Heart rate variability based machine learning models for risk prediction of suspected sepsis patients in the emergency department
Heart rate variability based machine learning models for risk prediction of suspected sepsis patients in the emergency department
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DOI：
10.1097/md.0000000000014197
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发表时间：
2019-02-01
期刊：
影响因子：
1.6
通讯作者：
Ong, Marcus E. H.
中科院分区：
文献类型：
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作者：
Chiew, Calvin J.;Liu, Nan;Ong, Marcus E. H.
在急诊科（艾德）早期识别高危脓毒症患者可以指导适当的管理和处置，从而改善预后。我们比较了机器学习模型与传统风险分层工具的性能，即快速序贯器官衰竭评估（qSOFA），国家早期预警评分（NEWS），改良早期预警评分（MEWS）和我们之前描述的新加坡艾德脓毒症（SEDS）模型，预测艾德疑似脓毒症患者的30天住院死亡率（IHM）。到新加坡总医院（SGH）就诊的成人患者纳入了2014年9月至2016年4月期间的艾德，并且符合4项全身炎症反应综合征（SIRS）标准中的2项。患者的人口统计学资料，生命体征和心率变异性（HRV）的措施，在分诊作为预测。使用qSOFA、NEWS、MEWS和SEDS评分创建基线模型。候选模型使用k-近邻，随机森林，自适应增强，梯度增强和支持向量机进行训练。以F1评分和精确-召回曲线下面积（AUPRC）对模型进行评价，共纳入214例患者，其中40例（18.7%）符合结局。梯度增强是最好的模型，F1评分为0.50，AUPRC为0.35，表现优于所有基线对照品（SEDS，F1 0.40，AUPRC 0.22; qSOFA，F1 0.32，AUPRC 0.21; NEWS，F1 0.38，AUPRC 0.28; MEWS，F1 0.30，AUPRC 0.25）。与传统的风险分层工具相比，机器学习模型可用于改善艾德中疑似脓毒症患者中30天IHM的预测。
Early identification of high-risk septic patients in the emergency department (ED) may guide appropriate management and disposition, thereby improving outcomes. We compared the performance of machine learning models against conventional risk stratification tools, namely the Quick Sequential Organ Failure Assessment (qSOFA), National Early Warning Score (NEWS), Modified Early Warning Score (MEWS), and our previously described Singapore ED Sepsis (SEDS) model, in the prediction of 30-day in-hospital mortality (IHM) among suspected sepsis patients in the ED.Adult patients who presented to Singapore General Hospital (SGH) ED between September 2014 and April 2016, and who met 2 of the 4 Systemic Inflammatory Response Syndrome (SIRS) criteria were included. Patient demographics, vital signs and heart rate variability (HRV) measures obtained at triage were used as predictors. Baseline models were created using qSOFA, NEWS, MEWS, and SEDS scores. Candidate models were trained using k-nearest neighbors, random forest, adaptive boosting, gradient boosting and support vector machine. Models were evaluated on F1 score and area under the precision-recall curve (AUPRC).A total of 214 patients were included, of whom 40 (18.7%) met the outcome. Gradient boosting was the best model with a F1 score of 0.50 and AUPRC of 0.35, and performed better than all the baseline comparators (SEDS, F1 0.40, AUPRC 0.22; qSOFA, F1 0.32, AUPRC 0.21; NEWS, F1 0.38, AUPRC 0.28; MEWS, F1 0.30, AUPRC 0.25).A machine learning model can be used to improve prediction of 30-day IHM among suspected sepsis patients in the ED compared to traditional risk stratification tools.