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Sensitivity of the EQ-5D-5L for fatigue, memory and concentration problems, and dyspnea, and their added value in patients after COVID-19 with persistent long-term symptoms : - An application of multiple linear regression and LASSO

This thesis examined the sensitivity of the EQ-5D-5L instrument in measuring health-related quality of life (HRQoL) among patients with persistent symptoms following COVID-19, including fatigue, memory and concentration problems, and dyspnea. Additionally, it was analyzed whether adding these symptoms to the EQ-5D-5L improved the explained variance for HRQoL. Patients from Uppsala University Hospital, Sweden, answered a survey that included questions on five dimensions of health represented by the EQ-5D-5L and an additional question on general health score called EQ-VAS. Multiple linear regression, Spearman’s rank correlation coefficient, and Least Absolute Shrinkage and Selection Operator (LASSO) were used to examine the sensitivity of the EQ-5D-5L. For the explanatory analysis, the Adjusted 𝑅2 was used to evaluate explanatory power with and without the presence of the symptoms. The results showed that the EQ-5D-5L dimensions explained a moderate proportion of the variance for fatigue and memory/concentration problems and a weak proportion for dyspnea. The explanatory analysis provided findings that fatigue significantly improved the explained variance of EQ-VAS by 5.5%, adding memory/concentration problems only improved it marginally, and adding dyspnea was non-significant. Additionally, strong to moderate correlations between fatigue and memory/concentration problems were found with multiple dimensions of the EQ-5D-5L. These findings suggest that the EQ-5D-5L instrument may be a valuable tool in assessing HRQoL in patients with persistent COVID-19 symptoms and that adding fatigue to the EQ-5D-5L could be beneficial for improving explanatory power to HRQoL in patients suffering from infectious disease. / COMBAT post-covid

Identiferoai:union.ndltd.org:UPSALLA1/oai:DiVA.org:uu-504216
Date January 2023
CreatorsWadsten, Carl
PublisherUppsala universitet, Statistiska institutionen
Source SetsDiVA Archive at Upsalla University
LanguageEnglish
Detected LanguageEnglish
TypeStudent thesis, info:eu-repo/semantics/bachelorThesis, text
Formatapplication/pdf
Rightsinfo:eu-repo/semantics/openAccess

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