Development and Validation of a Patient Self-assessment Score for Diabetes Risk

Baylor College of Medicine, Houston, Texas, United States
Annals of internal medicine (Impact Factor: 17.81). 12/2009; 151(11):775-83. DOI: 10.1059/0003-4819-151-11-200912010-00005
Source: PubMed


National guidelines disagree on who should be screened for undiagnosed diabetes. No existing diabetes risk score is highly generalizable or widely followed.
To develop a new diabetes screening score and compare it with other available screening instruments (Centers for Disease Control and Prevention, American Diabetes Association, and U.S. Preventive Services Task Force guidelines; 2 American Diabetes Association risk questionnaires; and the Rotterdam model).
Cross-sectional data.
NHANES (National Health and Nutrition Examination Survey) 1999 to 2004 for model development and 2005 to 2006, plus a combined cohort of 2 community studies, ARIC (Atherosclerosis Risk in Communities) Study and CHS (Cardiovascular Health Study), for validation.
U.S. adults aged 20 years or older.
A risk-scoring algorithm for undiagnosed diabetes, defined as fasting plasma glucose level of 7.0 mmol/L (126 mg/dL) or greater without known diabetes, was developed in the development data set. Logistic regression was used to determine which participant characteristics were independently associated with undiagnosed diabetes. The new algorithm and other methods were evaluated by standard diagnostic and feasibility measures.
Age, sex, family history of diabetes, history of hypertension, obesity, and physical activity were associated with undiagnosed diabetes. In NHANES (ARIC/CHS), the cut-point of 5 or more points selected 35% (40%) of persons for diabetes screening and yielded a sensitivity of 79% (72%), specificity of 67% (62%), positive predictive value of 10% (10%), and positive likelihood ratio of 2.39 (1.89). In contrast, the comparison scores yielded a sensitivity of 44% to 100%, specificity of 10% to 73%, positive predictive value of 5% to 8%, and positive likelihood ratio of 1.11 to 1.98.
Data during pregnancy were not available.
This easy-to-implement diabetes screening score seems to demonstrate improvements over existing methods. Studies are needed to evaluate it in diverse populations in real-world settings.
Clinical and Translational Science Center at Weill Cornell Medical College.

