Performance of a predictive model to identify undiagnosed diabetes in a health care setting.

Baan, Caroline A.; Ruige, Johannes; Stolk, Ronald P.; Witteman, Jacqueline C. M.; Dekker, Jacqueline; Heine, Robert J.; Feskens, Edith J. M.

doi:10.2337/diacare.22.2.213

Cited by 159 publications

(157 citation statements)

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“…The prediction models for the risk of diabetes can help to guide screening and interventions and to predict diabetes occurrence [3][4][5]. Routinely available and easily collected clinical and lifestyle-related information has been found to be effective for identifying diabetes cases [6][7][8][9][10][11][12]. In addition, a prediction model has been developed in Mexican Americans [13] and further tested in Japanese Americans [14].…”

Section: Introductionmentioning

confidence: 99%

A prediction model for type 2 diabetes risk among Chinese people

et al. 2008

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Aims/hypothesis A range of prediction rules for the onset of type 2 diabetes have been proposed. However, most studies have been conducted in white groups and it is not clear whether these models apply to Asian populations. The purpose of this study was to construct a simple points model for predicting incident diabetes among Chinese people.Methods We estimated the 10 year risk of diabetes in a cohort study of middle-aged and elderly participants who were free from diabetes at baseline. Cox regression coefficients were used to construct the simple points model and the discriminatory ability of the resulting prediction rule was determined using AUC and net reclassification improvement and integrated discrimination improvement statistics. Fivefold random splitting was used to test the internal validity and obtain bootstrap estimates of the AUC. Results Of the 2,960 participants without diabetes at the baseline examination, 548 developed type 2 diabetes during a median 10 year follow-up period. Age (four points), elevated fasting glucose (11 points), body mass index (eight points), triacylglycerol (five points), white blood cell count (four points) and a higher HDL-cholesterol (negative four points) were found to strongly predict diabetes incidence in a multivariate model. The estimated AUC for the model was 0.702 (95% CI 0.676-0.727). This model performed better than existing prediction models developed in other populations, including the Prospective Cardiovascular Münster, Cambridge, San Antonia and Framingham models for diabetes risk. Conclusions/interpretation We have constructed a model for predicting the 10 year incidence of diabetes in Chinese people that could be useful for identifying individuals at high risk of diabetes in the Chinese population.

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Section: Introductionmentioning

confidence: 99%

A prediction model for type 2 diabetes risk among Chinese people

et al. 2008

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“…The risk score tested was the Rotterdam Predictive Model (RPM) (12). It was developed from the Rotterdam Study cohort (28) and was externally validated in the Hoorn study (29).…”

Section: Research Design Andmentioning

confidence: 99%

“…Because of the rapidly increasing prevalence of type 2 diabetes (5)(6)(7)(8), screening individuals at high risk of having undiagnosed diabetes is recommended in several countries (9 -11). Several questionnaires have been developed to detect this high-risk group (12)(13)(14)(15)(16)(17). They all perform equally well, with a sensitivity of 70 -75% and a specificity of 55-70%.…”

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Risk Scores for Type 2 Diabetes Can Be Applied in Some Populations but Not All

