Search

Your search keyword '"Marsden, Max"' showing total 131 results
Start Over Author "Marsden, Max"
131 results on '"Marsden, Max"'

7. The impact of a Bayesian network for pre-hospital decision-support after trauma

Author

Marsden, Max

Abstract

In patients with major traumatic injuries, early intervention can be lifesaving. However, identifying high-risk patients can be difficult, and judgement errors may compromise optimal care. Prediction models can be used to augment clinical judgment. The aim of the thesis was to assess whether Bayesian Networks (BN), which are causal probabilistic models able to fuse knowledge and data, can augment clinical judgment in pre-hospital trauma care. The thesis focuses on decisions relating to Trauma Induced Coagulopathy (TIC), a difficult to diagnose condition that is central to resuscitation decisions, and haemorrhage. To assess decision making, thematic analysis of interviews with expert clinicians was performed. The interviews revealed that situational recognition is key to effective decision making, and uncertain information results in difficult decisions. An example of uncertainty was evident in clinician's decisions to activate the major haemorrhage protocol. In such decisions, the predictive performance of an existing BN model ("ED TIC BN") was not significantly different to native clinical judgement (at equal specificity, sensitivity = clinicians: 86% vs BN: 96%, p = 0.13). The ED TIC BN risk prediction model was updated for pre-hospital use to enable earlier decision support, and the PH TIC BN performed better than any other TIC model. The impact of the pre-hospital model on decision-making was analysed in a prospective, multicentre study. The discriminative performance of the BN was not significantly different from clinical assessment. The performance of clinical assessment did not improve after decision support information. However, there was evidence of the positive influence of the PH TIC BN on clinical assessment and increased ease of decision making. This thesis advances the understanding of impact analysis in pre-hospital decision support after injury. The knowledge generated will be used to design subsequent trials to assess the impact of decision support on patient and health system outcomes.

Published
2020

9. Trauma Laparotomy in the UK: A Prospective National Service Evaluation

Author

Nnajiuba, Henry Obinna, Perkins, Zane, Pearce, Phillip, Yalamanchili, Seema, Malik, Nabeela S., Tai, Nigel, Sim, Tony, Kaye, Callum T., Gourgiotis, Stavros, Lunevicius, Raimundas, Akhtar, Adeel, Finnegan, Chris, Ezidi, Francis, McGeown, Daniel, Gowdy, Adam, Sains, Parv, Bull, Charles, Pegna, Victoria, Florence, Charlotte, O’Reilly, David, Roberts, Bryony, Davies, Emma, Bassett-Davies, Maria, Hardwicke, Joseph, Smith, Michael, Briggs, Christopher, Mossadegh, Somayyeh, Enemosah, Ibrahim, Stubbs, Ben, West, Charles, Wilson, Michael, Vaughan, Elizabeth, McKechnie, Stuart, Graham, Alice, Chandarana, Mihir, Dorrance, Helen, Gall, Lewis, McGuigan, Mari-Claire, Cowlam, Tom, Bell, Alexander, Hardcastle, Christine, Bew, Duncan, Prior, Kate REJ., Winstanley, Mark, Moore, Jon, Parker, Emma Stewart, Jones, Jonathan, Smith, Stella Ruth, Varley, Rebecca, Paterson, Johanna, Evans, Laura, Lipscombe, Nicola, Griffiths, Ben, Watson, Laura Jayne, Brooks, Adam, Moody, Nick, Kourdouli, Amar, Blackburn, Lauren, Bond-Smith, Giles, Muthusami, Anitha, Shivakumar, Natesh, Griffiths, Ewen A., Bergin, Colin, Challen, Kirsty, Daley, Michael, Smith, Iain M., Williams, Gethin L., Rutka, Olga, Ross, Jennifer, Thaventhiran, Anthony J., Hancorn, Kate, Rossetto, Andrea, Badminton, Rebecca, Moreno-Camacho, Esau, Farrelly, Paul, Harrison, Oliver, Maytham, Gary, Alfa-Wali, Maryam, Farrah, Helen, Owen, Harriet, Navein, Jack, Kodakadath, Harsha, Khan, Mansoor, Troller, Rebekka, Thompson, Julian, Thompson, Michael, Thompson, Charlotte, Bailey, Ian, Manson, Jo, Sharrock, Anna, Greenway, Michael, Chesworth, Alexandra, Shrestha, Ashish, Mehta, Disha, Watts, Alan, Cronin, Una, Maoileannaigh, Lorcan o, Docherty, Sam, Marsden, Max E.R., Vulliamy, Paul E.D., Carden, Rich, Naumann, David N., and Davenport, Ross A.

