PDF(Pubmed)
Abstract：
To develop a machine learning model and nomogram to predict the probability of persistent virus shedding (PVS) in hospitalized patients with coronavirus disease 2019 (COVID-19), the clinical symptoms and signs, laboratory parameters, cytokines, and immune cell data of 429 patients with nonsevere COVID-19 were retrospectively reviewed. Two models were developed using the Akaike information criterion (AIC). The performance of these two models was analyzed and compared by the receiver operating characteristic (ROC) curve, calibration curve, net reclassification index (NRI), and integrated discrimination improvement (IDI). The final model included the following independent predictors of PVS: sex, C-reactive protein (CRP) level, interleukin-6 (IL-6) level, the neutrophil-lymphocyte ratio (NLR), monocyte count (MC), albumin (ALB) level, and serum potassium level. The model performed well in both the internal validation (corrected C-statistic = 0.748, corrected Brier score = 0.201) and external validation datasets (corrected C-statistic = 0.793, corrected Brier score = 0.190). The internal calibration was very good (corrected slope = 0.910). The model developed in this study showed high discriminant performance in predicting PVS in nonsevere COVID-19 patients. Because of the availability and accessibility of the model, the nomogram designed in this study could provide a useful prognostic tool for clinicians and medical decision-makers.
摘要：
建立机器学习模型和列线图，以预测2019年冠状病毒病住院患者（COVID-19）持续病毒脱落（PVS）的概率，临床症状和体征，实验室参数,细胞因子,对429例非重度COVID-19患者的免疫细胞数据进行回顾性分析.使用Akaike信息标准(AIC)开发了两个模型。通过接收机工作特性(ROC)曲线对这两种模型的性能进行了分析和比较,校正曲线,净重新分类指数(NRI)，和综合歧视改进(IDI)。最终模型包括以下PVS的独立预测因子：性别，C反应蛋白(CRP)水平,白细胞介素-6（IL-6）水平，中性粒细胞-淋巴细胞比率(NLR)，单核细胞计数（MC），白蛋白（ALB）水平，和血清钾水平。该模型在内部验证（校正的C统计量=0.748，校正的Brier得分=0.201）和外部验证数据集（校正的C统计量=0.793，校正的Brier得分=0.190）中均表现良好。内部校准非常好(校正斜率=0.910)。本研究开发的模型在预测非重症COVID-19患者的PVS方面表现出很高的判别能力。由于模型的可用性和可访问性，本研究设计的列线图可以为临床医生和医疗决策者提供有用的预后工具.
