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首页> 外文期刊>Journal of Clinical Medicine >Development and Validation of a Quick Sepsis-Related Organ Failure Assessment-Based Machine-Learning Model for Mortality Prediction in Patients with Suspected Infection in the Emergency Department
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Development and Validation of a Quick Sepsis-Related Organ Failure Assessment-Based Machine-Learning Model for Mortality Prediction in Patients with Suspected Infection in the Emergency Department

机译：急诊部疑似感染患者患者死亡率预测的快速脓毒症相关器官失效评估模型的开发与验证

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The quick sepsis-related organ failure assessment (qSOFA) score has been introduced to predict the likelihood of organ dysfunction in patients with suspected infection. We hypothesized that machine-learning models using qSOFA variables for predicting three-day mortality would provide better accuracy than the qSOFA score in the emergency department (ED). Between January 2016 and December 2018, the medical records of patients aged over 18 years with suspected infection were retrospectively obtained from four EDs in Korea. Data from three hospitals ( n = 19,353) were used as training-validation datasets and data from one ( n = 4234) as the test dataset. Machine-learning algorithms including extreme gradient boosting, light gradient boosting machine, and random forest were used. We assessed the prediction ability of machine-learning models using the area under the receiver operating characteristic (AUROC) curve, and DeLong’s test was used to compare AUROCs between the qSOFA scores and qSOFA-based machine-learning models. A total of 447,926 patients visited EDs during the study period. We analyzed 23,587 patients with suspected infection who were admitted to the EDs. The median age of the patients was 63 years (interquartile range: 43–78 years) and in-hospital mortality was 4.0% ( n = 941). For predicting three-day mortality among patients with suspected infection in the ED, the AUROC of the qSOFA-based machine-learning model (0.86 [95% CI 0.85–0.87]) for three -day mortality was higher than that of the qSOFA scores (0.78 [95% CI 0.77–0.79], p 0.001). For predicting three-day mortality in patients with suspected infection in the ED, the qSOFA-based machine-learning model was found to be superior to the conventional qSOFA scores.
机译：已经引入了快速败血症相关器官失败评估（QSOFA）得分以预测疑似感染患者器官功能障碍的可能性。我们假设使用QSOFA变量来预测三天死亡率的机器学习模型将提供比紧急部门（ED）的QSOFA得分更好的准确性。 2016年1月至2018年12月，令人疑妥感染18岁以上的患者的病程，从韩国四个EDS回顾性。来自三个医院（n = 19,353）的数据用作培训验证数据集和来自一个（n = 4234）的数据作为测试数据集。使用机器学习算法，包括极端梯度升压，轻梯度升压机和随机林。我们评估了使用接收器操作特性（AUROC）曲线下区域的机器学习模型的预测能力，并且德隆的测试用于比较QSOFA分数和基于QSOFA的机器学习模型之间的菌波。共有447,926名患者在研究期间访问了EDS。我们分析了23,587名患有疑似感染的患者，患者被录取为EDS。患者的中位年龄为63岁（四分位数：43-78岁）和住院死亡率为4.0％（n = 941）。用于预测患有疑似感染的患者的三天死亡率，基于QSOFA的机器学习模型的菌波（0.86 [95％CI 0.85-0.87]），三次死亡率高于QSOFA分数（0.78 [95％CI 0.77-0.79]，P <0.001）。为了预测患有疑似感染的患者的三天死亡率，发现基于QSOFA的机器学习模型优于传统的QSOFA分数。

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