Published
2021
Full Text
View/download PDF

10. Prehospital Partial Resuscitative Endovascular Balloon Occlusion of the Aorta for Exsanguinating Subdiaphragmatic Hemorrhage.

Author

Lendrum, Robbie A., Perkins, Zane, Marsden, Max, Cochran, Claire, Davenport, Ross, Chege, Frank, Fitzpatrick-Swallow, Virginia, Greenhalgh, Rob, Wohlgemut, Jared M., Henry, Christine L., Singer, Ben, Grier, Gareth, Davies, Gareth, Bunker, Nick, Nevin, Daniel, Christian, Mike, Campbell, Marion K., Tai, Nigel, Johnson, Austin, and Jansen, Jan O.

Published
2024
Full Text
View/download PDF

12. Global trends in opioid use for pain management in acute pancreatitis: A multicentre prospective observational study

Author

Knoph, Cecilie Siggaard, Lucocq, James, Kamarajah, Sivesh Kathir, Olesen, Søren Schou, Jones, Michael, Samanta, Jayanta, Talukdar, Rupjyoti, Capurso, Gabriele, de‐Madaria, Enrique, Yadav, Dhiraj, Siriwardena, Ajith K., Windsor, John, Drewes, Asbjørn Mohr, Nayar, Manu, Cark, Neil, Pius, Riinu, Houghton, Eduardo, Gimenéz, Mariano, Uribe, Karla, Rodriguez, Florencia, Gundara, Justin, Mackay, Thomas, Phan, Huynh, Lewin, Joel, McElhatton, Claire, Siriwardhane, Mehan, Hodgson, Russell, Malik, Hassan, Ward, Ryan, Young, Kerilee, Bappayya, Shaneel, Loveday, Benjamin, Samra, Jaswinder, Gall, Tamara, Mittal, Anubhav, Chan, Ting Ting, Lo, Vincent Wing‐ho, Liang, Hui, Wang, Cong, Huang, Wei, Jin, Tao, Wu, Yongzi, Xia, Qing, Georgio, Nikolaou, Koronakis, Nikolaos, Davidsen, Line, Hamed, Emad, Mohamed, Salem, Demetrashvili, Zaza, Tvaladze, Ana, Kachakhidze, Irakli, Zurabashvili, Tea, Ioannidis, Orestis, Kapiris, Stylianos, Mavrodimitraki, Eleni, Sotiropoulou, Maria, Machairas, Nikolaos, Schizas, Dimitrios, Syllaios, Athanasios, Vailas, Michail, Chlorakis, Georgios, Kalaitzakis, Evangelos, Tsafaridou, Maria, Mulita, Francesk, Verras, Georgios‐Ioannis, Gupta, Amit, Rajput, Deepak, Sharma, Oshin, Goud, Rajesh, Unnisa, Misbah, Bains, Lovenish, Singh, Nishu, Dhar, Jahnvi, Abdelmoeti, Mahmoud, Súilleabháin, Criostóir Ó, O'Connell, Robert, Calabro, Marcello, La Terra, Antonio, Muretore, Andrea, Contul, Riccardo Brachet, Diotallevi, Margherita, Mascaro, Annamaria, Millo, Paolo, Biondo, Santino Antonio, Mazzeo, Carmelo, Cucinotta, Eugenio, Fleres, Francesco, Marinak, AOUG, Brocco, Veronica, Ceresoli, Marco, Rennis, Maria, Centonze, Danilo, Distefano, Coatanza, Veroux, Massimiliano, Zerbo, Domenico, Bogoni, Selene, Biloslavo, Alan, Bianchi, Velentina, Candelli, Marcello, Franceschi, Francesco, Gasbarrini, Antonio, Nista, Enrico, Sganga, Gabriele, Tropeano, Giuseppe, Policlinico, Fondazione, Altieri, Caterina, Dinuzzi, Vincenza, Marconi, Matteo, Rivolta, Umberto, Dameno, Vitale Roberto, Papa, Mario V., Balla, Andrea, Lepiane, Pasquale, Saraceno, Federica, Aiolfi, Alberto, Bona, Davide, Sozzi, Andrea, Cianci, Pasquale, Varesano, Marco, Conversano, Ivana, Abete, Roberta, D'Avino, Raffaele, Marra, Ester, Marte, Gianpaolo, Tammaro, Pasquale, Gobatti, Davide, Marmaggi, Serena, Palmieri, Francesco, Sampietro, Roberto, Manca, Roberto, Pilla, Federica, Piras, Enrico, Pignata, Giusto, Canfora, Ilaria, Andreuccetti, Jacopo, D'Alessio, Rossella, Armellin, Claudia, Grossi, Ugo, Massani, Marco, Pontin, Alessandro, Stecca, Tommaso, Pilia, Tiaizna, Pisanu, Adolfo, Podda, Mauro, Giuffrida, Mario, Perrone, Gennaro, Guadagni, Simone, Morelli, Luca, Frontali, Alice, Basurto, Francesca, D'Ugo, Stefano, Manoochehri, Farshad, Spampinato, Marcello, Apadula, Laura, Preatoni, Paoletta, Sartarelli, Lodovico, Al‐Jaiuossi, Osama, Ernisova, Mairam, Sopuev, Andrey, Sua, Bruce, Farfus, Anthony, Teo, Keith, Smith, Brittany, Ratnayake, Bathiya, Buchanan, Jayvee, Clark, Elinor, Connor, Saxon, Hore, Todd, Attari, Salman, Kadir, Bushra, Memon, Sadik, Abbas, Zaigham, Quadeer, Muhammad