Prediction of Neurological Outcome After Pediatric Cardiac Arrest Using Heart Rate Variability and Machine Learning

AIMS: Heart rate variability (HRV), a non-invasive measure of autonomic function, may offer prognostic value after pediatric cardiac arrest. We used machine learning models to determine whether HRV features within the first 24 hours after return of spontaneous circulation can predict outcomes in children following cardiac arrest, and whether adding clinical cardiac arrest characteristics improves model performance. METHODS: Retrospective study of children who received post-arrest care in the PICU at the Children's Hospital of Philadelphia from 2020 to 2023. Thirty-six HRV features were extracted from ECG recordings and Extreme Gradient Boosting (XGB) models were trained to predict unfavorable neurological outcome, defined as Pediatric Cerebral Performance Category 4-6 and an increase >1 from baseline. Models were evaluated by cross-validation across the entire 24-hour period and within sequential 6-hour epochs. Additional models included clinical arrest characteristics. Performance was assessed by area under the receiver operating characteristic curve (AUROC). RESULTS: Of the 75 patients who met inclusion criteria (median age 6.8 IQR 10.4 years), 51% had an unfavorable outcome. Model considering HRV features and age models achieved an AUROC of 0.80 (95% CI: 0.68-0.88). Top HRV predictors included standard deviation (SDNN), power at very low and low frequency bands, entropy, and fractal scaling. Performance was similar across the 6-hour epochs (p's>0.1). Adding cardiac arrest characteristics did not improve model performance (AUROC 0.83 0.73-0.92, p>0.41). CONCLUSION: Using machine learning, HRV features within 24 hours after pediatric cardiac arrest predict unfavorable outcome with AUROC 0.8. Adding clinical variables did not improve model performance.