Estimated glomerular filtration rate and albuminuria for prediction of cardiovascular outcomes: a collaborative meta-analysis of individual participant data

Abstract

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.

Figure 1

Adjusted hazard ratios and 95% CIs (shaded areas or whisker plots) of cardiovascular mortality (top row), coronary heart disease (second row), stroke (third row), and heart failure (bottom row) according to eGFR (left column) and ACR (right column) in the combined general population and high-risk cohorts. The reference is eGFR 95 ml/min/1.73m² and ACR 5 mg/g (diamond). Dots represent statistical significance (P<0.05). *Adjustments were for age, sex, race/ethnicity, smoking, systolic blood pressure, antihypertensive drugs, diabetes, total and high-density lipoprotein cholesterol concentrations, and albuminuria (ACR or dipstick) or eGFR, as appropriate. In the analyses of eGFR, there were 629,776 participants for cardiovascular mortality, 144,874 for coronary heart disease, 137,658 for stroke, and 105,127 for heart failure. In the analyses of ACR, there were 120,148 participants for cardiovascular mortality, 91,185 for coronary heart disease, 82,646 for stroke, and 55,855 for heart failure.

Figure 2

Difference in C-statistic for cardiovascular outcomes by adding kidney measure(s) to traditional models in the combined general population and high-risk cohorts. There was only one study with dipstick proteinuria and heart failure, and thus meta-analysis was not performed.

Figure 3

Change in c-statistics for cardiovascular outcomes by adding eGFR, ACR, and both to traditional risk factors in general population and high risk cohorts, according to the status of diabetes and hypertension.

Figure 4

Number needed to screen (NNS) for preventing one event among individuals at high risk of each CVD outcome. High risk was defined as 5-y risk ≥10%, and NNS is based on the assumption of 20% risk reduction by interventions. * indicates statistical significance (p <0.05) compared to NNS based on the base model with traditional predictors. In these analyses there were 27,745 participants for cardiovascular mortality, 17,531 for coronary heart disease, 16,869 for stroke, and 19,265 for heart failure.

Figure 5

C-statistic difference for four cardiovascular outcomes by omitting kidney disease measures and traditional risk factors from a model with all risk factors in a CKD population