Chen Yuanyuan, Zhou Xiaolin, Yan Huadong, Huang Huihong, Li Shengjun, Jiang Zicheng, Zhao Jun, Meng Zhongji
Department of Infectious Diseases, Taihe Hospital, Hubei University of Medicine, Shiyan, China.
Institute of Biomedical Research, Taihe Hospital, Hubei University of Medicine, Shiyan, China.
Front Med (Lausanne). 2021 Feb 18;8:608107. doi: 10.3389/fmed.2021.608107. eCollection 2021.
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. 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. 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). The CANPT and CAN scores can predict the risk of severe disease and mortality in COVID-19 patients on admission.
2019年冠状病毒病（COVID-19）危重症患者的死亡率高于50%。本研究的目的是建立一个模型，用于预测COVID-19患者入院时发生重症和/或死亡的风险。对2020年1月22日至2020年4月15日在中国四家医院确诊为COVID-19的患者进行回顾性纳入研究。收集COVID-19患者的人口统计学、实验室和临床数据。通过多因素逻辑回归确定与COVID-19严重程度和死亡相关的独立危险因素；建立列线图和预测模型。采用受试者工作特征曲线下面积（AUROC）和预测准确性来评估模型的有效性。共纳入582例COVID-19患者，其中116例为重症患者。他们的合并症、体温、中性粒细胞与淋巴细胞比值（NLR）、血小板（PLT）计数以及总胆红素（Tbil）、肌酐（Cr）、肌酸激酶（CK）和白蛋白（Alb）水平是重症的独立危险因素。基于这八个变量生成了列线图，预测准确性为85.9%，AUROC为0.858（95%CI，0.823 - 0.893）。基于列线图，建立了CANPT评分，截断值为12和16。CANPT评分<12、≥12且<16以及≥16组的重症患者百分比分别为4.15%、27.43%和69.64%。17例患者死亡。NLR、Cr、CK和Alb是死亡的独立危险因素，并建立了CAN评分来预测死亡率。截断值为15时，预测准确性为97.4%，AUROC为0.903（95%CI 0.832，0.974）。CANPT和CAN评分可以预测COVID-19患者入院时发生重症和死亡的风险。
