Health-related quality of life of different patient groups in Swedish national quality registers : longitudinal studies of EQ-5D data and application of different value sets

Abstract

Background: EQ-5D data on patients’ health-related quality of life (HRQoL) are collected in roughly 40% of about 100 National Quality Registers (NQRs) in Sweden. The aim of the thesis was to increase knowledge on the use of the EQ-5D-3L in assessing HRQoL of patients in different NQRs and to compare different value sets used to describe HRQoL.

Methods: In Study I, data on patients from 11 NQRs with spine surgery, hip arthroplasty, knee arthroplasty, ankle surgery, anterior cruciate ligament, osteoarthritis, fractures, heart failure, respiratory failure, psoriasis, and rheumatology were included. A total of 266,241 patients from the NQRs and 49,169 members of the general population were included. In Study II, 69,290 patients in the Swedish Hip Arthroplasty Register at baseline and 1-year follow-up, and 21,305 patients at 6-year follow-up were included. Data on demographic and clinical characteristics and EQ-5D-3L data were retrieved in both studies. In Study I, descriptive analyses, Paretian Classification of Health Change (PCHC) and a two-level random intercept model of the impact of diagnoses on EQ VAS scores were performed. EQ-5D-3L indices were calculated using eight value sets from Sweden, Germany, Denmark, and the UK. One-way analysis of variance was used to assess the discriminative ability of the value sets across American Society of Anaesthesiologists (ASA) classes.

Results: In Study I, the pain/discomfort dimension was the dimension with the highest proportion of problems reported both in most registers and in the general population. The highest proportion of improved category of PCHC were reported by patients from the ankle, hip, knee, and spine registers. The two-level random intercept models of EQ VAS score, as predicted by diagnoses, showed patients in most registers, with the exception of fractures, had lower scores than the general population at baseline and at 1-year follow-up. In Study II, all value sets were able to discriminate HRQoL among the ASA classes, and showed the predictive ability of ASA classes on HRQoL.

Conclusion: Both studies demonstrated the importance of the EQ-5D-3L instrument in providing HRQoL data to complement clinical data. The studies documented HRQoL of different patient groups and demonstrated the consistency of the EQ-5D-3L dimensions and the EQ VAS score. All the value sets used to summarize EQ-5D-3L data showed that ASA classes predicted HRQoL and demonstrated ability to differentiate across ASA classes.