External Validation of a Tool Predicting 7-Year Risk of Developing Cardiovascular Disease, Type 2 Diabetes or Chronic Kidney Disease

Background Chronic cardiometabolic diseases, including cardiovascular disease (CVD), type 2 diabetes (T2D) and chronic kidney disease (CKD), share many modifiable risk factors and can be prevented using combined prevention programs. Valid risk prediction tools are needed to accurately identify indiv...

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Published in:	Journal of general internal medicine : JGIM Vol. 33; no. 2; pp. 182 - 188
Main Authors:	Rauh, Simone P., Rutters, Femke, van der Heijden, Amber A. W. A., Luimes, Thomas, Alssema, Marjan, Heymans, Martijn W., Magliano, Dianna J., Shaw, Jonathan E., Beulens, Joline W., Dekker, Jacqueline M.
Format:	Journal Article
Language:	English
Published:	New York Springer US 01-02-2018 Springer Nature B.V
Subjects:	Adult Aged Antihypertensives Australia Body mass Calibration Cardiovascular disease Cardiovascular diseases Cardiovascular Diseases - epidemiology Cerebral infarction Cohort Studies Diabetes Diabetes mellitus Diabetes mellitus (non-insulin dependent) Diabetes Mellitus, Type 2 - epidemiology Discrimination Family medical history Female Genetics Goodness of fit Health risk assessment Health risks Humans Internal Medicine Kidney diseases Mathematical models Medicine Medicine & Public Health Middle Aged Myocardial infarction Original Research Predictions Prevention Renal Insufficiency, Chronic - epidemiology Risk analysis Risk Assessment - methods Risk Factors Smoking Software Statistical tests Australia generalizability cardiovascular disease, type 2 diabetes, chronic kidney disease prediction tool
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Summary:	Background Chronic cardiometabolic diseases, including cardiovascular disease (CVD), type 2 diabetes (T2D) and chronic kidney disease (CKD), share many modifiable risk factors and can be prevented using combined prevention programs. Valid risk prediction tools are needed to accurately identify individuals at risk. Objective We aimed to validate a previously developed non-invasive risk prediction tool for predicting the combined 7-year-risk for chronic cardiometabolic diseases. Design The previously developed tool is stratified for sex and contains the predictors age, BMI, waist circumference, use of antihypertensives, smoking, family history of myocardial infarction/stroke, and family history of diabetes. This tool was externally validated, evaluating model performance using area under the receiver operating characteristic curve (AUC)—assessing discrimination—and Hosmer–Lemeshow goodness-of-fit (HL) statistics—assessing calibration. The intercept was recalibrated to improve calibration performance. Participants The risk prediction tool was validated in 3544 participants from the Australian Diabetes, Obesity and Lifestyle Study (AusDiab). Key Results Discrimination was acceptable, with an AUC of 0.78 (95% CI 0.75–0.81) in men and 0.78 (95% CI 0.74–0.81) in women. Calibration was poor (HL statistic: p < 0.001), but improved considerably after intercept recalibration. Examination of individual outcomes showed that in men, AUC was highest for CKD (0.85 [95% CI 0.78–0.91]) and lowest for T2D (0.69 [95% CI 0.65–0.74]). In women, AUC was highest for CVD (0.88 [95% CI 0.83–0.94)]) and lowest for T2D (0.71 [95% CI 0.66–0.75]). Conclusions Validation of our previously developed tool showed robust discriminative performance across populations. Model recalibration is recommended to account for different disease rates. Our risk prediction tool can be useful in large-scale prevention programs for identifying those in need of further risk profiling because of their increased risk for chronic cardiometabolic diseases.
Bibliography:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 ObjectType-Undefined-3
ISSN:	0884-8734 1525-1497
DOI:	10.1007/s11606-017-4231-7