Survival of hemodialysis patients : modeling differences in risk of dialysis centers.

Sá Carvalho, Marilia and Henderson, Robin and Shimakura, Silvia and Pereira Silva Cunha Sousa, Ines (2003) Survival of hemodialysis patients : modeling differences in risk of dialysis centers. International Journal for Quality in Health Care, 15 (3). pp. 189-196. ISSN 1464-3677

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<Objective>: Dialysis is the most common renal replacement therapy for patients with end stage renal disease. This paper considers survival of dialysis patients, aiming to assess quality of renal replacement therapy at dialysis centers in Rio de Janeiro, Brazil, and to investigate differences in survival between health facilities. <Methods>: A Cox proportional hazards model, allowing for time-varying covariates and prevalent data, was the basic method used to analyze the survival of 11 579 patients on hemodialysis in 67 health facilities in Rio de Janeiro State from January 1998 until August 2001, using data obtained from routine information systems. A frailty random effects model was applied to investigate differences in mortality between health centers not explained by measured characteristics. <Results>: The individual variables associated with the outcome were age and underlying disease, with diabetes being the main isolated risk factor. Considering covariates of the health unit, two factors were associated with performance: bigger units had on average better survival times than smaller ones and units which offered cyclic peritoneal dialysis performed less well than those that did not. There were significant frailty effects among centers, with relative risks varying between 0.24 and 3.15, and an estimated variance of 0.43. <Conclusions>: Routine assessment based on health registries of the outcome of any high technology medical treatment is extremely important in maintaining quality of care and in estimating the impact of changes in therapies, units, and patient profiles. The frailty model allowed estimation of variation in risk between centers not attributable to any measured covariates. This can be used to guide more specific investigation and changes in health policies related to renal transplant therapies.

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Journal Article
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International Journal for Quality in Health Care
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08 Oct 2016 00:06
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21 Nov 2022 17:52