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1. Use of a Machine Learning Program to Correctly Triage Incoming Text Messaging Replies From a Cardiovascular Text–Based Secondary Prevention Program: Feasibility Study

Author

Lowres, Nicole, Duckworth, Andrew, Redfern, Julie, Thiagalingam, Aravinda, and Chow, Clara K

Subjects
Information technology, T58.5-58.64, Public aspects of medicine, RA1-1270
Abstract

BackgroundSMS text messaging programs are increasingly being used for secondary prevention, and have been shown to be effective in a number of health conditions including cardiovascular disease. SMS text messaging programs have the potential to increase the reach of an intervention, at a reduced cost, to larger numbers of people who may not access traditional programs. However, patients regularly reply to the SMS text messages, leading to additional staffing requirements to monitor and moderate the patients’ SMS text messaging replies. This additional staff requirement directly impacts the cost-effectiveness and scalability of SMS text messaging interventions. ObjectiveThis study aimed to test the feasibility and accuracy of developing a machine learning (ML) program to triage SMS text messaging replies (ie, identify which SMS text messaging replies require a health professional review). MethodsSMS text messaging replies received from 2 clinical trials were manually coded (1) into “Is staff review required?” (binary response of yes/no); and then (2) into 12 general categories. Five ML models (Naïve Bayes, OneVsRest, Random Forest Decision Trees, Gradient Boosted Trees, and Multilayer Perceptron) and an ensemble model were tested. For each model run, data were randomly allocated into training set (2183/3118, 70.01%) and test set (935/3118, 29.98%). Accuracy for the yes/no classification was calculated using area under the receiver operating characteristics curve (AUC), false positives, and false negatives. Accuracy for classification into 12 categories was compared using multiclass classification evaluators. ResultsA manual review of 3118 SMS text messaging replies showed that 22.00% (686/3118) required staff review. For determining need for staff review, the Multilayer Perceptron model had highest accuracy (AUC 0.86; 4.85% false negatives; and 4.63% false positives); with addition of heuristics (specified keywords) fewer false negatives were identified (3.19%), with small increase in false positives (7.66%) and AUC 0.79. Application of this model would result in 26.7% of SMS text messaging replies requiring review (true + false positives). The ensemble model produced the lowest false negatives (1.43%) at the expense of higher false positives (16.19%). OneVsRest was the most accurate (72.3%) for the 12-category classification. ConclusionsThe ML program has high sensitivity for identifying the SMS text messaging replies requiring staff input; however, future research is required to validate the models against larger data sets. Incorporation of an ML program to review SMS text messaging replies could significantly reduce staff workload, as staff would not have to review all incoming SMS text messages. This could lead to substantial improvements in cost-effectiveness, scalability, and capacity of SMS text messaging–based interventions.

Published
2020
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6. A national multicentre study of outcomes and patient satisfaction with the virtual fracture clinic and the influence of the COVID-19 pandemic: The MAVCOV study