Ali, Altaf, Abeer, Ameet, Pooja, Devi, Jalpa, Seerani, Nandlal, Afzal, Ameer, Akbar, Ali, Asghar, Mohammad Sohail, Sa, Tiago, Barreira, Ana Lucia, Carvalho, Numo, Cismasiu, Brigitta, Henriques, Susana, Luiz, Francisco Vara, Draghici, Andreea, Grigorean, Valentin, Porojan, Vlad, Stoian, Alexandru‐Rares, Teaca, Lucia, Arbutina, Dragana, Cuk, Vladica, Kovacevic, Bojan, Mandic, Luka, Bonney, Glenn, Gao, Yujia, Pang, Ning Qi, Bellil, Abdalla, Devar, John, Khan, Zafar, Khumalo, Vusi, Smith, Martin, Estevez‐Fernandez, Sergio, Mosquera, Beatriz Romero, Rodriguez, Sergio, Garcia‐Rayado, Guillermo, Piñerua‐Gonsalvez, Jean Felix, Ruiz Rebollo, M Lourdes, Olmos, Jose M., Tejedor‐Tejada, Javier, Diez‐Alonso, Manuel, Matias‐Garcia, Belen, Moreno, Fernando Mendoza, Vera‐Mansilla, Cristina, Roses, Helena Salvador, Gómez, Diego Vázquez, Oballe, Juan Rodriguez, Jayarajah, Umesh, Nandasena, Malith, Pathirana, Aloka, Galal‐Eldin, Sami, Hajibandeh, Shahab, Hamid, Hytham, Colak, Elif, Sydorchuk, Larysa, Knut, Ruslan, Voronyuk, Ksenia, Chooklin, Serge, Baryskyi, Vitalii, Sydorchuk, Ruslan, Mukherjee, Samrat, Patel, Maitreyi, Akhtar, Amina, Asarbakhsh, Miriam, Nolan, Frances, Schuijtvlot, Nicholaas, Prem, Sandhya, Thrikandiyur, Anuradha, Morris, Millicent, Mroczek, Thomas, Sgourakis, George, Sultana, Asma, Varley, Rebecca, Groot‐Wassink, Thomas, Labinoti, Roland, Packham, Brett, Seebah, Keving, Allen, Sophie, Mokhtassi, Shiva, Belgaumkar, Ajay, De'Ath, Henry, Cook, Amy, Delaney, Christopher, Johnson, Roisin, Azibaodinami, Becky Olali, Sartini, Ashley, Stanfield, Mea, Tomasi, Ivan, Kanakala, Venkat, Mbarushimana, Simon, McKeever, Mark, Batilli, Mamata, Bhatta, Gakul, Rai, Subash, Bond‐smith, Giles, Elserafy, Amr, Shams, Mohamed, Al Saoudi, Tareq, Bhardwaj, Neil, Hussain, Wajith, Lancellotti, Francesco, Montagnini, Greta, Cairns, George, Hollyman, Marianne, Rakin, Asef, Shahid, Mishal, Barbour, Fraser, Hawkyard, Jake, McTeer, Matthew, Pandanaboyana, Sanjay, Taylor, Ellie, Kuzman, Matta, Dyer, Sarah, Hopkins, James, Pournaras, Dimitri, Sudlow, Alexis, Kumar, SK, Aujayeb, Avinash, Leo, Alex, Senra, Fatima Lorenzana, Watfah, Josef, Barrie, Jenifer, Brown, Chris, Gomez, Dhanny, Aroori, Somaiah, Ciprani, Debora, Karmarkar, Rahi, Almomani, Eyas, Roberts, Keith, Fale, Madeleine, Gupta, Ajay, Marsden, Max, Seet, Chris, Soni, Lakshya, Hamdan, Mohammed, Sadera, Rohan, Sud, Vikas, Chinnah, Edith, Di Mauro, Davide, Manzelli, Antonio, Orabi, Amira, Presa, Roberto, Reece‐Smith, Alex, Wajed, Shahjehan, Fingret, Jacob, Shah, Nehal, Jatania, Jignesh, Krishna, Arun, Berry, David, Kitsikosta, Loukiani, Helliwell, Jack, Huntley, Benjamin, Pine, James, Yau, Jih‐Dar, Lee, Shiela, Mahawar, Kamal, Shetty, Neehar, Britton, Emily, Shaw, Alice, Laarhoven, Stijn, Gahunia, Sukhpreet, Ortega, Miguel Gargia, Lee, Adam, Ng, Cho Ee, El Kafsi, Jihene, Mason, John, Vithlani, Gauri, Benhmida, Rami, Gunell, James, Parmar, Chetan, Dorkeh, Da‐Costa, Elnagar, Mohamed, Lee, Jih Ian, Nessa, Ashrafun, Yeap, Zhu Hui, Hemadasa, Niroshini, Javed, Saria, Sami, Sharuk, Damaskos, Dimitrios, Healey, Andrew, Soupashi, Maria, Triantafyllou, Tania, Coats, Maria, Douglass, Benjamin, Hendry, Brid, Hussain, Yasmin, Javid, Zhara, Mantyla, Mia, Rajkumar, Khaman, Chin, Carven, Hajibandeh, Shahab, Kumar, Nagappan, Gerogiannis, Ioannis, Kapsampelis, Panagiotis, Gerge, Farid, Anderson, Gulsum, Dinh, Vu, Phillips, Anna, Yadav, Dhiraj, and Pandanaboyana, Sanjay

