ArticlePLoS neglected tropical diseases2025
Development and validation of a simple-to-use nomogram for predicting severe scrub typhus in children.
Article in PLoS neglected tropical diseases, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Clinical features and risk factors for severe pediatric scrub typhus in Liangshan Prefecture, China: A retrospective cohort study.BMC pediatrics · 2026Article
- Clinical characteristics and an admission-time predictive model for severe scrub typhus and secondary HLH in adults.BMC infectious diseases · 2026Article
- Cytokine and chemokine profiles linked to early severity of scrub typhus: multicenter validation of soluble PD-L1.Journal of clinical microbiology · 2026Article
- Differentiating scrub typhus meningitis from brucellar meningitis in children: A single-center retrospective study.PLoS neglected tropical diseases · 2026Article
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4 authors.
Funding
Abstract
objectiveThis study aimed to develop and validate a simple-to-use nomogram for predicting severe scrub typhus (ST) in children.
methodsA 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.
resultsLASSO 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.
conclusionsThis 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.
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