ArticleClinical rheumatology2025
Establishment and validation of a nomogram for predicting intravenous immunoglobulin resistance and coronary artery lesion involvement in Kawasaki disease: a retrospective study.
Article in Clinical rheumatology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 2 of them syntheses that pooled it.
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Who cites it
3 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Quality and performance of machine learning versus logistic regression for predicting IVIG resistance in Kawasaki disease: a PROBAST+AI systematic comparison.BMC medical research methodology · 2026Pooled it
- Machine learning prediction models for intravenous immunoglobulin resistance in Kawasaki disease: a meta-analysis.BMC medical informatics and decision making · 2026Pooled it
- Predicting coronary artery lesions in Kawasaki disease: a nomogram based on LASSO regression feature selection.BMC medical informatics and decision making · 2026Article
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4 authors.
Funding
Abstract
objectiveWe aimed to develop a useful nomogram for early identification of Kawasaki disease (KD) children at a high risk of intravenous immunoglobulin (IVIG) resistance and coronary artery lesion (CAL) complications to improve KD management.
methodsClinical data from 400 patients treated at our hospital between January 1, 2016, and December 31, 2023, were collected. Lasso regression was utilized to screen risk factors for IVIG resistance and CAL involvement. Subsequently, a Logistic regression model incorporating parameters screened by Lasso regression was established and visualized as a nomogram. The discrimination, calibration, clinical applicability, and universality of the model were evaluated using receiver operating characteristic (ROC) curves, calibration curves, decision curve analysis (DCA), and internal validation.
resultsNEU%, HDL-C, and MHR were identified as predictors of IVIG resistance by Lasso regression, with C-index of the Logistic model being 0.886 for the training set and 0.855 for the validation set. For predicting CAL development, sex, fever date before the first IVIG administration, KD type, and the level of HDL-C and MHR were the optimal variables, yielding C-index of 0.915 and 0.866 for the training and validation set, respectively. Calibration curves for both validation sets performed well, indicating strong predictive abilities of the models.
conclusionsWe established a nomogram for predicting IVIG resistance that incorporates NEU%, HDL-C, and MHR and a second nomogram for CAL complications incorporating sex, fever date, KD type, and the level of HDL-C and MHR in KD patients, based on the Lasso-Logistic regression model. These nomograms were of guiding significance for screening KD children at high risk of developing IVIG resistance and CAL complications, thereby improving prognosis. Key Points • Two nomograms were established to predict IVIG resistance and CAL complications in KD patients, based on the Lasso-Logistic regression model.
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