Abstract

Since there is no current international consensus on the optimal approach for pain management in acute pancreatitis (AP), analgesic practices may vary across different healthcare settings. This study explored global disparities in analgesic use, in particular opioids, during admission and at discharge in hospitalised AP patients. This was a post hoc analysis of the prospective PAINAP database, which included all admissions for AP between April and June 2022 with a 1‐month follow‐up. Demographic details, analgesic use, and clinical outcomes were recorded during admission and at discharge. Odds ratios (ORs) for opioid use during admission and at discharge were identified using multivariable regression analyses. Amongst the 1864 patients (52% males, median age 56 (interquartile range, 41–71)) across three different continents, simple analgesics were predominantly used as the primary analgesic (70%). Opioid use during admission was lowest in European centres (67%). Admission in Asian (OR, 2.53 (95% confidence interval (CI), 1.59–4.04), p< 0.001), and Australian (OR, 5.81 (95% CI, 3.19–10.56), p< 0.001) centres was associated with opioid administration during admission compared with European centres. Increased pain severity, longer pre‐admission pain duration, organ failure, and longer length of admission increased opioid use during admission. At discharge, Asian (OR, 2.01 (95% CI, 1.40–2.88), p< 0.001) and Australian (OR, 1.91 (95% CI, 1.28–2.85), p= 0.002) centres were associated with opioid prescription compared with European centres. Increased pain severity, longer pre‐admission pain duration, acute necrotic collections, and walled‐off necrosis also increased the likelihood of opioid prescription at discharge. There are substantial intercontinental differences in opioid use for AP pain. Accordingly, there is a need for international guidelines on pain management in AP.

Published
2024
Full Text
View/download PDF

23. Reporting guideline for the early-stage clinical evaluation of decision support systems driven by artificial intelligence

