对创伤后出血复苏需求进行机器学习增强预测的试点部署 - ShockMatrix 试点研究。

IF 4.3 3区 材料科学 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC ACS Applied Electronic Materials Pub Date : 2024-10-28 DOI:10.1186/s12911-024-02723-9
Tobias Gauss, Jean-Denis Moyer, Clelia Colas, Manuel Pichon, Nathalie Delhaye, Marie Werner, Veronique Ramonda, Theophile Sempe, Sofiane Medjkoune, Julie Josse, Arthur James, Anatole Harrois
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引用次数: 0

摘要

重要性:创伤患者的决策制定仍然具有挑战性,往往会导致偏离指南。机器学习(ML)增强型决策支持可改善出血复苏。目的:开发一种ML增强型决策支持工具,用于预测出血复苏需求(NHR)(第一部分),并测试在智能手机应用程序中实时收集预测变量(第二部分):设计、环境和参与者:从登记册中建立一个 ML 模型,完全依靠院前预测因素来预测 NHR。测试了多个模型和估算技术。评估在院前警报期间在定制的智能手机应用程序中收集模型预测因子的可行性,并在四个一级创伤中心生成预测结果,将预测结果与创伤负责人的酝酿结果进行比较:第 1 部分:第一部分:模型输出为NHR,定义为:1)复苏中至少输注一次RBC;2)6小时内输注≥4次RBC;3)6小时内任何出血控制过程;或4)24小时内因出血死亡。在第二部分中,用似然比(LR)对模型和临床医生的预测进行了比较:在登记(2010 年 11 月至 2022 年 5 月)的 36,325 名符合条件的患者中,28,614 人被纳入模型开发(第 1 部分)。年龄中位数为 36 [25-52],ISS 中位数为 13 [5-22],3249/28614(11%)人符合 NHR 的定义。根据 F4 评分,包含九个院前变量的 XGBoost 模型对 NHR 的预测效果最好,为 0.76 [0.73-0.78]。在 3 个月的时间里(2022 年 8 月至 10 月),391 名符合条件的患者中有 139 人被纳入第二部分(38.5%),其中 22/139 人患有 NHR。临床医生的满意度很高,没有观察到工作流程中断,模型和临床医生的 LR 值相当:ShockMatrix 试验研究开发了一种简单的 ML 增强型 NHR 预测工具,其性能与临床参考评分和临床医生相当。在预警前实时收集预测变量是可行的,而且不会影响工作流程。
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Pilot deployment of a machine-learning enhanced prediction of need for hemorrhage resuscitation after trauma - the ShockMatrix pilot study.

Importance: Decision-making in trauma patients remains challenging and often results 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 h, 3) any hemorrhage control procedure within 6 h or 4) death from hemorrhage within 24 h. 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 36,325 eligible patients in the registry (Nov 2010-May 2022), 28,614 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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