目的 构建并验证危重症型流行性感冒(以下简称"流感")患者早期预测模型.方法 选择2017年1月 1日—2020年6月 30日就诊于四川大学华西医院急诊科、四川大学华西医院上锦医院急诊科和攀枝花市中心医院急诊科的流感患者根据K折交叉验证法将70％的患者随机分配至模型建立组,30％的患者分配至模型验证组.并将模型建立组和模型验证组中的患者分别分为危重型组和非危重型组.基于修订版本国家早期预警评分(modified National Early Warning Score,MEWS)和简化英国胸科协会改良肺炎评分(confusion,uremia,respiratory,BP,age 65 years,CRB-65),构建危重症型流感早期预测模型,并评估该早期预测模型的准确度.结果 共纳入患者612例.其中,模型建立组427例,模型验证组185例.在模型建立组中,非危重症型304例,危重症型123例 在模型验证组中,非危重型152例,危重型33例.二分类logistic回归分析结果显示,年龄、高血压、出现首发症状至急诊就诊间隔天数、意识状态、血氧饱和度、白细胞计数、淋巴细胞绝对值是危重症型流感的独立危险因素.根据这7个危险因素建立危重症型流感早期预测模型,该模型对非危重症型及危重症型患者预测的正确百分比分别为95.4％及77.2％,总体预测正确百分比为90.2％.受试者操作特征曲线分析结果显示,危重症型流感早期预测模型在预测危重症患者中的灵敏度为0.909,特异度为0.921.曲线下面积及其95％置信区间为0.931(0.860,0.999);危重症型流感早期预测模型的灵敏度、特异度及曲线下面积(0.935、0.865、0.942)高于MEWS(0.642、0.595、0.536)和CRB-65(0.628、0.862、0.703).结论 年龄、高血压、出现首发症状至急诊就诊间隔天数、意识状态、血氧饱和度、白细胞计数、淋巴细胞绝对值是预测危重症流感患者的独立危险因素.危重症型流感早期预测模型在预测危重症流感患者中具有较高的准确性,且其预测价值和准确度优于MEWS 和 CRB-65.
Construction and validation of predictive model for critical illness patients in emergency department with influenza in early stages
Objective To establish and verify the early prediction model of critical illness patients with influenza.Methods Critical illness patients with influenza who diagnosed with influenza in the emergency departments from West China Hospital of Sichuan University,Shangjin Hospital of West China Hospital of Sichuan University,and Panzhihua Central Hospital between January 1,2017 and June 30,2020 were selected.According to K-fold cross validation method,70％of patients were randomly assigned to the model group,and 30％of patients were assigned to the model verification group.The patients in the model group and the model verification group were divided into the critical illness group and the non-critical illness group,respectively.Based on the modified National Early Warning Score(MEWS)and the Simplified British Thoracic Society Score(confusion,uremia,respiratory,BP,age 65 years,CRB-65 score),a critical illness influenza early prediction model was constructed and its accuracy was evaluated.Results A total of 612 patients were included.Among them,there were 427 cases in the model group and 185 cases in the model verification group.In the model group,there were 304 cases of non-critical illness and 123 cases of critical illness.In the model verification group,there were 152 cases of non-critical illness and 33 cases of critical illness.The results of binary logistic regression analysis showed that age,hypertension,the number of days between the onset of symptoms and presentation at the emergency department,consciousness state,white blood cell count,and lymphocyte count,oxygen saturation of blood were the independent risk factors for critical illness influenza.Based on these 7 risk factors,an early prediction model for critical illness influenza was established.The correct percentages of the model for non-critical illness and critical illness patients were 95.4％and 77.2％,respectively,with an overall correct prediction percentage of 90.2％.The results of the receiver operator characteristic curve showed that the sensitivity and specificity of the early prediction model for critical illness influenza in predicting critical illness patients were 0.909,0.921,and the area under the curve and its 95％confidence interval were 0.931(0.860,0.999).The sensitivity,specificity,and area under the curve(0.935,0.865,0.942)of the early prediction model for critical illness influenza were higher than those of MEWS(0.642,0.595,0.536)and CRB-65(0.628,0.862,0.703).Conclusions The conclusion is that age,hypertension,the number of days between the onset of symptoms and presentation at the emergency department,consciousness,oxygen saturation,white blood cell count,and absolute lymphocyte count are independent risk factors for predicting severe influenza cases.The early prediction model for critical illness patients with influenza has high accuracy in predicting severe influenza cases,and its predictive value and accuracy are superior to those of the MEWS score and CRB-65 score.
Emergency departmentinfluenzacritical illnessroutine blood testprediction model
NETL
NSTL
万方数据