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Available from: Alvin I Mushlin, Feb 28, 2014
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    • "Therefore, development of a simple accurate screening method is needed. Historically, the majority of the clinical screening methods consisted of surveys developed using logistic regression analyses to predict diabetes [8] [9] [10] [11] [12] [13]. "
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    ABSTRACT: The global prevalence of diabetes is rapidly increasing. Studies support the necessity of screening and interventions for prediabetes, which could result in serious complications and diabetes. This study aimed at developing an intelligence-based screening model for prediabetes. Data from the Korean National Health and Nutrition Examination Survey (KNHANES) were used, excluding subjects with diabetes. The KNHANES 2010 data (n = 4685) were used for training and internal validation, while data from KNHANES 2011 (n = 4566) were used for external validation. We developed two models to screen for prediabetes using an artificial neural network (ANN) and support vector machine (SVM) and performed a systematic evaluation of the models using internal and external validation. We compared the performance of our models with that of a screening score model based on logistic regression analysis for prediabetes that had been developed previously. The SVM model showed the areas under the curve of 0.731 in the external datasets, which is higher than those of the ANN model (0.729) and the screening score model (0.712), respectively. The prescreening methods developed in this study performed better than the screening score model that had been developed previously and may be more effective method for prediabetes screening.
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    • "70 for women . A risk score for undiagnosed diabetes using the data of NHANES 1999 - 2004 was developed by Bang et al . in 2009 [ 12 ] . This risk score ( NHANES DRS ) is calculated out of 6 variables , including age , sex , family history of diabetes , personal history of hypertension , obesity , and physical activity . "
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    ABSTRACT: Objective To evaluate the performance of Finnish Diabetes Risk Score (FINDRISC) in detecting undiagnosed diabetes and prediabetes among U.S. adults by gender and race. Methods This cross-sectional analysis included participants (aged ≥20 years) from the National Health and Nutrition Examination Survey (NHANES) 1999–2010. Sensitivity, specificity, area under the receiver operating characteristic (ROC) curve and the optimal cutoff points for identifying undiagnosed diabetes and prediabetes were calculated for FINDRISC by gender and race/ethnicity. Results Among the 20,633 adults (≥20 years), 49.8% were women and 53.0% were non-Hispanic White. The prevalence of undiagnosed diabetes and prediabetes was 4.1% and 35.6%, respectively. FINDRISC was positively associated with the prevalence of diabetes (OR = 1.48 for 1 unit increase, p<0.001) and prediabetes (OR = 1.15 for 1 unit increase, p<0.001). The area under ROC for detecting undiagnosed diabetes was 0.75 for total population, 0.74 for men and 0.78 for women (p = 0.04); 0.76 for White, 0.76 for Black and 0.72 for Hispanics (p = 0.03 for White vs. Hispanics). The area under ROC for detecting prediabetes was 0.67 for total population, 0.66 for men and 0.70 for women (p<0.001); 0.68 for White, 0.67 for Black and 0.65 for Hispanics (p<0.001 for White vs. Hispanics). The optimal cutoff point was 10 (sensitivity = 0.75) for men and 12 (sensitivity = 0.72) for women for detecting undiagnosed diabetes; 9 (sensitivity = 0.61) for men and 10 (sensitivity = 0.69) for women for detecting prediabetes. Conclusions FINDRISC is a simple and non-invasive screening tool to identify individuals at high risk for diabetes in the U.S. adults.
    Full-text · Article · May 2014 · PLoS ONE
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    • "We selected explanatory variables for the regression analysis (described later) based on established [9,10,23-27] risk factors for diabetes that are common to the NHANES and MEPS databases, as well as variables associated with greater access to or use of health care services. All variables were coded as dichotomous indicators (characteristic applies = 1, else = 0), with the exception of family income (measured continuously in thousands of 2010 dollars). "
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    ABSTRACT: Background Screening to detect prediabetes and diabetes enables early prevention and intervention. This study describes the number and characteristics of asymptomatic, undiagnosed adults in the United States who could be detected with prediabetes and type 2 diabetes using the American Diabetes Association (ADA) guidelines compared to the United States Preventive Services Task Force (USPSTF) guidelines. Methods We developed predictive models for undiagnosed diabetes and prediabetes using polytomous logistic regression from data on risk factors in the 2003–2010 National Health and Nutrition Examination Survey (n = 19,056). We applied these predictive models to the 2010 Medical Expenditure Panel Survey, which contains health care use data, to generate probabilities of undiagnosed diabetes and undetected prediabetes for each adult. We summed individual probabilities to estimate the number of adults who would be detected with prediabetes and/or type 2 diabetes if screened under ADA or USPSTF guidelines. We analyzed health care use patterns of people at high risk for diabetes. Results In 2010, 59.1 million adults met the USPSTF screening criteria including 24.4 million people with undetected prediabetes and 3.7 million people with undiagnosed diabetes. In comparison, among the 86.3 million people who met the ADA screening criteria, there were 33.9 million with undetected prediabetes and 4.6 million with undiagnosed type 2 diabetes. The ADA guidelines detected 38.9% more cases of prediabetes and 24.3% more cases of type 2 diabetes compared to the USPSTF guidelines. Subgroup analysis showed that ADA guidelines would detect 78% more cases of diabetes among the age 54 and younger population, in 40% more blacks, and in more than twice as many Hispanics than USPSTF guidelines. Only 58% of adults meeting ADA guidelines and 70% meeting USPSTF guidelines had ≥ 1 primary care office visit in 2010. Conclusions Compared to USPSTF guidelines, ADA guidelines would screen more people and detect more cases of both prediabetes and type 2 diabetes, though a substantial percentage of patients with undetected cases had no contact with a primary care provider in 2010. Addressing the problem of large numbers of undetected prediabetes and type 2 diabetes cases will require new strategies for screening.
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