Self-Rated Health and Adverse Events in CKD

Background and objectives

Little is known about the utility of self-rated general health assessments in persons with moderate-to-severe CKD. This study examined the ability of a single self-rated health measure to predict all-cause mortality and kidney disease progression in a cohort of 443 patients with stages 3–4 CKD, recruited between 2005 and 2011, and followed until the end of 2012. The performance of models incorporating self-rated health measures was compared with previously published predictive models and more complex models comprising a multibiomarker panel.

Design, setting, participants, & measurements

Participants were asked “In general, would you say your health is excellent, very good, good, fair, or poor?” Outcomes examined were time to all-cause mortality, kidney disease progression (initiation of RRT or 30% loss of eGFR), and a composite of these events. Model performances were compared using a nonparametric area under the curve (AUC) analysis.

Results

Over a median follow-up of 3.3 years, 118 (27%) participants died and 138 (31%) had progression of kidney disease. Fair-to-poor self-rated health status was associated with significantly greater risks of mortality (fully adjusted hazard ratio [HR] for relative to good-to-excellent self-rated health, 2.76; 95% confidence interval [95% CI], 1.28 to 5.89), kidney disease progression (HR, 1.94; 95% CI, 1.49 to 2.56), and the combined end point (HR, 2.21; 95% CI, 1.66 to 2.96). For 3-year mortality prediction, the self-rated health model (AUC, 0.80; 95% CI, 0.76 to 0.85) had significantly higher AUCs than the base model (AUC, 0.71; 95% CI, 0.66 to 0.76) and the multibiomarker panel model (AUC, 0.74; 95% CI, 0.68 to 0.80) (P=0.03 and P=0.04, respectively).

Conclusions

A single, easily obtained measure of self-rated health helps identify patients with CKD at high risk of mortality and kidney disease progression. Routine evaluation of self-rated health may help target individuals who might benefit from more intensive monitoring strategies.