality Improvement Programme risk model; this provides a risk estimate for a range of postoperative complications. This model, widely used in North America, is not open source and therefore cannot be applied to patient populations in other settings. Data from a prospective multicentre clinical dataset of 118 NHS hospitals (the peri‐operative quality improvement programme) were used to develop a bespoke risk‐adjustment model for postoperative morbidity. Patients aged ≥ 18 years who underwent colorectal surgery were eligible for inclusion. Postoperative morbidity was defined using the postoperative morbidity survey at postoperative day 7. Thirty‐one candidate variables were considered for inclusion in the model. Death or morbidity occurred by postoperative day 7 in 3098 out of 11,646 patients (26.6%). Twelve variables were incorporated into the final model, including (among others): Rockwood clinical frailty scale; body mass index; and index of multiple deprivation quintile. The C‐statistic was 0.672 (95%CI 0.660–0.684), with a bootstrap optimism corrected C‐statistic of 0.666 at internal validation. The model demonstrated good calibration across the range of morbidity estimates with a mean slope gradient of predicted risk of 0.959 (95%CI 0.894–1.024) with an index‐corrected intercept of −0.038 (95%CI −0.112–0.036) at internal validation. Our model provides parsimonious case‐mix adjustment to quantify risk of morbidity on postoperative day 7 for a UK population of patients undergoing major colorectal surgery. Despite the C‐statistic of < 0.7, our model outperformed existing risk‐models in widespread use. We therefore recommend application in case‐mix adjustment, where incorporation into a continuous monitoring tool such as the variable life adjusted display or exponentially‐weighted moving average‐chart could support high‐level monitoring and quality improvement of risk‐adjusted outcome at the population level. [ABSTRACT FROM AUTHOR]
Published: 2022
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42. NHS ‘Learning from Deaths’ reports: a qualitative and quantitative document analysis of the first year of a countrywide patient safety programme

Author: Dorit Braun, S Ramani Moonesinghe, Cecilia Vindrola-Padros, and Zoe Brummell
Subjects: medicine.medical_specialty, State Medicine, health & safety, quality in health care, Secondary care, 03 medical and health sciences, Patient safety, 0302 clinical medicine, Organization development, Statutory law, Health care, medicine, Humans, 030212 general & internal medicine, Health policy, organisational development, business.industry, Health Policy, 030503 health policy & services, General Medicine, Document analysis, England, Family medicine, Medicine, Patient Safety, 0305 other medical science, business, Delivery of Health Care, qualitative research, Qualitative research
Abstract: ObjectivesTo review how National Health Service (NHS) Secondary Care Trusts (NSCTs) are using the Learning from Deaths (LfDs) programme to learn from and prevent, potentially preventable deaths.IntroductionPotentially preventable deaths occur worldwide within healthcare organisations. In England, inconsistencies in how NSCTs reviewed, investigated and shared LfDs, resulted in the introduction of national guidance on ‘LfDs’ in 2017. This guidance provides a ‘framework for identifying, reporting, investigating and LfDs’. Amendments to NHS Quality Account regulations, legally require NSCTs in England to report quantitative and qualitative information relating to patient deaths annually. The programme intended NSCTs would share this learning and take measurable action to prevent future deaths.MethodWe undertook qualitative and quantitative secondary data, document analysis of all NSCTs LfDs reports within their 2017/2018 Quality Accounts (n=222).ResultsAll statutory elements of LfDs reporting were reported by 98 out of 222 (44%) NSCTs. The percentage of deaths judged more likely than not due to problems in healthcare was between 0% and 13%. The majority of NSCTs (89%) reported lessons learnt; the most common learning theme was poor communication. 106 out of 222 NSCTs (48%) have shared or plan to share the learning within their own organisation. The majority of NSCTs (86%) reported actions taken and 47% discussed or had a plan for assessment of impact. 37 out of 222 NSCTs (17%) mentioned involvement of bereaved families.ConclusionsThe wide variation in reporting demonstrates that some NSCTs have engaged fully with LfDs, while other NSCTs appear to have disengaged with the programme. This may reveal a disparity in organisational learning and patient safety culture which could result in inequity for bereaved families. Many themes identified from the LfDs reports have previously been identified by national and international reports and inquiries.
Published: 2021

43. Complications and consequences: short-term harm has long-term impact

Author: Jackson, Alexander I.R., Moonesinghe, S. Ramani, and Grocott, Michael P.W.
Published: 2023
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44. Perioperative care for older people.

