International journal of gynaecology and obstetrics: the official organ of the International Federation of Gynaecology and Obstetrics · 2026
Hematologic markers and machine learning in predicting placenta accreta: A case-control study.
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Jochum Michael D, Albrecht Kelly D, Martinez Yamely Mendez, Zhang Victoria, Sarada Sanmay, Burnett Brian, Reed Christina C, Fox Karin A, Shamshirsaz Amir A, Belfort Michael A, Munoz Jessian L, Lombaard Hennie A
Аннотация
OBJECTIVE: This study aims to enhance antenatal detection of placenta accreta spectrum (PAS) and predict severe hemorrhage at delivery using machine learning by evaluating the association between antenatal hematologic index trends across trimesters, imaging markers, and patient history.
METHODS: We retrospectively analyzed 2017-2023 data from a PAS referral center, including demographics, laboratory results, ultrasounds, and outcomes. Patients with confirmed PAS (cases) were compared to those with antenatal risk but no histopathologic evidence of PAS (controls). Statistical analyses and machine learning models were developed to predict PAS. We also used machine learning to predict severe hemorrhage (>1500 mL) in the cases.
RESULTS: A total of 186 PAS cases and 217 controls were identified, showing significant differences in body mass index, gravidity, parity, prior cesarean deliveries, gestational age at delivery, and PAS ultrasound findings. Logistic regression highlighted prior cesarean deliveries (odds ratio [OR] 1.8; 95% confidence interval [CI] 1.3-2.4) and second (OR 28.1; 95% CI 12.7-60.8) or third trimester ultrasound markers (OR 27.6; 95% CI 13.2-61.1) as strong predictors of PAS. Third trimester mean platelet volume was inversely associated with PAS (OR 0.55; 95% CI 0.39-0.78). Machine learning models achieved high accuracy. Model 1 predicted PAS with 90% accuracy. Model 2 predicted PAS with 88.8% accuracy using early gestational hematologic markers. Model 3 predicted severe hemorrhage (>1500 mL) with 74.3% accuracy.
CONCLUSION: Machine learning models combining patient history, imaging, and hematologic markers detect PAS and predict hemorrhage with up to 90% accuracy. These tools improve antenatal diagnosis of PAS, which enhances maternal outcomes by enabling early identification and better resource allocation.
METHODS: We retrospectively analyzed 2017-2023 data from a PAS referral center, including demographics, laboratory results, ultrasounds, and outcomes. Patients with confirmed PAS (cases) were compared to those with antenatal risk but no histopathologic evidence of PAS (controls). Statistical analyses and machine learning models were developed to predict PAS. We also used machine learning to predict severe hemorrhage (>1500 mL) in the cases.
RESULTS: A total of 186 PAS cases and 217 controls were identified, showing significant differences in body mass index, gravidity, parity, prior cesarean deliveries, gestational age at delivery, and PAS ultrasound findings. Logistic regression highlighted prior cesarean deliveries (odds ratio [OR] 1.8; 95% confidence interval [CI] 1.3-2.4) and second (OR 28.1; 95% CI 12.7-60.8) or third trimester ultrasound markers (OR 27.6; 95% CI 13.2-61.1) as strong predictors of PAS. Third trimester mean platelet volume was inversely associated with PAS (OR 0.55; 95% CI 0.39-0.78). Machine learning models achieved high accuracy. Model 1 predicted PAS with 90% accuracy. Model 2 predicted PAS with 88.8% accuracy using early gestational hematologic markers. Model 3 predicted severe hemorrhage (>1500 mL) with 74.3% accuracy.
CONCLUSION: Machine learning models combining patient history, imaging, and hematologic markers detect PAS and predict hemorrhage with up to 90% accuracy. These tools improve antenatal diagnosis of PAS, which enhances maternal outcomes by enabling early identification and better resource allocation.