External validation of a clinical scoring system for the risk of gestational diabetes mellitus

Leeuwen, van; Opmeer, Brent C.; Zweers, E. J. K.; Ballegooie, E. van; Brugge, Henk G. ter; Valk, Harold W. de; Visser, Gerard H.A.; Mol, Ben W J

doi:10.1016/j.diabres.2009.04.025

Cited by 18 publications

(14 citation statements)

References 9 publications

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“…Our study differs from previous validation studies [15][16][17][18] by performing a head-tohead comparison, whereas other studies validated a single prognostic model or only a small selection of the prognostic models for gestational diabetes mellitus. However, our findings are similar to those of these external validation studies, except for the external validation of the Van Leeuwen 2010 model by Lovati and colleagues.…”

Section: Comparison With Other Studiesmentioning

confidence: 84%

“…However, these prognostic models are not commonly used in routine clinical care nor are they recommended by current guidelines. This might be due to the fact that external validation of these prognostic models are scarce, [15][16][17][18] let alone that all these models have been directly evaluated and compared on the basis of their predictive accuracy in one independent cohort by independent investigators. To acquire a fair comparison of their predictive accuracy, and thus of their clinical value, it is essential to perform a head-to-head comparison of all published prognostic models in one independent cohort.…”

Section: What This Study Addsmentioning

confidence: 99%

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External validation of prognostic models to predict risk of gestational diabetes mellitus in one Dutch cohort: prospective multicentre cohort study

Ruiter

Kwee

Naaktgeboren

et al. 2016

BMJ

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ObjeCtiveTo perform an external validation and direct comparison of published prognostic models for early prediction of the risk of gestational diabetes mellitus, including predictors applicable in the first trimester of pregnancy. DesignExternal validation of all published prognostic models in large scale, prospective, multicentre cohort study. setting 31 independent midwifery practices and six hospitals in the Netherlands. PartiCiPantsWomen recruited in their first trimester (<14 weeks) of pregnancy between December 2012 and January 2014, at their initial prenatal visit. Women with pre-existing diabetes mellitus of any type were excluded. Main OutCOMe MeasuresDiscrimination of the prognostic models was assessed by the C statistic, and calibration assessed by calibration plots. results 3723 women were included for analysis, of whom 181 (4.9%) developed gestational diabetes mellitus in pregnancy. 12 prognostic models for the disorder could be validated in the cohort. C statistics ranged from 0.67 to 0.78. Calibration plots showed that eight of the 12 models were well calibrated.The four models with the highest C statistics included almost all of the following predictors: maternal age, maternal body mass index, history of gestational diabetes mellitus, ethnicity, and family history of diabetes. Prognostic models had a similar performance in a subgroup of nulliparous women only. Decision curve analysis showed that the use of these four models always had a positive net benefit. COnClusiOnsIn this external validation study, most of the published prognostic models for gestational diabetes mellitus show acceptable discrimination and calibration. The four models with the highest discriminative abilities in this study cohort, which also perform well in a subgroup of nulliparous women, are easy models to apply in clinical practice and therefore deserve further evaluation regarding their clinical impact. IntroductionIn the field of obstetrics, the number of publications on prognostic models has more than tripled in the past decade, 1 which reflects an increasing interest in risk based medicine. Risk based medicine aims to provide the most appropriate care to each patient, often guided by outcome risk estimates based on individual patient characteristics, test results, or even genetic information. 2 As a result of the obesity pandemic, the incidence of gestational diabetes mellitus, notably occurring in the second or third trimester, is rising and is increasingly contributing to perinatal complications such as macrosomia, shoulder dystocia, caesarean section, and neonatal hypoglycaemia. 3 4 Moreover, long term sequelae of gestational diabetes mellitus are type 2 diabetes in mothers and obesity in their offspring. 5 6 Early diagnosis and treatment of gestational diabetes mellitus have been proven to improve pregnancy outcomes. 7 8 Some guidelines propose a population strategy for diagnosing the disorder [9][10][11][12] (that is, an oral glucose tolerance test) in all pregnant women, whereas others opt for a high ri...

