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Protocol for development and validation of a clinical prediction model for adverse pregnancy outcomes in women with gestational diabetes.

ABSTRACT: INTRODUCTION:Gestational diabetes (GDM) is a common yet highly heterogeneous condition. The ability to calculate the absolute risk of adverse pregnancy outcomes for an individual woman with GDM would allow preventative and therapeutic interventions to be delivered to women at high-risk, sparing women at low-risk from unnecessary care. The Prediction for Risk-Stratified care for women with GDM (PeRSonal GDM) study will develop, validate and evaluate the clinical utility of a prediction model for adverse pregnancy outcomes in women with GDM. METHODS AND ANALYSIS:We undertook formative research to conceptualise and design the prediction model. Informed by these findings, we will conduct a model development and validation study using a retrospective cohort design with participant data collected as part of routine clinical care across three hospitals. The study will include all pregnancies resulting in births from 1 July 2017 to 31 December 2018 coded for a diagnosis of GDM (estimated sample size 2430 pregnancies). We will use a temporal split-sample development and validation strategy. A multivariable logistic regression model will be fitted. The performance of this model will be assessed, and the validated model will also be evaluated using decision curve analysis. Finally, we will explore modes of model presentation suited to clinical use, including electronic risk calculators. ETHICS AND DISSEMINATION:This study was approved by the Human Research Ethics Committee of Monash Health (RES-19-0000713?L). We will disseminate results via presentations at scientific meetings and publication in peer-reviewed journals. TRIAL REGISTRATION DETAILS:Systematic review proceeding this work was registered on PROSPERO (CRD42019115223) and the study was registered on the Australian and New Zealand Clinical Trials Registry (ACTRN12620000915954); Pre-results.

SUBMITTER: Cooray SD

PROVIDER: S-EPMC7646337 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature

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Protocol for development and validation of a clinical prediction model for adverse pregnancy outcomes in women with gestational diabetes.

Cooray Shamil D SD Boyle Jacqueline A JA Soldatos Georgia G Zamora Javier J Fernández Félix Borja M BM Allotey John J Thangaratinam Shakila S Teede Helena J HJ

BMJ open 20201105 11

<h4>Introduction</h4>Gestational diabetes (GDM) is a common yet highly heterogeneous condition. The ability to calculate the absolute risk of adverse pregnancy outcomes for an individual woman with GDM would allow preventative and therapeutic interventions to be delivered to women at high-risk, sparing women at low-risk from unnecessary care. The Prediction for Risk-Stratified care for women with GDM (PeRSonal GDM) study will develop, validate and evaluate the clinical utility of a prediction mo ...[more]

PMID: 33154055

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Project description:AimsTo explore the association between higher serum ferritin (SF) levels in mid-pregnancy and adverse pregnancy outcomes in gestational diabetes mellitus (GDM) pregnancies, then develop a predictive cut-off value that might effectively predict the risk of adverse pregnancy outcomes in future clinical.MethodsThe study involved 201 pregnant women with GDM. 201 gestational age and parity matched normoglycemic pregnant women were taken as control group. The differences in clinical data were compared by the Mann-Whitney U test and Chi-square tests. Multivariate logistic regression was used to determine the relationship between SF and GDM-relate adverse pregnancy outcomes. The predicted value of SF level was determined through receiver operating characteristic (ROC) curve analysis.ResultsSF level was significantly higher in women with GDM [16.10 (27.30-9.50) (ng/mL) vs. 12.04 (18.11-7.06) (ng/mL), (p < 0.001)]. Meanwhile, higher levels of SF were also discovered in GDM women with preeclampsia and neonatal hypoglycemia and respiratory distress (all p < 0.05). In the adjusted model, a positive association was shown between SF and preeclampsia [adjusted odds ratio (AOR) = 1.032, 95%CI = 1.004-1.060, p = 0.024], neonatal hypoglycemia [adjusted odds ratio (AOR) = 1.047, 95%CI = 1.022-1.072, p < 0.001] and respiratory distress outcomes (AOR = 1.034, 95%CI = 1.011-1.058, p = 0.004) respectively. The area under ROC curve (AUC) for prediction of preeclampsia by SF combined with serum calcium, age, pre-pregnancy BMI and gestational weight gain (GWG) was 0.658 (95% CI = 50.8-80.8%, p = 0.028) with the cut-off value of 24.45 ng/mL, and the sensitivity and specificity were 58.8.0% and 64.3%, respectively. To predict neonatal hypoglycemia, the clinical point value of SF was 27.43 ng/mL with AUC was 0.800, sensitivity and specificity was 90.5% and 68.0% respectively. Predicting neonatal respiratory distress, the AUC value of the SF level was 0.730, with a cut-off value of 27.37 ng/mL and the sensitivity and specificity were 52.0% and 86.5%, respectively.ConclusionsHigher level of SF in mid-pregnancy was significantly associated with the risk of GDM and GDM-relate adverse pregnancy outcomes. Moreover, SF levels have moderate clinical value in predicting the adverse outcomes of maternal preeclampsia, neonatal hypoglycemia and respiratory distress.

