gms | German Medical Science

20. Jahrestagung des Deutschen Netzwerks Evidenzbasierte Medizin e. V.

Deutsches Netzwerk Evidenzbasierte Medizin e. V.

21. - 23.03.2019, Berlin

Health-related quality of life in multiple myeloma and mapping algorithms to derive health-state utility values: an overview

Meeting Abstract

  • Vjollca Qerimi Rushaj - UMIT – University for Health Sciences, Medical Informatics and Technology, Institute of Public Health, Medical Decision Making and Health Technology Assessment, Department of Public Health, Health Services Research and Health Technology Assessment, Österreich; Ss. Cyril and Methodius University in Skopje, Faculty of Pharmacy, Skopje, Mazedonien
  • Beate Jahn - UMIT – University for Health Sciences, Medical Informatics and Technology, Institute of Public Health, Medical Decision Making and Health Technology Assessment, Department of Public Health, Health Services Research and Health Technology Assessment, Österreich
  • Sibylle Puntscher - UMIT – University for Health Sciences, Medical Informatics and Technology, Institute of Public Health, Medical Decision Making and Health Technology Assessment, Department of Public Health, Health Services Research and Health Technology Assessment, Österreich
  • Igor Stojkov - UMIT – University for Health Sciences, Medical Informatics and Technology, Institute of Public Health, Medical Decision Making and Health Technology Assessment, Department of Public Health, Health Services Research and Health Technology Assessment, Österreich
  • Andrea Manca - University of York, Centre for Health Economics, York, Großbritannien; Luxembourg Institute of Health, Department of Population Health, Luxemburg
  • Bernhard Holzner - Medical University of Innsbruck, Department of Psychiatry, Psychotherapy and Psychosomatics, Innsbruck, Österreich
  • Eva Gamper - Medical University of Innsbruck, Department of Psychiatry, Psychotherapy and Psychosomatics, Innsbruck, Österreich
  • Wolfgang Willenbacher - Innsbruck University Hospital, Internal Medicine V – Hematology & Oncology, Innsbruck, Österreich; ONCOTYROL– Center for Personalized Cancer Medicine, Österreich
  • Georg Kemmler - Medical University of Innsbruck, Department of Psychiatry, Psychotherapy and Psychosomatics, Innsbruck, Österreich
  • Roman Weger - ONCOTYROL– Center for Personalized Cancer Medicine, Österreich; ACMIT – Austrian Center for Medical Innovation & Technology, Österreich
  • Lucia Neppl - ONCOTYROL– Center for Personalized Cancer Medicine, Österreich
  • Dan Greenberg - Ben-Gurion University of the Negev, Department of Health Systems Management, Faculty of Health Sciences, Israel
  • Uwe Siebert - Harvard Medical School/ Harvard T. H. Chan School of Public Health, Institute for Technology Assessment, Department of Radiology, Massachusetts General Hospital, and Harvard T. H. Chan School of Public Health, Center for Health Decision Science, Department, USA; UMIT – University for Health Sciences, Medical Informatics and Technology, Institute of Public Health, Medical Decision Making and Health Technology Assessment, Department of Public Health, Health Services Research and Health Technology Assessment/, ONCOTYROL – Center for Personalized Cancer Medicine, Österreich
  • Ursula Rochau - UMIT – University for Health Sciences, Medical Informatics and Technology, Institute of Public Health, Medical Decision Making and Health Technology Assessment, Department of Public Health, Health Services Research and Health Technology Assessment, Österreich

EbM und Digitale Transformation in der Medizin. 20. Jahrestagung des Deutschen Netzwerks Evidenzbasierte Medizin. Berlin, 21.-23.03.2019. Düsseldorf: German Medical Science GMS Publishing House; 2019. Doc19ebmP-OG09-04

doi: 10.3205/19ebm118, urn:nbn:de:0183-19ebm1182

Published: March 20, 2019

© 2019 Qerimi Rushaj et al.
This is an Open Access article distributed under the terms of the Creative Commons Attribution 4.0 License. See license information at http://creativecommons.org/licenses/by/4.0/.


Outline

Text

Background/research question: Health-related quality of life (HRQoL) and health-state utility values (HSUVs) are measured with different methods and instruments. Deriving HSUVs from generic preference-based instruments (e.g., EQ-5D, SF-6D, HUI) is recommended by academic institutions, HTA agencies and reimbursement authorities. In clinical trials of patients with multiple myeloma (MM), condition-specific instruments are most frequently used to measure HRQoL. When HSUVs are not available, “mapping” can be applied to link outcomes from different measures of HRQoL to HSUVs. Therefore, our aim is to give an overview on published HRQoL data in MM patients and to assess if the identified data could be applied to derive HSUVs with already published mapping techniques.

Methods: We performed a systematic literature search in PubMed/MEDLINE, Cochrane, Tufts CEA Registry, Web of Science, EQ-5D and EORTC databases to identify studies reporting on HRQoL data and/or HSUVs in patients diagnosed and treated for MM, derived from EQ-5D and/or EORTC (QLQ-C30 or QLQ-MY20). We used evidence tables to systematically extract and summarize HRQoL data, HSUVs and study characteristics. To assess if published mapping algorithms could be used to derive HSUVs from the extracted HRQoL data, we compared population characteristics, treatments and functional scores reported.

Results: From the overall 847 identified studies, we included 31 studies for analysis based on our inclusion criteria. All included studies reported data from the QLQ-C30 for MM patients, five studies reported HSUVs derived from the EQ-5D and 18 studies showed HRQoL data derived from the QLQ-MY20. Treatment combinations included bortezomib, thalidomide, lenalidomide, carfilzomib, pamidronate melphalan, prednisone, dexamethasone, and stem cell transplantation. Only 16 studies explained the complete range of functional scores from the QLQ-C30 or QLQ-MY20 and could be used to calculate HSUVs applying the mapping algorithms [1], [2], [3].

Conclusions: We identified studies reporting on HRQoL data in patients with MM from the QLQ-C30 or QLQ-MY20 that could be used in mapping estimations to derive HSUVs for MM. Several studies did not report full functional scores of the questionnaire as needed for the mapping algorithms, thus limiting their applicability for mapping to derive HSUVs.


References

1.
Kharroubi SA, Edlin R, Meads D, Browne C, Brown J, McCabe C. Use of Bayesian Markov chain Monte Carlo methods to estimate EQ-5D utility scores from EORTC QLQ data in myeloma for use in cost-effectiveness analysis. Med Decis Making. 2015;35(3):351-60.
2.
McKenzie L, van der Pol M. Mapping the EORTC QLQ C-30 onto the EQ-5D instrument: the potential to estimate QALYs without generic preference data. Value Health. 2009;12(1):167-71.
3.
Proskorovsky I, Lewis P, Williams CD, Jordan K, Kyriakou C, Ishak J, et al. Mapping EORTC QLQ-C30 and QLQ-MY20 to EQ-5D in patients with multiple myeloma. Health Qual Life Outcomes. 2014;12:35.