Development and validation of a simple-to-use nomogram for predicting severe scrub typhus in children.

Abstract

Objective: This study aimed to develop and validate a simple-to-use nomogram for predicting severe scrub typhus (ST) in children.

Methods: A retrospective study of 256 patients with ST was performed at the Kunming Children's Hospital from January 2015 to November 2022. ALL patients were divided into a common and severe group based on the severity of the disease. A least absolute shrinkage and selection operator (LASSO) regression model was used to identify the optimal predictors, and the predictive nomogram was plotted by multivariable logistic regression. The nomogram was assessed by calibration, discrimination, and clinical utility.

Results: LASSO regression analysis identified that hemoglobin count (Hb), platelet count (PLT), lactate dehydrogenase (LDH), blood urea nitrogen (BUN), creatine kinase isoenzyme MB(CK-MB) and hypoproteinemia were the optimal predictors for severe ST. The nomogram was plotted by the six predictors. The area under the receiver operating characteristic (ROC) curve of the nomogram was 0.870(95% CI = 0.812 ~ 0.928) in training set and 0.839(95% CI = 0.712 ~ 0.967) in validation set. The calibration curve demonstrated that the nomogram was well-fitted, and the decision curve analysis (DCA) showed that the nomogram was clinically beneficial.

Conclusions: This study developed and validated a simple-to-use nomogram for predicting severe ST in children based on six predictors including Hb, PLT, LDH, BUN, CK-MB and hypoproteinemia, demonstrating excellent predictive accuracy for the data, though external and prospective validation is required to assess its potential clinical utility.