Author: Partridge, Judith S L, Moonesinghe, S Ramani, Lees, Nicholas, and Dhesi, Jugdeep K
Subjects: *PERIOPERATIVE care, *ELECTIVE surgery, *STAKEHOLDER analysis, *GERIATRIC assessment, *SURGICAL complications, *RISK assessment, *LABOR supply, *COST effectiveness
Abstract: Increasing numbers of older people are undergoing surgery with benefits including symptom relief and extended longevity. Despite these benefits, older people are more likely than younger patients to experience postoperative complications, which are predominantly medical as opposed to surgical. Comprehensive Geriatric Assessment and optimisation offers a systematic approach to risk assessment and risk modification in the perioperative period. Clinical evidence shows that Comprehensive Geriatric Assessment and optimisation reduces postoperative medical complications and is cost effective in the perioperative setting. These benefits have been observed in patients undergoing elective and emergency surgery. Challenges in the implementation of perioperative Comprehensive Geriatric Assessment and optimisation services are acknowledged. These include the necessary involvement of a wide stakeholder group, limited available geriatric medicine workforce and ensuring fidelity to Comprehensive Geriatric Assessment methodology with adaptation to the local context. Addressing these challenges needs a cross-specialty, interdisciplinary approach underpinned by evidence-based medicine and implementation science with upskilling to facilitate innovative use of the extended workforce. Future delivery of quality patient-centred perioperative care requires proactive engagement with national audit, collaborative guidelines and establishment of networks to share best practice. [ABSTRACT FROM AUTHOR]
Published: 2022
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45. Surgical antibiotic prophylaxis in an era of antibiotic resistance: common resistant bacteria and wider considerations for practice

Author: Menz, Bradley D, Charani, Esmita, Gordon, David L, Leather, Andrew J M, Moonesinghe, S Ramani, Phillips, Cameron J, Menz, Bradley D, Charani, Esmita, Gordon, David L, Leather, Andrew JM, Ramani Moonesinghe, S, and Phillips, Cameron J
Subjects: Pharmacology, antimicrobial stewardship, Infectious Diseases, antibiotic resistance, perioperative care, surgical antibiotic prophylaxis, Pharmacology (medical), Review, surgical site infection
Abstract: Refereed/Peer-reviewed The increasing incidence of antimicrobial resistance (AMR) presents a global crisis to healthcare, with longstanding antimicrobial agents becoming less effective at treating and preventing infection. In the surgical setting, antibiotic prophylaxis has long been established as routine standard of care to prevent surgical site infection (SSI), which remains one of the most common hospital-acquired infections. The growing incidence of AMR increases the risk of SSI complicated with resistant bacteria, resulting in poorer surgical outcomes (prolonged hospitalisation, extended durations of antibiotic therapy, higher rates of surgical revision and mortality). Despite these increasing challenges, more data are required on approaches at the institutional and patient level to optimise surgical antibiotic prophylaxis in the era of antibiotic resistance (AR). This review provides an overview of the common resistant bacteria encountered in the surgical setting and covers wider considerations for practice to optimise surgical antibiotic prophylaxis in the perioperative setting.
Published: 2021

46. Women empowerment in anaesthesia research and clinical practice:meeting report from the British Journal of Anaesthesia Women in Anaesthesia Research symposium

Author: Christa Boer and S Ramani Moonesinghe
Subjects: Gender equality, business.industry, media_common.quotation_subject, education, Academic promotion, humanities, Clinical Practice, 03 medical and health sciences, 0302 clinical medicine, Anesthesiology and Pain Medicine, 030202 anesthesiology, Anesthesia, Medicine, Empowerment, Peer coaching, business, health care economics and organizations, media_common
Abstract: The Women in Anaesthesia Research Symposium (Prato, Italy; 4 June, 2019), supported by the British Journal of Anaesthesia in collaboration with Monash University, was organised to discuss challenges facing women in anaesthesia clinical practice and research. We provide an overview of institutional or departmental measures that were proposed during the symposium that may empower women in anaesthesia today.
Published: 2020

47. Perioperative interventions for prevention of postoperative pulmonary complications: systematic review and meta-analysis

