Abstract
Abstract
Background
Dystocia is a common obstetric complication among nulliparous women, which requires medical intervention and carries the risk of negative maternal and neonatal outcomes. Our aim was to examine the association between body mass index (BMI) and the occurrence of dystocia. We also identified cutoffs of gestational weight gain, based on pre-pregnancy BMI, associated with the risk of dystocia.
Methods
This was a multicenter, retrospective, cohort study conducted in two tertiary Maternal–Fetal medicine units in Fukushima, Japan. The study population included nullipara women who delivered at either of the two units between January 1, 2013, and December 31, 2020. Women (n = 2597) were categorized into six groups (G) based on their pre-pregnancy BMI: G1 (< 18.5 kg/m2), G2 (18.5 to < 20.0 kg/m2), G3 (20.0 to < 23.0 kg/m2), G4 (23.0 to < 25.0 kg/m2), G5 (25.0 to < 30.0 kg/m2), and G6 (≥ 30.0 kg/m2). Using G3 as a reference, multiple logistic regression analyses were performed to estimate the risk of dystocia for each BMI category. Receiver operating characteristic curve analyses were performed to determine the cutoff value of gestational weight gain for the risk of dystocia.
Results
The highest BMI category (G6) was an independent risk factor for dystocia (adjusted odds ratio, 3.0; 95% confidence interval, 1.5–5.8). The receiver operating characteristic curve analysis revealed no association between gestational weight gain and the occurrence of dystocia in G5 and G6 (P = 0.446 and P = 0.291, respectively). For G1 to G4, AUC and predictive cutoffs of gestational weight gain for dystocia were as follows: G1, AUC 0.64 and cutoff 11.5 kg (P < 0.05); G2, AUC 0.63 and cutoff 12.3 kg (P < 0.05); G3, AUC 0.67 and cutoff 14.3 kg (P < 0.01); and G4, AUC 0.63 and cutoff 11.5 kg (P < 0.05).
Conclusion
A pre-pregnancy BMI > 30.0 kg/m2 was an independent risk factor for dystocia. For women with a pre-pregnancy BMI < 25.0 kg/m2, the risk of dystocia increases as a function of gestational weight gain. These findings could inform personalized preconception care for women to optimize maternal and neonatal health.
Publisher
Springer Science and Business Media LLC
Subject
Obstetrics and Gynecology
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