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

Abstract

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.

A two-step screening algorithm including fasting plasma glucose measurement and a risk estimation model is an accurate strategy for detecting gestational diabetes mellitus

“…The first clinical scoring system for GDM prediction, presented by Naylor et al, was based on preconception BMI in addition to maternal age and ethnicity [17]. In addition, other risk scores have revealed a contributing effect of BMI in GDM prediction [16,18,24]. It is, however, striking that in contrast to our model, none of the above-mentioned prediction algorithms included data on FPG.…”

“…2. In the past, several research groups have focused on clinical risk equations for GDM in different ethnic groups [16][17][18]24]. While most of these studies were technically well performed, they reflect the inconsistency of strategies and thresholds for diagnosing GDM [11].…”

“…Used separately, these proposed risk indicators have shown limited diagnostic accuracy [15,16]. Some researchers have further suggested that GDM screening based on risk factors might be more effective with the use of appropriate prediction models that include statistical combinations of several risk factors [16][17][18]. However, the design of a sufficient risk score requires an adequate number of cases and, moreover, additional validation to assess if the risk estimation model is effective in routine clinical use.…”

“…In the past, we and others have identified several risk factors for the occurrence of GDM, such as maternal age, obesity, history of GDM, family history of type 2 diabetes and ethnicity [5,13,14]. Used separately, these proposed risk indicators have shown limited diagnostic accuracy [15,16]. Some researchers have further suggested that GDM screening based on risk factors might be more effective with the use of appropriate prediction models that include statistical combinations of several risk factors [16][17][18].…”

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

“…However, as the number of women in the various country-of-origin categories was too small for statistical analysis, the researchers differentiated instead only between Caucasians and non-Caucasians. No definition or conceptualisation of the categories Caucasian or non-Caucasian was provided, nor was any information given on how the eight country-of-origin ethnic categories had been divided into the categories Caucasian and non-Caucasian (Van Leeuwen et al, 2010).…”