Safitri, Atika; (2024) Investigating Quality of Life in Ocular Hypertension and Glaucoma: A Patient-Centred Analysis of Preferences and Utilities. Doctoral thesis (Ph.D), UCL (University College London).

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Abstract

Glaucoma has a profound impact on patients’ quality of life, not only due to the disease but also its treatment. Several clinical trials have used health-related quality of life (HRQoL) as their outcomes; however, no significant improvement was found as reflected in HRQoL scores. Clearly, there is a gap in understanding how to measure the HRQoL from the perspective of patients and clinicians. The aims of this thesis were: 1) to explore the HRQoL measures that are sensitive to the unique expectation and experiences of glaucoma patients, 2) to assess individual priorities and their treatment preferences, 3) to predict the HRQoL assesed using utilities, and 4) to compare patient-reported outcome and clinician-reported outcomes. This PhD thesis consists of four workstreams using a mixed-method design recruiting 25 patients for qualitative design, and 513 patients and 134 clinicians for quantitative design. In the first study, a one-to-one interview was used. In the second study, a preference elicitation design was used. In the third study, linear and non-linear prediction models were developed. In the fourth study, the results from the second and third study were incorporated using Rasch analysis and compared. In the first study, treatment-related outcomes were found to be distinct to disease-related outcomes and were considered an important aspect for glaucoma patients regardless of their disease severity. In the second study, drop-freedom was found to be one of the preferences which were affected by the ethnicity. In the third study, HRQoL could be accurately predicted using utilities. In the fourth study, a lack of agreement on the perception of HRQoL was found between clinicians and patients. This study highlights the aspect of HRQoL where treatment is burdensome for patients. Various methods can be used to measure HRQoL – a very subjective outcome, ranging from interview and prediction model using machine learning.

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