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Performance of early risk assessment tools to predict the later development of gestational diabetes
Author(s) -
Kotzaeridi Grammata,
Blätter Julia,
Eppel Daniel,
Rosicky Ingo,
Mittlböck Martina,
YerlikayaSchatten Gülen,
Schatten Christian,
Husslein Peter,
Eppel Wolfgang,
Huhn Evelyn A.,
Tura Andrea,
Göbl Christian S.
Publication year - 2021
Publication title -
european journal of clinical investigation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.164
H-Index - 107
eISSN - 1365-2362
pISSN - 0014-2972
DOI - 10.1111/eci.13630
Subject(s) - gestational diabetes , medicine , gestation , pregnancy , gestational age , obstetrics , risk assessment , diabetes mellitus , prospective cohort study , cohort study , cohort , receiver operating characteristic , endocrinology , genetics , computer security , computer science , biology
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.