Author

Brown, Jonathon, Lim, Jun Wei, Smith, Matthew, Lau, Nicole, Kamaraj, Achi, Zhou, Andrew, Lim, Jiang An, Jenkins, Lydia, Tennyson, Maria, Khan, Wasim, Foster, Amy, Rutherfoord, Catharine, Nathoo, Nikita, Domos, Peter, Padwick, Thomas, White, William, Robinson, Andrew, Vusirikala, Anuhya, Ensor, David, Akula, Maheswara, Vignaraja, Vikram, Rogers, Benedict, Kalabi, Fatemeh, Chan, Gareth, Clayton-Yeomans, Jacob, Gibbs, James, Titheradge, Rachel, Spalvieri, Stefano, Nair, Abhinav, Nathwani, Dinesh, Selvakumar, Jashan, Wihba, Kinan, Zakieh, Omar, Park, Andrew, Crewe, Claire, Ayeni, Funbi, Prowse, James, Kumar, Pradeep, Akinyemi, Tosin, Solim, Zahirah Nahaboo, Shaerf, Daniel, Sheikh, Hamza, Poustie, Mark, Rai, Sumnima, Birole, Umesh, Russell, Aimee, Loh, Cheryl, Drury, Colin, Cemm, Nicholas, Pravinkumar, Ruth, Baig, Saira, Bigsby, Ewan, Sellers, Grace, Dow, Joseph, Jenner, Lydia, Rogers, Michaela, Rankin, Sally, Mangtani, Anisha, Brameier, Devon, Rooker, Jemma, Anwer, Mohammed Azher, Kumar, Nakulan Nantha, Lam, Ryan, Andronic, Adrian, Phillips, Hannah, Attwood, Joe, Sturdee, Simon, Panagiotopoulos, Andreas, McVie, James, Boylan, Luke, Reid, Michael, Cheekoty, Prashasth, Edwards, Rebecca, Lindh, Amy, Kettle, Sarah, Genena, Ahmed, Hourston, George, Gopaul, Steven, Garg, Sunil, Gowers, Benjamin, Lennox-Warburton, Hannah, Sambhwani, Sharan, Shyamsundar, Srinivasan, Srinivasan, Suresh, Tavakkolizadeh, Adel, Petohazi, Alexandra, O'Hanlon, Ciaran, Everton, Harry Donkin, Pilkington, Isobel, Graziadei, Vivien, Anwar, Walid, Wignall, Alice, Jassal, Arun, Ilyas, Haris, Palan, Jeya, Monkhouse, Ray, Nijamudeen, Aysha, Caruana, Ethan, Gilbody, Julian, Malik, Maymunah, Saadat, Waqar, Yusuf, Karshe, Davies, Kimberley, Mueller, Marion, Tint, Pyi Han, Sengupta, Shreya, James, Andrew, Virji, Faizan, Bond, Gemma, Thumbadoo, Ruben, Teng, Wai Huang, Gee, Christopher, Warren, Craig, Nair, Gopikrishnan, Bohler, Iain, Periasamy, Kumar, Liao, Quintin, Ramasubbu, Rohan, Baston, Tessa, Witek, Alexander, Parwaiz, Hammad, Williams, James, Aboelmagd, Karim, Haines, Samuel, Bänziger, Carina, Harrold, Fraser, Abishek, Joshua, Davies, Peter, Pennington, Rachel, Parulekar, Ramaa, Sinnerton, Robert, Ravury, Sheetal, Macdonald, Angus, Emmerson, Benjamin, Inman, Dominic, Moedbeck, Edwina, Barber, Margaret, Jaiswal, Sharlene, Lewis, Rebecca, Downie, Samantha, Ng, Samantha, Shue, Wan Nee, Fosu, Augustine, Ko, Chai Chi, Lewis, Charlotte, Thiruchandran, Gajendiran, Jain, Siddharth, Ng, Zhаn, Wilson, Christopher, Toh, Ching, Miller, Gillian, Duncan, Katherine, McKee, Lesley, Mitchell, Martin, Ferguson, David, Hambidge, John, Wickramarachchi, Lilanthi, Abouelela, Amr, Hong, Anna, Oo, Aung Phyo, Kitsis, Christos, Elgalli, Mosab, Aros-Atolagbe, Robert, Clement, Adeline, Gibb, Catriona, Goffin, Joaquim, Jalal, Murtadhah, Leung, Billy, Neal-Smith, Gregory, Khatkar, Harman, Maher, Mark, Davies, Neville, Aboelmagd, Tariq, Jarrat, Jeremy, McKay, Joseph, Walmsley, Matthew, Greenhalgh, Michael, McCabe-Robinson, Olivia, Berry, Alexander, Iftinca, Dorin, Easwaran, Palakkad, Patel, Akash, Awow, Fatima, Pan, Myat, Patel, Nikhil, Khanal, Pragalva, Wagner, Boris, Athawes, Kirsten, Makaram, Navnit, Hegde, Rutwik, White, Tim, Williamson, Thomas, McAskill, Victoria, Stevens, Chloe, Malagelada, Francesc, Nasser, Mohammad Zeeshan, Ogunfuye, Omodolapo, Zahoor, Ubaid, Rogers, Gareth, Telford, Jeremy, Cowie, Jonathan, Hoggett, Lee, Havenhand, Tom, Do, Anh Duy, Cross, Charlotte, Morris, James, Hunter, Joel, Nabahin, Maram, Choudhary, Tashi, Campbell, Aaron, Mercer, Alexandra, Deyermond, Chloe, Fowler, Chris, McGoldrick, Peter, McCormac, Philip, Kakar, Sahil, McKenna, Sean, Boutros, Ihab, Vial, Izziddine, Bhullar, Samraj, Lyle, Victoria, Thomas, Alexander, Shean, Kate, Jacobs, Neal, Townsend, Oliver, Sharma, Akshara, van der Broeck, Alyssa, Hartley, Laura, O'Riordan, Marguerite, Gella, Sreenadh, Adlawan, Anjeli, Ijaz, Arhum, Al-kamali, Aws, Livesey, Jon, Yeshitila, Natania, Thayaparan, Anthony, Hayter, Edward, McLean, Elizabeth, Davies, Jessica, Walker, Joseph, Anakwe, Raymond, Fell, Adam, Haque, Aniqa, Sobti, Anshul, Jacob, Joshua, Moore, Thomas, Bhandari, Farzaan, Boitnott, Jasmine, Cheong, Jeremy, Lim, Wei Boon, Michla, Yusuf, Gilmore, Catherine, McConway, Jim, Elliott, Jonathan, Khan, Azizah, Madhu, Divine, Yusuf, Hannah, Gibby, Mary Louise, Thomas, Phillip, Abdel-Fattah, Areej, Martinson, Eliott, Akhtar, Muhammad Adeel, Hossain, Fahad, Ismayl, Ghiath, Khadabadi, Nikhil, Moores, Thomas, Goude, William, Ibrahim, Edward, Sivaganesh, Karthikeyan, Ogunjimi, Michael, Wilson, Michael, Shah, Sachi, McCluskey, Aled, Zakaria, Benjamin, Berber, Onur, Cuthbert, Rory, Mergia, Teresa, Khawaja, Abdullah, Hayman, Gregory, Relwani, Jai, Shaeir, Mohammed, Shah, Sohail, Peach, Christopher, Cook, Edward, Helston, Olivia, Koh, Yun Jee, Ng, Zhan H., Makaram, Navnit S., Kolhe, Shivam N., Mackenzie, Samuel P., Clement, Nick D., Duckworth, Andrew D., and White, Tim O.

