Association of pre-pregnancy body mass index and gestational weight gain on postpartum pelvic floor muscle morphology and function in Chinese primiparous women: A retrospective cohort study.

OBJECTIVE: Our study aimed to investigate the association between maternal pre-pregnancy body mass index (BMI), gestational weight gain (GWG), and impaired pelvic floor muscle (PFM) morphology and function during the early postpartum period. METHODS: This retrospective cohort study was conducted at Shanghai First Maternity and Infant Hospital from December 2020 to December 2022. A total of 1118 primiparous women with singleton pregnancies who underwent vaginal deliveries and participated in postpartum PFM assessments were included. Maternal pre-pregnancy BMI and GWG were considered as exposures. PFM morphology and function impairment were the primary outcomes. PFM morphology impairment, defined as levator ani muscle avulsion, was assessed using transperineal ultrasound. PFM function impairment, manifested as diminished PFM fiber strength, was assessed through vaginal manometry. Multivariable logistic regression analysis was employed to calculate adjusted odds ratios (aOR) with 95% confidence intervals (CI). Restricted cubic spline models were used to validate and visualize the relationship. RESULTS: Women with lower pre-pregnancy BMI were at an increased risk of levator ani muscle avulsion (aOR = 1.73, 95% CI: 1.10-2.70, P = 0.017), particularly when combined with excessive GWG during pregnancy (aOR = 3.20, 95% CI: 1.15-8.97, P = 0.027). Lower pre-pregnancy BMI was also identified as an independent predictor of PFM weakness (aOR = 1.53, 95% CI: 1.08-2.16, P = 0.017 for type I fiber injuries). Notably, regardless of the avulsion status, both underweight and overweight/obese women faced an elevated risk of reduced PFM strength (aOR = 1.74, 95% CI: 1.17-2.59, P = 0.006 for underweight women with type I fiber injuries; aOR = 1.67, 95% CI: 1.06-2.64, P = 0.027; and aOR = 1.73, 95% CI: 1.09-2.76, P = 0.021 for overweight/obese women with type I and type II fibers injuries, respectively). CONCLUSIONS: Both lower and higher pre-pregnancy BMI, as well as excessive GWG, were strongly associated with PFM impairments. These findings highlighted the critical importance of comprehensive weight management throughout pregnancy to effectively promote women's pelvic health.

Prediction of preeclampsia from retinal fundus images via deep learning in singleton pregnancies: a prospective cohort study.

INTRODUCTION: Early prediction of preeclampsia (PE) is of universal importance in controlling the disease process. Our study aimed to assess the feasibility of using retinal fundus images to predict preeclampsia via deep learning in singleton pregnancies. METHODS: This prospective cohort study was conducted at Shanghai First Maternity and Infant Hospital, Tongji University School of Medicine. Eligible participants included singleton pregnancies who presented for prenatal visits before 14 weeks of gestation from September 1, 2020, to February 1, 2022. Retinal fundus images were obtained using a nonmydriatic digital retinal camera during their initial prenatal visit upon admission before 20 weeks of gestation. In addition, we generated fundus scores, which indicated the predictive value of hypertension, using a hypertension detection model. To evaluate the predictive value of the retinal fundus image-based deep learning algorithm for preeclampsia, we conducted stratified analyses and measured the area under the curve (AUC), sensitivity, and specificity. We then conducted sensitivity analyses for validation. RESULTS: Our study analyzed a total of 1138 women, 92 pregnancies developed into hypertension disorders of pregnancy (HDP), including 26 cases of gestational hypertension and 66 cases of preeclampsia. The adjusted odds ratio (aOR) of the fundus scores was 2.582 (95% CI, 1.883-3.616; P  < 0.001). Otherwise, in the categories of prepregnancy BMI less than 28.0 and at least 28.0, the aORs were 3.073 (95%CI, 2.265-4.244; P  < 0.001) and 5.866 (95% CI, 3.292-11.531; P  < 0.001). In the categories of maternal age less than 35.0 and at least 35.0, the aORs were 2.845 (95% CI, 1.854-4.463; P  < 0.001) and 2.884 (95% CI, 1.794-4.942; P  < 0.001). The AUC of the fundus score combined with risk factors was 0.883 (sensitivity, 0.722; specificity, 0.934; 95% CI, 0.834-0.932) for predicting preeclampsia. CONCLUSION: Our study demonstrates that the use of deep learning algorithm-based retinal fundus images offers promising predictive value for the early detection of preeclampsia.