Author

Vasey, Baptiste, Nagendran, Myura, Campbell, Bruce, Clifton, David A, Collins, Gary S., Denaxas, Spiros, Denniston, Alastair K., Faes, Livia, Geerts, Bart, Ibrahim, Mudathir, Liu, Xiaoxuan, Mateen, Bilal A., Mathur, Piyush, Mccradden, Melissa D., Morgan, Lauren, Ordish, Johan, Rogers, Campbell, Saria, Suchi, Ting, Daniel S. W., Watkinson, Peter, Weber, Wim, Wheatstone, Peter, Mcculloch, Peter, Lee, Aaron Y., Fraser, Alan G., Connell, Ali, Vira, Alykhan, Esteva, Andre, Althouse, Andrew D., Beam, Andrew L., de Hond, Anne, Boulesteix, Anne-Laure, Bradlow, Anthony, Ercole, Ari, Paez, Arsenio, Tsanas, Athanasios, Kirby, Barry, Glocker, Ben, Velardo, Carmelo, Park, Chang Min, Hehakaya, Charisma, Baber, Chris, Paton, Chris, Johner, Christian, Kelly, Christopher J., Vincent, Christopher J., Yau, Christopher, Mcgenity, Clare, Gatsonis, Constantine, Faivre-Finn, Corinne, Simon, Crispin, Sent, Danielle, Bzdok, Danilo, Treanor, Darren, Clifton, David A., Wong, David C., Steiner, David F., Higgins, David, Benson, Dawn, O’Regan, Declan P., Gunasekaran, Dinesh V., Danks, Dominic, Neri, Emanuele, Kyrimi, Evangelia, Schwendicke, Falk, Magrabi, Farah, Ives, Frances, Rademakers, Frank E., Fowler, George E., Frau, Giuseppe, Hogg, H. D. Jeffry, Marcus, Hani J., Chan, Heang-Ping, Xiang, Henry, Mcintyre, Hugh F., Harvey, Hugh, Kim, Hyungjin, Habli, Ibrahim, Fackler, James C., Shaw, James, Higham, Janet, Wohlgemut, Jared M., Chong, Jaron, Bibault, Jean-Emmanuel, Cohen, Jérémie F., Kers, Jesper, Morley, Jessica, Krois, Joachim, Monteiro, Joao, Horovitz, Joel, Fletcher, John, Taylor, Jonathan, Yoon, Jung Hyun, Singh, Karandeep, Moons, Karel G. M., Karpathakis, Kassandra, Catchpole, Ken, Hood, Kerenza, Balaskas, Konstantinos, Kamnitsas, Konstantinos, Militello, Laura, Wynants, Laure, Oakden-Rayner, Lauren, Lovat, Laurence B., Smits, Luc J. M., Hinske, Ludwig C., Elzarrad, M. Khair, van Smeden, Maarten, Giavina-Bianchi, Mara, Daley, Mark, Sendak, Mark P., Sujan, Mark, Rovers, Maroeska, Decamp, Matthew, Woodward, Matthew, Komorowski, Matthieu, Marsden, Max, Mackintosh, Maxine, Abramoff, Michael D., de la Hoz, Miguel Ángel Armengol, Hambidge, Neale, Daly, Neil, Peek, Niels, Redfern, Oliver, Ahmad, Omer F., Bossuyt, Patrick M., Keane, Pearse A., Ferreira, Pedro N. P., Schnell-Inderst, Petra, Mascagni, Pietro, Dasgupta, Prokar, Guan, Pujun, Barnett, Rachel, Kader, Rawen, Chopra, Reena, Mann, Ritse M., Sarkar, Rupa, Mäenpää, Saana M., Finlayson, Samuel G., Vollam, Sarah, Vollmer, Sebastian J., Park, Seong Ho, Laher, Shakir, Joshi, Shalmali, van der Meijden, Siri L., Shelmerdine, Susan C., Tan, Tien-En, Stocker, Tom J. W., Giannini, Valentina, Madai, Vince I., Newcombe, Virginia, Wei Yan, Ng, Rogers, Wendy A., Ogallo, William, Park, Yoonyoung, and Perkins, Zane B.

Subjects
Research Report, Consensus, Artificial Intelligence, Research Design, Urological cancers Radboud Institute for Health Sciences [Radboudumc 15], Immunology, Humans, General Medicine, DECIDE-AI expert group, General Biochemistry, Genetics and Molecular Biology, 11 Medical and Health Sciences, Women's cancers Radboud Institute for Health Sciences [Radboudumc 17], Checklist
Abstract

A growing number of artificial intelligence (AI)-based clinical decision support systems are showing promising performance in preclinical, in silico evaluation, but few have yet demonstrated real benefit to patient care. Early-stage clinical evaluation is important to assess an AI system’s actual clinical performance at small scale, ensure its safety, evaluate the human factors surrounding its use and pave the way to further large-scale trials. However, the reporting of these early studies remains inadequate. The present statement provides a multi-stakeholder, consensus-based reporting guideline for the Developmental and Exploratory Clinical Investigations of DEcision support systems driven by Artificial Intelligence (DECIDE-AI). We conducted a two-round, modified Delphi process to collect and analyze expert opinion on the reporting of early clinical evaluation of AI systems. Experts were recruited from 20 pre-defined stakeholder categories. The final composition and wording of the guideline was determined at a virtual consensus meeting. The checklist and the Explanation & Elaboration (E&E) sections were refined based on feedback from a qualitative evaluation process. In total, 123 experts participated in the first round of Delphi, 138 in the second round, 16 in the consensus meeting and 16 in the qualitative evaluation. The DECIDE-AI reporting guideline comprises 17 AI-specific reporting items (made of 28 subitems) and ten generic reporting items, with an E&E paragraph provided for each. Through consultation and consensus with a range of stakeholders, we developed a guideline comprising key items that should be reported in early-stage clinical studies of AI-based decision support systems in healthcare. By providing an actionable checklist of minimal reporting items, the DECIDE-AI guideline will facilitate the appraisal of these studies and replicability of their findings.

