Performance of early risk assessment tools to predict the later development of gestational diabetes. (18th June 2021)

• Record Type: Journal Article
• Authors: Kotzaeridi, Grammata; Blätter, Julia; Eppel, Daniel; Rosicky, Ingo; Mittlböck, Martina; Yerlikaya‐Schatten, Gülen; Schatten, Christian; Husslein, Peter; Eppel, Wolfgang; Huhn, Evelyn A.; Tura, Andrea; Göbl, Christian S.
• Is Part Of: European journal of clinical investigation. Volume 51:Number 12(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-06-18
• Subjects: early gestation -- gestational diabetes mellitus -- risk prediction model; Pathology -- Periodicals; Medical research -- Periodicals; 616.075
• DOI: 10.1111/eci.13630
• Languages: English
• ISSNs: 0014-2972

Abstract

Abstract: Background: Several prognostic models for gestational diabetes mellitus (GDM) are provided in the literature; however, their clinical significance has not been thoroughly evaluated, especially with regard to application at early gestation and in accordance with the most recent diagnostic criteria. This external validation study aimed to assess the predictive accuracy of published risk estimation models for the later development of GDM at early pregnancy. Methods: In this cohort study, we prospectively included 1132 pregnant women. Risk evaluation was performed before 16 + 0 weeks of gestation including a routine laboratory examination. Study participants were followed‐up until delivery to assess GDM status according to the IADPSG 2010 diagnostic criteria. Fifteen clinical prediction models were calculated according to the published literature. Results: Gestational diabetes mellitus was diagnosed in 239 women, that is 21.1% of the study participants. Discrimination was assessed by the area under the ROC curve and ranged between 60.7% and 76.9%, corresponding to an acceptable accuracy. With some exceptions, calibration performance was poor as most models were developed based on older diagnostic criteria with lower prevalence and therefore tended to underestimate the risk of GDM. The highest variable importance scores were observed for history of GDM and routine laboratory parameters. Conclusions: Most prediction models showed acceptable accuracy in terms of discrimination but lacked in calibration, which was strongly dependent on study settings. Simple biochemical variables such as fasting glucose, HbA1c and triglycerides can improve risk prediction. One model consisting of clinical and laboratory parameters showed satisfactory accuracy and could be used for further investigations.