Machine learning models using anthropometric data and heart rate variability predicted recurrent vasovagal syncope in children with 71.0% accuracy, 76.3% sensitivity, and 63.3% specificity.
Observational (n=87)
Can machine learning models incorporating anthropometric data and heart rate variability predict recurrent vasovagal syncope in children without blood pressure monitoring?
Machine learning models utilizing anthropometric and heart rate variability data show potential as a blood pressure-independent screening tool to identify children at high risk for recurrent vasovagal syncope.
Effect estimate: 71.0% accuracy, 76.3% sensitivity, 63.3% specificity
Vasovagal syncope (VVS) affects 17% of children, significantly impairing quality of life. Machine learning (ML) models achieve high predictive accuracy of VVS in adults using blood pressure (BP) monitoring, but pediatric implementation remains challenging. The aim of the study was to evaluate whether ML models incorporating anthropometric data and heart rate variability (HRV) can predict VVS without BP monitoring in children with prior syncope or suspected VVS. We analyzed 87 participants (7–18 years) with VVS history. HRV indices (time-domain, frequency-domain, and nonlinear) were extracted from 5 min supine and standing ECG recordings using NeuroKit2. Multiple algorithms were tested with 10-fold cross-validation; SHAP analysis identified feature importance. AdaBoost achieved the performance of 71.0% accuracy, 76.3% sensitivity, and 63.3% specificity—78% of adult BP-dependent algorithm sensitivity. Weight, multifractal detrended fluctuation analysis during standing, and normalized low-frequency power were most influential. Alterations in symbolic dynamics and multiscale entropy indicated compromised autonomic complexity. ML models with anthropometric and HRV data show potential as an adjunctive screening tool to identify children at higher risk for syncope recurrence, requiring clinical confirmation.
Wieniawski et al. (Mon,) conducted a observational in Vasovagal syncope (VVS) (n=87). Machine learning models incorporating anthropometric data and heart rate variability was evaluated on Prediction of recurrent vasovagal syncope (71.0% accuracy, 76.3% sensitivity, 63.3% specificity). Machine learning models using anthropometric data and heart rate variability predicted recurrent vasovagal syncope in children with 71.0% accuracy, 76.3% sensitivity, and 63.3% specificity.
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