Published
2022
Full Text
View/download PDF

26. Trauma emergency thoracotomy for resuscitation in shock—a multi-centre evaluation of current UK practice of pre-hospital and emergency department resuscitative thoracotomy in trauma

Author

Tucker, Harriet, primary, Ramage, Lisa, additional, Greenhalgh, Rob, additional, Griggs, Jo, additional, Lang, Nigel, additional, Marsden, Max, additional, Vulliamy, Paul, additional, Wohlgemut, Jared M, additional, Christian, Mike, additional, and Davenport, Ross, additional

Published
2022
Full Text
View/download PDF

32. A Twenty-one Year Retrospective Observational Study of Pre-hospital Thoracotomies Conducted by London's Air Ambulance for Penetrating and Blunt Trauma Analysis Plan

Author

Christian, Michael D, Thomas, Stephen H, Shadman Aziz, Greenhalgh, Rob, Lendrum, Robbie, Perkins, Zane, Whitehouse, Andy, Read, Steve, Foster, Liz, Henry, Christine, Davies, Gareth, Lockey, David J, Carden, Rich, Chege, Frank, Davenport, Ross, Marsden, Max, Riggs, Andrea, Dobbie, Anna, and Hurst, Tom

Published
2021
Full Text
View/download PDF

34. Defence Medical Services’ REBOA training course

Author

Marsden, Max E R, Park, C, Barratt, J, Tai, N, and Rees, P

Abstract

Resuscitative Endovascular Balloon Occlusion of the Aorta (REBOA) enables temporary haemorrhage control and physiological stabilisation. This article describes the bespoke Defence Medical Services (DMS) training package for effectively using REBOA. The article covers how the course was designed, how the key learning objectives are taught, participant feedback and the authors’ perceptions of future training challenges and opportunities. Since the inaugural training course in April 2019, the authors have delivered six courses, training over 100 clinicians. For the first time in the UK DMS, we designed and delivered a robust specialist endovascular training programme, with demonstrable, significant increases in confidence and competence. As a result of this course, the first DMS REBOA-equipped forward surgical teams deployed in June 2019. Looking to the future, there is a requirement to develop an assessment of skill retention and the potential need for revalidation.

Published
2023
Full Text
View/download PDF

35. Balloons on the battlefield: REBOA implementation in the UK Defence Medical Services

Author

Marsden, Max E R, Buckley, A M, Park, C, Tai, N, and Rees, P

Abstract

Established in 2018, the Defence Endovascular Resuscitation (DefER) group recognised that resuscitative endovascular balloon occlusion of the aorta (REBOA) offered an option to improve survival in battle casualties dying from haemorrhage, particularly in remote and austere surgical settings. Following a successful jHub opportunity assessment, DefER purchased training and operational kit at pace. By 1 April 2019, the first forward surgical group undertook a bespoke endovascular training and assessment package. Results of the pilot were presented back to a jHub 4* Innovation Board, which initially awarded £500 000 to fund the project to full implementation. Med Op Cap provided a solution to establish REBOA as a core capability on to the 370 modules. REBOA catheters and arterial access kit are now available to deployed Role 2 facilities across defence as an adjunct to damage control resuscitation in specific circumstances. REBOA has, from a standing start, gained pan-Defence Medical Services (DMS) endorsement and has been integrated into deployed damage control resuscitation. To establish a new resuscitation capability across all Role 2 platforms within 15 months of inception represents implementation at pace. This agility was unlocked by empowering clinicians to develop the platform in conjunction with commercial procurement. This article describes how this innovative pathway facilitated the rapid introduction of a lifesaving haemorrhage control technique to equip DMS clinicians.