Author: Benedict C. Creagh-Brown, Peter M. Odor, S Ramani Moonesinghe, Sohail Bampoe, and David Gilhooly
Subjects: Relative risk reduction, medicine.medical_specialty, Respiratory Therapy, Respiratory Tract Diseases, Psychological intervention, 03 medical and health sciences, 0302 clinical medicine, Postoperative Complications, 030202 anesthesiology, Medicine, Humans, Vasoconstrictor Agents, 030212 general & internal medicine, Adverse effect, Intensive care medicine, Physical Therapy Modalities, Expectorants, Intraoperative Care, business.industry, Research, Hemodynamics, Respiratory infection, General Medicine, Perioperative, Confidence interval, Analgesia, Epidural, Relative risk, Meta-analysis, Critical Pathways, Fluid Therapy, business
Abstract: ObjectiveTo identify, appraise, and synthesise the best available evidence on the efficacy of perioperative interventions to reduce postoperative pulmonary complications (PPCs) in adult patients undergoing non-cardiac surgery.DesignSystematic review and meta-analysis of randomised controlled trials.Data sourcesMedline, Embase, CINHAL, and CENTRAL from January 1990 to December 2017.Eligibility criteriaRandomised controlled trials investigating short term, protocolised medical interventions conducted before, during, or after non-cardiac surgery were included. Trials with clinical diagnostic criteria for PPC outcomes were included. Studies of surgical technique or physiological or biochemical outcomes were excluded.Data extraction and synthesisReviewers independently identified studies, extracted data, and assessed the quality of evidence. Meta-analyses were conducted to calculate risk ratios with 95% confidence intervals. Quality of evidence was summarised in accordance with GRADE methods. The primary outcome was the incidence of PPCs. Secondary outcomes were respiratory infection, atelectasis, length of hospital stay, and mortality. Trial sequential analysis was used to investigate the reliability and conclusiveness of available evidence. Adverse effects of interventions were not measured or compared.Results117 trials enrolled 21 940 participants, investigating 11 categories of intervention. 95 randomised controlled trials enrolling 18 062 participants were included in meta-analysis; 22 trials were excluded from meta-analysis because the interventions were not sufficiently similar to be pooled. No high quality evidence was found for interventions to reduce the primary outcome (incidence of PPCs). Seven interventions had low or moderate quality evidence with confidence intervals indicating a probable reduction in PPCs: enhanced recovery pathways (risk ratio 0.35, 95% confidence interval 0.21 to 0.58), prophylactic mucolytics (0.40, 0.23 to 0.67), postoperative continuous positive airway pressure ventilation (0.49, 0.24 to 0.99), lung protective intraoperative ventilation (0.52, 0.30 to 0.88), prophylactic respiratory physiotherapy (0.55, 0.32 to 0.93), epidural analgesia (0.77, 0.65 to 0.92), and goal directed haemodynamic therapy (0.87, 0.77 to 0.98). Moderate quality evidence showed no benefit for incentive spirometry in preventing PPCs. Trial sequential analysis adjustment confidently supported a relative risk reduction of 25% in PPCs for prophylactic respiratory physiotherapy, epidural analgesia, enhanced recovery pathways, and goal directed haemodynamic therapies. Insufficient data were available to support or refute equivalent relative risk reductions for other interventions.ConclusionsPredominantly low quality evidence favours multiple perioperative PPC reduction strategies. Clinicians may choose to reassess their perioperative care pathways, but the results indicate that new trials with a low risk of bias are needed to obtain conclusive evidence of efficacy for many of these interventions.Study registrationProspero CRD42016035662.
Published: 2020

48. The immediate effects of a novel auditory and proprioceptive training device on gait after stroke

Author: Eric G. Johnson, Everett B. Lohman, Abel Rendon, Ektaben G. Dobariya, Shubhada S. Ramani, and Lissie E. Mayer
Subjects: gait, stroke, rehabilitation, physical therapy, tib trainer®, auditory cues, Medicine (General), R5-920
Abstract: This case report describes the immediate effects of a new rehabilitation tool on gait in a chronic stroke patient. Specifically, we measured step length symmetry and gait velocity in a 47 year-old male stroke patient who was currently receiving outpatient physical therapy. Objective gait measurements were taken using the GAITRite before, during, and after a 5 minute training session. Step length symmetry improved 26% during the first minute of training, 71% by the fifth minute of training, and 72% after a 5 minute rest period post-training. Gait velocity increased by 5.5% after 5 minutes of training. Clinical research is warranted to validate this new training tool as a useful adjunctive rehabilitation activity for improving spatial and temporal aspects of gait after stroke.
Published: 2011
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49. Peak power output testing: novel method for preoperative assessment of exercise capacity.

