A predictive model and nomogram for coronary artery injury in Kawasaki disease based on laboratory indicators: a retrospective study - Report - MDSpire
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A predictive model and nomogram for coronary artery injury in Kawasaki disease based on laboratory indicators: a retrospective study
Clinical Report: Predictive Model for Coronary Artery Damage in Kawasaki Disease
Overview
This study developed predictive models and nomograms utilizing laboratory indicators to differentiate typical and incomplete Kawasaki disease (KD) and assess the risk of coronary artery lesions (CAL). The models demonstrated good calibration and clinical utility, particularly for early screening in primary care settings.
Background
Kawasaki disease (KD) is a significant cause of acquired heart disease in children, with incomplete forms often leading to misdiagnosis and severe complications like CAL. Identifying reliable laboratory indicators for early diagnosis and risk stratification is crucial for improving patient outcomes and preventing long-term cardiovascular issues.
Data Highlights
Group
Sample Size
AUC
Typical KD
95
0.762
Coronary Artery Lesion
39
0.790
Key Findings
Total protein (TP) is the only independent factor for differentiating typical from incomplete KD.
Hypoalbuminemia, hyponatremia, and elevated lactate dehydrogenase (LDH) are independent risk factors for KD with CAL.
Hypoalbuminemia is identified as the strongest predictor of CAL (OR = 0.783, P = 0.001).
The predictive model for typical KD achieved an AUC of 0.762.
The predictive model for CAL achieved an AUC of 0.790.
Both models showed good calibration and positive clinical net benefit.
Clinical Implications
The developed predictive models and nomograms can assist clinicians in the early identification of incomplete KD and the assessment of CAL risk. These tools are particularly beneficial in primary care settings where diagnostic resources may be limited.
Conclusion
The study highlights the importance of routine laboratory indicators in the differentiation of KD phenotypes and the prediction of CAL risk, providing practical tools for enhancing early diagnosis and treatment.
