基于机器学习技术的预测模型在急诊科应用的风险分层（MARS-ED）研究方案：一项随机对照预试验研究方案，旨在预测急诊科 31 天死亡率。

Machine learning for risk stratification in the emergency department (MARS-ED) study protocol for a randomized controlled pilot trial on the implementation of a prediction model based on machine learning technology predicting 31-day mortality in the emergency department.

机构信息

Department of Internal Medicine, Division of General Internal Medicine, Section Acute Medicine, Maastricht University Medical Center +, PO Box 5800, Maastricht, 6202 AZ, The Netherlands.

Central Diagnostic Laboratory, Department of Clinical Chemistry, Maastricht University Medical Center +, Maastricht, The Netherlands.

出版信息

Scand J Trauma Resusc Emerg Med. 2024 Jan 23;32(1):5. doi: 10.1186/s13049-024-01177-2.

DOI:10.1186/s13049-024-01177-2

PMID:38263188

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10804603/

Abstract

BACKGROUND

Many prediction models have been developed to help identify emergency department (ED) patients at high risk of poor outcome. However, these models often underperform in clinical practice and their actual clinical impact has hardly ever been evaluated. We aim to perform a clinical trial to investigate the clinical impact of a prediction model based on machine learning (ML) technology.

METHODS

The study is a prospective, randomized, open-label, non-inferiority pilot clinical trial. We will investigate the clinical impact of a prediction model based on ML technology, the RISK, which has been developed to predict the risk of 31-day mortality based on the results of laboratory tests and demographic characteristics. In previous studies, the RISK was shown to outperform internal medicine specialists and to have high discriminatory performance. Adults patients (18 years or older) will be recruited in the ED. All participants will be randomly assigned to the control group or the intervention group in a 1:1 ratio. Participants in the control group will receive care as usual in which the study team asks the attending physicians questions about their clinical intuition. Participants in the intervention group will also receive care as usual, but in addition to asking the clinical impression questions, the study team presents the RISK to the attending physician in order to assess the extent to which clinical treatment is influenced by the results.

DISCUSSION

This pilot clinical trial investigates the clinical impact and implementation of an ML based prediction model in the ED. By assessing the clinical impact and prognostic accuracy of the RISK, this study aims to contribute valuable insights to optimize patient care and inform future research in the field of ML based clinical prediction models.

TRIAL REGISTRATION

ClinicalTrials.gov NCT05497830. Machine Learning for Risk Stratification in the Emergency Department (MARS-ED). Registered on August 11, 2022. URL: https://clinicaltrials.gov/study/NCT05497830 .

摘要

背景

已经开发出许多预测模型来帮助识别急诊科（ED）中预后不良风险较高的患者。然而，这些模型在临床实践中的表现往往不尽如人意，其实际临床影响几乎从未得到过评估。我们旨在进行一项临床试验，以调查基于机器学习（ML）技术的预测模型的临床影响。

方法

本研究为前瞻性、随机、开放标签、非劣效性试验性临床试验。我们将研究一种基于 ML 技术的预测模型 RISK 的临床影响，该模型旨在基于实验室检测结果和人口统计学特征预测 31 天死亡率风险。在之前的研究中，RISK 被证明优于内科专家，具有较高的区分性能。将招募成年患者（18 岁或以上）到急诊科。所有参与者将按照 1:1 的比例随机分配到对照组或干预组。对照组的参与者将接受常规护理，研究团队会向主治医生询问他们的临床直觉。干预组的参与者也将接受常规护理，但除了询问临床印象问题外，研究团队还会向主治医生提供 RISK，以评估临床治疗受结果影响的程度。

讨论

本试验性临床试验调查了 ED 中基于 ML 的预测模型的临床影响和实施。通过评估 RISK 的临床影响和预后准确性，本研究旨在为优化患者护理提供有价值的见解，并为基于 ML 的临床预测模型领域的未来研究提供信息。

试验注册

ClinicalTrials.gov NCT05497830. Machine Learning for Risk Stratification in the Emergency Department (MARS-ED). 注册于 2022 年 8 月 11 日。网址：https://clinicaltrials.gov/study/NCT05497830。