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Section: Comparison With Other Studiesmentioning

confidence: 84%

Section: What This Study Addsmentioning

confidence: 99%

External validation of prognostic models to predict risk of gestational diabetes mellitus in one Dutch cohort: prospective multicentre cohort study

Ruiter

Kwee

Naaktgeboren

et al. 2016

BMJ

View full text Add to dashboard Cite

show abstract

“…Reference cross-checking of the selected papers yielded no additional studies. Thus, a total of 17 studies on firsttrimester prediction modeling for GDM were identified for inclusion in this review [19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34]. Fourteen studies were development studies and another three studies were external validation studies.…”

Section: Study Selectionmentioning

confidence: 99%

Prediction models for the risk of gestational diabetes: a systematic review

et al. 2017

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Background: Numerous prediction models for gestational diabetes mellitus (GDM) have been developed, but their methodological quality is unknown. The objective is to systematically review all studies describing first-trimester prediction models for GDM and to assess their methodological quality. Methods: MEDLINE and EMBASE were searched until December 2014. Key words for GDM, first trimester of pregnancy, and prediction modeling studies were combined. Prediction models for GDM performed up to 14 weeks of gestation that only include routinely measured predictors were eligible. Data was extracted by the CHecklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies (CHARMS). Data on risk predictors and performance measures were also extracted. Each study was scored for risk of bias. Results: Our search yielded 7761 articles, of which 17 were eligible for review (14 development studies and 3 external validation studies). The definition and prevalence of GDM varied widely across studies. Maternal age and body mass index were the most common predictors. Discrimination was acceptable for all studies. Calibration was reported for four studies. Risk of bias for participant selection, predictor assessment, and outcome assessment was low in general. Moderate to high risk of bias was seen for the number of events, attrition, and analysis. Conclusions: Most studies showed moderate to low methodological quality, and few prediction models for GDM have been externally validated. External validation is recommended to enhance generalizability and assess their true value in clinical practice.

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“…These inconsistencies draw attention to the importance of taking into consideration the unique research situation on the enactment of ethnicity. For example, the cohort study participants from the Van Leeuwen et al (2010) article discussed above are, in a different publication, recategorised from the original country-of-origin categories into the racial categories ''Caucasian'', ''Black'', ''Asian'', and ''Other'' (instead of ''Caucasian'' and ''non-Caucasian'', as in the first article discussed above) (Van Leeuwen et al, 2009. Thus, the same data are categorised into different racialised groups in different publications by the same authors.…”

Section: Multiplicitymentioning

confidence: 99%

“…Hispanic American, Native American, Asian American, African American, and Pacific Islander)'' (American Diabetes Association, 2009: S66), a phrase which is transformed by the authors into ''non-Caucasian'', after they found that the individual country-of-origin ethnic groups were too small for any meaningful statistical analysis to take place. In the second article based on the same cohort study, the authors refer specifically to a different model developed by Naylor et al, (1997) in which the clinical risk groups are described as ''Black'', ''Asian'', and ''Other'', with ''White'' being used as a reference group (Naylor et al, 1997. in Van Leeuwen et al, 2009).…”

Section: Multiplicitymentioning

confidence: 99%

The multiplicity and situationality of enacting ‘ethnicity’ in Dutch health research articles

et al. 2017

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Previous research has problematised the diversity of conceptualisations and operationalisations of ethnicity within health research and the field of Ethnicity and Health. In this article, we explore how practices in health research and the field of Ethnicity and Health themselves contribute to the enactment of different versions of ethnicity. Using a qualitative content analysis of contemporary peer-reviewed Dutch biomedical and health research, we identified various dynamics in research practices and the research situation, which are relevant to understanding the enactment of multiple versions of ethnicity and specific ethnic and racial categories in health research in the Netherlands. Specifically, we discuss the production of academic publications and the manner in which researchers must establish the premises for ethnicity-specific health research; the organisation and ethnic and racial labelling of the data; and the discussion of new research findings in comparison with previous ethnicity-specific research. Ultimately, our analysis illustrates that, in health research and publications, ethnicity and its relation to health are not simply discovered or found; rather we discuss how the manner in which ethnicity and specific categories of ethnicity are enacted is contingent upon these everyday dynamics of research practices and the specific research situation in which research takes place.

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External validation of a clinical scoring system for the risk of gestational diabetes mellitus

Cited by 18 publications

References 9 publications

External validation of prognostic models to predict risk of gestational diabetes mellitus in one Dutch cohort: prospective multicentre cohort study

External validation of prognostic models to predict risk of gestational diabetes mellitus in one Dutch cohort: prospective multicentre cohort study

Prediction models for the risk of gestational diabetes: a systematic review

The multiplicity and situationality of enacting ‘ethnicity’ in Dutch health research articles

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