Machine Learning for the Prediction of Surgical Morbidity in Placenta Accreta Spectrum

Itamar D. Futterman; Olivia Sher; Chaskin Saroff; Alexa Cohen; Georgios Doulaveris; Pe'er Dar; Myah M. Griffin; Meghana Limaye; Thomas Owens; Lois Brustman; Henri Rosenberg; Rebecca Jessel; Scott Chudnoff; Shoshana Haberman

doi:10.1055/a-2405-3459

American Journal of Perinatology, Table of Contents

Am J Perinatol 2025; 42(03): 281-292
DOI: 10.1055/a-2405-3459

SMFM Fellowship Series Article

Machine Learning for the Prediction of Surgical Morbidity in Placenta Accreta Spectrum

Itamar D. Futterman

¹Division of Maternal Fetal Medicine, Department of Obstetrics and Gynecology, Maimonides Medical Center, Brooklyn, New York

²Division of Complex Obstetrical Surgery, Department of Obstetrics and Gynecology, Maimonides Medical Center, Brooklyn, New York

,

Olivia Sher

¹Division of Maternal Fetal Medicine, Department of Obstetrics and Gynecology, Maimonides Medical Center, Brooklyn, New York

,

Chaskin Saroff

³BioticAI Inc., San Francisco, California

,

Alexa Cohen

⁴Division of Fetal Medicine and Ultrasound, Obstetrics, Gynecology and Women's Health, Montefiore Medical Center, Albert Einstein College of Medicine, Bronx, New York

,

Georgios Doulaveris

⁴Division of Fetal Medicine and Ultrasound, Obstetrics, Gynecology and Women's Health, Montefiore Medical Center, Albert Einstein College of Medicine, Bronx, New York

,

Pe'er Dar

⁴Division of Fetal Medicine and Ultrasound, Obstetrics, Gynecology and Women's Health, Montefiore Medical Center, Albert Einstein College of Medicine, Bronx, New York

,

Myah M. Griffin

⁵Division of Maternal Fetal Medicine, Department of Obstetrics and Gynecology, NYU Langone Medical Center, New York, New York

,

Meghana Limaye

⁵Division of Maternal Fetal Medicine, Department of Obstetrics and Gynecology, NYU Langone Medical Center, New York, New York

,

Thomas Owens

⁶Division of Maternal Fetal Medicine, Mount Sinai West, Icahn School of Medicine at Mount Sinai, New York, New York

,

Lois Brustman

⁶Division of Maternal Fetal Medicine, Mount Sinai West, Icahn School of Medicine at Mount Sinai, New York, New York

,

Henri Rosenberg

⁷Division of Maternal Fetal Medicine, Department of Obstetrics, Gynecology and Reproductive Science, Icahn School of Medicine at Mount Sinai, New York, New York

,

Rebecca Jessel

⁷Division of Maternal Fetal Medicine, Department of Obstetrics, Gynecology and Reproductive Science, Icahn School of Medicine at Mount Sinai, New York, New York

,

Scott Chudnoff

¹Division of Maternal Fetal Medicine, Department of Obstetrics and Gynecology, Maimonides Medical Center, Brooklyn, New York

,

Shoshana Haberman

¹Division of Maternal Fetal Medicine, Department of Obstetrics and Gynecology, Maimonides Medical Center, Brooklyn, New York

› Author Affiliations

Abstract

Objective We 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).

Study Design A 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 (>1,500 mL), 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.

Results Among 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.

Conclusion By 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.

Key Points

ML models are useful models are useful in predicting PAS-related surgical morbidity.
Optimal management for PAS remains unclear.
Utilizing ML can serve as a foundation for risk and complexity stratification in cases of PAS.

Keywords

placenta accreta spectrum - surgical morbidity - machine learning

Full Text

References

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Supplementary Material