Dataset Information

Smoking, HIV, and risk of pregnancy loss.

ABSTRACT:

Objective

Cigarette smoking during pregnancy increases risks of poor pregnancy outcomes including miscarriage and stillbirth (pregnancy loss), but the effect of smoking on pregnancy loss among HIV-infected women has not been explored. Here, investigated the impact of smoking on risk of pregnancy loss among HIV-positive and HIV-negative women, and estimated the potential impact of realistic smoking cessation interventions on risk of pregnancy loss among HIV-positive women.

Design

We analyzed pregnancy outcomes in HIV-positive and HIV-negative participants in the Women's Interagency HIV Study between 1994 and 2014.

Methods

We estimated effects of current smoking at or immediately before pregnancy on pregnancy loss; we controlled for confounding using regression approaches, and estimated potential impact of realistic smoking cessation interventions using a semiparametric g-formula approach.

Results

Analysis examined 1033 pregnancies among 659 women. The effect of smoking on pregnancy loss differed dramatically by HIV status: adjusted for confounding, the risk difference comparing current smokers to current nonsmokers was 19.2% (95% confidence limit 10.9-27.5%) in HIV-positive women and 9.7% (95% confidence limit 0.0-19.4%) in HIV-negative women. These results were robust to sensitivity analyses. We estimated that we would need to offer a realistic smoking cessation intervention to 36 women to prevent one pregnancy loss.

Conclusion

Smoking is a highly prevalent exposure with important consequences for pregnancy in HIV-positive pregnant women in the United States, even in the presence of potent highly active antiretroviral therapy. This evidence supports greater efforts to promote smoking cessation interventions among HIV-positive women, especially those who desire to become pregnant.

SUBMITTER: Westreich D

PROVIDER: S-EPMC5263172 | biostudies-literature | 2017 Feb

REPOSITORIES: biostudies-literature

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Publications

Smoking, HIV, and risk of pregnancy loss.

Westreich Daniel D Cates Jordan J Cohen Mardge M Weber Kathleen M KM Seidman Dominika D Cropsey Karen K Wright Rodney R Milam Joel J Young Mary A MA Mehta C Christina CC Gustafson Deborah R DR Golub Elizabeth T ET Fischl Margaret A MA Adimora Adaora A AA

AIDS (London, England) 20170201 4

<h4>Objective</h4>Cigarette smoking during pregnancy increases risks of poor pregnancy outcomes including miscarriage and stillbirth (pregnancy loss), but the effect of smoking on pregnancy loss among HIV-infected women has not been explored. Here, investigated the impact of smoking on risk of pregnancy loss among HIV-positive and HIV-negative women, and estimated the potential impact of realistic smoking cessation interventions on risk of pregnancy loss among HIV-positive women.<h4>Design</h4>W ...[more]

PMID: 27902507

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Project description:BackgroundFor women who have experienced recurrent pregnancy loss (RPL), it is crucial not only to treat them but also to evaluate the risk of recurrence. The study aimed to develop a risk predictive model to predict the subsequent early pregnancy loss (EPL) in women with RPL based on preconception data.MethodsA prospective, dynamic population cohort study was carried out at the Second Hospital of Lanzhou University. From September 2019 to December 2022, a total of 1050 non-pregnant women with RPL were participated. By December 2023, 605 women had subsequent pregnancy outcomes and were randomly divided into training and validation group by 3:1 ratio. In the training group, univariable screening was performed on RPL patients with subsequent EPL outcome. The least absolute shrinkage and selection operator (LASSO) regression and multivariate logistic regression were utilized to select variables, respectively. Subsequent EPL prediction model was constructed using generalize linear model (GLM), gradient boosting machine (GBM), random forest (RF), and deep learning (DP). The variables selected by LASSO regression and multivariate logistic regression were then established and compared using the best prediction model. The AUC, calibration curve, and decision curve (DCA) were performed to assess the prediction performances of the best model. The best model was validated using the validation group. Finally, a nomogram was established based on the best predictive features.ResultsIn the training group, the GBM model achieved the best performance with the highest AUC (0.805). The AUC between the variables screened by the LASSO regression (16-variables) and logistic regression (9-variables) models showed no significant difference (AUC: 0.805 vs. 0.777, P = 0.1498). Meanwhile, the 9-variable model displayed a well discrimination performance in the validation group, with an AUC value of 0.781 (95%CI 0.702, 0.843). The DCA showed the model performed well and was feasible for making beneficial clinical decisions. Calibration curves revealed the goodness of fit between the predicted values by the model and the actual values, the Hosmer-Lemeshow test was 7.427, and P = 0.505.ConclusionsPredicting subsequent EPL in RPL patients using the GBM model has important clinical implications. Future prospective studies are needed to verify the clinical applicability.Trial registrationThis study was registered in the Chinese Clinical Trial Registry with the registration number of ChiCTR2000039414 (27/10/2020).

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Dataset Information

Smoking, HIV, and risk of pregnancy loss.

Objective

Design

Methods

Results

Conclusion

Publications

Smoking, HIV, and risk of pregnancy loss.

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