Department of Medical Informatics, Gulhane Faculty of Medicine, University of Health Sciences, Ankara, Turkey.
Department of Physiology, Faculty of Medicine, Hacettepe University, Ankara, Turkey.
J Med Virol. 2021 May;93(5):3194-3201. doi: 10.1002/jmv.26890. Epub 2021 Mar 1.
Coronavirus disease 2019 (COVID-19) has become a global pandemic that has affected millions of people worldwide. The presence of multiple risk factors for COVID-19 makes it difficult to plan treatment and optimize the use of medical resources. The aim of this study is to determine potential risk factors for hospitalization or mortality in patients with COVID-19 via two novel naive Bayesian nomograms. The publicly available COVID-19 National data published by the Mexican Ministry of Health through the "Dirección General de Epidemiología" website was analyzed. Univariable logistic regression was utilized to identify potential risk factors that may affect hospitalization or mortality in patients with COVID-19. The naïve Bayesian classifier method was implemented to predict nomograms. The nomograms were verified by the area under the receiver operating characteristic curve (AUC), classification accuracy (CA), F1 score, precision, recall, and calibration plot. A total of 979,430 patients (45.3 ± 15.9 years old, and 51.1% male) tested positive for COVID-19 from January 1 to November 22, 2020. Among them, 22.3% of the patients required hospitalization and 99,964 patients (9.8%) died. The most important risk factors to predict the probability of hospitalization and mortality were pneumonia, age, chronic kidney failure, chronic obstructive respiratory disease, and diabetes. The performance measures demonstrated good discrimination and calibration (hospitalization: AUC = 0.896, CA = 0.880; mortality: AUC = 0.903, CA = 0.899). Two novel nomograms to estimate the risk of hospitalization and mortality were proposed, which could be used to facilitate individualized decision-making for patients newly diagnosed with COVID-19.
新型冠状病毒病（COVID-19）已成为全球性大流行疾病，影响了全球数百万人。COVID-19 存在多种危险因素，这使得治疗计划和优化医疗资源的使用变得困难。本研究旨在通过两个新的朴素贝叶斯列线图确定 COVID-19 患者住院或死亡的潜在危险因素。分析了墨西哥卫生部通过“Dirección General de Epidemiología”网站发布的 COVID-19 国家公开数据。使用单变量逻辑回归确定可能影响 COVID-19 患者住院或死亡的潜在危险因素。实施朴素贝叶斯分类器方法来预测列线图。通过接收者操作特征曲线下面积（AUC）、分类准确性（CA）、F1 评分、精度、召回率和校准图来验证列线图。2020 年 1 月 1 日至 11 月 22 日，共有 979430 名（45.3±15.9 岁，51.1%为男性）COVID-19 检测呈阳性的患者。其中，22.3%的患者需要住院治疗，99964 名（9.8%）患者死亡。预测住院和死亡概率的最重要危险因素是肺炎、年龄、慢性肾衰竭、慢性阻塞性呼吸疾病和糖尿病。性能指标表明良好的区分度和校准度（住院：AUC=0.896，CA=0.880；死亡：AUC=0.903，CA=0.899）。提出了两个新的列线图来估计住院和死亡风险，这有助于为新诊断为 COVID-19 的患者做出个体化决策。