Published
2024
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12. IMPACT-Global Hip Fracture Audit: Nosocomial infection, risk prediction and prognostication, minimum reporting standards and global collaborative audit: Lessons from an international multicentre study of 7,090 patients conducted in 14 nations during the COVID-19 pandemic

Author

Abdul-Jabar, Hani, Abu-Rajab, Rashid, Abugarja, Ahmed, Adam, Karen, Aguado Hernández, Héctor J., Améstica Lazcano, Gedeón, Anderson, Sarah, Ansar, Mahmood, Antrobus, Jonathan, Aragón Achig, Esteban Javier, Archunan, Maheswaran, Arrieta Salinas, Mirentxu, Ashford–Wilson, Sarah, Assens Gibert, Cristina, Athanasopoulou, Katerina, Awadelkarim, Mohamed, Baird, Stuart, Bajada, Stefan, Balakrishnan, Shobana, Balasubramanian, Sathishkumar, Ballantyne, James A., Bárcena Goitiandia, Leopoldo, Barkham, Benjamin, Barmpagianni, Christina, Barres-Carsi, Mariano, Barrett, Sarah, Baskaran, Dinnish, Bell, Jean, Bell, Katrina, Bell, Stuart, Bellelli, Giuseppe, Benchimol, Javier Alberto, Boietti, Bruno Rafael, Boswell, Sally, Braile, Adriano, Brennan, Caitlin, Brent, Louise, Brooke, Ben, Bruno, Gaetano, Burahee, Abdus, Burns, Shirley, Calabrò, Giampiero, Campbell, Lucy, Carabelli, Guido Sebastian, Carnegie, Carol, Carretero Cristobal, Guillermo, Caruana, Ethan, Cassinello Ogea, M.a Concepción, Castellanos Robles, Juan, Castillon, Pablo, Chakrabarti, Anil, Cecere, Antonio Benedetto, Chen, Ping, Clarke, Jon V., Collins, Grace, Corrales Cardenal, Jorge E., Corsi, Maurizio, Cózar Adelantado, Gara María, Craxford, Simon, Crooks, Melissa, Cuarental-García, Javier, Cuthbert, Rory, Dall, Graham, Daskalakis, Ioannis, De Cicco, Annalisa, de la Fuente de Dios, Diana, Demaria, Pablo, Dereix, John, Díaz Jiménez, Julian, Dinamarca Montecinos, José Luis, Do Le, Ha Phuong, Donoso Coppa, Juan Pablo, Drosos, Georgios, Duffy, Andrew, East, Jamie, Eastwood, Deborah, Elbahari, Hassan, Elias de Molins Peña, Carmen, Elmamoun, Mamoun, Emmerson, Ben, Escobar Sánchez, Daniel, Faimali, Martina, Farré-Mercadé, Maria Victòria, Farrow, Luke, Fayez, Almari, Fell, Adam, Fenner, Christopher, Ferguson, David, Finlayson, Louise, Flores Gómez, Aldo, Freeman, Nicholas, French, Jonathan, Gabardo Calvo, Santiago, Gagliardo, Nicola, Garcia Albiñana, Joan, García Cruz, Guillermo, García de Cortázar Antolín, Unai, García Virto, Virginia, Gealy, Sophie, Gil Caballero, Sandra Marcela, Gill, Moneet, González González, María Soledad, Gopireddy, Rajesh, Guntley, Diane, Gurung, Binay, Guzmán Rosales, Guadalupe, Haddad, Nedaa, Hafeez, Mahum, Haller, Petra, Halligan, Emer, Hardie, John, Hawker, Imogen, Helal, Amr, Herrera Cruz, Mariana, Herreros Ruiz-Valdepeñas, Ruben, Horton, James, Howells, Sean, Howieson, Alan, Hughes, Luke, Hünicken Torrez, Flavia Lorena, Hurtado Ortega, Ana, Huxley, Peter, Hamid, Hytham K.S., Ilahi, Nida, Iliadis, Alexis, Inman, Dominic, Jadhao, Piyush, Jandoo, Rajan, Jawad, Lucy, Jayatilaka, Malwattage Lara Tania, Jenkins, Paul J., Jeyapalan, Rathan, Johnson, David, Johnston, Andrew, Joseph, Sarah, Kapoor, Siddhant, Karagiannidis, Georgios, Karanam, Krishna