No abstract

Worldwide, obesity has been shown to negatively affect women especially during pregnancy. In this work, a retrospective cohort study for 1308 women, who gave birth between 2014 and 2016 in public and private hospitals, was conducted to evaluate the impact of weight, demographic and lifestyle indicators on many pregnancy and fetal outcomes in Northern Lebanon. The frequent health complications related to pregnancy were cesarean-section (31.1%) followed by post-hemorrhage (25.1%), induced labor (23.5%) and macrosomia (11.4%). Multivariate analysis showed that the main complications were highly correlated to obesity, macrosomia, weight gain, multiparity and mother’s age. High values from adjusted odds ratios were mainly associated to obesity, multiparity and weight gain. Obese pregnant women had a significant increased risk of having cesarean-section (p<0.001), preeclampsia (p<0.0001), labor induction (p<0.0001) and postpartum hemorrhage (p<0.0001). Adverse fetal outcomes such as macrosomia were also correlated with high BMI (p<0.0001). The risk was even greater for multiparous, older women that carried excessive weight gain. There is therefore a need to increase awareness among the target population and encourage prevention of the dangers related to obesity and weight gain.

Preterm Birth (PTB) can negatively affect the health of mothers as well as infants. Prediction of this gynecological complication remains difficult especially in Middle and Low-Income countries because of limited access to specific tests and data collection scarcity. Machine learning methods have been used to predict PTB but the low prevalence of this pregnancy complication led to rather low prediction values. The objective of this study was to produce a nomogram based on improved prediction for low prevalence PTB using up sampling and lasso penalized regression. We used data from a cohort study in Northern Lebanon of 922 multiparous presenting a PTB prevalence of 8%. We analyzed the personal, demographic, and health indicators available for this group of women. The improved Positive Predictive Value for PTB reached around 88%. The regression coefficients of the 6 selected variables (Pre-hemorrhage, Social status, Residence, Age, BMI, and Weight gain) were used to create a nomogram to screen multiparous women for PTB risk. The nomogram based on readily available indicators for multiparous women reasonably predicted most of the at PTB risk women. The physicians can use this tool to screen for women at high risk for spontaneous preterm birth to improve medical surveillance that can reduce PTB incidence.

Background: Preterm Birth (PTB) can negatively affect the health of mothers as well as infants. Prediction of this gynecological complication remains difficult especially in Middle and Low-Income countries because of limited access to specific tests and data collection scarcity. Multiparous women in our study presented a higher PTB prevalence compared to nulliparous women. Methods: In a cohort study from Northern Lebanon of 1996 women, 922 were multiparous presenting a PTB prevalence of 8%. We analyzed the personal, demographic, and health indicators available for this group of women. We compared 4 modified logistic regression models (up-sampling, lasso penalized regression) to develop a nomogram that can screen for preterm in multi-parous women. The models were trained and validated on different data sets.Results: The best PTB prediction of the Logistic regression model reached around 88%. This was obtained using a Logistic Regression Model trained on up-sampled datasets and LASSO (Least Absolute Shrinkage and Selection Operator) penalized. The regression coefficients of the 6 selected variables (Pre-hemorrhage, Social status, Residence, Age, BMI, and Weight gain) were used to create a nomogram to screen multiparous women for PTB risk. Conclusions: The nomogram based on readily available indicators for multiparous women reasonably predicted most of the at PTB risk women. This tool will allow physicians to screen women that represent a high risk for spontaneous preterm birth and run furthermore adequate additional tests leading to better medical surveillance that can reduce PTB incidence.