Artificial intelligence and machine learning for hemorrhagic trauma care

Peng, Henry T.; Siddiqui, M Musaab; Rhind, Shawn G.; Zhang, Jing; Luz, Luís Teodoro da; Beckett, Andrew

doi:10.1186/s40779-023-00444-0

Cited by 13 publications

(17 citation statements)

References 118 publications

(146 reference statements)

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“…Two recent reviews provide an extensive overview of algorithm-based prediction of haemorrhage and trauma outcomes [10,11]. These models can be classi ed according to the outcome, predictors, data source and methods applied.…”

Section: Discussionmentioning

confidence: 99%

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: Discussionmentioning

confidence: 99%

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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“…Recent works on Machine Learning based decision-support in trauma stratify into data source, predictors (so-called features ), Machine Learning approaches, predicted outcomes (outputs), the timing of the outcome, and availability of the features [6 ▪▪ ,7 ▪▪ ].…”

Section: Current Evidence Of Algorithm-based Decision Support For Traumamentioning

confidence: 99%

“…stepwise, Poisson, Cox, General Linear Models), including logistic regression (least absolute shrinkage, Ridge, Elastic Net regression) over neural, artificial or deep networks to tree or kernel-based methods, Gradient Boosting, Support Vector Machines, or Bayes Networks. Network-based approaches implemented feed-forward and deep neural network, multilayer perceptions, etc., tree-based approaches included random-forest or decision trees [6 ▪▪ ,7 ▪▪ ,8]. Among this spectrum, aggregation such as Random Forest and Gradient Boosting are considered more reliable [8].…”

Section: Current Evidence Of Algorithm-based Decision Support For Traumamentioning

confidence: 99%

“…Over the last 15 years, technological advances and increased computational capacity at lower cost, facilitated exploitation of complex mathematical theories. These advances triggered the exploration of algorithm-based strategies for enhanced real-time decision-support and prediction algorithms [6 ▪▪ ,7 ▪▪ ]. These algorithms correspond to what is colloquially termed Artificial Intelligence and correspond to a computational process able to perform a task that is usually the result of a human cognitive process.…”

Section: Introductionmentioning

confidence: 99%

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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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“…Machine learning is an emerging tool in clinical research and one that may provide a novel approach to this complex problem. Given the algorithmic nature of trauma and ML's affinity for pattern recognition, recent review articles have highlighted the potential benefit of using ML to predict outcomes in trauma 28,29 . Therefore, we sought to use time-specific ML models to identify if parameters associated with survivability change over time and to predict mortality during the active resuscitation period in trauma patients undergoing UMT.…”

mentioning

confidence: 99%

TiME OUT: Time-specific machine-learning evaluation to optimize ultramassive transfusion

Meyer,

Nguyen,

ElHabr

et al. 2023

J Trauma Acute Care Surg

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Background Ultra-massive transfusion (UMT) is a resource-demanding intervention for trauma patients in hemorrhagic shock and associated mortality rates remains high. Current research has been unable to identify a transfusion ceiling, or point where UMT transitions from life-saving to futility. Furthermore, little consideration has been given to how time-specific patient data points impact decisions with ongoing high-volume resuscitation. Therefore, this study sought to utilize time-specific machine learning (ML) modeling to predict mortality and identify parameters associated with survivability in trauma patients undergoing UMT. Methods A retrospective review was conducted at a Level I trauma (2018-2021) and included trauma patients meeting criteria for UMT, defined as >20 red blood cell products within 24 h of admission. Cross-sectional data was obtained from the blood bank and trauma registries and time-specific data was obtained from the electronic medical record. Time-specific decision-tree models (TS-DTM) predicating mortality were generated and evaluated using AUC. Results In the 180 patients included, mortality rate was 40.5% at 48-hours and 52.2% overall. The deceased received significantly more blood products with a median of 71.5 total units compared to 55.5 in the survivors (p < 0.001) and significantly greater rates of pRBC and FFP at each time interval. TS-DTM predicted mortality with an accuracy as high as 81%. In the early time intervals, hemodynamic stability, undergoing an emergency department thoracotomy and injury severity were most predictive of survival while in the later intervals, markers of adequate resuscitation such as arterial pH and lactate level became more prominent. Conclusions This study supports that the decision of “when to stop” in UMT resuscitation is not based exclusively on the number of units transfused, but rather the complex integration of patient and time-specific data. ML is an effective tool to investigate this concept and further research is needed to refine and validate these TS-DTM. Level of Evidence IV, Retrospective cohort review

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Artificial intelligence and machine learning for hemorrhagic trauma care

Cited by 13 publications

References 118 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

TiME OUT: Time-specific machine-learning evaluation to optimize ultramassive transfusion

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