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

被引:11
作者
Rauh, Simone P. [1 ]
Rutters, Femke [1 ]
van der Heijden, Amber A. W. A. [2 ]
Luimes, Thomas [1 ]
Alssema, Marjan [1 ,3 ]
Heymans, Martijn W. [1 ]
Magliano, Dianna J. [4 ,5 ]
Shaw, Jonathan E. [4 ,5 ]
Beulens, Joline W. [1 ]
Dekker, Jacqueline M. [1 ]
机构
[1] Vrije Univ Amsterdam, Med Ctr, Amsterdam Publ Hlth Res Inst, Dept Epidemiol & Biostat, Amsterdam, Netherlands
[2] Vrije Univ Amsterdam Med Ctr, Amsterdam Publ Hlth Res Inst, Dept Gen Practice & Elderly Care Med, Amsterdam, Netherlands
[3] Unilever Res Labs, Vlaardingen, Netherlands
[4] Baker IDI Heart & Diabet Inst, Dept Clin Diabet & Epidemiol, Melbourne, Vic, Australia
[5] Monash Univ, Dept Epidemiol & Prevent Med, Melbourne, Vic, Australia
基金
英国医学研究理事会;
关键词
prediction tool; generalizability; cardiovascular disease; type; 2; diabetes; chronic kidney disease; LIFE-STYLE; OBESITY; AUSDIAB; HEALTH; IMPACT; MODEL;
D O I
10.1007/s11606-017-4231-7
中图分类号
R19 [保健组织与事业(卫生事业管理)];
学科分类号
摘要
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. We aimed to validate a previously developed non-invasive risk prediction tool for predicting the combined 7-year-risk for chronic cardiometabolic diseases. 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. The risk prediction tool was validated in 3544 participants from the Australian Diabetes, Obesity and Lifestyle Study (AusDiab). 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]). 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.
引用
收藏
页码:182 / 188
页数:7
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