Predicting cesarean delivery among gravidas with morbid obesity–a machine learning approachAJOG Global Reports at a Glance

Rajasri Kolli, MS; Talayeh Razzaghi, PhD; Stephanie Pierce, MD; Rodney K. Edwards, MD; Marta Maxted, MD, MS; Pavan Parikh, MD, MS

AJOG Global Reports (Nov 2023)

Predicting cesarean delivery among gravidas with morbid obesity–a machine learning approachAJOG Global Reports at a Glance

Rajasri Kolli, MS,
Talayeh Razzaghi, PhD,
Stephanie Pierce, MD,
Rodney K. Edwards, MD,
Marta Maxted, MD, MS,
Pavan Parikh, MD, MS

Affiliations

Rajasri Kolli, MS: Data Science and Analytics Institute, University of Oklahoma, Norman, OK (Ms Kolli and Dr Razzaghi)
Talayeh Razzaghi, PhD: Data Science and Analytics Institute, University of Oklahoma, Norman, OK (Ms Kolli and Dr Razzaghi); School of Industrial and Systems Engineering, University of Oklahoma, Norman, OK (Dr Razzaghi)
Stephanie Pierce, MD: Department of Obstetrics and Gynecology, University of Oklahoma Health Sciences Center, Oklahoma City, OK (Drs Pierce, Edwards, Maxted, and Parikh)
Rodney K. Edwards, MD: Department of Obstetrics and Gynecology, University of Oklahoma Health Sciences Center, Oklahoma City, OK (Drs Pierce, Edwards, Maxted, and Parikh)
Marta Maxted, MD, MS: Department of Obstetrics and Gynecology, University of Oklahoma Health Sciences Center, Oklahoma City, OK (Drs Pierce, Edwards, Maxted, and Parikh)
Pavan Parikh, MD, MS: Department of Obstetrics and Gynecology, University of Oklahoma Health Sciences Center, Oklahoma City, OK (Drs Pierce, Edwards, Maxted, and Parikh); Department of Obstetrics and Gynecology, Oregon Health Science University, Portland, OR (Dr Parikh).; Corresponding author: Pavan Parikh, MD, MS

Journal volume & issue: Vol. 3, no. 4
p. 100276

Abstract

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BACKGROUND: Women with obesity have higher rates of complications following cesarean delivery, such as wound infection and endometritis, with risks being the highest if a cesarean delivery is performed after labor. Previous efforts at predicting whether a patient's labor course would ultimately result in cesarean delivery have been intermediate with area under the curve in the 0.75 to 78 range. OBJECTIVE: This study aimed to assess whether machine learning algorithms would outperform traditional modeling in developing a cesarean delivery prediction model among gravidas with morbid obesity (body mass index of ≥40 kg/m2) to determine whether a primary cesarean delivery may be beneficial. STUDY DESIGN: This was a secondary analysis of a retrospective cohort of 1298 patients with morbid obesity presenting for vaginal delivery at ≥37 weeks of gestation between 2011 and 2016 at a single institution. Data available at the time of admission and delivery were modeled using logistic regression, decision tree, random forest, and support vector modeling with evaluation of area under the curve, accuracy, sensitivity, and specificity. RESULTS: Logistic regression demonstrated an area under the curve of 0.816 (95% confidence interval, 0.810–0.817), which was superior to machine learning models when evaluating data at the time of delivery (demographic data, initial cervical examinations, comorbidities, and obstetrical interventions) (P<.001). However, there was no significant difference between most machine learning models and logistic regression area under the curve of 0.799 (95% confidence interval, 0.795–0.804) when evaluating parameters available at the time of admission (demographic data, initial cervical examinations, and comorbidities). Race was noted to be a significant predictor in both models (P<.001). CONCLUSION: Machine learning and traditional modeling techniques are likely equivalent concerning cesarean delivery prediction in this population. The models developed showed good discrimination and may be used to guide clinical decision-making concerning the optimal mode of delivery.

Published in AJOG Global Reports

ISSN: 2666-5778 (Online)
Publisher: Elsevier
Country of publisher: United States
LCC subjects: Medicine: Gynecology and obstetrics
Website: https://www.journals.elsevier.com/ajog-global-reports

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