Association of uterine rupture with pregestational diabetes in women undergoing trial of labor after cesarean delivery.
Rodney A McLarenChima NdubizuFouad AtallahHoward MinkoffPublished in: The journal of maternal-fetal & neonatal medicine : the official journal of the European Association of Perinatal Medicine, the Federation of Asia and Oceania Perinatal Societies, the International Society of Perinatal Obstetricians (2019)
Objective: To evaluate the association of pregestational diabetes with uterine rupture during a trial of labor with one prior cesarean delivery.Study design: A retrospective study of women undergoing a trial of labor after cesarean. The study group consisted of women with pregestational diabetes and the control group was women without pregestational diabetes. Primary outcome was a uterine rupture. Data were extracted from the USA. Natality Database from 2012 to 2016. Maternal and neonatal outcomes were analyzed. Multivariable logistic regression analysis was used to estimate risks of uterine rupture and maternal and neonatal outcomes.Results: There were 359,504 women undergoing labor after cesarean, with 3508 women with pregestational diabetes and 355,996 without. The prevalence of uterine rupture among women with pregestational diabetes undergoing labor after cesarean was 0.5%, while among women without pregestational diabetes, it was 0.2% (adjusted odds ratio [OR] 2.03 [95% CI 1.18-3.51]; p = .01). There was an increased risk of unplanned hysterectomy among pregnancies complicated by pregestational diabetes (adjusted OR 3.06 [95% CI 1.41-6.66]).Conclusion: Women undergoing a trial of labor, who have pregestational diabetes had a higher rate of uterine rupture than women without a history of pregestational diabetes.
Keyphrases
- type diabetes
- cardiovascular disease
- glycemic control
- polycystic ovary syndrome
- pregnancy outcomes
- clinical trial
- study protocol
- cervical cancer screening
- pregnant women
- risk factors
- emergency department
- adipose tissue
- metabolic syndrome
- breast cancer risk
- climate change
- deep learning
- skeletal muscle
- phase ii
- human health
- data analysis
- open label