Extending the training window from the final hour to the full duration of labor and adding time from labor onset let a model identify 40.3% of hypoxic-ischemic encephalopathy cases at least 3 hours before delivery, an 8.4-point gain over the last-hour classifier
Using 174,186 deliveries at gestational age ≥35 weeks from Kaiser Permanente Northern California, the study extracted 40 cardiotocography (CTG) features from 20-minute epochs to train Random Forest classifiers targeting severe acidosis (N=2,636) and clinically validated hypoxic-ischemic encephalopathy (HIE, N=304), progressively extended the training window from the final hour before delivery to the full duration of labor, and added time from labor onset (TLO) as a feature; evaluated continuously at a fixed 15% false positive rate, early identification improved as the window was extended and plateaued at 18 hours, and adding TLO gave the best early-warning performance, with 40.3% of HIE cases identified at least 3 hours and 27.3% at least 6 hours before delivery, absolute gains of 8.