Prediction nomogram for risk stratification in patients with COVID-19
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Abstract
COVID-19 can progress to severe forms of the disease with high mortality, so it has been necessary to identify predictive factors that allow stratifying the risk in patients. A retrospective analytical study was carried out in a cohort of 150 patients from Manuel Fajardo Hospital in Villa Clara, Cuba, from March to June 2020. With the information obtained, a severity prognostic index was constructed by means of a multivariate binary logistic regression model, in which the probability of the patient evolving towards severity was expressed as a function of the set of variables that were identified as predictors of the health event of interest. R software version 4.0.2 (22-06-2020) was used to summarize the data and apply the hypothesis tests. With the final results, a prognostic index was elaborated through a mathematical equation on which the model is based. A prediction nomogram was constructed to facilitate its interpretation, which constituted the main output of this study. The variables with the highest predictive power, which definitely remained in the model and with the nomogram was constructed were: age (p=0.049), arterial hypertension (p=0.013), neutrophil/lymphocyte ratio (p=0.004), lactate dehydrogenase (p=0.039) and arterial oxygen saturation (p=0.044). The Hosmer-Lemeshow test statistic result was p= 0.976 and the discriminatory capacity given by the area under the ROC curve (receiver operating characteristic curve) was equal to 0.988 (AUC: 0.9882, 95% CI: 0.9756-1). The optimal cut-off point was 0.099. We conclude that our nomogram is a very useful tool for the early identification of patients at risk of progressing to severe forms of COVID-19. In this way, it facilitates better stratification and the adequacy of timely treatment, capable of slowing disease progression
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