MEWS plus plus : Enhancing the Prediction of Clinical Deterioration in Admitted Patients through a Machine Learning Model
MEWS plus plus : Enhancing the Prediction of Clinical Deterioration in Admitted Patients through a Machine Learning Model
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DOI：
10.3390/jcm9020343
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发表时间：
2020-02-01
影响因子：
3.9
通讯作者：
Levin, Matthew A.
中科院分区：
文献类型：
--
作者：
Kia, Arash;Timsina, Prem;Levin, Matthew A.
早期发现有临床恶化风险的患者对于及时干预至关重要。传统的检测系统依赖于一组有限的变量，无法预测下降的时间。我们描述了一种名为MEWS++的机器学习模型，该模型能够在事件发生前6小时识别有护理升级或死亡风险的患者。一项回顾性单中心队列研究于2011年7月至2017年7月在成人（年龄> 18岁）住院患者中进行，不包括精神病患者、产妇和临终关怀患者。三种机器学习模型进行了训练和测试：随机森林（RF），线性支持向量机和逻辑回归。我们使用灵敏度、特异性和受试者操作特征曲线下面积（AUC-ROC）和精确度-召回曲线（AUC-PR）将模型的性能与传统的改良早期预警评分（MEWS）进行了比较。主要结局是6小时内从地板床到重症监护室或降压病房的护理升级，或死亡。共纳入96，645例患者，其中157，984例医院就诊和244，343例病床移动。总体升级率或死亡率为3.4%。RF模型具有最佳性能，灵敏度为81.6%，特异性为75.5%，AUC-ROC为0.85，AUC-PR为0.37。与传统MEWS相比，灵敏度提高了37%，特异性提高了11%，AUC-ROC提高了14%。这项研究发现，使用机器学习和现成的临床数据，可以在事件发生前6小时预测临床恶化或死亡。我们开发的模型可以在事件发生前几个小时警告患者恶化，从而帮助及时做出临床决策。
Early detection of patients at risk for clinical deterioration is crucial for timely intervention. Traditional detection systems rely on a limited set of variables and are unable to predict the time of decline. We describe a machine learning model called MEWS++ that enables the identification of patients at risk of escalation of care or death six hours prior to the event. A retrospective single-center cohort study was conducted from July 2011 to July 2017 of adult (age > 18) inpatients excluding psychiatric, parturient, and hospice patients. Three machine learning models were trained and tested: random forest (RF), linear support vector machine, and logistic regression. We compared the models' performance to the traditional Modified Early Warning Score (MEWS) using sensitivity, specificity, and Area Under the Curve for Receiver Operating Characteristic (AUC-ROC) and Precision-Recall curves (AUC-PR). The primary outcome was escalation of care from a floor bed to an intensive care or step-down unit, or death, within 6 h. A total of 96,645 patients with 157,984 hospital encounters and 244,343 bed movements were included. Overall rate of escalation or death was 3.4%. The RF model had the best performance with sensitivity 81.6%, specificity 75.5%, AUC-ROC of 0.85, and AUC-PR of 0.37. Compared to traditional MEWS, sensitivity increased 37%, specificity increased 11%, and AUC-ROC increased 14%. This study found that using machine learning and readily available clinical data, clinical deterioration or death can be predicted 6 h prior to the event. The model we developed can warn of patient deterioration hours before the event, thus helping make timely clinical decisions.