Intrahospital trauma flowcharts — Cognitive aids for intrahospital trauma management from the French Society of Anaesthesia and Intensive Care Medicine (SFAR) and the French Society of Emergency Medicine (SFMU)

Gauss, Tobias; Quintard, Hervé; Bijok, Benjamin; Bouhours, G; Clavier, Thomas; Cook, Fabrice; Courson, Hugues de; David, Jean-Stéphane; Duracher-Gout, Caroline; Garrigue, Delphine; Geeraerts, Thomas; Hamada, Sophie; Joannès-Boyau, Olivier; Jouffroy, Romain; Lamblin, Antoine; Langeron, Olivier; Lanot, Pierre; Lasocki, Sigismond; Léone, Marc; Mirek, Sébastien; Muller, Laurent; Pasquier, Pierre; Prunet, Bertrand; Parratte, Sébastien; Raux, Mathieu; Richards, Justin E.; Roger, Claire; Roquilly, Antoine; Weiss, Emmanuel; Bouzat, Pierre; Pottecher, Julien

doi:10.1016/j.accpm.2022.101069

Cited by 6 publications

(3 citation statements)

References 8 publications

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“…Median age was 36 years [IQR 25-52] and the cohort was mainly composed of men 79% (22 356 male, 6 062 female). Median ISS was 13 [5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22]. Table 1 provides a detailed description of the cohort.…”

Section: Sample Cohortmentioning

confidence: 99%

“…Trauma-speci c decision-support systems offer the potential to mitigate variable decision-making and guideline deviation. These systems range from simple checklists [5], owcharts [6,7] to digital tools [8,9]. Recent advances in mathematical and technological paradigms have enabled the exploration of algorithm-based strategies for enhanced real-time decision-making and complex prediction in trauma.…”

Section: Introductionmentioning

confidence: 99%

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Machine-Learning Enhanced Prediction of Need for Hemorrhage Resuscitation after Trauma – The ShockMatrix Pilot Study

GAUSS,

MOYER,

COLAS

et al. 2024

Preprint

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Importance: Decision-making in trauma patients remains challenging and often result in deviation from guidelines. Machine-Learning (ML) enhanced decision-support could improve hemorrhage resuscitation. Aim To develop a ML enhanced decision support tool to predict Need for Hemorrhage Resuscitation (NHR) (part I) and test the collection of the predictor variables in real time in a smartphone app (part II). Design, Setting, and Participants: Development of a ML model from a registry to predict NHR relying exclusively on prehospital predictors. Several models and imputation techniques were tested. Assess the feasibility to collect the predictors of the model in a customized smartphone app during prealert and generate a prediction in four level-1 trauma centers to compare the predictions to the gestalt of the trauma leader. Main Outcomes and Measures: Part 1: Model output was NHR defined by 1) at least one RBC transfusion in resuscitation, 2) transfusion ≥ 4 RBC within 6 hours, 3) any hemorrhage control procedure within 6 hours or 4) death from hemorrhage within 24 hours. The performance metric was the F4-score and compared to reference scores (RED FLAG, ABC). In part 2, the model and clinician prediction were compared with Likelihood Ratios (LR). Results From 36325 eligible patients in the registry (Nov 2010 - May 2022), 28614 were included in the model development (Part 1). Median age was 36 [25–52], median ISS 13 [5–22], 3249/28614 (11%) corresponded to the definition of NHR. A XGBoost model with nine prehospital variables generated the best predictive performance for NHR according to the F4-score with a score of 0.76 [0.73–0.78]. Over a 3-month period (Aug - Oct 2022), 139 of 391 eligible patients were included in part II (38.5%), 22/139 with NHR. Clinician satisfaction was high, no workflow disruption observed and LRs comparable between the model and the clinicians. Conclusions and Relevance: The ShockMatrix pilot study developed a simple ML-enhanced NHR prediction tool demonstrating a comparable performance to clinical reference scores and clinicians. Collecting the predictor variables in real-time on prealert was feasible and caused no workflow disruption.

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Section: Sample Cohortmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Machine-Learning Enhanced Prediction of Need for Hemorrhage Resuscitation after Trauma – The ShockMatrix Pilot Study

GAUSS,

MOYER,

COLAS

et al. 2024

Preprint

Self Cite

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“…One answer consisted in the rigorous protocolization of trauma care, embodied by the Advanced Trauma Life Support (ATLS) embedded into simple decision-support systems. This support ranges from checklists [3], flowcharts [4] to digital tools [5].…”

Section: Andandmentioning

confidence: 99%

Current knowledge and availability of machine learning across the spectrum of trauma science

Gauss,

Perkins,

Tjardes

2023

Current Opinion in Critical Care

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Purpose of review Recent technological advances have accelerated the use of Machine Learning in trauma science. This review provides an overview on the available evidence for research and patient care. The review aims to familiarize clinicians with this rapidly evolving field, offer perspectives, and identify existing and future challenges. Recent findings The available evidence predominantly focuses on retrospective algorithm construction to predict outcomes. Few studies have explored actionable outcomes, workflow integration, or the impact on patient care. Machine Learning and data science have the potential to simplify data capture and enhance counterfactual causal inference research from observational data to address complex issues. However, regulatory, legal, and ethical challenges associated with the use of Machine Learning in trauma care deserve particular attention. Summary Machine Learning holds promise for actionable decision support in trauma science, but rigorous proof-of-concept studies are urgently needed. Future research should assess workflow integration, human-machine interaction, and, most importantly, the impact on patient outcome. Machine Learning enhanced causal inference for observational data carries an enormous potential to change trauma research as complement to randomized studies. The scientific trauma community needs to engage with the existing challenges to drive progress in the field.

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Intrahospitale Trauma-Flowcharts

et al. 2023

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Intrahospital trauma flowcharts — Cognitive aids for intrahospital trauma management from the French Society of Anaesthesia and Intensive Care Medicine (SFAR) and the French Society of Emergency Medicine (SFMU)

Cited by 6 publications

References 8 publications

Machine-Learning Enhanced Prediction of Need for Hemorrhage Resuscitation after Trauma – The ShockMatrix Pilot Study

Machine-Learning Enhanced Prediction of Need for Hemorrhage Resuscitation after Trauma – The ShockMatrix Pilot Study

Current knowledge and availability of machine learning across the spectrum of trauma science

Intrahospitale Trauma-Flowcharts

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