Saga, Kattakayam, Freddy, Konarski, Alastair, Kontakis, Georgios, Labrador Hernández, Gregorio, Lancaster, Victoria, Landi, Giovanni, Le, Brian, Liew, Ignatius, Logishetty, Kartik, Lopez Marquez, Andrew Carlomaria Daniel, Lopez, Judit, Lum, Joann, Macpherson, Gavin J., Madan, Suvira, Mahroof, Sabreena, Malik-Tabassum, Khalid, Mallina, Ravi, Maqsood, Afnan, Marson, Ben, Martin Legorburo, M. José, Martin-Perez, Encarna, Martínez Jiménez, Tania, Martinez Martin, Javier, Mayne, Alistair, Mayor, Amy, McAlinden, Gavan, McLean, Lucille, McDonald, Lorna, McIntyre, Joshua, McKay, Pamela, McKean, Greg, McShane, Heather, Medici, Antonio, Meeke, Chelsea, Meldrum, Evonne, Mendez, Mijail, Mercer, Scott, Merino Perez, Josu, Mesa-Lampré, María-Pilar, Mighton, Shuna, Milne, Kirsty, Mohamed Yaseen, Muhammed, Moppett, Iain, Mora, Jesus, Morales-Zumel, Sira, Moreno Fenoll, Irene Blanca, Mousa, Adham, Murray, Alastair W., Murray, Elspeth V., Nair, Radhika, Neary, Fiona, Negri, Giacomo, Negus, Oliver, Newham-Harvey, Fiona, Ng, Nigel, Nightingale, Jess, Noor Mohamed Anver, Sumiya, Nunag, Perrico, O'Hare, Matthew, Ollivere, Ben, Ortés Gómez, Raquel, Owens, AnneMarie, Page, Siobhan, Palloni, Valentina, Panagiotopoulos, Andreas, Panagiotopoulos, Elias, Panesar, Paul, Papadopoulos, Antonios, Spyridon, Papagiannis, Pareja Sierra, Teresa, Park, Chang, Parwaiz, Hammad, Paterson-Byrne, Paul, Patton, Sam, Pearce, Jack, Porter, Marina, Pellegrino, Achille, Pèrez Cuellar, Arturo, Pezzella, Raffaele, Phadnis, Ashish, Pinder, Charlotte, Piper, Danielle, Powell-Bowns, Matilda, Prieto Martín, Rocío, Probert, Annabel, Ramesh, Ashwanth, Ramírez de Arellano, Manuel Vicente Mejía, Renton, Duncan, Rickman, Stephen, Robertson, Alastair, Roche Albero, Adrian, Rodrigo Verguizas, José Alberto, Rodríguez Couso, Myriam, Rooney, Joanna, Sáez-López, Pilar, Saldaña-Díaz, Andres, Santulli, Adriano, Sanz Pérez, Marta Isabel, Sarraf, Khaled M., Scarsbrook, Christine, Scott, Chloe E.H., Scott, Jennifer, Shah, Sachi, Sharaf, Sharief, Sharma, Sidharth, Shirley, Denise, Siano, Antonio, Simpson, James, Singh, Abhinav, Singh, Amit, Sinnett, Tim, Sisodia, Gurudatt, Smith, Philomena, Sophena Bert, Eugenia, Steel, Michael, Stewart, Avril, Stewart, Claire, Sugand, Kapil, Sullivan, Niall, Sweeting, Lauren, Symes, Michael, Tan, Dylan Jun Hao, Tancredi, Francesco, Tatani, Irini, Thomas, Philip, Thomson, Fraser, Toner, Niamh S., Tong, Anna, Toro, Antonio, Tosounidis, Theodoros, Tottas, Stylianos, Trinidad Leo, Andrea, Tucker, Damien, Vemulapalli, Krishna, Ventura Garces, Diego, Vernon, Olivia Katherine, Viveros Garcia, Juan Carlos, Ward, Alex, Ward, Kirsty, Watson, Kate, Weerasuriya, Thisara, Wickramanayake, Udara, Wilkinson, Hannah, Windley, Joseph, Wood, Janet, Wynell-Mayow, William, Zatti, Giovanni, Zeiton, Moez, Zurrón Lobato, Miriam, Hall, Andrew J., Clement, Nicholas D., Ojeda-Thies, Cristina, MacLullich, Alasdair MJ., Toro, Giuseppe, Johansen, Antony, White, Tim O., and Duckworth, Andrew D.

