The EORTC QLU-C10D distinguished better between cancer patients and the general population than PROPr and EQ-5D-5L in a cross-sectional study

Annika Döhmen; Alexander Obbarius; Milan Kock; Sandra Nolte; Christopher J Sidey-Gibbons; José M Valderas; Jens Rohde; Kathrin Rieger; Felix Fischer; Ulrich Keilholz; Matthias Rose; Christoph Paul Klapproth

doi:10.1016/j.jclinepi.2024.111592

The EORTC QLU-C10D distinguished better between cancer patients and the general population than PROPr and EQ-5D-5L in a cross-sectional study

J Clin Epidemiol. 2024 Nov 7:177:111592. doi: 10.1016/j.jclinepi.2024.111592. Online ahead of print.

Affiliations

¹ Center for Internal Medicine and Dermatology, Department of Psychosomatic Medicine, Charité - Universitätsmedizin Berlin, Berlin, Germany. Electronic address: annika.doehmen@charite.de.
² Center for Internal Medicine and Dermatology, Department of Psychosomatic Medicine, Charité - Universitätsmedizin Berlin, Berlin, Germany; BIH Charité Digital Clinician Scientist Program, BIH Biomedical Innovation Academy, Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Charitéplatz 1 10117, Berlin, Germany; Dornsife Center for Self-Report Science, University of Southern California, Los Angeles, CA, USA.
³ Center for Internal Medicine and Dermatology, Department of Psychosomatic Medicine, Charité - Universitätsmedizin Berlin, Berlin, Germany.
⁴ Faculty of Medicine, Nursing and Health Sciences, Eastern Health Clinical School, Monash University, Melbourne, Victoria, Australia; School of Health Sciences, Swinburne University of Technology, Melbourne, Victoria, Australia.
⁵ MD Anderson Center for INSPiRED Cancer Care (Integrated Systems for Patient-Reported Data), The University of Texas, MD Anderson Cancer Center, Houston, TX, USA; Department of Symptom Research, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
⁶ Division of Family Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore; Centre for Research in Health Systems Performance, National University of Singapore, Singapore, Singapore.
⁷ Department of Hematology, Oncology and Cancer Immunity, Campus Benjamin Franklin, Charité - Universitätsmedizin Berlin, Berlin, Germany.
⁸ Charité Comprehensive Cancer Center (CCCC), Department of Oncology, Charité - Universitätsmedizin Berlin, Germany.
⁹ Center for Internal Medicine and Dermatology, Department of Psychosomatic Medicine, Charité - Universitätsmedizin Berlin, Berlin, Germany; BIH Charité Digital Clinician Scientist Program, BIH Biomedical Innovation Academy, Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Charitéplatz 1 10117, Berlin, Germany.

PMID: 39515489
DOI: 10.1016/j.jclinepi.2024.111592

Abstract

Objectives: Health state utility (HSU) instruments for calculating quality-adjusted life years, such as the European Organisation for Research and Treatment of Cancer (EORTC) Quality of Life Utility - Core 10 Dimensions (QLU-C10D), derived from the EORTC QLQ-30 questionnaire, the Patient-Reported Outcome Measurement Information System (PROMIS) preference score (PROPr), and the EuroQoL-5-Dimensions-5-Levels (EQ-5D-5L), yield different HSU values due to different modeling and different underlying descriptive scales. For example the QLU-C10D includes cancer-relevant dimensions such as nausea. This study aimed to investigate how these differences in descriptive scales contribute to differences in HSU scores by comparing scores of cancer patients receiving chemotherapy to those of the general population.

Study design and setting: EORTC QLU-C10D, PROPr, and EQ-5D-5L scores were obtained for a convenience sample of 484 outpatients of the Department of Oncology, Charité - Universitätsmedizin Berlin, Germany. Convergent and known group's validity were assessed using Pearson's correlation and intraclass correlation coefficients (ICC). We assessed each descriptive dimension score's discriminatory power and compared them to those of the general population (n > 1000) using effect size (ES; Cohen's d) and area under the curve (AUC).

Results: The mean scores of QLU-C10D (0.64; 95% CI 0.62-0.67), PROPr (0.38; 95% CI 0.36-0.40), and EQ-5D-5L (0.72; 95% CI 0.70-0.75) differed significantly, irrespective of sociodemographic factors, condition, or treatment. Conceptually similar descriptive scores as obtained from the HSU instruments showed varying degrees of discrimination in terms of ES and AUC between patients and the general population. The QLU-C10D and its dimensions showed the largest ES and AUC.

Conclusion: The QLU-C10D and its domains distinguished best between health states of the two populations, compared to the PROPr and EQ-5D-5L. As the EORTC Core Quality of Life Questionnaire (QLQ-C30) is widely used in clinical practice, its data are available for economic evaluation.

Plain language summary: The assessment of dimensions of health-related quality of life (HRQoL), such as physical functioning or depression, is important to cancer patients and physicians for treatment and side effect monitoring. Descriptive HRQoL is measured by patient-reported outcomes measures (PROM). The European Organisation for Research and Treatment of Cancer (EORTC) QLQ-C30 questionnaire and the Patient-Reported Outcome Measurement Information System (PROMIS) are the most common PROM in the clinical HRQoL assessment. In recent years, multidimensional preference-based HRQoL measures were developed using these PROM as dimensions. These preference-based measures, also referred to as health state utility (HSU) scores, are needed for economic evaluations of treatments. The QLQ-C30's corresponding HSU score is the quality-of-life utility measure-core 10 dimensions (QLU-C10D), and PROMIS' HSU score is the PROMIS preference score (PROPr). Both new HSU scores are frequently compared to the well-established EuroQoL-5-dimensions-5-levels (EQ-5D-5L). They all conceptualize HSU differently, as they assess different dimensions of HRQoL und use different models. Both the QLU-C10D and the PROPr have thus shown systematic differences to the EQ-5D-5L but these were largely consistent across the subgroups. Convergent and known groups validity can therefore be considered established. However, as HSU is a multidimensional construct, it remains unclear how differences in its dimensions, for example, its descriptive scales, contribute to differences in HSU scores. This is of importance as it is the descriptive scales that measure clinical HRQoL. We investigated this question by assessing each dimension's ability to distinguish between a sample of 484 cancer patients and the German general population. We could show that the ability to distinguish depended on the domain: for example, for depression, the QLU-C10D and EQ-5D-5L distinguished clearer, while for physical function, PROMIS did. Overall, the QLU-C10D and its dimensions distinguish best between cancer patients and general population.

Keywords: Cancer; Chemotherapy; EORTC QLQ-C30; EQ-5D-5L; Health-related quality of life; PROMIS-29; PROPr; Patient reported outcomes; QLU-C10D.