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
• 来源期刊: 工程（英文）
• 年，卷（期）: 2022,（1）
• 所属期刊栏目: Coronavirus Disease 2019-Article
• 页码范围: 122-129
• 页数: 8页
• 语种: 英文

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 admit-ted to hospital with laboratory-confirmed COVID-19 infection from 10 January 2020 to 26 February 2020 were collected.Clinical information and laboratory findings were collected and compared between the outcomes of improved patients and non-improved patients.The 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 patients.The concordance index(C-index)was used to assess the discrimination of the model,and inter-nal validation was performed through bootstrap resampling.A novel predictive nomogram was con-structed by incorporating these features.Of 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 improve-ment.There were numerous differences in clinical characteristics and laboratory findings between patients with improved outcomes and patients without improved outcomes.After 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 Ⅱ.The 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 com-pared with the ideal model on the calibration plot and was clinically practical according to the decision curve and clinical impact curve.The nomogram we constructed is useful for clinicians to predict improved clinical outcome probability for each COVID-19 patient,which may facilitate personalized counselling and treatment.