Published
2022
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15. A Machine Learning Algorithm to Estimate the Probability of a True Scaphoid Fracture After Wrist Trauma

Author

Buijze, Geert A., Bulstra, Anne Eva J., Cohen, Abigail, Colaris, Joost W., Court-Brown, Charles M., Doornberg, Job N., Duckworth, Andrew D., Goslings, J. Carel, Gray, Alasdair, Hendrickx, Laurent A.M., Jaarsma, Ruurd L., Mallee, Wouter H., Mulders, Marjolein A.M., McQueen, Margaret M., Moran, Matthew, Obdeijn, Miryam C., Kerkhoffs, Gino M.M.J., Ring, David, Schep, Niels W.L., and Walenkamp, Monique M.J.

Published
2022
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23. Interactions of fluorophores with complex surfaces and spectroscopic examinations of ancient manuscripts

Author

Duckworth, Andrew Robert

Subjects
543
Abstract

In the first part of this thesis, it was found by fibre-optic fluorescence spectroscopy, that the greening of fabrics washed in optical brighteners is due to a reabsorption effect. The quantum yield of fluorescence of the optical brighteners OB15, OB36 and OB49 in water are 0.11, 0.08 and 0.71 respectively. Their respective fluorescence natural lifetimes are 6700±109, 5971±712 and 1685±22ps. In solution, the excited state of OB15 experiences more competing relaxation processes as the solvatochromic shift increases. OB49 displays the opposite trend. A literature cellulose model surface is employed as a cotton mimic for evanescent wave fluorescence studies. Two model greases are similarly developed and used, and a third is presented for future work. These are based on surface-specific reactions with glass substrates, and the doping of a regenerated cellulose film with long chain alcohols. On doped cellulose surfaces, some low quantum yields occur compared to clean cellulose and bulk solution. Photobleaching behaviours are also observed. Both dyes physisorb rigidly to cellulose and grease models. The second part of this thesis identifies the pigment palette of the earliest Northumbrian manuscripts pre- and post-1066, by Raman and diffuse reflectance spectroscopy. It develops a suite of multispectral imaging programs in MATLAB for facile classification of pigments across a page ab initio, using data reduction and colour spaces. Raman and reflectance data are meta-analysed using symmetric permutation to split manuscripts and pigments into groups ab initio. It was also generalised, that the palette of the pre-Hastings selected manuscripts contained vergaut, indigo, orpiment, impure red lead, and copper green pigments, as well as orcein purples. Immediately post-1066 white lead, red ochre, vermilion and lapis lazuli appear in the palette in England, though vergaut and indigo disappear and the red lead used is essentially pure.

Published
2017

27. Proximal forearm fractures : epidemiology, functional results and predictors of outcome

Author

Duckworth, Andrew David, McQueen, Margaret, and Court-Brown, Charles

Subjects
617.1, elbow, trauma, radial head, olecranon, fracture, epidemiology, management, outcome
Abstract

Proximal forearm fractures account for over 10% of all upper limb fractures. There is limited epidemiological data available and much of the literature focuses on the more complex fracture patterns, with the role of non-operative management for the isolated proximal forearm fracture still to be defined. Prospective short and long-term patient reported outcome data for simple isolated fractures of the radial head and olecranon would help define the indications for the non-operative management of these injuries. This thesis aims to test the hypothesis that non-operative management provides a comparable outcome to operative intervention for defined fractures of the proximal forearm. A large prospective database of 6872 fractures collected over a one-year period was used to define the epidemiology of proximal forearm fractures. A separate large prospective study carried out over an eighteen-month period using a pre-defined management protocol for all isolated radial head and neck fractures was analysed to determine the short and long-term outcome. Additional retrospective databases were collected and analysed to determine the short and long-term outcome for the non-operative and operative management of olecranon fractures, as well as the operative management of complex radial head fractures. Finally, two prospective randomised controlled trials (PRCTs) of isolated displaced fractures of the olecranon were carried out to compare 1) tension band wire (TBW) versus plate fixation in younger patients (< 75 years) and 2) operative versus non-operative management in elderly patients (≥75 years). The primary outcome measure for these studies was the upper limb specific patient reported Disabilities of the Arm, Shoulder and Hand (DASH) score. Secondary outcome measures included surgeon reported outcome scores, complication rates and cost. The incidence of proximal forearm fractures was 68 per 100,000. Radial head fractures fit a type D distribution curve (unimodal young man, bimodal woman) and radial neck type A (unimodal young man, unimodal older woman). Proximal ulna and olecranon fractures were both a type F (unimodal older man, unimodal older woman), with an increasing incidence after the 6th decade. Over 90% of proximal radial fractures were isolated stable fractures. Prospective analysis of 201 isolated proximal radius fractures found that the patient and surgeon reported outcome following primary non-operative management for Mason type 1 and type 2 (n=185) fractures was excellent in the short and long-term, with < 2% of patients undergoing secondary surgical intervention. At a mean of 10 years post injury (n=100), the mean DASH score was 5.8 and 92% of patients were satisfied. Factors associated with a poorer short and long-term patient reported outcome included increasing fracture displacement (≥5mm) and socio-economic deprivation. Retrospective analysis of 105 acute unstable complex radial head fractures found that the mean short-term functional outcome was good (mean Broberg and Morrey Score 80) following radial head replacement. In the long-term (mean 7 years), 28% of patients required removal or revision of the prosthesis, with younger patients and silastic implants independent risk factors (both p < 0.05). Retrospective analysis of 36 operatively managed isolated displaced olecranon fractures found satisfactory short and long-term outcomes, with the symptomatic metalwork removal rate 47% and the mean DASH 2.5 at a mean of seven years post injury. In the PRCT of plate (n=34) versus TBW (n=33) fixation, comparable functional and patient reported outcomes (DASH 8.5 vs 13.5; p=0.252) were found at one year following injury. Complication rates were significantly higher in the TBW group (63.3% vs 37.5%; p=0.042), predominantly due to a significantly higher rate of symptomatic metalwork removal (50.0% vs 21.9%; p=0.021), resulting in equivocal costs for both techniques (p=0.131). In older lower-demand patients, short and long-term retrospective analysis found very satisfactory outcomes following non-operative management of isolated displaced fractures of the olecranon, with patient satisfaction 91% and no patients requiring surgery for a symptomatic non-union. The preliminary results of the PRCT of non-operative (n=8) versus operative (n=11) management demonstrated comparable functional and patient reported outcomes at all points over the one-year following injury (all p≥0.05), with a higher rate of complications (81.8% vs 14.3%; p=0.013) and cost (p=0.01) following surgical intervention. The association found between fragility and the epidemiology of proximal forearm fractures highlighted the importance of considering non-operative management for these injuries. These findings support non-operative management for isolated stable radial head and neck fractures. For more complex injuries when radial head replacement is indicated, there is a high rate of removal or revision, with younger patients most at risk. In younger active patients with an isolated displaced fracture of the olecranon, TBW and plate fixation provide comparable short-term results, with TBW fixation as cost effective despite an increased rate of metalwork removal. In older lower demand patients, this data provides strong evidence for the non-operative management of isolated displaced olecranon fractures.