Published
2023
Full Text
View/download PDF

42. British Society of Breast Radiology Annual Scientific Meeting 2016

Author

Azmil, Ameerah Mohd, primary, Bansal, Gaurav Jyoti, additional, Davies, Eleri, additional, Wallis, Matthew, additional, Kilburn-Toppin, Fleur, additional, Taylor-Phillip, Sian, additional, Buckley, Anne, additional, Healy, Nuala, additional, Quinn, Aine, additional, O’Keeffe, Sylvia, additional, Ang, Teri, additional, Maxwell, Anthony, additional, Lim, Yit Y., additional, Harkness, Elaine, additional, Emsley, Richard, additional, Astley, Susan, additional, Gadde, Soujanya, additional, Ferguson, Jennifer, additional, Stevens, Guy, additional, Hills, Robert, additional, Thomas, Kate Gower, additional, Rajan, Sabrina, additional, Sharma, Nisha, additional, Kishore, Ajit, additional, O’ Dowd, John, additional, Murphy, Claire, additional, Lindsay, Ken, additional, Dadayal, Guneesh, additional, Moody, Anne Nielsen, additional, Whitaker, Madeleine, additional, Wolstenhulme, Stephen, additional, Bull, Jane, additional, Culpan, Anne-Marie, additional, Roszkowski, Natalia, additional, Grima, Alexis, additional, Stahnke, Michaela, additional, Rubin, Caroline, additional, Walker, Ruth, additional, Oeppen, Rachel, additional, Puttagunta, Srikanth, additional, Gray, Karen, additional, Puri, Shama, additional, Millican-Slater, Rebecca, additional, Pascaline, Sana, additional, Hajaj, Mohamad, additional, Flinn, Janet, additional, Thomson, Kim, additional, Elsberger, Beatrix, additional, Turnbull, Anne, additional, Bagnall, Mark, additional, York, Joanne, additional, Benney, Stuart, additional, Goyal, Amit, additional, Topps, Ashley, additional, Barr, Simon, additional, Pikoulas, Panagiotis, additional, Pritchard, Susan, additional, Greenhalgh, Sophie, additional, Sreenivas, Muthyala, additional, Nandhra, Anju, additional, Sharma, Sheetal, additional, Gulati, Archita, additional, Razzaq, Furhan, additional, Khan, Sana, additional, McKillen, Jacqueline, additional, Larkin, Rupert, additional, Kamat, Sachin, additional, Goncalves, Carla, additional, Tan, Alan, additional, Eleti, Asha, additional, Vidyaprakash, Nithya, additional, McMahon, Michelle A., additional, Dall, Barbara J. G., additional, Majid, Faisal, additional, Rattehalli, Ramachadra, additional, Gaur, Vandana, additional, Mackenzie, Alistair, additional, Warren, Lucy, additional, Patel, Mishal, additional, Halling-Brown, Mark, additional, Wilkinson, Louise, additional, Young, Kenneth, additional, Tanner, James, additional, Currie, Rachael, additional, Balyes, Sharron, additional, Wragg, Julie, additional, Zamfir, Georgiana, additional, Ayra, Pallavi, additional, Marsden, Max, additional, Butterworth, James, additional, Sinha, Shona, additional, Desai, Anil, additional, Metafa, Anna, additional, Nielsen-Moody, Anne, additional, Ward, Laura, additional, Heller, Samantha, additional, Hudson, Sue, additional, Taylor, Kathryn, additional, Xyda, Argyro, additional, Allen, Steven, additional, Orlowski, Elizabeth, additional, Downey, Kate, additional, Sidebottom, Richard, additional, Lodhi, Ahmad, additional, Howell, Anthony, additional, Evans, Gareth, additional, Lim, Yit, additional, Chen, Yan, additional, Lepine-Williams, Elizabeth, additional, Henderson, Steven, additional, Litherland, Janet, additional, Altaf, Naveed, additional, Bury, Yvonne, additional, Forester, Nerys, additional, Chin, Mei Chan, additional, Taylor, Caroline, additional, Skippage, Pippa, additional, Sian, Aneet, additional, McAvinchey, Rita, additional, Evans, Tom, additional, Nisbet, Sian, additional, Murray, Alex, additional, Castillo, Raquel Clark, additional, Markham, Hannah, additional, Thomas, Timothy, additional, Jain, Anil, additional, Molderson, Jaanilka, additional, Wan, Joseph, additional, Newman, Helen, additional, Lowes, Simon, additional, Lunt, Linsley, additional, Sultana, Sadia, additional, Chillal, Varun, additional, Dalton, Rebecca, additional, Reeves, David, additional, Sergeant, Jamie, additional, Edwards, Liz, additional, Jenkins, Kate, additional, Shaikh, Shaheeda, additional, O’Flynn, Elizabeth, additional, Bombroffe, Laura, additional, Hughes, Julie, additional, Scurr, Erica, additional, Wilson, Robin, additional, Jones, Lyn, additional, Govindarajulu, Sasirekha, additional, Sahu, Ajay K., additional, Brown, Matthew, additional, Allen, Steve, additional, O’Flynn, Liz, additional, deSouza, Nandita, additional, Rahim, Rumana, additional, Wasan, Rema, additional, Goligher, Jane, additional, Wijesuriya, Shalini, additional, Morel, Juliet, additional, Peacock, Clare, additional, Michell, Michael, additional, Iqbal, Asif, additional, Evans, David, additional, Batohi, Bhavna, additional, Wasan, Tavleen, additional, Phillips, Vivien, additional, Manuel, Eden, additional, Bowman, Gillian, additional, Satchithananda, Keshthra, additional, Fang, Cheng, additional, Shah, Chirag, additional, Hodkin, Leanne, additional, Horgan, Keiran, additional, Dodwell, David, additional, Moloney, BM, additional, Casey, M. C., additional, Breathnach, A., additional, Dwyer, R. M., additional, McLoughlin, R., additional, Sweeney, K. J., additional, Malone, C., additional, Healy, M. J., additional, Kerin, M. J., additional, Mitchell, Elizabeth, additional, Twiddy, Maureen, additional, Dobson, Hilary, additional, Vinnicombe, Sarah, additional, Evans, Andrew, additional, Grant, David, additional, Bishop, Briony, additional, Giggens, Roxanne, additional, Bhuva, Shaheel, additional, Royds, Jennifer, additional, Camilleri, Francesca, additional, Babu, Gauripriya, additional, Beveridge, Carolyn, additional, Geach, Rebecca, additional, Klimczak, Katherine, additional, Massey, Helen, additional, Lyburn, Iain, additional, Salehi-Bird, Seema, additional, Nishtala, Sahithi, additional, Gunning, Liz, additional, Changaradil, Deepthi Vinayan, additional, Bansal, Jyoti, additional, Davis, Joanna, additional, Sharma, Ashwini, additional, Del Voscovo, Elena, additional, Etheridge, Harriet, additional, Butler, Aisling, additional, Fenne, David S. J., additional, Rubin, Gary, additional, Burt, Helen, additional, Ridley, Nick, additional, Taylor, Sarah, additional, Seth, Subodh, additional, and Seth, Archana, additional

