Background: Numerous prediction models for gestational diabetes mellitus (GDM) have been developed, but their methodological quality is unknown. The objective is to systematically review all studies describing first-trimester prediction models for GDM and to assess their methodological quality. Methods: MEDLINE and EMBASE were searched until December 2014. Key words for GDM, first trimester of pregnancy, and prediction modeling studies were combined. Prediction models for GDM performed up to 14 weeks of gestation that only include routinely measured predictors were eligible. Data was extracted by the CHecklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies (CHARMS). Data on risk predictors and performance measures were also extracted. Each study was scored for risk of bias. Results: Our search yielded 7761 articles, of which 17 were eligible for review (14 development studies and 3 external validation studies). The definition and prevalence of GDM varied widely across studies. Maternal age and body mass index were the most common predictors. Discrimination was acceptable for all studies. Calibration was reported for four studies. Risk of bias for participant selection, predictor assessment, and outcome assessment was low in general. Moderate to high risk of bias was seen for the number of events, attrition, and analysis. Conclusions: Most studies showed moderate to low methodological quality, and few prediction models for GDM have been externally validated. External validation is recommended to enhance generalizability and assess their true value in clinical practice.
Background Maternal body mass index (BMI) below or above the reference interval (18.5–24.9 kg/m2) is associated with adverse pregnancy outcomes. Whether BMI exerts an effect within the reference interval is unclear. Therefore, we assessed the association between adverse pregnancy outcomes and BMI, in particular within the reference interval, in a general unselected pregnant population. Methods Data was extracted from a prospective population-based multicentre cohort (Risk Estimation for PrEgnancy Complications to provide Tailored care (RESPECT) study) conducted between December 2012 to January 2014. BMI was studied in categories (I: <18.5, II: 18.5–19.9, III: 20.0–22.9, IV: 23.0–24.9, V: 25.0–27.4, VI: 27.5–29.9, VII: >30.0 kg/m2) and as a continuous variable within the reference interval. Adverse pregnancy outcomes were defined as composite endpoints for maternal, neonatal or any pregnancy complication, and for adverse pregnancy outcomes individually. Linear trends were assessed using linear-by-linear association analysis and (adjusted) relative risks by regression analysis. Results The median BMI of the 3671 included women was 23.2 kg/m2 (IQR 21.1–26.2). Adverse pregnancy outcomes were reported in 1256 (34.2%). Linear associations were observed between BMI categories and all three composite endpoints, and individually for pregnancy-induced hypertension (PIH), preeclampsia, gestational diabetes mellitus (GDM), large-for-gestational-age (LGA) neonates; but not for small-for-gestational-age neonates and preterm birth. Within the reference interval, BMI was associated with the composite maternal endpoint, PIH, GDM and LGA, with adjusted relative risks of 1.15 (95%CI 1.06–1.26), 1.12 (95%CI 1.00–1.26), 1.31 (95%CI 1.11–1.55) and 1.09 (95%CI 1.01–1.17). Conclusions Graded increase in maternal BMI appears to be an indicator of risk for adverse pregnancy outcomes even among women with a BMI within the reference interval. The extent to which BMI directly contributes to the increased risk in this group should be evaluated in order to determine strategies most valuable for promoting safety and long-term health for mothers and their offspring.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.