Published
2016

29. Delirium detection tools show varying completion rates and positive score rates when used at scale in routine practice in general hospital settings: A systematic review.

Author

Penfold, Rose S., Squires, Charlotte, Angus, Alisa, Shenkin, Susan D., Ibitoye, Temi, Tieges, Zoë, Neufeld, Karin J., Avelino‐Silva, Thiago J., Davis, Daniel, Anand, Atul, Duckworth, Andrew D., Guthrie, Bruce, and MacLullich, Alasdair M. J.

Subjects
DIAGNOSIS of delirium, MEDICAL information storage & retrieval systems, RESEARCH funding, PATIENTS, CINAHL database, HOSPITAL admission & discharge, DESCRIPTIVE statistics, EMERGENCY medical services, SYSTEMATIC reviews, MEDLINE, GERIATRIC assessment, HOSPITAL care of older people, DATA analysis software, PSYCHOLOGY information storage & retrieval systems, OLD age
Abstract

Background: Multiple short delirium detection tools have been validated in research studies and implemented in routine care, but there has been little study of these tools in real‐world conditions. This systematic review synthesized literature reporting completion rates and/or delirium positive score rates of detection tools in large clinical populations in general hospital settings. Methods: PROSPERO (CRD42022385166). Medline, Embase, PsycINFO, CINAHL, and gray literature were searched from 1980 to December 31, 2022. Included studies or audit reports used a validated delirium detection tool performed directly with the patient as part of routine care in large clinical populations (n ≥ 1000) within a general acute hospital setting. Narrative synthesis was performed. Results: Twenty‐two research studies and four audit reports were included. Tools used alone or in combination were the Confusion Assessment Method (CAM), 4 'A's Test (4AT), Delirium Observation Screening Scale (DOSS), Brief CAM (bCAM), Nursing Delirium Screening Scale (NuDESC), and Intensive Care Delirium Screening Checklist (ICDSC). Populations and settings varied and tools were used at different stages and frequencies in the patient journey, including on admission only; inpatient, daily or more frequently; on admission and as inpatient; inpatient post‐operatively. Tool completion rates ranged from 19% to 100%. Admission positive score rates ranged from: CAM 8%–51%; 4AT 13%–20%. Inpatient positive score rates ranged from: CAM 2%–20%, DOSS 6%–42%, and NuDESC 5–13%. Postoperative positive score rates were 21% and 28% (4AT). All but two studies had moderate–high risk of bias. Conclusions: This systematic review of delirium detection tool implementation in large acute patient populations found clinically important variability in tool completion rates, and in delirium positive score rates relative to expected delirium prevalence. This study highlights a need for greater reporting and analysis of relevant healthcare systems data. This is vital to advance understanding of effective delirium detection in routine care. [ABSTRACT FROM AUTHOR]

Published
2024
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37. Postfasciotomy Classification System for Acute Compartment Syndrome of the Leg

Author

Bouklouch, Yasser, primary, Bernstein, Mitchell, additional, Bosse, Michael, additional, Cota, Adam, additional, Duckworth, Andrew D., additional, Dunbar, Robert P., additional, Gamulin, Axel, additional, Guy, Pierre, additional, Hak, David J., additional, Haller, Justin M., additional, Hayda, Roman, additional, Jarragh, Ali, additional, Johnstone, Alan J., additional, Karunakar, Madhav, additional, Lawendy, Abdel-Rahman, additional, Leighton, Ross, additional, Mavrogenis, Andreas F., additional, Mauffrey, Cyril, additional, Miclau, Theodore, additional, Obremskey, William T., additional, Renninger, Chris, additional, Sanders, David W., additional, Schmidt, Andrew H., additional, Schneider, Prism, additional, Sen, Milan K., additional, Taitsman, Lisa, additional, Van Lancker, Hans, additional, and Harvey, Edward J., additional

