Digital biomarkers as predictors of brain injury in neonatal encephalopathy - Zaghloul, Nahla; Singh, Neel Kamal; Xu, Weihuang; Lagnese, Kaitlyn; Sura, Livia; Roig, Juan Carlos; Albayram, Mehmet; Rajderkar, Dhanashree; Wynn, James L.; Zare, Alina; Weiss, Michael D. (Frontiers in Pediatrics, 2025)
BackgroundNeonatal encephalopathy (NE) is a significant cause of neurodevelopmental impairment, with therapeutic hypothermia (TH) being the current standard of care for mitigating brain injury in affected neonates. Despite advances, there is a critical need for early, reliable biomarkers that can predict brain injury severity and long-term outcomes, particularly during the 72-h hypothermia window. This study explores the potential of digital biomarkers derived from continuous bedside physiologic monitoring to predict MRI-confirmed brain injury in neonates with NE.MethodsWe collected continuous physiologic data from 138 neonates undergoing TH, including heart rate, systemic oxygen saturation (SpO₂), cerebral oxygen saturation (rcSO₂), systolic and diastolic blood pressure, and mean arterial pressure (MAP). Using a Long Short-Term Memory (LSTM) neural network, we developed predictive models to classify neonates into no/mild or moderate/severe brain injury groups based on MRI findings. Model performance was evaluated at 24 and 48 h of data collection. An ablation study was conducted to assess the relative importance of individual biomarkers.ResultsSeventy-three neonates (52.9%) were classified as having moderate/severe injury, while 65 neonates (47.1%) had no/mild injury on MRI. The predictive accuracy of the LSTM model improved significantly with extended data duration, achieving an accuracy of 91.2% at 48 h compared to 84.6% at 24 h. The ablation study identified heart rate as the most significant biomarker, whereas rcSO₂ trends showed potential but did not consistently contribute to prediction accuracy in later models.ConclusionOur study highlights the potential of digital biomarkers in predicting brain injury severity during the therapeutic hypothermia window. Machine learning models, such as LSTM networks, offer an opportunity for real-time prediction and risk stratification, ultimately enhancing clinical decision-making and neuroprotective strategies in neonates with NE. Future studies will focus on integrating real-time data capture and improving predictive accuracy.