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Impaired Retinal Vessel Dilation Predicts Mortality in End-Stage Renal Disease
Roman Günthner1, Henner Hanssen2, Christine Hauser1
1Nephrology, Technical University of Munich.
Rationale:
Patients with end-stage renal disease (ESRD) are characterized by increased cardiovascular (CV) and all-cause mortality due to advanced remodeling of the macro- and microvascular beds.
Objective:
The aim of this study was to determine whether retinal microvascular function can predict all-cause and CV mortality in patients with ESRD.
Methods And Results:
In the multicenter prospective observational ISAR (Risk Stratification in End-Stage Renal Disease) study, data on dynamic retinal vessel analysis (DVA) was available in a sub-cohort of 214 dialysis patients (mean age 62.6{plus minus}15.0; 32% female). Microvascular dysfunction was quantified by measuring maximum arteriolar (aMax) and venular dilation (vMax) of retinal vessels in response to flicker light stimulation. During a mean follow-up of 44 months, 55 patients died, including 25 CV and 30 non-CV fatal events. vMax emerged as a strong independent predictor for all-cause mortality. In the Kaplan-Meier analysis, individuals within the lowest tertile of vMax showed significantly shorter three-year survival rates than those within the highest tertile (66.9{plus minus}5.8% vs 92.4{plus minus}3.3%). Uni- and multivariate hazard ratios for all-cause mortality per SD increase of vMax were 0.62 [0.47;0.82] and 0.65[0.47;0.91], respectively. aMax and vMax were able to significantly predict nonfatal and fatal CV events (HR 0.74[0.57;0.97] and 0.78[0.61;0.99], respectively).
Conclusions:
Our results provide the first evidence that impaired retinal venular dilation is a strong and independent predictor of all-cause mortality in hemodialyzed ESRD patients. DVA provides added value for prediction of all-cause mortality and may be a novel diagnostic tool to optimize CV risk stratification in ESRD and other high-risk CV cohorts.
Clinical Trial Registration:
NCT01152892.
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Prediction Intervals
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.

