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Maternal cardiovascular disease risk factors as predictors of preterm birth in California: a case-control study.

ABSTRACT: OBJECTIVE:To determine whether maternal cardiovascular disease (CVD) risk factors predict preterm birth. DESIGN:Case control. SETTING:California hospitals. PARTICIPANTS:868 mothers with linked demographic information and biospecimens who delivered singleton births from July 2009 to December 2010. METHODS:Logistic regression analysis was employed to calculate odds ratios for the associations between maternal CVD risk factors before and during pregnancy (including diabetes, hypertensive disorders and cholesterol levels) and preterm birth outcomes. PRIMARY OUTCOME:Preterm delivery status. RESULTS:Adjusting for the other maternal CVD risk factors of interest, all categories of hypertension led to increased odds of preterm birth, with the strongest magnitude observed in the pre-eclampsia group (adjusted OR (aOR), 13.49; 95%?CI 6.01 to 30.27 for preterm birth; aOR, 10.62; 95%?CI 4.58 to 24.60 for late preterm birth; aOR, 17.98; 95%?CI 7.55 to 42.82 for early preterm birth) and chronic hypertension alone for early preterm birth (aOR, 4.58; 95%?CI 1.40 to 15.05). Diabetes (types 1 and 2 and gestational) was also associated with threefold increased risk for preterm birth (aOR, 3.06; 95%?CI 1.12 to 8.41). A significant and linear dose response was found between total and low-density lipoprotein (LDL) cholesterol and aORs for late and early preterm birth, with increasing cholesterol values associated with increased risk (likelihood ?2 differences of 8.422 and 8.019 for total cholesterol for late and early, and 9.169 and 10.896 for LDL for late and early, respectively). Receiver operating characteristic curves using these risk factors to predict late and early preterm birth produced C statistics of 0.601 and 0.686. CONCLUSION:Traditional CVD risk factors are significantly associated with an increased risk of preterm birth; these findings reinforce the clinical importance of integrating obstetric and cardiovascular risk assessment across the healthcare continuum in women.

SUBMITTER: Rohlfing AB

PROVIDER: S-EPMC7282308 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature

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Maternal cardiovascular disease risk factors as predictors of preterm birth in California: a case-control study.

Rohlfing Anne B AB Nah Gregory G Ryckman Kelli K KK Snyder Brittney D BD Kasarek Deborah D Paynter Randi A RA Feuer Sky K SK Jelliffe-Pawlowski Laura L Parikh Nisha I NI

BMJ open 20200603 6

<h4>Objective</h4>To determine whether maternal cardiovascular disease (CVD) risk factors predict preterm birth.<h4>Design</h4>Case control.<h4>Setting</h4>California hospitals.<h4>Participants</h4>868 mothers with linked demographic information and biospecimens who delivered singleton births from July 2009 to December 2010.<h4>Methods</h4>Logistic regression analysis was employed to calculate odds ratios for the associations between maternal CVD risk factors before and during pregnancy (includi ...[more]

