新型冠状病毒肺炎患者重症转归风险预测

目的 建立新型冠状病毒肺炎（COVID-19）患者转归为重症的预测模型，为早期、动态地监测患者转归提供更加全面、准确、及时的指标。方法 基于患者的入院检测指标和轻、重症分型，以及检测指标的动态改变（即入院后两次检测指标测量值差）等输入变量，使用XGBoost方法建立预测模型，评估患者在入院之后转归为重症的风险。然后将入选的患者从入院随访至出院，观察其病情转归情况，对模型预测结果进行验证。结果 在100例COVID-19患者的训练集中，筛选出具有较高评分的预测变量并建立模型，计算出预测变量取值的高风险范围：血氧饱和度<94%、外周血白细胞计数>8.0×10⁹个、SBP变化<-2.5 mmHg（1 mmHg=0.133 kPa）、心率>90次/min、有多发小斑片影、年龄>30岁、心率变化<12.5次/min。训练集的模型预测结果的敏感率为61.7%，漏诊率为38.3%；使用模型对测试集进行预测的敏感性为75.0%，漏诊率为25.0%。结论 与传统的预测判断方法（即采用入院时第一次检测的指标和重症入选条件进行评估患者是否为轻、重症）相比，模型的预测考虑到了COVID-19患者的基线生理指标与病情变化指标，因此能够全面、准确地预测患者重症转归的风险，减少重症患者的漏诊率。

Prediction of severe outcomes of patients with COVID-19

Objective To establish a new model for the prediction of severe outcomes of COVID-19 patients and provide more comprehensive, accurate and timely indicators for the early identification of severe COVID-19 patients. Methods Based on the patients'' admission detection indicators, mild or severe status of COVID-19, and dynamic changes in admission indicators (the differences between indicators of two measurements) and other input variables, XGBoost method was applied to establish a prediction model to evaluate the risk of severe outcomes of the COVID-19 patients after admission. Follow up was done for the selected patients from admission to discharge, and their outcomes were observed to evaluate the predicted results of this model. Results In the training set of 100 COVID-19 patients, six predictors with higher scores were screened and a prediction model was established. The high-risk range of the predictor variables was calculated as: blood oxygen saturation <94%, peripheral white blood cells count >8.0×10⁹, change in systolic blood pressure <-2.5 mmHg, heart rate >90 beats/min, multiple small patchy shadows, age >30 years, and change in heart rate <12.5 beats/min. The prediction sensitivity of the model based on the training set was 61.7%, and the missed diagnosis rate was 38.3%. The prediction sensitivity of the model based on the test set was 75.0%, and the missed diagnosis rate was 25.0%. Conclusions Compared with the traditional prediction (i.e. using indicators from the first test at admission and the critical admission conditions to assess whether patients are in mild or severe status), the new model''s prediction additionally takes into account of the baseline physiological indicators and dynamic changes of COVID-19 patients, so it can predict the risk of severe outcomes in COVID-19 patients more comprehensively and accurately to reduce the missed diagnosis of severe COVID-19.