Abstract
Abstract
Existing prediction models for obstetric anal sphincter injuries (OASIS) often rely on traditional regression, lack external validation, and, when fitted on multicenter data, fail to account for hospital-level clustering. We aimed to develop and temporally validate mixed-effects machine learning models for OASIS and compare them with traditional regression models. A secondary aim was to evaluate whether macrosomia (≥4000 g) could substitute for continuous birth weight. We conducted a retrospective nationwide cohort study of singleton, term (≥37 weeks), cephalic vaginal deliveries in the Dutch Perinatal Registry. Data from 2016 to 2019 were used for model development with three-fold cross-validation; data from 2020 were reserved for temporal validation. Candidate predictors included 18 demographic, obstetric, and neonatal factors. We developed six models: mixed-effects gradient boosting (MEGB), mixed-effects LASSO (ME-LASSO), and backward stepwise logistic regression, each with either birth weight or macrosomia. Discrimination was assessed by the area under the receiver operating characteristic curve (AUC), calibration by calibration plots, and prediction accuracy by the Brier score. Among 614,255 deliveries, OASIS occurred in 2.6%. In temporal validation including macrosomia, MEGB achieved the highest discrimination (AUC 0.727; 95% CI 0.719–0.735), significantly outperforming ME-LASSO (AUC 0.719; 95% CI 0.710-0.727; P
Author summary
Obstetric Anal Sphincter Injuries (OASIS) after giving birth, can lead to long-term issues such as incontinence, pelvic floor dysfunction, chronic pain, and sexual dysfunction. Identifying women at increased risk before birth could support counselling and help clinicians take preventive measures. Although several prediction tools have been developed, many do not account for possible differences between hospitals, where clinical practices and rates of OASIS may vary.
Using nationwide data from more than 600,000 births in the Netherlands, we developed prediction models for OASIS that also accounted for differences between hospitals. We used machine-learning approaches and compared their performance with that of a traditional regression model. The machine-learning models performed slightly better in predicting OASIS. We also found that classifying babies as large (birth weight above 4,000 g) performed almost as well as using exact birth weight. This is relevant because fetal weight can only be estimated before birth using ultrasound. After further external validation, these models may support earlier and more individualized assessment of OASIS risk.