Fetal Acidemia Prediction Using a Random Forest Model With Cumulative Fetal Stress Assessment During Labor.

AIM: To improve prediction of fetal acidemia during labor using a random forest model incorporating cumulative fetal stress assessment, focusing on an umbilical cord arterial pH < 7.20 as an early clinically relevant threshold. METHODS: This retrospective case-enriched model development study was conducted at Toho University Omori Medical Center. Data were collected from full-term vaginal deliveries between January 2017 and June 2021 for cases with umbilical cord arterial pH < 7.20 and between September 2018 and April 2019 for those with pH ≥ 7.20. Fetal acidemia was defined as pH < 7.20 to account for the time gap between fetal heart rate-based prediction and actual pH measurement at birth. A random forest model was developed using fetal heart rate data, including all decelerations within 30 min before delivery. Model performance was evaluated using five-fold cross-validation. Two datasets were created: Dataset 1 included fetal heart rate data alone, whereas Dataset 2 additionally included maternal and perinatal factors. RESULTS: Dataset 1 achieved a test accuracy of 91.0% and an area under the receiver operating characteristic curve (AUC) of 0.95. Dataset 2 showed improved test accuracy of 93.0% with a similar AUC. Variable importance analysis identified variability as the strongest predictor, followed by the duration of mild and severe prolonged decelerations. CONCLUSIONS: In this case-enriched derivation dataset, a random forest model incorporating cumulative CTG-derived fetal stress variables showed high discrimination for umbilical artery pH < 7.20. External validation in independent consecutive populations is required before clinical implementation.