OBJECTIVE: To develop a clinical prediction rule that can help the clinician to identify women at high and low risk for gestational diabetes mellitus (GDM) early in pregnancy in order to improve the efficiency of GDM screening. DESIGN: We used data from a prospective cohort study to develop the clinical prediction rule. SETTING: The original cohort study was conducted in a university hospital in the Netherlands. POPULATION: Nine hundred and ninety-five consecutive pregnant women underwent screening for GDM. METHODS: Using multiple logistic regression analysis, we constructed a model to estimate the probability of development of GDM from the medical history and patient characteristics. Receiver operating characteristics analysis and calibration were used to assess the accuracy of the model. MAIN OUTCOME MEASURE: The development of a clinical prediction rule for GDM. We also evaluated the potential of the prediction rule to improve the efficiency of GDM screening. RESULTS: The probability of the development of GDM could be predicted from the ethnicity, family history, history of GDM and body mass index. The model had an area under the receiver operating characteristic curve of 0.77 (95% CI 0.69-0.85) and calibration was good (Hosmer and Lemeshow test statistic, P = 0.25). If an oral glucose tolerance test was performed in all women with a predicted probability of 2% or more, 43% of all women would be tested and 75% of the women with GDM would be identified. CONCLUSIONS: The use of a clinical prediction model is an accurate method to identify women at increased risk for GDM, and could be used to select women for additional testing for GDM.
OBJECTIVE: To develop a clinical prediction rule that can help the clinician to identify women at high and low risk for gestational diabetes mellitus (GDM) early in pregnancy in order to improve the efficiency of GDM screening. DESIGN: We used data from a prospective cohort study to develop the clinical prediction rule. SETTING: The original cohort study was conducted in a university hospital in the Netherlands. POPULATION: Nine hundred and ninety-five consecutive pregnant women underwent screening for GDM. METHODS: Using multiple logistic regression analysis, we constructed a model to estimate the probability of development of GDM from the medical history and patient characteristics. Receiver operating characteristics analysis and calibration were used to assess the accuracy of the model. MAIN OUTCOME MEASURE: The development of a clinical prediction rule for GDM. We also evaluated the potential of the prediction rule to improve the efficiency of GDM screening. RESULTS: The probability of the development of GDM could be predicted from the ethnicity, family history, history of GDM and body mass index. The model had an area under the receiver operating characteristic curve of 0.77 (95% CI 0.69-0.85) and calibration was good (Hosmer and Lemeshow test statistic, P = 0.25). If an oral glucose tolerance test was performed in all women with a predicted probability of 2% or more, 43% of all women would be tested and 75% of the women with GDM would be identified. CONCLUSIONS: The use of a clinical prediction model is an accurate method to identify women at increased risk for GDM, and could be used to select women for additional testing for GDM.
Authors: C S Göbl; L Bozkurt; P Rivic; G Schernthaner; R Weitgasser; G Pacini; M Mittlböck; D Bancher-Todesca; M Lechleitner; A Kautzky-Willer Journal: Diabetologia Date: 2012-09-22 Impact factor: 10.122
Authors: Katrien Benhalima; Paul Van Crombrugge; Carolien Moyson; Johan Verhaeghe; Sofie Vandeginste; Hilde Verlaenen; Chris Vercammen; Toon Maes; Els Dufraimont; Christophe De Block; Yves Jacquemyn; Farah Mekahli; Katrien De Clippel; Annick Van Den Bruel; Anne Loccufier; Annouschka Laenen; Caro Minschart; Roland Devlieger; Chantal Mathieu Journal: Acta Diabetol Date: 2020-01-08 Impact factor: 4.280
Authors: Grammata Kotzaeridi; Julia Blätter; Daniel Eppel; Ingo Rosicky; Martina Mittlböck; Gülen Yerlikaya-Schatten; Christian Schatten; Peter Husslein; Wolfgang Eppel; Evelyn A Huhn; Andrea Tura; Christian S Göbl Journal: Eur J Clin Invest Date: 2021-06-18 Impact factor: 5.722