Published
2023
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40. Section Editors

Author

Obert, Laurent, primary, Medoff, Robert J., additional, Kim, Jong Pil, additional, Zenke, Yukichi, additional, Duckworth, Andrew D., additional, Schep, Niels W.L., additional, Bouyer, Michael, additional, Lafosse, Thibault, additional, Lindau, Tommy R., additional, Grewal, Ruby, additional, Doornberg, Job N., additional, Osterman, A. Lee, additional, Colaris, Joost W., additional, Rozental, Tamara, additional, Shin, Alexander Y., additional, Dias, Joseph, additional, and Axelsson, Peter, additional

Published
2021
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41. Contributing Authors

Author

Abe, Yukio, primary, Aita, Márcio Aurélio, additional, Perez Alba, Jose Manuel, additional, Axelrod, Daniel, additional, Axelsson, Peter, additional, Bauer, Thomas, additional, Bell, Katrina R., additional, Bessems, J.H.J.M., additional, Bhandari, Mohit, additional, Blackburn, Julia, additional, Blokhuis, Taco J., additional, Boeckstyns, Michel E.H., additional, Bouyer, Michael, additional, Carratalá Baixauli, Vicente, additional, Calcagni, Maurizio, additional, Chan, Andrea, additional, Chantelot, Christophe, additional, Chiche, Léo, additional, Chung, Kevin C., additional, Colaris, Joost W., additional, Corella Montoya, Fernando, additional, Davis, Elissa S., additional, Piñal, Francisco del, additional, Dias, Joseph, additional, Doornberg, Job N., additional, Drijfhout van Hooff, C.C., additional, Duckworth, Andrew D., additional, Ehlinger, Matthieu, additional, Esposito, K.R., additional, Facca, Sybille, additional, Farnebo, Simon, additional, Fredrikson, Per, additional, Freibott, C.E., additional, Gómez, Ignacio Miranda, additional, Gouzou, Stéphanie, additional, Grewal, Ruby, additional, Guidi, Marco, additional, Hannemann, Pascal F.W., additional, Harper, Carl M., additional, Haynes, Sara F., additional, Jaarsma, R.L., additional, Johal, Herman, additional, Johnson, Nick, additional, Jung, Hyoung-Seok, additional, Kadar, Assaf, additional, Kim, Jong Pil, additional, Koehler, Steven M., additional, Laane, C.L.E., additional, Lafosse, Thibault, additional, Lee, Hyun Il, additional, Lee, Jae-Sung, additional, Lindau, Tommy R., additional, Lindqvist, Sandra, additional, Liverneaux, Philippe, additional, Loisel, François, additional, Lucas García, Francisco J., additional, Luchetti, Riccardo, additional, Meaike, Jesse D., additional, Meaike, Joshua J., additional, Medoff, Robert J., additional, Michielsen, Maartje, additional, Miller, Andrew, additional, Molyneux, Samuel G., additional, Obert, Laurent, additional, Osterman, A. Lee, additional, Paul, Ryan, additional, Pientka, William F., additional, Ploegmakers, J.J.W., additional, Pocnetz, Sasa, additional, Poublon, A.R., additional, Ring, D., additional, Rozental, Tamara, additional, Saab, Marc, additional, Saka, Natsumi, additional, Sandow, Michael J., additional, Schep, Niels W.L., additional, Schoolmeesters, B.J.A., additional, Shin, Alexander Y., additional, Shoap, S.C., additional, Sims, Laura, additional, Strauch, R.J., additional, Strelzow, Jason A., additional, Suh, Nina, additional, Takahashi, Youhei, additional, Tang, Jin Bo, additional, Ten Bosch, Jan A., additional, The, B., additional, Tosti, Rick, additional, van der Windt, A.E., additional, Vanhees, Matthias, additional, Vernet, Paul, additional, Verstreken, Frederik, additional, White, Timothy O., additional, Wijffels, M.M.E., additional, Woolnough, Taylor, additional, Xiong, Grace, additional, Zenke, Yukichi, additional, and Zhang, Yiyang, additional

Published
2021
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43. Early mobilisation after hip fracture surgery is associated with improved patient outcomes: A systematic review and meta‐analysis.

Author

Agarwal, Nikhil, Feng, Tony, Maclullich, Alasdair, Duckworth, Andrew, and Clement, Nicholas

Subjects
MEDICAL information storage & retrieval systems, STATISTICAL models, HIP fractures, FRACTURE fixation, EARLY ambulation (Rehabilitation), TREATMENT effectiveness, META-analysis, HOSPITAL mortality, DESCRIPTIVE statistics, CHI-squared test, SYSTEMATIC reviews, MEDLINE, ODDS ratio, SURGICAL complications, MEDICAL databases, POSTOPERATIVE period, CONFIDENCE intervals, LENGTH of stay in hospitals, DATA analysis software, DISEASE risk factors
Abstract

