{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T19:58:40Z","timestamp":1774468720591,"version":"3.50.1"},"reference-count":43,"publisher":"Georg Thieme Verlag KG","issue":"03","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Am J Perinatol"],"published-print":{"date-parts":[[2025,2]]},"abstract":"<jats:p>\n            Objective\u2003We sought to create a machine learning (ML) model to identify variables that would aid in the prediction of surgical morbidity in cases of placenta accreta spectrum (PAS).<\/jats:p><jats:p>\n            Study Design\u2003A multicenter analysis including all cases of PAS identified by pathology specimen confirmation, across five tertiary care perinatal centers in New York City from 2013 to 2022. We developed models to predict operative morbidity using 213 variables including demographics, obstetrical information, and limited prenatal imaging findings detailing placental location. Our primary outcome was prediction of a surgical morbidity composite defined as including any of the following: blood loss (&gt;1,500\u2009mL), transfusion, intensive care unit admission, vasopressor use, mechanical ventilation\/intubation, and organ injury. A nested, stratified, cross-validation approach was used to tune model hyperparameters and estimate generalizability. Gradient boosted tree classifier models incorporated preprocessing steps of standard scaling for numerical variables and one-hot encoding for categorical variables. Model performance was evaluated using area under the receiver operating characteristic curve (AUC), positive and negative predictive values (PPV, NPV), and F1 score. Variable importance ranking was also determined.<\/jats:p><jats:p>\n            Results\u2003Among 401 PAS cases, 326 (81%) underwent hysterectomy. Of the 401 cases of PAS, 309 (77%) had at least one event defined as surgical morbidity. Our predictive model had an AUC of 0.79 (95% confidence interval: 0.69, 0.89), PPV 0.79, NPV 0.76, and F1 score of 0.88. The variables most predictive of surgical morbidity were completion of a hysterectomy, prepregnancy body mass index (BMI), absence of a second trimester ultrasound, socioeconomic status zip code, BMI at delivery, number of prenatal visits, and delivery time of day.<\/jats:p><jats:p>\n            Conclusion\u2003By identifying social and obstetrical characteristics that increase patients' risk, ML models are useful in predicting PAS-related surgical morbidity. Utilizing ML could serve as a foundation for risk and complexity stratification in cases of PAS to optimize surgical planning.<\/jats:p><jats:p>\n            Key Points\n          <\/jats:p>","DOI":"10.1055\/a-2405-3459","type":"journal-article","created":{"date-parts":[[2024,9,17]],"date-time":"2024-09-17T23:11:32Z","timestamp":1726614692000},"page":"281-292","source":"Crossref","is-referenced-by-count":3,"title":["Machine Learning for the Prediction of Surgical Morbidity in Placenta Accreta Spectrum"],"prefix":"10.1055","volume":"42","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6766-3437","authenticated-orcid":false,"given":"Itamar D.","family":"Futterman","sequence":"additional","affiliation":[{"name":"Division of Maternal Fetal Medicine, Department of Obstetrics and Gynecology, Maimonides Medical Center, Brooklyn, New York"},{"name":"Division of Complex Obstetrical Surgery, Department of Obstetrics and Gynecology, Maimonides Medical Center, Brooklyn, New York"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Olivia","family":"Sher","sequence":"additional","affiliation":[{"name":"Division of Maternal Fetal Medicine, Department of Obstetrics and Gynecology, Maimonides Medical Center, Brooklyn, New York"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chaskin","family":"Saroff","sequence":"additional","affiliation":[{"name":"BioticAI Inc., San Francisco, California"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alexa","family":"Cohen","sequence":"additional","affiliation":[{"name":"Division of Fetal Medicine and Ultrasound, Obstetrics, Gynecology and Women's Health, Montefiore Medical Center, Albert Einstein College of Medicine, Bronx, New York"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Georgios","family":"Doulaveris","sequence":"additional","affiliation":[{"name":"Division of Fetal Medicine and Ultrasound, Obstetrics, Gynecology and Women's Health, Montefiore Medical Center, Albert Einstein College of Medicine, Bronx, New York"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pe'er","family":"Dar","sequence":"additional","affiliation":[{"name":"Division of Fetal Medicine and Ultrasound, Obstetrics, Gynecology and Women's Health, Montefiore Medical Center, Albert Einstein College of Medicine, Bronx, New York"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Myah M.","family":"Griffin","sequence":"additional","affiliation":[{"name":"Division of Maternal Fetal Medicine, Department of Obstetrics and Gynecology, NYU Langone Medical Center, New York, New York"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3514-1183","authenticated-orcid":false,"given":"Meghana","family":"Limaye","sequence":"additional","affiliation":[{"name":"Division of Maternal Fetal Medicine, Department of Obstetrics and Gynecology, NYU Langone Medical Center, New York, New 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