BACKGROUND: The usefulness of estimated glomerular filtration rate (eGFR) and albuminuria for prediction of cardiovascular outcomes is controversial. We aimed to assess the addition of creatinine-based eGFR and albuminuria to traditional risk factors for prediction of cardiovascular risk with a meta-analytic approach. METHODS: We meta-analysed individual-level data for 637 315 individuals without a history of cardiovascular disease from 24 cohorts (median follow-up 4.2-19.0 years) included in the Chronic Kidney Disease Prognosis Consortium. We assessed C statistic difference and reclassification improvement for cardiovascular mortality and fatal and non-fatal cases of coronary heart disease, stroke, and heart failure in a 5 year timeframe, contrasting prediction models for traditional risk factors with and without creatinine-based eGFR, albuminuria (either albumin-to-creatinine ratio [ACR] or semi-quantitative dipstick proteinuria), or both. FINDINGS: The addition of eGFR and ACR significantly improved the discrimination of cardiovascular outcomes beyond traditional risk factors in general populations, but the improvement was greater with ACR than with eGFR, and more evident for cardiovascular mortality (C statistic difference 0.0139 [95% CI 0.0105-0.0174] for ACR and 0.0065 [0.0042-0.0088] for eGFR) and heart failure (0.0196 [0.0108-0.0284] and 0.0109 [0.0059-0.0159]) than for coronary disease (0.0048 [0.0029-0.0067] and 0.0036 [0.0019-0.0054]) and stroke (0.0105 [0.0058-0.0151] and 0.0036 [0.0004-0.0069]). Dipstick proteinuria showed smaller improvement than ACR. The discrimination improvement with eGFR or ACR was especially evident in individuals with diabetes or hypertension, but remained significant with ACR for cardiovascular mortality and heart failure in those without either of these disorders. In individuals with chronic kidney disease, the combination of eGFR and ACR for risk discrimination outperformed most single traditional predictors; the C statistic for cardiovascular mortality fell by 0.0227 (0.0158-0.0296) after omission of eGFR and ACR compared with less than 0.007 for any single modifiable traditional predictor. INTERPRETATION: Creatinine-based eGFR and albuminuria should be taken into account for cardiovascular prediction, especially when these measures are already assessed for clinical purpose or if cardiovascular mortality and heart failure are outcomes of interest. ACR could have particularly broad implications for cardiovascular prediction. In populations with chronic kidney disease, the simultaneous assessment of eGFR and ACR could facilitate improved classification of cardiovascular risk, supporting current guidelines for chronic kidney disease. Our results lend some support to also incorporating eGFR and ACR into assessments of cardiovascular risk in the general population. FUNDING: US National Kidney Foundation, National Institute of Diabetes and Digestive and Kidney Diseases.
AD - Johns Hopkins Bloomberg School of Public Health, Baltimore, MD.BACKGROUND: The usefulness of estimated glomerular filtration rate (eGFR) and albuminuria for prediction of cardiovascular outcomes is controversial. We aimed to assess the addition of creatinine-based eGFR and albuminuria to traditional risk factors for prediction of cardiovascular risk with a meta-analytic approach. METHODS: We meta-analysed individual-level data for 637 315 individuals without a history of cardiovascular disease from 24 cohorts (median follow-up 4.2-19.0 years) included in the Chronic Kidney Disease Prognosis Consortium. We assessed C statistic difference and reclassification improvement for cardiovascular mortality and fatal and non-fatal cases of coronary heart disease, stroke, and heart failure in a 5 year timeframe, contrasting prediction models for traditional risk factors with and without creatinine-based eGFR, albuminuria (either albumin-to-creatinine ratio [ACR] or semi-quantitative dipstick proteinuria), or both. FINDINGS: The addition of eGFR and ACR significantly improved the discrimination of cardiovascular outcomes beyond traditional risk factors in general populations, but the improvement was greater with ACR than with eGFR, and more evident for cardiovascular mortality (C statistic difference 0.0139 [95% CI 0.0105-0.0174] for ACR and 0.0065 [0.0042-0.0088] for eGFR) and heart failure (0.0196 [0.0108-0.0284] and 0.0109 [0.0059-0.0159]) than for coronary disease (0.0048 [0.0029-0.0067] and 0.0036 [0.0019-0.0054]) and stroke (0.0105 [0.0058-0.0151] and 0.0036 [0.0004-0.0069]). Dipstick proteinuria showed smaller improvement than ACR. The discrimination improvement with eGFR or ACR was especially evident in individuals with diabetes or hypertension, but remained significant with ACR for cardiovascular mortality and heart failure in those without either of these disorders. In individuals with chronic kidney disease, the combination of eGFR and ACR for risk discrimination outperformed most single traditional predictors; the C statistic for cardiovascular mortality fell by 0.0227 (0.0158-0.0296) after omission of eGFR and ACR compared with less than 0.007 for any single modifiable traditional predictor. INTERPRETATION: Creatinine-based eGFR and albuminuria should be taken into account for cardiovascular prediction, especially when these measures are already assessed for clinical purpose or if cardiovascular mortality and heart failure are outcomes of interest. ACR could have particularly broad implications for cardiovascular prediction. In populations with chronic kidney disease, the simultaneous assessment of eGFR and ACR could facilitate improved classification of cardiovascular risk, supporting current guidelines for chronic kidney disease. Our results lend some support to also incorporating eGFR and ACR into assessments of cardiovascular risk in the general population. FUNDING: US National Kidney Foundation, National Institute of Diabetes and Digestive and Kidney Diseases.
PY - 2015 SN - 2213-8595 (Electronic) SP - 514 EP - 25 T2 - Lancet Diabetes Endocrinol TI - Estimated glomerular filtration rate and albuminuria for prediction of cardiovascular outcomes: a collaborative meta-analysis of individual participant data VL - 3 Y2 - FY16 ER -