Background: Early prognostication of the outcome in pediatric cardiac arrest (CA) patients is crucial for clinical decision-making. Heart rate variability (HRV) has shown potential in predicting outcomes after CA in adult patients. This study investigates whether HRV can be used to predict survival outcomes after pediatric CA using machine learning techniques. Methods: This retrospective study included children with CA, who achieved return of spontaneous circulation (ROSC), and were admitted to the pediatric intensive care unit (PICU) of a tertiary hospital between 2012 and 2021. A 5-min electrocardiogram (ECG) segment acquired at 24 h after CA was used to calculate HRV parameters (time-, frequency-, and non-linear domains). These parameters were used to train a random forest model. The primary outcome was 12-month survival or death. Model performance was evaluated using receiver-operating characteristics (ROC) analysis and predictive values. Feature importance was assessed using Shapley values. Results: A total of 76 patients (male: 63.2%, median age: 2.5 [IQR: 0.4–8.0] years) were divided into survival (34) or death (42) groups based on 12-month outcomes. The machine learning model achieved an accuracy of 77.6% and a positive predictive value of 0.879 for mortality prediction. The most influential features for model predictions were the frequency-domain parameters total power and very-low frequency (VLF) power, with lower values associated with an increased probability of death. Conclusions: Analysis of HRV at 24 h after ROSC may serve as a strong predictor of 12-month survival after pediatric CA.

Prognostication after moderate-to-severe traumatic brain injury (TBI) remains challenging in the intensive care unit (ICU) despite the existence of well-validated online prognostication tools. Changes in brain activity related to TBI can be measured using electroencephalography (EEG), making it a potentially interesting diagnostic tool to refine prognostication. The primary objective of this systematic review was to evaluate the literature concerning the prognostic value of EEG among patients with TBI in the ICU. Five databases were searched from inception until August 13, 2024. The search identified 1492 unique records. Eventually, 27 manuscripts met the inclusion criteria (>18 years old, Glasgow Coma Scale ≤12, EEG performed in the ICU). The QUIPS (QUality In Prognostic Studies) and PROBAST (Prediction model Risk Of Bias ASsessment Tool) tools were used to assess the study quality and bias. Due to high heterogeneity in EEG feature and outcome definitions and a lack of correction for confounding factors, all studies had a moderate-to-high risk of bias. Nonetheless, specific EEG features (identified through visual and quantitative EEG, EEG reactivity, and machine learning techniques) were found to be predictive of neurological outcomes up to 1.5 years after TBI. While epileptiform discharges and seizures were not consistently associated with outcomes, a higher alpha variability, a more continuous EEG, present EEG reactivity, and present EEG sleep features were predictive of better outcomes. The combination of EEG features with clinical parameters demonstrated improved predictive performance compared with models using standard clinical parameters alone. Still, the EEG features described and their potential additional value in outcome prediction after TBI merit further investigation.

Background and Objectives Early neuroprognostication in children with reduced consciousness after cardiac arrest (CA) is a major clinical challenge. EEG is frequently used for neuroprognostication in adults, but has not been sufficiently validated for this indication in children. Using machine learning techniques, we studied the predictive value of quantitative EEG (qEEG) features for survival 12 months after CA, based on EEG recordings obtained 24 hours after CA in children. The results were confirmed through visual analysis of EEG background patterns. Methods This is a retrospective single-center study including children (0–17 years) with CA, who were subsequently admitted to the pediatric intensive care unit (PICU) of a tertiary care hospital between 2012 and 2021 after return of circulation (ROC) and were monitored using EEG at 24 hours after ROC. Signal features were extracted from a 30-minute EEG segment 24 hours after CA and used to train a random forest model. The background pattern from the same EEG fragment was visually classified. The primary outcome was survival or death 12 months after CA. Analysis of the prognostic accuracy of the model included calculation of receiver-operating characteristic and predictive values. Feature contribution to the model was analyzed using Shapley values. Results Eighty-six children were included (in-hospital CA 27%, out-of-hospital CA 73%). The median age at CA was 2.6 years; 53 (62%) were male. Mortality at 12 months was 56%; main causes of death on the PICU were withdrawal of life-sustaining therapies because of poor neurologic prognosis (52%) and brain death (31%). The random forest model was able to predict death at 12 months with an accuracy of 0.77 and positive predictive value of 1.0. Continuity and amplitude of the EEG signal were the signal parameters most contributing to the model classification. Visual analysis showed that no patients with a background pattern other than continuous with amplitudes exceeding 20 μV were alive after 12 months. Discussion Both qEEG and visual EEG background classification for registrations obtained 24 hours after ROC form a strong predictor of nonsurvival 12 months after CA in children.