一项基于逻辑回归的机器学习临床决策支持预后算法的 12 家医院前瞻性评估，旨在为疑似 COVID-19 患者的决策提供便利。
A 12-hospital prospective evaluation of a clinical decision support prognostic algorithm based on logistic regression as a form of machine learning to facilitate decision making for patients with suspected COVID-19.
机构信息
Division of Critical Care, Department of Anesthesiology, University of Minnesota Medical School, Minneapolis, Minnesota, United States of America.
Department of Laboratory Medicine and Pathology, University of Minnesota Medical School, Minneapolis, Minnesota, United States of America.
出版信息
PLoS One. 2022 Jan 5;17(1):e0262193. doi: 10.1371/journal.pone.0262193. eCollection 2022.
OBJECTIVE
To prospectively evaluate a logistic regression-based machine learning (ML) prognostic algorithm implemented in real-time as a clinical decision support (CDS) system for symptomatic persons under investigation (PUI) for Coronavirus disease 2019 (COVID-19) in the emergency department (ED).
METHODS
We developed in a 12-hospital system a model using training and validation followed by a real-time assessment. The LASSO guided feature selection included demographics, comorbidities, home medications, vital signs. We constructed a logistic regression-based ML algorithm to predict "severe" COVID-19, defined as patients requiring intensive care unit (ICU) admission, invasive mechanical ventilation, or died in or out-of-hospital. Training data included 1,469 adult patients who tested positive for Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) within 14 days of acute care. We performed: 1) temporal validation in 414 SARS-CoV-2 positive patients, 2) validation in a PUI set of 13,271 patients with symptomatic SARS-CoV-2 test during an acute care visit, and 3) real-time validation in 2,174 ED patients with PUI test or positive SARS-CoV-2 result. Subgroup analysis was conducted across race and gender to ensure equity in performance.
RESULTS
The algorithm performed well on pre-implementation validations for predicting COVID-19 severity: 1) the temporal validation had an area under the receiver operating characteristic (AUROC) of 0.87 (95%-CI: 0.83, 0.91); 2) validation in the PUI population had an AUROC of 0.82 (95%-CI: 0.81, 0.83). The ED CDS system performed well in real-time with an AUROC of 0.85 (95%-CI, 0.83, 0.87). Zero patients in the lowest quintile developed "severe" COVID-19. Patients in the highest quintile developed "severe" COVID-19 in 33.2% of cases. The models performed without significant differences between genders and among race/ethnicities (all p-values > 0.05).
CONCLUSION
A logistic regression model-based ML-enabled CDS can be developed, validated, and implemented with high performance across multiple hospitals while being equitable and maintaining performance in real-time validation.
目的
前瞻性评估基于逻辑回归的机器学习 (ML) 预测算法，该算法作为实时临床决策支持 (CDS) 系统，用于对因 2019 年冠状病毒病 (COVID-19) 而接受调查的有症状者 (PUI) 进行评估。
方法
我们在 12 家医院系统中开发了一个模型，该模型经过训练和验证，然后进行实时评估。LASSO 引导的特征选择包括人口统计学特征、合并症、家庭用药、生命体征。我们构建了一个基于逻辑回归的 ML 算法，用于预测“严重”COVID-19，定义为需要入住重症监护病房 (ICU)、接受有创机械通气或在院内或院外死亡的患者。训练数据包括 1469 名在急性护理后 14 天内检测出严重急性呼吸综合征冠状病毒 2 (SARS-CoV-2) 阳性的成年患者。我们进行了以下 3 项验证：1) 对 414 例 SARS-CoV-2 阳性患者进行时间验证；2) 在急性就诊期间对 SARS-CoV-2 检测呈阳性的 13271 例 PUI 患者进行验证；3) 在对 PUI 进行检测或 SARS-CoV-2 检测呈阳性的 2174 例 ED 患者中进行实时验证。我们进行了跨种族和性别进行的亚组分析，以确保在性能方面公平。
结果
该算法在预测 COVID-19 严重程度的预实施验证中表现良好：1) 时间验证的受试者工作特征曲线下面积 (AUROC) 为 0.87(95%CI：0.83，0.91)；2) 在 PUI 人群中的验证的 AUROC 为 0.82(95%CI：0.81，0.83)。ED CDS 系统在实时验证中表现良好，AUROC 为 0.85(95%CI，0.83，0.87)。最低五分位数的患者无一例发生“严重”COVID-19。最高五分位数的患者中，有 33.2%的患者发生“严重”COVID-19。模型在性别和种族/民族之间没有显著差异(所有 p 值均>0.05)。
结论
可以开发、验证和实施基于逻辑回归的 ML 启用 CDS，该系统具有高性能，可在多家医院使用，并且公平且在实时验证中保持性能。
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