A visualized nomogram to predict intravenous immunoglobulin resistance in Kawasaki disease: a study based on the population in Southern China
Abstract Background A significant proportion of Kawasaki disease (KD) patients suffer from intravenous immunoglobulin (IVIG) resistance after initial IVIG treatment, which results in persistent coronary artery injury. This study aimed to analyze the risk factors including coagulation indicators and...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
BMC
2025-04-01
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| Series: | Italian Journal of Pediatrics |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s13052-025-01964-2 |
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| Summary: | Abstract Background A significant proportion of Kawasaki disease (KD) patients suffer from intravenous immunoglobulin (IVIG) resistance after initial IVIG treatment, which results in persistent coronary artery injury. This study aimed to analyze the risk factors including coagulation indicators and develop a visualized nomogram model to early predict KD patients who would be at high risk of IVIG-resistant. Methods Consecutive KD patients receiving standard dose of IVIG in Xiamen Women and Children’s Hospital between April 2014 and June 2024 were included in the study. Baseline variables were analyzed using univariate logistic regression and multivariable logistic regression to identify the predictors of IVIG-resistance and derive a nomogram model for the assessment of IVIG-resistance in KD patients. The performance of the nomogram was evaluated with the area under curve (AUC) of receiver operating characteristic, calibration curve, and decision curve analysis. Results A total of 541 KD patients were finally enrolled in the present study, and 7.6% of KD patients suffered from IVIG-resistant. The predictive value of coagulation indicators for IVIG-resistance may be limited except for activated partial thromboplastin time (APTT). Other independent predictors include red blood cell count, globulin, Alanine aminotransferase, and weight. The training and testing sets of nomogram scored an AUC of 0.781 (95% CI, 0.688–0.874) and 0.749 (95% CI, 0.597–0.902). The nomogram was calibrated well, and the decision curve analysis showed that the nomogram would generate more net benefit when the threshold probabilities ranged from 10 to 70%. Conclusion A visualized nomogram model was constructed to accurately predict the risk of IVIG-resistance for KD patients, and APTT may be a potential predictor of IVIG-resistant. |
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| ISSN: | 1824-7288 |