Performance of early risk assessment tools to predict the later development of gestational diabetes

TY - JOUR

T1 - Performance of early risk assessment tools to predict the later development of gestational diabetes

AU - Kotzaeridi, Grammata

AU - Blätter, Julia

AU - Eppel, Daniel

AU - Rosicky, Ingo

AU - Mittlböck, Martina

AU - Yerlikaya-Schatten, Gülen

AU - Schatten, Christian

AU - Husslein, Peter

AU - Eppel, Wolfgang

AU - Huhn, Evelyn A

AU - Tura, Andrea

AU - Göbl, Christian S

N1 - © 2021 The Authors. European Journal of Clinical Investigation published by John Wiley & Sons Ltd on behalf of Stichting European Society for Clinical Investigation Journal Foundation.

PY - 2021/12

Y1 - 2021/12

N2 - 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.

AB - 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.

KW - Adult

KW - Blood Glucose/metabolism

KW - Blood Pressure

KW - Cohort Studies

KW - Diabetes, Gestational/diagnosis

KW - Ethnicity

KW - Fasting

KW - Female

KW - Glycated Hemoglobin/metabolism

KW - Humans

KW - Medical History Taking

KW - Obesity, Maternal/epidemiology

KW - Pregnancy

KW - Prenatal Diagnosis

KW - ROC Curve

KW - Risk Assessment

KW - Triglycerides/metabolism

U2 - 10.1111/eci.13630

DO - 10.1111/eci.13630

M3 - SCORING: Journal article

C2 - 34142723

VL - 51

SP - e13630

JO - EUR J CLIN INVEST

JF - EUR J CLIN INVEST

SN - 0014-2972

IS - 12

ER -