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Long interdialytic interval and mortality among patients receiving hemodialysis
Robert N Foley1, David T Gilbertson, Thomas Murray
1United States Renal Data System, University of Minnesota, Minneapolis, MN 55404, USA.
Background:
Patients with end-stage renal disease requiring dialysis have limited tolerance of metabolic and volume-related deviations from normal ranges; in addition, the prevalence of cardiovascular disease is high among such patients. Given these problems, we hypothesized that a long interdialytic interval is associated with adverse events in patients receiving hemodialysis.
Methods:
We studied 32,065 participants in the End-Stage Renal Disease Clinical Performance Measures Project, a nationally representative sample of U.S. patients receiving hemodialysis three times weekly, at the end of calendar years 2004 through 2007. We compared rates of death and cardiovascular-related hospital admissions on the day after the long (2-day) interdialytic interval with rates on other days.
Results:
The mean age of the cohort was 62.2 years; 24.2% of the patients had been receiving dialysis treatment for 1 year or less. Over a mean follow-up interval of 2.2 years, the following event rates were higher on the day after the long interval than on other days: all-cause mortality (22.1 vs. 18.0 deaths per 100 person-years, P<0.001), mortality from cardiac causes (10.2 vs. 7.5, P<0.001), infection-related mortality (2.5 vs. 2.1, P = 0.007), mortality from cardiac arrest (1.3 vs. 1.0, P = 0.004), mortality from myocardial infarction (6.3 vs. 4.4, P<0.001), and admissions for myocardial infarction (6.3 vs. 3.9, P<0.001), congestive heart failure (29.9 vs. 16.9, P<0.001), stroke (4.7 vs. 3.1, P<0.001), dysrhythmia (20.9 vs. 11.0, P<0.001), and any cardiovascular event (44.2 vs. 19.7, P<0.001).
Conclusions:
The long (2-day) interdialytic interval is a time of heightened risk among patients receiving hemodialysis. (Funded by the National Institutes of Health.).
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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.