Dataset Information

External validation and clinical utility of prognostic prediction models for gestational diabetes mellitus: A prospective cohort study.

ABSTRACT: INTRODUCTION:We performed an independent validation study of all published first trimester prediction models, containing non-invasive predictors, for the risk of gestational diabetes mellitus. Furthermore, the clinical potential of the best performing models was evaluated. MATERIAL AND METHODS:Systemically selected prediction models from the literature were validated in a Dutch prospective cohort using data from Expect Study I and PRIDE Study. The predictive performance of the models was evaluated by discrimination and calibration. Clinical utility was assessed using decision curve analysis. Screening performance measures were calculated at different risk thresholds for the best model and compared with current selective screening strategies. RESULTS:The validation cohort included 5260 women. Gestational diabetes mellitus was diagnosed in 127 women (2.4%). The discriminative performance of the 12 included models ranged from 68% to 75%. Nearly all models overestimated the risk. After recalibration, agreement between the observed outcomes and predicted probabilities improved for most models. CONCLUSIONS:The best performing prediction models showed acceptable performance measures and may enable more personalized medicine-based antenatal care for women at risk of developing gestational diabetes mellitus compared with current applied strategies.

SUBMITTER: Meertens LJE

PROVIDER: S-EPMC7317858 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

External validation and clinical utility of prognostic prediction models for gestational diabetes mellitus: A prospective cohort study.

Meertens Linda J E LJE Scheepers Hubertina C J HCJ van Kuijk Sander M J SMJ Roeleveld Nel N Aardenburg Robert R van Dooren Ivo M A IMA Langenveld Josje J Zwaan Iris M IM Spaanderman Marc E A MEA van Gelder Marleen M H J MMHJ Smits Luc J M LJM

Acta obstetricia et gynecologica Scandinavica 20200214 7

<h4>Introduction</h4>We performed an independent validation study of all published first trimester prediction models, containing non-invasive predictors, for the risk of gestational diabetes mellitus. Furthermore, the clinical potential of the best performing models was evaluated.<h4>Material and methods</h4>Systemically selected prediction models from the literature were validated in a Dutch prospective cohort using data from Expect Study I and PRIDE Study. The predictive performance of the mod ...[more]

PMID: 31955406

Similar Datasets

Project description:Gestational diabetes mellitus (GDM) is closely related to adverse pregnancy outcomes and other diseases. Early intervention in pregnant women who are at high risk of developing GDM could help prevent adverse health consequences. The study aims to develop a simple model using the stacking ensemble method to predict GDM for women in the first trimester based on easily available factors. We used the data from the Chinese Pregnant Women Cohort Study from July 2017 to November 2018. A total of 6,848 pregnant women in the first trimester were included in the analysis. Logistic regression (LR), random forest (RF), and extreme gradient boosting (XGBoost) were considered as base learners. Optimal feature subsets for each learner were chosen by using recursive feature elimination cross-validation. Then, we built a pipeline to process imbalance data, tune hyperparameters, and evaluate model performance. The learners with the best hyperparameters were employed in the first layer of the proposed stacking method. Their predictions were obtained using optimal feature subsets and served as meta-learner's inputs. Another LR was used as a meta-learner to obtain the final prediction results. Accuracy, specificity, error rate, and other metrics were calculated to evaluate the performance of the models. A paired samples t-test was performed to compare the model performance. In total, 967 (14.12%) women developed GDM. For base learners, the RF model had the highest accuracy (0.638 (95% confidence interval (CI) 0.628-0.648)) and specificity (0.683 (0.669-0.698)) and lowest error rate (0.362 (0.352-0.372)). The stacking method effectively improved the accuracy (0.666 (95% CI 0.663-0.670)) and specificity (0.725 (0.721-0.729)) and decreased the error rate (0.333 (0.330-0.337)). The differences in the performance between the stacking method and RF were statistically significant. Our proposed stacking method based on easily available factors has better performance than other learners such as RF.

Dataset Information

External validation and clinical utility of prognostic prediction models for gestational diabetes mellitus: A prospective cohort study.

Publications

External validation and clinical utility of prognostic prediction models for gestational diabetes mellitus: A prospective cohort study.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets