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Quality of Life in Multiple Myeloma

其他書名	Longitudinal Trajectories and Monitoring Symptoms and Quality of Life to Improve Quality of Care
出版	King's College London, 2016
URL	http://books.google.com.hk/books?id=jXyL0AEACAAJ&hl=&source=gbs_api

註釋Background: Multiple myeloma is an increasingly common disease, but there is little evidence about the change in symptoms and problems in more advanced stages. Aim: To describe the health-related quality of life (QOL) trajectories in multiple myeloma, and to evaluate the longitudinal validity of the Myeloma Patient Outcome Scale (MyPOS), a questionnaire to monitor QOL and palliative care concerns. Methods: A national, multi-centre, observational study comprising (1) a cross-sectional analysis merging data from two studies, and (2) a longitudinal study, recruiting patients at various stages of the disease. Demographic and clinical data was collected alongside QOL measures. Analysis: (i) prevalence of symptoms and independently associated factors with poor quality of life, (ii) latent growth mixture analysis of quality of life trajectories, (iii) longitudinal validity and reliability via Rasch analysis, Generalizability theory and responsiveness to change. Results: (i) Cross-sectional study: 557 patients reported a mean of 7.2 symptoms with the most common symptoms, pain, fatigue and breathlessness, being present in 61-78% of patients. General symptom level, pain, anxiety and depression, physical decline, age and phase of illness had significant independent associations with high palliative care concerns. (ii) Longjtudinal study: Four classes of individual QOL trajectories were identified (n=224): declining HRQOL over 8 months, stable moderate to good QOL, improving QOL, and fluctuating poor QOL. Logistic regression analysis revealed general symptom level (OR = 1.28), pain (OR=1.03) and presence of clinically relevant anxiety or depression (OR=l.19) to be predictors for a declining or poor QOL trajectory. (iii) The MyPOS demonstrated good to excellent test-retest reliability. Rasch analysis identified limitations of suboptimal scale-to-sample targeting, resulting in floor effects.