摘要
目的评价2019冠状病毒病(COVID-19)危重症风险预测评分模型对医疗资源充足地区患者的适用性。方法纳入2020年1月11日—2月22日深圳市第三人民医院COVID-19定点收治医院的病例,并进行严重程度分型。危重症COVID-19患者被定义为入住ICU、有创机械通气或死亡的患者。使用COVID-19危重症风险预测评分模型的线上计算器对研究对象评分并进行危险分层。用SPSS 16.0软件进行Hosmer-Lemeshow检验,用ROC曲线的曲线下面积(AUC)验证模型的校准度和区分度。结果共112例患者被纳入回顾性验证研究。其中严重COVID-19患者26例(23.2%),3例死亡(2.7%)。COVID-19患者危重症风险预测评分模型Hosmer-Lemeshow检验结果P>0.05,AUC为0.681(P=0.002,95%CI 0.547~0.814),特异度为0.977,灵敏度为0.385。结论该模型预测在医疗资源充足条件下的COVID-19患者危重症发生风险的效能较差。未来应该建立更适合用于医疗资源充足时的COVID-19危重症风险预测模型。
Objective To evaluate the feasibility of using COVID-19 risk scoring system in clinical settings with adequate medical resources.Methods Patients admitted into the Third People’s Hospital of Shenzhen due to COVID-19 from January 11 to February 22,2020 were included and classified according to severity of illness.Critically ill COVID-19 patients were defined as those who were admitted into ICU,under invasive mechanical ventilation,or deceased.With the online calculation tool,the COVID-19 risk prediction model identified potential critically ill patients and assigned the COVID-19 patients scores according to their symptom severity,and analyzed according to their scores.SPSS 16.0 was used to perform Hosmer-Lemeshow test and AUC to confirm the performance of the model in terms of calibration and discrimination.Results A total of 112 patients were included in this retrospective validation study,including 26 critically ill COVID-19 patients(deadly outcome in 3 cases).The validation demonstrated consistent results between model-predicted disease state and actual disease progression(Hosmer-Lemeshow test P>0.05).The risk prediction model showed that the area under the receiver operating characteristic curve was 0.681 in predicting critically ill COVID-19(P=0.002,95%CI 0.547-0.814)with specificity of 0.977 and sensitivity of 0.385.Conclusions The risk prediction model performed poorly in predicting the emergence of critical illness in COVID-19 patients in clinical settings with adequate medical resources.An improved model should be established to adapt to the clinical settings with adequate medical resources.
作者
郭颖异
李晓鹤
刘宁静
卓楚越
肖书念
卓超
刘映霞
GUO Yingyi;LI Xiaohe;LIU Ningjing;ZHUO Chuyue;XIAO Shunian;ZHUO Chao;LIU Yingxia(Department of Infectious Diseases,First Affiliated Hospital of Guangzhou Medical University,Guangzhou Institute of Respiratory Health,State Key Laboratory of Respiratory Disease,Guangzhou 510030,China)
出处
《中国感染与化疗杂志》
CAS
CSCD
北大核心
2022年第2期129-133,共5页
Chinese Journal of Infection and Chemotherapy
