Symptom clusters predict mortality among dialysis patients in Norway: a prospective observational cohort study

Amin Amro; Bård Waldum; Nanna von der Lippe; Fredrik Barth Brekke; Toril Dammen; Christine Miaskowski; Ingrid Os

doi:10.1016/j.jpainsymman.2014.04.005

Symptom clusters predict mortality among dialysis patients in Norway: a prospective observational cohort study

J Pain Symptom Manage. 2015 Jan;49(1):27-35. doi: 10.1016/j.jpainsymman.2014.04.005. Epub 2014 May 22.

Authors

Amin Amro¹, Bård Waldum², Nanna von der Lippe², Fredrik Barth Brekke², Toril Dammen³, Christine Miaskowski⁴, Ingrid Os⁵

Affiliations

¹ Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway; Department of Nephrology Ullevål, Oslo University Hospital, Oslo, Norway. Electronic address: a.a.g.amro@medisin.uio.no.
² Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway.
³ Institute of Basic Medical Sciences, Faculty of Medicine, University of Oslo, Oslo, Norway.
⁴ Department of Physiological Nursing, University of California at San Francisco, San Francisco, California, USA.
⁵ Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway; Department of Nephrology Ullevål, Oslo University Hospital, Oslo, Norway.

PMID: 24858738
DOI: 10.1016/j.jpainsymman.2014.04.005

Abstract

Context: Patients with end-stage renal disease on dialysis have reduced survival rates compared with the general population. Symptoms are frequent in dialysis patients, and a symptom cluster is defined as two or more related co-occurring symptoms.

Objectives: The aim of this study was to explore the associations between symptom clusters and mortality in dialysis patients.

Methods: In a prospective observational cohort study of dialysis patients (n = 301), Kidney Disease and Quality of Life Short Form and Beck Depression Inventory questionnaires were administered. To generate symptom clusters, principal component analysis with varimax rotation was used on 11 kidney-specific self-reported physical symptoms. A Beck Depression Inventory score of 16 or greater was defined as clinically significant depressive symptoms. Physical and mental component summary scores were generated from Short Form-36. Multivariate Cox regression analysis was used for the survival analysis, Kaplan-Meier curves and log-rank statistics were applied to compare survival rates between the groups.

Results: Three different symptom clusters were identified; one included loading of several uremic symptoms. In multivariate analyses and after adjustment for health-related quality of life and depressive symptoms, the worst perceived quartile of the "uremic" symptom cluster independently predicted all-cause mortality (hazard ratio 2.47, 95% CI 1.44-4.22, P = 0.001) compared with the other quartiles during a follow-up period that ranged from four to 52 months. The two other symptom clusters ("neuromuscular" and "skin") or the individual symptoms did not predict mortality.

Conclusion: Clustering of uremic symptoms predicted mortality. Assessing co-occurring symptoms rather than single symptoms may help to identify dialysis patients at high risk for mortality.

Keywords: End-stage renal disease; dialysis; mortality; quality of life; symptom cluster; symptoms.

Publication types

Observational Study
Research Support, Non-U.S. Gov't

MeSH terms

Cohort Studies
Depression / epidemiology
Female
Follow-Up Studies
Humans
Kaplan-Meier Estimate
Kidney Failure, Chronic / mortality*
Kidney Failure, Chronic / physiopathology
Kidney Failure, Chronic / psychology
Kidney Failure, Chronic / therapy*
Male
Middle Aged
Multivariate Analysis
Norway / epidemiology
Principal Component Analysis
Prospective Studies
Quality of Life
Renal Dialysis*
Surveys and Questionnaires