Introduction: The aims of this systematic review and meta‐analysis were to determine if after hip fracture surgery (1) early mobilisation is associated with improved clinical outcomes, and if so (2) are benefits directly proportional to how soon after surgery the patient mobilises. Methods: A Preferred Reporting Items for Systematic Reviews and Meta‐Analyses systematic review was conducted using four databases to identify all studies that compared postoperative early mobilisation with delayed mobilisation, in patients after hip fracture surgery. The Critical Appraisal Skills Programme checklist was employed for critical appraisal and evaluation of all studies that met the inclusion criteria. Results: A total of 13 studies, including 297,435 patients were identified, of which 235,275 patients were mobilised early and 62,160 were mobilised late. Six studies assessed 30‐day mortality, of which two also investigated 30‐day complication rates. Pooled meta‐analysis demonstrated that there were significantly lower 30‐day mortality rates (odds ratio [OR] 0.35, 95% confidence interval [CI] 0.31–0.41, p < 0.001) and complication rates (OR 0.43, 95% CI 0.36–0.51, p < 0.001) in patients mobilising early after hip fracture surgery. Five studies investigated length of stay (LOS) and meta‐analysis revealed no difference between groups (mean difference −0.57 days, 95% CI −1.89–0.74, p = 0.39). Conclusion: Early mobilisation in hip fracture patients is associated with a reduction in 30‐day mortality and complication rates compared to delayed mobilisation, but no difference in LOS. These findings illustrate that early mobilisation is associated with superior post operative outcomes. However, a direct casual effect remains to be demonstrated, and further work on the factors underlying delayed mobilisation is required. [ABSTRACT FROM AUTHOR]

Published
2024
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48. Artificial intelligence fracture recognition on computed tomography

Author

Dankelman, Lente H. M., Schilstra, Sanne, IJpma, Frank F. A., Doornberg, Job N., Colaris, Joost W., Verhofstad, Michael H. J., Wijffels, Mathieu M. E., Prijs, Jasper, Algra, Paul, van den Bekerom, Michel, Bhandari, Mohit, Bongers, Michiel, Court-Brown, Charles, Bulstra, Anne-Eva, Buijze, Geert, Bzovsky, Sofia, Colaris, Joost, Chen, Neil, Doornberg, Job, Duckworth, Andrew, Goslings, J. Carel, Gordon, Max, Gravesteijn, Benjamin, Groot, Olivier, Guyatt, Gordon, Hendrickx, Laurent, Hintermann, Beat, Hofstee, Dirk-Jan, IJpma, Frank, Jaarsma, Ruurd, Janssen, Stein, Jeray, Kyle, Jutte, Paul, Karhade, Aditya, Keijser, Lucien, Kerkhoffs, Gino, Langerhuizen, David, Lans, Jonathan, Mallee, Wouter, Moran, Matthew, McQueen, Margaret, Mulders, Marjolein, Nelissen, Rob, Obdeijn, Miryam, Oberai, Tarandeep, Olczak, Jakub, Oosterhoff, Jacobien H. F., Petrisor, Brad, Poolman, Rudolf, Ring, David, Tornetta, Paul, Sanders, David, Schwab, Joseph, Schemitsch, Emil H., Schep, Niels, Schipper, Inger, Schoolmeesters, Bram, Swiontkowski, Marc, Sprague, Sheila, Steyerberg, Ewout, Stirler, Vincent, Walter, Stephen D., Walenkamp, Monique, Wijffels, Mathieu, and Laane, Charlotte

Subjects
Artificial intelligence, Orthopedics, Emergency Medicine, Orthopedics and Sports Medicine, Surgery, Convolutional neural networks, Critical Care and Intensive Care Medicine, Computed tomography, Fractures
Abstract

Purpose The use of computed tomography (CT) in fractures is time consuming, challenging and suffers from poor inter-surgeon reliability. Convolutional neural networks (CNNs), a subset of artificial intelligence (AI), may overcome shortcomings and reduce clinical burdens to detect and classify fractures. The aim of this review was to summarize literature on CNNs for the detection and classification of fractures on CT scans, focusing on its accuracy and to evaluate the beneficial role in daily practice. Methods Literature search was performed according to the PRISMA statement, and Embase, Medline ALL, Web of Science Core Collection, Cochrane Central Register of Controlled Trials and Google Scholar databases were searched. Studies were eligible when the use of AI for the detection of fractures on CT scans was described. Quality assessment was done with a modified version of the methodologic index for nonrandomized studies (MINORS), with a seven-item checklist. Performance of AI was defined as accuracy, F1-score and area under the curve (AUC). Results Of the 1140 identified studies, 17 were included. Accuracy ranged from 69 to 99%, the F1-score ranged from 0.35 to 0.94 and the AUC, ranging from 0.77 to 0.95. Based on ten studies, CNN showed a similar or improved diagnostic accuracy in addition to clinical evaluation only. Conclusions CNNs are applicable for the detection and classification fractures on CT scans. This can improve automated and clinician-aided diagnostics. Further research should focus on the additional value of CNN used for CT scans in daily clinics.

Published
2023

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