OBJECTIVES: To develop and validate a prediction model for gestational diabetes mellitus (GDM) at 11-13 weeks' gestation based on maternal characteristics and history and to compare its performance with the method recommended by the National Institute of Health and Care Excellence (NICE) and five other published prediction models. METHODS: A predictive logistic regression model for GDM was developed from 1,827 cases (2.4%) who developed GDM and 73,334 unaffected controls. A 5-fold cross-validation study was performed to validate this model and to compare its performance with those of the NICE guidelines and the previously published models. RESULTS: In the logistic regression model, maternal age, weight, height, racial origin, family history of diabetes, use of ovulation drugs, birth weight, and previous history of GDM were found to be significant predictors of GDM. In screening for GDM in the 5-fold cross-validation study, detection rates (DRs) were higher (p < 0.0001) for the proposed model (DR = 83.2%) than for the NICE guidelines (DR = 77.5%) for a false positive rate of approximately 40% (determined by NICE). The area under the receiver operating characteristic curve of the new model was higher (p < 0.0001) than that of the previous five models (0.823 vs. 0.688-786). CONCLUSIONS: Early effective screening for GDM can be achieved based on maternal characteristics and history.
OBJECTIVES: To develop and validate a prediction model for gestational diabetes mellitus (GDM) at 11-13 weeks' gestation based on maternal characteristics and history and to compare its performance with the method recommended by the National Institute of Health and Care Excellence (NICE) and five other published prediction models. METHODS: A predictive logistic regression model for GDM was developed from 1,827 cases (2.4%) who developed GDM and 73,334 unaffected controls. A 5-fold cross-validation study was performed to validate this model and to compare its performance with those of the NICE guidelines and the previously published models. RESULTS: In the logistic regression model, maternal age, weight, height, racial origin, family history of diabetes, use of ovulation drugs, birth weight, and previous history of GDM were found to be significant predictors of GDM. In screening for GDM in the 5-fold cross-validation study, detection rates (DRs) were higher (p < 0.0001) for the proposed model (DR = 83.2%) than for the NICE guidelines (DR = 77.5%) for a false positive rate of approximately 40% (determined by NICE). The area under the receiver operating characteristic curve of the new model was higher (p < 0.0001) than that of the previous five models (0.823 vs. 0.688-786). CONCLUSIONS: Early effective screening for GDM can be achieved based on maternal characteristics and history.
Authors: Diane Farrar; Mark Simmonds; Maria Bryant; Debbie A Lawlor; Fidelma Dunne; Derek Tuffnell; Trevor A Sheldon Journal: PLoS One Date: 2017-04-06 Impact factor: 3.240
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Authors: F van Hoorn; Mph Koster; C A Naaktgeboren; F Groenendaal; A Kwee; M Lamain-de Ruiter; A Franx; M N Bekker Journal: BJOG Date: 2020-09-01 Impact factor: 6.531