Dataset Information

Development and validation of a clinical model for preconception and early pregnancy risk prediction of gestational diabetes mellitus in nulliparous women.

ABSTRACT: 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.

SUBMITTER: Donovan BM

PROVIDER: S-EPMC6461273 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Development and validation of a clinical model for preconception and early pregnancy risk prediction of gestational diabetes mellitus in nulliparous women.

Donovan Brittney M BM Breheny Patrick J PJ Robinson Jennifer G JG Baer Rebecca J RJ Saftlas Audrey F AF Bao Wei W Greiner Andrea L AL Carter Knute D KD Oltman Scott P SP Rand Larry L Jelliffe-Pawlowski Laura L LL Ryckman Kelli K KK

PloS one 20190412 4

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 thei ...[more]

PMID: 30978258

Similar Datasets

Project description:ObjectiveMost small for gestational age pregnancies are unrecognised before birth, resulting in substantial avoidable perinatal mortality and morbidity. Our objective was to develop multivariable prediction models for small for gestational age combining clinical risk factors and biomarkers at 15±1 weeks' with ultrasound parameters at 20±1 weeks' gestation.MethodsData from 5606 participants in the Screening for Pregnancy Endpoints (SCOPE) cohort study were divided into Training (n = 3735) and Validation datasets (n = 1871). The primary outcomes were All-SGA (small for gestational age with birthweight <10th customised centile), Normotensive-SGA (small for gestational age with a normotensive mother) and Hypertensive-SGA (small for gestational age with an hypertensive mother). The comparison group comprised women without the respective small for gestational age phenotype. Multivariable analysis was performed using stepwise logistic regression beginning with clinical variables, and subsequent additions of biomarker and then ultrasound (biometry and Doppler) variables. Model performance was assessed in Training and Validation datasets by calculating area under the curve.Results633 (11.2%) infants were All-SGA, 465(8.2%) Normotensive-SGA and 168 (3%) Hypertensive-SGA. Area under the curve (95% Confidence Intervals) for All-SGA using 15±1 weeks' clinical variables, 15±1 weeks' clinical+ biomarker variables and clinical + biomarkers + biometry /Doppler at 20±1 weeks' were: 0.63 (0.59-0.67), 0.64 (0.60-0.68) and 0.69 (0.66-0.73) respectively in the Validation dataset; Normotensive-SGA results were similar: 0.61 (0.57-0.66), 0.61 (0.56-0.66) and 0.68 (0.64-0.73) with small increases in performance in the Training datasets. Area under the curve (95% Confidence Intervals) for Hypertensive-SGA were: 0.76 (0.70-0.82), 0.80 (0.75-0.86) and 0.84 (0.78-0.89) with minimal change in the Training datasets.ConclusionModels for prediction of small for gestational age, which combine biomarkers, clinical and ultrasound data from a cohort of low-risk nulliparous women achieved modest performance. Incorporation of biomarkers into the models resulted in no improvement in performance of prediction of All-SGA and Normotensive-SGA but a small improvement in prediction of Hypertensive-SGA. Our models currently have insufficient reliability for application in clinical practice however, they have potential utility in two-staged screening tests which include third trimester biomarkers and or fetal biometry.

Project description:To compare intrapartum results associated with differing degrees of ketonuria in nulliparous women with gestational diabetes mellitus (GDM), we implemented a retrospective cohort study comparing clinical characteristics among differing degrees of ketonuria and the duration and distribution of ketonuria at different stages of labor. We also analyzed adverse maternal and neonatal outcomes for each group. A total of 570 GDM deliveries were included; of these, 238 had negative ketonuria (41.8%), 180 had moderate ketonuria (31.6%), and 152 had ketosis (26.6%). The proportion of patients with a family history of diabetes significantly increased as the degree of ketonuria increased (P < 0.001). Moreover, a significantly lower level of HOMA-IR (the insulin resistance index) was observed for the Negative group (P < 0.001). The triglyceride (TG) level was significantly higher in the Ketosis group (P < 0.001), and the total cholesterol (TC) levels significantly increased as the degree of ketonuria progressed (P < 0.001). There were also higher maternal blood sugar levels and a significantly higher proportion of oxytocin augmentation in ketonuria cases (P < 0.001). Over three-fourths of patients (75.6%) had a ketonuria duration of ≤2 hours in the Moderate group, 61.2% had a ketonuria duration of between 3 and 4 h in the Ketosis group, and most of the ketonuria cases resolved in the first stage of labor. As the degree of ketonuria progressed, we observed a significantly higher number of cases with fetal heart rate pattern III (FHR pattern III), meconium-stained amniotic fluid III (MSAF III), postpartum hemorrhages, prolonged labor, neonatal hypoglycemia, an umbilical cord arterial pH of <7.2, low Apgar scores, increased neonatal intensive care admissions, augmented forceps-assisted deliveries, and conversions to cesarean sections. The results showed that ketonuria is common during the intrapartum period and that the risk of adverse maternal and neonatal outcomes may increase when complicated with ketonuria.

Dataset Information

Development and validation of a clinical model for preconception and early pregnancy risk prediction of gestational diabetes mellitus in nulliparous women.

Publications

Development and validation of a clinical model for preconception and early pregnancy risk prediction of gestational diabetes mellitus in nulliparous women.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets