A Predictive Nomogram for Predicting Improved Clinical Outcome Probability in Patients with COVID-19 in Zhejiang Province,China

作     者：Jiaojiao Xie Ding Shi Mingyang Bao Xiaoyi Hu Wenrui Wu Jifang Sheng Kaijin Xu Qing Wang Jingjing Wu Kaicen Wang Daiqiong Fang Yating Li Lanjuan Li Jiaojiao Xie;Ding Shi;Mingyang Bao;Xiaoyi Hu;Wenrui Wu;Jifang Sheng;Kaijin Xu;Qing Wang;Jingjing Wu;Kaicen Wang;Daiqiong Fang;Yating Li;Lanjuan Li

作者机构：State Key Laboratory for Diagnosis and Treatment of Infectious DiseasesNational Clinical Research Center for Infectious DiseasesCollaborative Innovation Center for Diagnosis and Treatment of Infectious DiseasesThe First Affiliated HospitalCollege of MedicineZhejiang UniversityHangzhou 310003China State Key Laboratory of Genetic EngineeringInstitute of BiostatisticsSchool of Life SciencesFudan UniversityShanghai 200433China Division of Hepatobiliary and Pancreatic SurgeryDepartment of SurgeryKey Lab of Combined Multi-organ Transplantation of the Ministry of HealthThe First Affiliated HospitalCollege of MedicineZhejiang UniversityHangzhou 310003China Division of of Endocrinology and MetabolismDepartment of Internal Medicine SystemThe First Affiliated HospitalCollege of MedicineZhejiang UniversityHangzhou 310003China 

出 版 物：《Engineering》 (工程（英文）)

年 卷 期：2022年第8卷第1期

页      面：122-129页

核心收录：

学科分类：1002[医学-临床医学] 100201[医学-内科学(含：心血管病、血液病、呼吸系病、消化系病、内分泌与代谢病、肾病、风湿病、传染病)] 10[医学] 

基　　金：supported by the research on the prevention and clinical treatment in patients with COVID-19(2020C03123) a funding of the Zhejiang Provincial Department of Science and Technology the National Natural Science Foundation of China(81790631) the National Key Research and Development Program of China(2018YFC2000500) 

主　　题：Coronavirus disease 2019(COVID-19) Nomogram Patient-relevant outcome 

摘      要：The aim of this research was to develop a quantitative method for clinicians to predict the probability of improved prognosis in patients with coronavirus disease 2019(COVID-19).Data on 104 patients admitted to hospital with laboratory-confirmed COVID-19 infection from 10 January 2020 to 26 February 2020 were *** information and laboratory findings were collected and compared between the outcomes of improved patients and non-improved *** least absolute shrinkage and selection operator(LASSO)logistics regression model and two-way stepwise strategy in the multivariate logistics regression model were used to select prognostic factors for predicting clinical outcomes in COVID-19 *** concordance index(C-index)was used to assess the discrimination of the model,and internal validation was performed through bootstrap resampling.A novel predictive nomogram was constructed by incorporating these *** the 104 patients included in the study(median age 55 years),75(72.1%)had improved short-term outcomes,while 29(27.9%)showed no signs of *** were numerous differences in clinical characteristics and laboratory findings between patients with improved outcomes and patients without improved *** a multi-step screening process,prognostic factors were selected and incorporated into the nomogram construction,including immunoglobulin A(IgA),C-reactive protein(CRP),creatine kinase(CK),acute physiology and chronic health evaluation II(APACHE II),and interaction between CK and APACHE *** C-index of our model was 0.962(95%confidence interval(CI),0.931-0.993)and still reached a high value of 0.948 through bootstrapping validation.A predictive nomogram we further established showed close performance compared with the ideal model on the calibration plot and was clinically practical according to the decision curve and clinical impact *** nomogram we constructed is useful for clinicians to predict improved clinical outcome p