Low predictive value of traditional risk factors in identifying women at risk for gestational diabetes

Ogonowski, Jarosław; Miazgowski, Tomasz; Homa, Katarzyna; Celewicz, Zbigniew; Kuczyńska, Magdalena

doi:10.1080/00016340701505044

Cited by 19 publications

(17 citation statements)

References 36 publications

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“…Our approach was similar to that of several studies designed with the aim to identify the best diagnostic approach for GDM (19,20,21,22,23). In the European countries, the nonconformity of screening and diagnostic procedures for GDM is responsible for the different reported prevalence rates of GDM (6).…”

Section: Discussionmentioning

confidence: 80%

“…However, it is widely accepted that previous GDM is one of the best predictors of GDM, as reported in many studies (22). Furthermore, the lack of information on the maternal and fetal outcomes did not allow us to assess the predictive role of individual RFs for GDM in the occurrence of these outcomes.…”

Section: Discussionmentioning

confidence: 93%

“…The predictive value of maternal age in the development of GDM has been evaluated in several studies and the risk associated has been shown to be higher with increasing age (20,21,28,29). However, other authors have not reported a much higher risk for older women (22,30). A recent study examining the individual association of maternal age, BMI, and racial origin with the development of GDM has found different associated risks in relation to the prevalence of GDM for the specific racial group considered.…”

Section: Discussionmentioning

confidence: 98%

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Improvement of selective screening strategy for gestational diabetes through a more accurate definition of high-risk groups

Pintaudi

Vieste

Corrado

et al. 2014

European Journal of Endocrinology

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Objective: This study aimed to assess the predictive value of risk factors (RFs) for gestational diabetes mellitus (GDM) established by selective screening (SS) and to identify subgroups of women at a higher risk of developing GDM. Design: A retrospective, single-center study design was employed. Methods: Data of 1015 women screened for GDM at 24-28 weeks of gestation and diagnosed according to the International Association of Diabetes and Pregnancy Study Groups criteria were evaluated. Information on RFs established by SS was also collected and their association with GDM was determined. To identify distinct and homogeneous subgroups of patients at a higher risk, the RECursive Partitioning and AMalgamation (RECPAM) method was used. Results: Overall, 113 (11.1%) women were diagnosed as having GDM. The application of the SS criteria would result in the execution of an oral glucose tolerance test (OGTT) in 58.3% of women and 26 (23.0%) cases of GDM would not be detected due to the absence of any RF. The RECPAM analysis identified high-risk subgroups characterized by fasting plasma glucose values O5.1 mmol/l (odds ratio (OR)Z26.5; 95% CI 14.3-49.0) and pre-pregnancy BMI (ORZ7.0; 95% CI 3.9-12.8 for overweight women). In a final logistic model including RECPAM classes, previous macrosomia (ORZ3.6; 95% CI 1.1-11.6), and family history of diabetes (ORZ1.8; 95% CI 1.1-2.8), but not maternal age, were also found to be associated with an increased risk of developing GDM. A screening approach based on the RECPAM model would reduce by over 50% (23.0 vs 10.6%) the number of undiagnosed GDM cases when compared with the current SS approach, at the expense of 50 additional OGTTs required. Conclusions: A screening approach based on our RECPAM model results in a significant reduction in the number of undetected GDM cases compared with the current SS procedure.

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Section: Discussionmentioning

confidence: 80%

Section: Discussionmentioning

confidence: 93%

Section: Discussionmentioning

confidence: 98%

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Improvement of selective screening strategy for gestational diabetes through a more accurate definition of high-risk groups

Pintaudi

Vieste

Corrado

et al. 2014

European Journal of Endocrinology

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“…Although many studies have reported the risk factors for GDM, [9][10][11][12][13][14] to our knowledge only a few studies have integrated patient characteristics and medical history in a quantitative manner by means of a risk scoring system or a prediction model. [15][16][17] In this article, we developed a multivariable logistic regression model in which we combined patient characteristics and medical history to predict the occurrence of GDM.…”

Section: Introductionmentioning

confidence: 99%

Estimating the risk of gestational diabetes mellitus: a clinical prediction model based on patient characteristics and medical history

Leeuwen

Opmeer

Zweers

et al. 2009

BJOG

106

110

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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.

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“…Diagnostic T a b l e 1 . P l a s ma Gl u c o s e Va l u e s d u r i n g GCT a n d OGT T i n P r e g n a n t Wo me n criteria for the 100-g (12). Glycosuria is also a poor predictor of GDM to be considered effective as a screening tool (and this pre-screening test does not seem to identify) GDM (13).…”

Section: Gestational Diabetes Mellitus (Gdm) Is Defined As Any Degreementioning

confidence: 99%

Glucose Challenge Test Does not Predict Gestational Diabetes Mellitus

et al. 2008

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Low predictive value of traditional risk factors in identifying women at risk for gestational diabetes

Abstract: Selective screening based on traditional risk factors for GDM had relatively low sensitivity, and identified <60% of Caucasian women at risk. The cut-off value for BMI as a risk indicator (23 kg/m2) was lower than that proposed by guidelines about screening for GDM.

Cited by 19 publications

References 36 publications

Improvement of selective screening strategy for gestational diabetes through a more accurate definition of high-risk groups

Improvement of selective screening strategy for gestational diabetes through a more accurate definition of high-risk groups

Estimating the risk of gestational diabetes mellitus: a clinical prediction model based on patient characteristics and medical history

Glucose Challenge Test Does not Predict Gestational Diabetes Mellitus

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