CANPT Score: A Tool to Predict Severe COVID-19 on Admission

Frontiers in Medicine

article

Yuanyuan Chen¹  Xiaolin Zhou³  Huadong Yan⁴  Huihong Huang⁵  Shengjun Li¹  Zicheng Jiang⁵  Jun Zhao¹  Zhongji Meng¹ 

[1] Department of Infectious Diseases, Taihe Hospital, Hubei University of Medicine;Institute of Biomedical Research, Taihe Hospital, Hubei University of Medicine;Department of Liver Diseases, Yichang Central People's Hospital, China Three Gorges University;Department of Liver Diseases, HwaMei Hospital, University of Chinese Academy of Sciences;Department of Infectious Diseases, Ankang Central Hospital, Hubei University of Medicine;School of Public Health, Hubei University of Medicine;Hubei Clinical Research Center for Precise Diagnosis and Treatment of Liver Cancer

关键词: SARS-CoV-2;    COVID-19;    severe illness;    prediction;    nomogram;   

DOI  :  10.3389/fmed.2021.608107

学科分类：社会科学、人文和艺术（综合）

来源: Frontiers

【 摘 要 】

Background and Aims: Patients with critical coronavirus disease 2019 (COVID-19) have a mortality rate higher than 50%. The purpose of this study was to establish a model for the prediction of the risk of severe disease and/or death in patients with COVID-19 on admission. Materials and Methods: Patients diagnosed with COVID-19 in four hospitals in China from January 22, 2020 to April 15, 2020 were retrospectively enrolled. The demographic, laboratory, and clinical data of the patients with COVID-19 were collected. The independent risk factors related to the severity of and death due to COVID-19 were identified with a multivariate logistic regression; a nomogram and prediction model were established. The area under the receiver operating characteristic curve (AUROC) and predictive accuracy were used to evaluate the model's effectiveness. Results: In total, 582 patients with COVID-19, including 116 patients with severe disease, were enrolled. Their comorbidities, body temperature, neutrophil-to-lymphocyte ratio (NLR), platelet (PLT) count, and levels of total bilirubin (Tbil), creatinine (Cr), creatine kinase (CK), and albumin (Alb) were independent risk factors for severe disease. A nomogram was generated based on these eight variables with a predictive accuracy of 85.9% and an AUROC of 0.858 (95% CI, 0.823–0.893). Based on the nomogram, the CANPT score was established with cut-off values of 12 and 16. The percentages of patients with severe disease in the groups with CANPT scores <12, ≥12, and <16, and ≥16 were 4.15, 27.43, and 69.64%, respectively. Seventeen patients died. NLR, Cr, CK, and Alb were independent risk factors for mortality, and the CAN score was established to predict mortality. With a cut-off value of 15, the predictive accuracy was 97.4%, and the AUROC was 0.903 (95% CI 0.832, 0.974). Conclusions: The CANPT and CAN scores can predict the risk of severe disease and mortality in COVID-19 patients on admission.

【 授权许可】

CC BY