ArticleBMC pediatrics2026
Novel biomarker-based prediction model for coronary artery lesions in Kawasaki disease.
Article in BMC pediatrics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
objectiveThis study aimed to develop and validate a biomarker-based prediction model for assessing the individual risk of coronary artery lesions (CAL) in Kawasaki disease (KD).
methodsA retrospective analysis was performed on 345 pediatric KD patients admitted between June 2018 and June 2022. Patients were randomly divided into training (n = 241) and validation (n = 104) sets. Univariate analysis identified candidate predictors, and Least Absolute Shrinkage and Selection Operator (LASSO) regression was used for feature selection. Multivariable logistic regression and machine learning models—random forest (RF), support vector machine, and k-nearest neighbors—were developed. Model performance was assessed using the area under the receiver operating characteristic curve (AUC), calibration curves, and decision curve analysis. A nomogram was constructed, and SHapley Additive exPlanations (SHAP) values were applied to interpret feature contributions.
resultsSeven biomarkers were significantly associated with CAL in univariate analysis (P < 0.05). LASSO and multivariable logistic regression analysis identified age, N-terminal pro-B-type natriuretic peptide, interleukin-6, calprotectin, endothelial microparticles, Matrix Metalloproteinase-9, and Galectin-3 as independent predictors. The RF model demonstrated superior performance, with AUCs of 0.888 (training) and 0.860 (validation). SHAP analysis confirmed these three variables as the top contributors to CAL prediction. The nomogram exhibited strong calibration and clinical utility.
conclusionThe machine learning-based prediction model incorporating novel biomarkers enables individualized risk assessment for CAL development in KD patients. This model exhibits excellent predictive performance and clinical applicability, facilitating early identification of high-risk patients and the implementation of targeted interventions, thereby optimizing healthcare resource allocation and improving long-term cardiovascular outcomes.
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.