The Combined Influence of Maternal Medical Conditions on the Risk of Primary Cesarean Delivery.
Robert FreschKendal StephensElizabeth KellyPublished in: AJP reports (2024)
Background Common maternal medical comorbidities such as hypertensive disorders, diabetes, tobacco use, and extremes of maternal age, body mass index, and gestational weight gain are known individually to influence the rate of cesarean delivery. Numerous studies have estimated the risk of individual conditions on cesarean delivery. Objective To examine the risk for primary cesarean delivery in women with multiple maternal medical comorbidities to determine the cumulative risk they pose on mode of delivery. Study Design In this population-based retrospective cohort study, we analyzed data from Ohio live birth records from 2006 to 2015 to estimate the influence of individual and combinations of maternal comorbidities on rates of singleton primary cesarean delivery. The exposures were individual and combinations of maternal medical conditions (chronic hypertension [CHTN], gestational hypertension, pregestational diabetes, gestational diabetes, tobacco use, advanced maternal age, and maternal obesity) and outcomes were rates and adjusted relative risk (aRR) of primary cesarean delivery. Results There were 1,463,506 live births in Ohio during the study period, of which 882,423 (60.3%) had one or more maternal medical condition, and of those 243,112 (27.6%) had primary cesarean delivery. The range of rates and aRR range of primary cesarean delivery were 13.9 to 29.3% (aRR 0.78-1.68) in singleton pregnancies with a single medical condition, and this increased to 21.9 to 48.6% (aRR 1.34-3.87) in pregnancies complicated by multiple medical comorbidities. The highest risk for primary cesarean occurred in advanced maternal age, obese women with pregestational diabetes, and CHTN. Conclusion A greater number of maternal medical comorbidities during pregnancy is associated with increasing cumulative risk of primary cesarean delivery. These data may be useful in counseling patients on risk of cesarean during pregnancy.
Keyphrases
- birth weight
- weight gain
- gestational age
- pregnancy outcomes
- healthcare
- type diabetes
- pregnant women
- end stage renal disease
- preterm birth
- chronic kidney disease
- adipose tissue
- metabolic syndrome
- machine learning
- electronic health record
- air pollution
- prognostic factors
- hiv infected
- peritoneal dialysis
- hepatitis c virus
- artificial intelligence
- antiretroviral therapy
- data analysis
- case control
- deep learning