Vistisen²,

et al. 2006

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OBJECTIVE -Risk scores based on phenotypic characteristics to identify individuals at high risk of having undiagnosed diabetes have been developed in Caucasian populations. The impact of known risk factors on having undiagnosed type 2 diabetes differs between populations from different ethnic origin, and risk scores developed in Caucasians may not be applicable to other ethnic groups. This study evaluated the performance of one risk score in nine populations of diverse ethnic origin.RESEARCH DESIGN AND METHODS -Data provided by centers from around the world to the DETECT-2 project were used. The database includes population-based surveys with information on at least 500 people without known diabetes having a 75-g oral glucose tolerance test. To date, 52 centers have contributed data on 190,000 individuals from 34 countries. In this analysis, nine cross-sectional studies were selected representing diverse ethnic and regional backgrounds. The risk score assessed uses information on age, sex, blood pressure treatment, and BMI.RESULTS -This analysis included 29,758 individuals; 1,805 individuals had undiagnosed diabetes. The performance of the risk score varied widely, with sensitivity, specificity, and percentage needing further testing ranging between 12 and 57%, 72 and 93%, and 2 and 25%, respectively, with the worse performance in non-Caucasian populations. This variation in performance was related to differences in the association between prevalence of undiagnosed diabetes and components of the risk score.CONCLUSIONS -A typical risk score developed in Caucasian populations cannot be applied to other populations of diverse ethnic origins. Diabetes Care 29:410 -414, 2006T ype 2 diabetes is a common and serious condition associated with reduced life expectancy and considerable morbidity. Recent estimates suggest that currently 195 million people throughout the world have diabetes, and this will increase to over 330 million by 2025 (1). Approximately 50% of people with diabetes are undiagnosed (2,3). Because type 2 diabetes may remain undetected for several years, at the time of clinical diagnosis, many people have one or more micro-or macrovascular complications (4).Detecting people with undiagnosed type 2 diabetes is important for both public health policy and everyday clinical practice. Because of the rapidly increasing prevalence of type 2 diabetes (5-8), screening individuals at high risk of having undiagnosed diabetes is recommended in several countries (9 -11). Several questionnaires have been developed to detect this high-risk group (12-17). They all perform equally well, with a sensitivity of 70 -75% and a specificity of 55-70%. Because the majority of risk scores have been developed and validated in Caucasians (12,18), their applicability to populations of different ethnic background and with different risk factor distribution is uncertain.The DETECT-2 project is an international data pooling collaboration specifically addressing issues related to screening for type 2 diabetes, with an emphasis on the imp...

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“…With this model, automatic calculation of risk scores in the health website becomes possible for people to undergo diabetes self-assessment (Baan et al, 1999). Therefore, our study provides an easy and practical tool to screen undiagnosed diabetes in Chinese population and can be divided into two steps.…”

Section: Discussionmentioning

confidence: 99%

Evaluation of a risk factor scoring model in screening for undiagnosed diabetes in China population

Dong

Lou

Zhao

et al. 2011

J. Zhejiang Univ. Sci. B

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Abstract:Objective: To develop a risk scoring model for screening for undiagnosed type 2 diabetes in Chinese population. Methods: A total of 5348 subjects from two districts of Jinan City, Shandong Province, China were enrolled. Group A (2985) included individuals from east of the city and Group B (2363) from west of the city. Screening questionnaires and a standard oral glucose tolerance test (OGTT) were completed by all subjects. Based on the stepwise logistic regression analysis of Group A, variables were selected to establish the risk scoring model. The validity and effectiveness of this model were evaluated in Group B. Results: Based on stepwise logistic regression analysis performed with data of Group A, variables including age, body mass index (BMI), waist-to-hip ratio (WHR), systolic pressure, diastolic pressure, heart rate, family history of diabetes, and history of high glucose were accepted into the risk scoring model. The risk for having diabetes increased along with aggregate scores. When Youden index was closest to 1, the optimal cutoff value was set up at 51. At this point, the diabetes risk scoring model could identify diabetes patients with a sensitivity of 83.3% and a specificity of 66.5%, making the positive predictive value 12.83% and negative predictive value 98.53%. We compared our model with the Finnish and Danish model and concluded that our model has superior validity in Chinese population. Conclusions: Our diabetes risk scoring model has satisfactory sensitivity and specificity for identifying undiagnosed diabetes in our population, which might be a simple and practical tool suitable for massive diabetes screening.

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Performance of a predictive model to identify undiagnosed diabetes in a health care setting.

Cited by 159 publications

References 0 publications

A prediction model for type 2 diabetes risk among Chinese people

A prediction model for type 2 diabetes risk among Chinese people

Risk Scores for Type 2 Diabetes Can Be Applied in Some Populations but Not All

Evaluation of a risk factor scoring model in screening for undiagnosed diabetes in China population

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