Background/Aims: Hemodialysis (HD) patients are hospitalized more frequentl
y than patients with other chronic diseases, averaging 11.5 hospital days/p
atient/year. Hospital costs attributable to renal failure in the US exceed
$2 billion per year. The present healthcare climate continues to force dial
ysis providers to focus on these issues in order to optimize patient care w
hile limiting cost. Methods: We used a novel method for analyzing hospitali
zation risk, a multiple-event Cox proportional hazards model, to identify f
actors that influenced hospitalization in a HD unit population over a two-y
ear period. This model allows individual patients to contribute multiple fa
ilure events to the model while controlling for the serial dependency of ev
ents. Results: 178 HD patients were retrospectively examined. There were 38
1 hospitalizations during the study period, averaging out to 1.9 hospitaliz
ations and 10.5 hospital days/patient-year. Substance abuse and diabetes co
nveyed the largest risks for hospitalization (diabetes RR: 2.09; substance
abuse RR: 2.24) in the study cohort, exposing the necessity for examining p
ractice patterns and behavioral interventions as means for improving HD pat
ient care. Conclusion: Despite the small numbers of patients in this single
-center HD population, the model achieved adequate statistical power. There
fore, it has the potential to serve as a continuous quality improvement (CQ
I) tool in particular HD patient sub-groups, or in individual HD units. Cop
yright (C) 1999 S. Karger AG. Basel.