PMID: 32499261

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Project description:BackgroundEarly neonatal death caused by preterm birth contributes the most for perinatal death. The prevalence of preterm birth continues to rise and is a significant public health problem. The exact cause of preterm birth is yet unanswered, as mostly preterm birth happens spontaneously. Predictors of preterm birth in developing countries like Ethiopia were not well investigated, and no study was conducted before this in the study area.ObjectivesTo identify predictors of preterm birth in Western Ethiopia, 2017/2018.MethodsHealth facility-based unmatched case-control study was conducted from October 20/2017-march 20/2018 in 4 Hospitals. A total sample size of 358 women was recruited. From this 72 were cases and 286 were controls. Cases were mothers who gave Preterm birth, and controls were mothers who gave birth at term. Ethical clearance was obtained from Wollega University ethical review committee. A pre tested, structured questionnaire was used to collect data. Data entry and analysis was done using Epi Data 3.1 and SPSS version 21, respectively. Logistic regression was done to identify predictors of preterm birth.ResultThree hundred fifty-eight women participated in this study of which 72 were cases and 286 were controls; making the overall response rate of 100%. Lack of antenatal care visit [AOR = 3.18, 95% CI 1.37-7.38]),(Having 1-2 antenatal care visit [AOR = 2.27, 95% CI 1.18-4.35]),history of previous preterm)[AOR = 5.19, 95% CI1.29-20.88],Short Interpregnancy Interval [AOR = 4.41.95% CI 2.05-9.47],Having Reproductive tract infections [AOR = 2.54, 95% CI 1.02-6.32] and having Obstetric complications [AOR = 2.48,95% CI 1.31-4.71] were found to be predictors of preterm birth.Conclusion and recommendationRisk factors of preterm delivery are multifactorial and depend on geographical and demographic features of the population studied. Hence results of studies from one area might not be applicable to another area. Antenatal care visits are unique opportunities for early diagnosis and treatment of problems. Therefore, antenatal care should be strengthened, and appropriate counseling should be given at each antenatal care follow up. Maintainning optimum birth interval through family planning, and early identification and treatment of reproductive tract infections are mandatory.

Project description:BackgroundPreterm birth is serious public health worldwide, and early prediction of preterm birth in pregnant women may provide assistance for timely intervention and reduction of preterm birth. This study aimed to develop a preterm birth prediction model that is readily available and convenient for clinical application.MethodsData used in this case-control study were extracted from the National Vital Statistics System (NVSS) database between 2018 and 2019. Univariate and multivariate logistic regression analyses were utilized to find factors associated with preterm birth. Odds ratio (OR) and 95% confidence interval (CI) were used as effect measures. The area under the curve (AUC), accuracy, sensitivity, and specificity were utilized as model performance evaluation metrics.ResultsData from 3,006,989 pregnant women in 2019 and 3,039,922 pregnant women in 2018 were used for the model establishment and external validation, respectively. Of these 3,006,989 pregnant women, 324,700 (10.8%) had a preterm birth. Higher education level of pregnant women [bachelor (OR = 0.82; 95%CI, 0.81-0.84); master or above (OR = 0.82; 95%CI, 0.81-0.83)], pre-pregnancy overweight (OR = 0.96; 95%CI, 0.95-0.98) and obesity (OR = 0.94; 95%CI, 0.93-0.96), and prenatal care (OR = 0.48; 95%CI, 0.47-0.50) were associated with a reduced risk of preterm birth, while age ≥ 35 years (OR = 1.27; 95%CI, 1.26-1.29), black race (OR = 1.26; 95%CI, 1.23-1.29), pre-pregnancy underweight (OR = 1.26; 95%CI, 1.22-1.30), pregnancy smoking (OR = 1.27; 95%CI, 1.24-1.30), pre-pregnancy diabetes (OR = 2.08; 95%CI, 1.99-2.16), pre-pregnancy hypertension (OR = 2.22; 95%CI, 2.16-2.29), previous preterm birth (OR = 2.95; 95%CI, 2.88-3.01), and plurality (OR = 12.99; 95%CI, 12.73-13.24) were related to an increased risk of preterm birth. The AUC and accuracy of the prediction model in the testing set were 0.688 (95%CI, 0.686-0.689) and 0.762 (95%CI, 0.762-0.763), respectively. In addition, a nomogram based on information on pregnant women and their spouses was established to predict the risk of preterm birth in pregnant women.ConclusionsThe nomogram for predicting the risk of preterm birth in pregnant women had a good performance and the relevant predictors are readily available clinically, which may provide a simple tool for the prediction of preterm birth.

Dataset Information

Maternal cardiovascular disease risk factors as predictors of preterm birth in California: a case-control study.

Publications

Maternal cardiovascular disease risk factors as predictors of preterm birth in California: a case-control study.

Similar Datasets

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