Project description:Implementation of dietary and lifestyle interventions prior to and early in pregnancy in high risk women has been shown to reduce the risk of gestational diabetes mellitus (GDM) development later in pregnancy. Although numerous risk factors for GDM have been identified, the ability to accurately identify women before or early in pregnancy who could benefit most from these interventions remains limited. As nulliparous women are an under-screened population with risk profiles that differ from their multiparous counterparts, development of a prediction model tailored to nulliparous women may facilitate timely preventive intervention and improve maternal and infant outcomes. We aimed to develop and validate a model for preconception and early pregnancy prediction of gestational diabetes mellitus based on clinical risk factors for nulliparous women. A risk prediction model was built within a large California birth cohort including singleton live birth records from 2007-2012. Model accuracy was assessed both internally and externally, within a cohort of women who delivered at University of Iowa Hospitals and Clinics between 2009-2017, using discrimination and calibration. Differences in predictive accuracy of the model were assessed within specific racial/ethnic groups. The prediction model included five risk factors: race/ethnicity, age at delivery, pre-pregnancy body mass index, family history of diabetes, and pre-existing hypertension. The area under the curve (AUC) for the California internal validation cohort was 0.732 (95% confidence interval (CI) 0.728, 0.735), and 0.710 (95% CI 0.672, 0.749) for the Iowa external validation cohort. The model performed particularly well in Hispanic (AUC 0.739) and Black women (AUC 0.719). Our findings suggest that estimation of a woman's risk for GDM through model-based incorporation of risk factors accurately identifies those at high risk (i.e., predicted risk >6%) who could benefit from preventive intervention encouraging prompt incorporation of this tool into preconception and prenatal care.

Project description:During pregnancy, many women gain excessive weight, which is related to adverse maternal and neonatal outcomes. In this study, we evaluated whether rate of gestational weight gain (RGWG) in early, mid, and late pregnancy is strongly associated with adverse pregnancy outcomes. A retrospective chart review of 2,789 pregnant Korean women was performed. Weights were recorded at the first clinic visit, during the screening test for fetal anomaly, and during the 50g oral glucose challenge test and delivery, to represent early, mid, and late pregnancy, respectively. A multivariate logistic regression analysis was performed to examine the relationship between RGWG and adverse pregnancy outcomes. At early pregnancy, the RGWG was significantly associated with high risk of developing gestational diabetes mellitus (GDM), pregnancy-induced hypertension (PIH), large for gestational age (LGA) infants, macrosomia, and primary cesarean section (P-CS). The RGWG of mid pregnancy was not significantly associated with any adverse pregnancy outcomes. The RGWG at late pregnancy was significantly associated with a lower risk of developing GDM, preterm birth and P-CS, but with a higher risk of developing LGA infants and macrosomia. When the subjects were divided into three groups (Underweight, Normal, and Obese), based on pre-pregnancy body mass index (BMI), the relationship between early RGWG and adverse pregnancy outcomes was significantly different across the three BMI groups. At early pregnancy, RGWG was not significantly associated to adverse pregnancy outcomes for subjects in the Underweight group. In the Normal group, however, early RGWG was significantly associated with GDM, PIH, LGA infants, macrosomia, P-CS, and small for gestational weight (SGA) infants, whereas early RGWG was significantly associated with only a high risk of PIH in the Obese group. The results of our study suggest that early RGWG is significantly associated with various adverse pregnancy outcomes and that proper preemptive management of early weight gain, particularly in pregnant women with a normal or obese pre-pregnancy BMI, is necessary to reduce the risk of developing adverse pregnancy outcomes.