Author: Milliken, Don, Rooms, Martin, Moonesinghe, S. Ramani, and Jhanji, Shaman
Subjects: PREHABILITATION, AEROBIC capacity, RECEIVER operating characteristic curves, EXERCISE tests, ABDOMINAL surgery, SURGICAL complications
Abstract: Background: Assessment of exercise capacity is an important component of risk assessment before major surgery. Cardiopulmonary exercise testing (CPET) provides comprehensive assessment but is resource-intensive, limiting widespread adoption. Measurement of a patient's peak power output (PPO) using a simplified test on a cycle ergometer has the potential to identify patients likely to have abnormal CPET findings and to be at increased perioperative risk. The aim of this study was to investigate the potential for PPO to identify those with abnormal CPET and to determine whether PPO predicted the risk of adverse postoperative outcomes. Methods: In a retrospective analysis of a single-centre cohort, the ability of PPO to predict a high-risk CPET result in patients undergoing major cancer surgery was analysed. The assessment was validated in patients undergoing major abdominal surgery from a UK national multicentre cohort. The association between PPO and adverse postoperative outcomes to traditional CPET-derived variables were compared. Results: In 2262 patients from a single centre, PPO was an excellent discriminator of high-risk CPET, with an area under the receiver operating characteristic curve (AUROC) of 0.901 (95 per cent c.i. 0.888 to 0.913). In the national cohort of 2742 patients, there was excellent discrimination, with an AUROC of 0.856 (0.842 to 0.871). A PPO cut-off of 1.5 W/kg may be appropriate for use in screening, with a sensitivity of 90 per cent in both cohorts. PPO and traditional CPET-derived predictors demonstrated similar discrimination of major postoperative complications and death. The association between PPO and major postoperative complications persisted on multivariable analysis. Conclusion: These results suggest a role for the PPO test in preoperative screening and risk stratification for major surgery. Prospective evaluation is recommended. [ABSTRACT FROM AUTHOR]
Published: 2022
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50. Contributors

Author: Aggarwal, Geeta, Andukuri, Venkata, Arnold, Susan LaFollette, Arriaga, Alexander F., Bader, Angela M., Balfour, Angie, Berger, Elizabeth R., Berlin, Nicholas L., Blitz, Jeanna D., Booth, Alexander, Botwinick, Laura K., Bozic, Kevin, Buehler, Kate, Chappell, Desiree, Chen, Yun-Yun K., Chereshneva, Maria, Clapp, Justin T., Cloyd, Benjamin H., Cruikshanks, Claire, Dauw, Casey A., Dhesi, Jugdeep, DiBianco, John Michael, Dixon-Woods, Mary, Duffy, Caoimhe C., Edwards, Angela F., Edwards, Kylie-Ellen, El-Ghazali, Sally, Englesbe, Michael, Esper, Stephen A., Falk, Scott, Fischer, Chelsea P., Fleisher, Lee A., Fogerty, Robert L., Fowler, Aidan, Franz, Amber, Ghani, Khurshid R., Grocott, Michael P.W., Gulotta, Lawrence V., Hallway, Alexander, Harpe-Bates, Jennifer, Healey, Frances, Azad, Kaveh Houshmand, Howard, Ryan, Incorvia, Joseph, Jackiewicz, Thomas E., Janda, Allison, Johnson, Emily H., Johnston, Carolyn, Jordan, Lesley, Kelz, Rachel R., Khan, Maria, Kheterpal, Sachin, King, Christopher J., Ko, Clifford Y., Lane-Fall, Meghan B., Lee, Thomas H., Levett, Denny Z.H., Lin, Della M., Lloyd, Robert, Lockett, Mark, Low, Daniel, Mahajan, Aman, Mainey, Chris, Manning, Michael W., Martin, Graham P., Martin, Lynn D., Mathis, Michael, McCulloch, Peter, Moonesinghe, S. Ramani, Murray, Dave, Myles, Paul S., Mythen, Monty G., Odell, Wendy, Offodile, Anaeze C., II, Parry, Gareth, Partridge, Judith, Peden, Carol J., Quiney, Nial, Ragheb, Jacqueline W., Raper, Steven E., Rogerson, Andrew, Rooney, Kevin, Roper, Madeleine, Santhirapala, Ramai, Scott, Michael, Shah, Nirav, Singleton, Georgina F., Soar, Harry, Spencer, Paula, Stephens, Timothy J., Stevens, Emma, Sweitzer, BobbieJean, Tong, Jason, Tung, Alan, Vetter, Thomas R., Vlisides, Phillip E., Wain, Hester, Wick, Elizabeth C., Wu, Christopher L., Wyatt, Ronald, and YaDeau, Jacques T.
Published: 2023
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