Development and Validation of a Nomogram for Predicting Severity in Patients With Hemorrhagic Fever With Renal Syndrome: a Retrospective Study

Abstract

Background: Hemorrhagic fever with renal syndrome (HFRS) is a zoonotic disease caused by hantavirus infection. China is one of the most endemic countries of HFRS in the world. Patients with severe HFRS may develop multiple organ failure or even death, which makes HFRS a serious public health problem in China. Therefore, we constructed and verified a reliable nomogram to predict the severity in patients with HFRS and provide guidance for medical practice.Methods: In this retrospective study, we included a total of 155 consecutive patients with HFRS who were diagnosed from January 1, 2015 to December 31, 2019, of which 109 patients served as a training cohort and 46 patients as an independent verification cohort. 54 laboratory and clinical indicators were applied to assess the severity of HFRS patients. In the training set, the least absolute shrinkage and selection operator (LASSO) regression was used to screen the characteristic variables of the risk model. Multivariate logistic regression analysis was used to construct a nomogram containing the characteristic variables selected in the LASSO regression model. The nomogram's performance was evaluated by the discrimination, calibration, and clinical applicability in the training set and validation set.Results: The prediction nomogram included six predictors such as neutrophils, hemoglobin (Hb), platelets, creatinine, calcium (Ca) and dyspnea, which were screened by LASSO regression. The area under the receiver operating characteristic curve (AUC) of the nomogram in the training and validation cohorts was 0.969 (95%CI:0.935-1.000) and 0.934(95%CI: 0.847-1.000), respectively, indicating that the model has good discrimination. The calibration curve exhibited that the nomogram was in good agreement between the prediction and the actual observation. Decision curve analysis and clinical impact curve indicated that the predictive nomogram had clinical utility.Conclusion: In this study, we established a simple and feasible model to predict severity in patients with HFRS, with which HFRS will be better identified and patients can be treated early.