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首页> 外文期刊>JMIR Medical Informatics >Nomogram for Predicting COVID-19 Disease Progression Based on Single-Center Data: Observational Study and Model Development
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Nomogram for Predicting COVID-19 Disease Progression Based on Single-Center Data: Observational Study and Model Development

机译：基于单中心数据预测Covid-19疾病进展的NOMO图：观察研究和模型开发

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Background In late December 2019, a pneumonia caused by SARS-CoV-2 was first reported in Wuhan and spread worldwide rapidly. Currently, no specific medicine is available to treat infection with COVID-19. Objective The aims of this study were to summarize the epidemiological and clinical characteristics of 175 patients with SARS-CoV-2 infection who were hospitalized in Renmin Hospital of Wuhan University from January 1 to January 31, 2020, and to establish a tool to identify potential critical patients with COVID-19 and help clinical physicians prevent progression of this disease. Methods In this retrospective study, clinical characteristics of 175 confirmed COVID-19 cases were collected and analyzed. Univariate analysis and least absolute shrinkage and selection operator (LASSO) regression were used to select variables. Multivariate analysis was applied to identify independent risk factors in COVID-19 progression. We established a nomogram to evaluate the probability of progression of the condition of a patient with COVID-19 to severe within three weeks of disease onset. The nomogram was verified using calibration curves and receiver operating characteristic curves. Results A total of 18 variables were considered to be risk factors after the univariate regression analysis of the laboratory parameters ( P .05), and LASSO regression analysis screened out 10 risk factors for further study. The six independent risk factors revealed by multivariate Cox regression were age (OR 1.035, 95% CI 1.017-1.054; P .001), CK level (OR 1.002, 95% CI 1.0003-1.0039; P =.02), CD4 count (OR 0.995, 95% CI 0.992-0.998; P =.002), CD8 % (OR 1.007, 95% CI 1.004-1.012, P .001), CD8 count (OR 0.881, 95% CI 0.835-0.931; P .001), and C3 count (OR 6.93, 95% CI 1.945-24.691; P =.003). The areas under the curve of the prediction model for 0.5-week, 1-week, 2-week and 3-week nonsevere probability were 0.721, 0.742, 0.87, and 0.832, respectively. The calibration curves showed that the model had good prediction ability within three weeks of disease onset. Conclusions This study presents a predictive nomogram of critical patients with COVID-19 based on LASSO and Cox regression analysis. Clinical use of the nomogram may enable timely detection of potential critical patients with COVID-19 and instruct clinicians to administer early intervention to these patients to prevent the disease from worsening.
机译：背景技术在2019年12月底，由SARS-COV-2引起的肺炎是在武汉报告的，并在全球迅速传播。目前，没有任何特定药物可以用Covid-19治疗感染。目的本研究的目标是总结175例SARS-COV-2感染患者的流行病学和临床特征，于2020年至1月31日至1月31日，武汉大学人民医院住院，建立了识别潜力的工具Covid-19的关键患者，帮助临床医生预防这种疾病的进展。方法在该回顾性研究中，收集并分析了175例确诊的Covid-19案例的临床特征。单变量分析和最低绝对收缩和选择运算符（套索）回归用于选择变量。应用多变量分析以确定Covid-19进展中的独立风险因素。我们建立了一个载体，以评估Covid-19在疾病发作的三周内患者患者病情进展的可能性。使用校准曲线和接收器操作特性曲线来验证NOM图。结果在实验室参数的单变量回归分析（P <0.05）的单变量回归分析后，总共18个变量被认为是风险因素，而卢斯回归分析筛选出10个危险因素进行进一步研究。多元COX回归揭示的六种独立风险因素是年龄（或1.035,95％CI 1.017-1.054; P <.001），CK水平（或1.002,95％CI 1.0003-1.0039; P = .02），CD4计数（或0.995,95％CI 0.992-0.998; p = .002），CD8％（或1.007,95％CI 1.004-1.012，P <.001），CD8计数（或0.881,95％CI 0.835-0.931; P. <.001）和C3计数（或6.93,95％CI 1.945-24.691; p = .003）。预测模型曲线下的区域为0.5周，1周，2周和3周的非概率分别为0.721,0.742,0.87和0.832。校准曲线表明，该模型在疾病发作的三周内具有良好的预测能力。结论本研究提出了基于套索和COX回归分析的Covid-19关键患者的预测性探测器。临床用途可以及时检测Covid-19的潜在关键患者，并指导临床医生向这些患者施用早期干预，以防止疾病恶化。

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