Abstract
BackgroundChronic 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.ObjectiveWe aimed to validate a previously developed non-invasive risk prediction tool for predicting the combined 7-year-risk for chronic cardiometabolic diseases.DesignThe 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.ParticipantsThe risk prediction tool was validated in 3544 participants from the Australian Diabetes, Obesity and Lifestyle Study (AusDiab).Key ResultsDiscrimination 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]).ConclusionsValidation 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.
Highlights
To improve the cost-effectiveness of prevention programs, a target population is needed,[8,9,10] and valid tools are needed to accurately identify individuals at risk for chronic cardiometabolic disease who may benefit most from interventions. We recently developed such a risk prediction tool, including only non-invasive measures, to predict the combined 7-year risk for cardiovascular disease (CVD), type 2 diabetes (T2D) and chronic kidney disease (CKD) in the general population.[7]
As performance is generally overestimated in the development population, external validation of a prediction tool is an essential step in determining its generalizability.[12,13,14]
Exclusion criteria were as follows: 1) prevalent diagnosed chronic cardiometabolic disease: 1a) CVD, including myocardial infarction, percutaneous transluminal coronary angioplasty, coronary artery bypass graft, angina pectoris, stroke, intermittent claudication, peripheral arterial intervention, or heart failure; 1b) T2D defined by self-reported T2D and/or use of antidiabetic medication; or 1c) CKD defined by self-reported or estimated glomerular filtration rate < 15 mL/min/1.73 m2; 2) no follow-up information on the three diseases of interest; 3) death of other than cardiovascular causes during follow-up
Summary
Chronic cardiometabolic diseases, including cardiovascular disease (CVD), type 2 diabetes (T2D) and chronic kidney disease (CKD), are associated with reduced quality of life and are a major cause of death.[1,2,3,4] As these diseases share many modifiable risk factors, common opportunities for prevention have been suggested.[5,6,7] To improve the cost-effectiveness of prevention programs, a target population is needed,[8,9,10] and valid tools are needed to accurately identify individuals at risk for chronic cardiometabolic disease who may benefit most from interventions.We recently developed such a risk prediction tool, including only non-invasive measures, to predict the combined 7-year risk for CVD, T2D and CKD in the general population.[7]. Chronic cardiometabolic diseases, including cardiovascular disease (CVD), type 2 diabetes (T2D) and chronic kidney disease (CKD), are associated with reduced quality of life and are a major cause of death.[1,2,3,4] As these diseases share many modifiable risk factors, common opportunities for prevention have been suggested.[5,6,7] To improve the cost-effectiveness of prevention programs, a target population is needed,[8,9,10] and valid tools are needed to accurately identify individuals at risk for chronic cardiometabolic disease who may benefit most from interventions. 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
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