Published
2016
Full Text
View/download PDF

45. Scanning and War

Author

Smith, Iain M., primary, Naumann, David N., additional, Marsden, Max E. R., additional, Ballard, Mark, additional, and Bowley, Douglas M., additional

Published
2015
Full Text
View/download PDF

50. 3* Evaluation of an Artificial Intelligence (AI) system to augment clinical risk prediction of Trauma Induced Coagulopathy in the pre-hospital setting: a prospective observational study

Author

Marsden, Max, Perkins, Zane, Marsh, William, Christian, Michael, Lyon, Richard, Davenport, Ross, and Tai, Nigel

Abstract

BackgroundThe potential of AI systems to support pre-hospital clinical decision-making in both military and civilian settings is significant. However, whilst such algorithms are increasingly available, far less attention has been paid to understanding the impact of such systems on clinical performance. This study had two aims; first, to compare the performance of expert clinicians againstan AI system in a real-world clinical setting and second, to assess the impact of augmenting expert clinical prediction withan AI system.MethodsTwo civilian UK Air Ambulances were selected as surrogate settings relevant to military practice. We performed a prospective study over a six-month period where expert pre-hospital clinicians’ judgement of the risk of Trauma Induced Coagulopathy (TIC) in injured patients was assessed and compared to the performance of an AI system. Two TIC risk predictions were generated for every patient: an AI prediction and a human prediction. Measures of predictive performance included discrimination, calibration, and overall accuracy.ResultsOverall, 51 expert clinicians were enrolled in the study providing 184 patient interactions for analysis. The studied patients had a median age of 31 (range 16, 89), median injury severity score of 17 (IQR 9, 34), 75% were male, and 19% developed TIC.Aim 1:The AI system performed better than clinicians; higher discrimination [AUROC 0.87 (0.79, 0.95) versus 0.83 (0.74, 0.92)] better calibration [0.37 (-0.14, 0.89) versus -1.19 (-1.73, -0.65)] and more accurate [Brier Skill Score 0.34 (0.19, 0.48) versus 0.00 (-0.41, 0.30)].Aim 2:Risk prediction was better in all performance metrics when clinicians were assisted with the AI system [AUROC 0.88 (0.80, 0.95) versus 0.83 (0.74, 0.92)]ConclusionsAI systems can improve human risk prediction in the pre-hospital setting. In the military environment, where austerity and lack of senior clinical expertise may affect outcomes, the benefit of implementing predictive AI should be substantial.(*awarded First Place)

Published
2022
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results