Project description:ObjectiveTo investigate the association between gestational diabetes mellitus and adverse outcomes of pregnancy after adjustment for at least minimal confounding factors.DesignSystematic review and meta-analysis.Data sourcesWeb of Science, PubMed, Medline, and Cochrane Database of Systematic Reviews, from 1 January 1990 to 1 November 2021.Review methodsCohort studies and control arms of trials reporting complications of pregnancy in women with gestational diabetes mellitus were eligible for inclusion. Based on the use of insulin, studies were divided into three subgroups: no insulin use (patients never used insulin during the course of the disease), insulin use (different proportions of patients were treated with insulin), and insulin use not reported. Subgroup analyses were performed based on the status of the country (developed or developing), quality of the study, diagnostic criteria, and screening method. Meta-regression models were applied based on the proportion of patients who had received insulin.Results156 studies with 7 506 061 pregnancies were included, and 50 (32.1%) showed a low or medium risk of bias. In studies with no insulin use, when adjusted for confounders, women with gestational diabetes mellitus had increased odds of caesarean section (odds ratio 1.16, 95% confidence interval 1.03 to 1.32), preterm delivery (1.51, 1.26 to 1.80), low one minute Apgar score (1.43, 1.01 to 2.03), macrosomia (1.70, 1.23 to 2.36), and infant born large for gestational age (1.57, 1.25 to 1.97). In studies with insulin use, when adjusted for confounders, the odds of having an infant large for gestational age (odds ratio 1.61, 1.09 to 2.37), or with respiratory distress syndrome (1.57, 1.19 to 2.08) or neonatal jaundice (1.28, 1.02 to 1.62), or requiring admission to the neonatal intensive care unit (2.29, 1.59 to 3.31), were higher in women with gestational diabetes mellitus than in those without diabetes. No clear evidence was found for differences in the odds of instrumental delivery, shoulder dystocia, postpartum haemorrhage, stillbirth, neonatal death, low five minute Apgar score, low birth weight, and small for gestational age between women with and without gestational diabetes mellitus after adjusting for confounders. Country status, adjustment for body mass index, and screening methods significantly contributed to heterogeneity between studies for several adverse outcomes of pregnancy.ConclusionsWhen adjusted for confounders, gestational diabetes mellitus was significantly associated with pregnancy complications. The findings contribute to a more comprehensive understanding of the adverse outcomes of pregnancy related to gestational diabetes mellitus. Future primary studies should routinely consider adjusting for a more complete set of prognostic factors.Review registrationPROSPERO CRD42021265837.

Project description:BackgroundContinuous glucose monitoring (CGM) has shown potential in improving maternal and neonatal outcomes in individuals with type 1/2 diabetes, but data in gestational diabetes mellitus (GDM) is limited. We aimed to explore the relationship between CGM-derived metrics during pregnancy and pregnancy outcomes among women with GDM.MethodsWe recruited 1302 pregnant women with GDM at a mean gestational age of 26.0 weeks and followed them until delivery. Participants underwent a 14-day CGM measurement upon recruitment. The primary outcome was any adverse pregnancy outcome, defined as having at least one of the outcomes: preterm birth, large-for-gestational-age (LGA) birth, fetal distress, premature rupture of membranes, and neonatal intensive care unit (NICU) admission. The individual outcomes included in the primary outcome were considered as secondary outcomes. We conducted multivariable logistic regression to evaluate the association of CGM-derived metrics with these outcomes.FindingsPer 1-SD difference in time above range (TAR), glucose area under the curve (AUC), nighttime mean blood glucose (MBG), daytime MBG, and daily MBG was associated with higher risk of any adverse pregnancy outcome, with odds ratio: 1.22 (95% CI 1.08-1.36), 1.22 (95% CI 1.09-1.37), 1.18 (95% CI 1.05-1.32), 1.21 (95% CI 1.07-1.35), and 1.22 (95% CI 1.09-1.37), respectively. Time in range, TAR, AUC, nighttime MBG, daytime MBG, daily MBG, and mean amplitude of glucose excursions were positively associated, while time blow range was inversely associated with the risk of LGA. Additionally, higher value for TAR was associated with higher risk of NICU admission. We further summarized the potential thresholds of TAR (2.5%) and daily MBG (4.8 mmol/L) to distinguish individuals with and without any adverse pregnancy outcome.InterpretationThe CGM-derived metrics may help identify individuals at higher risk of adverse pregnancy outcomes. These CGM biomarkers could serve as potential new intervention targets to maintain a healthy pregnancy status among women with GDM.FundingNational Key R&D Program of China, National Natural Science Foundation of China, and Westlake Laboratory of Life Sciences and Biomedicine.

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Protocol for development and validation of a clinical prediction model for adverse pregnancy outcomes in women with gestational diabetes.

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Protocol for development and validation of a clinical prediction model for adverse pregnancy outcomes in women with gestational diabetes.

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