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Meta-Analysis
. 2007 Sep-Oct;10(5):390-7.
doi: 10.1111/j.1524-4733.2007.00193.x.

Quality of life assessed with the Medical Outcomes Study Short Form 36-Item Health Survey of patients on renal replacement therapy: a systematic review and meta-analysis

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Meta-Analysis

Quality of life assessed with the Medical Outcomes Study Short Form 36-Item Health Survey of patients on renal replacement therapy: a systematic review and meta-analysis

Ylian S Liem et al. Value Health. 2007 Sep-Oct.
Free article

Abstract

Objectives: The Medical Outcomes Study Short Form 36-Item Health Survey (SF-36) is the most widely used generic instrument to estimate quality of life of patients on renal replacement therapy. Purpose of this study was to summarize and compare the published literature on quality of life of hemodialysis (HD), peritoneal dialysis (PD), and renal transplant (RTx) patients.

Methods: We used random-effects regression analyses to compare the SF-36 scores across treatment groups and adjusted this comparison for age and prevalence of diabetes using random-effects meta-regression analyses.

Results: We found 52 articles that met the inclusion criteria, reporting quality of life of 36,582 patients. The unadjusted scores of all SF-36 health dimensions were not significantly different between HD and PD patients, but the scores of RTx patients were higher than those of dialysis patients, except for the dimensions Mental Health and Bodily Pain. Point differences between dialysis and RTx patients varied from 2 to 32. With adjustment for age and diabetes, the differences became smaller (point difference 2-22). The significance of the differences of both dialysis groups compared with RTx recipients disappeared for the dimensions Vitality and Social Functioning. The significance of the differences between HD and RTx patients disappeared on the dimensions Physical Functioning, Role Physical, and Bodily Pain.

Conclusion: We conclude that dialysis patients have a lower quality of life than RTx patients, but this difference can partly be explained by differences in age and prevalence of diabetes.

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