Minimizing population health loss due to scarcity in OR capacity: validation of quality of life input.
Decision modeling
Prioritization
Quality of life
Surgery
Validation
Value based health care
Journal
BMC medical research methodology
ISSN: 1471-2288
Titre abrégé: BMC Med Res Methodol
Pays: England
ID NLM: 100968545
Informations de publication
Date de publication:
31 01 2023
31 01 2023
Historique:
received:
26
01
2022
accepted:
09
12
2022
entrez:
1
2
2023
pubmed:
2
2
2023
medline:
3
2
2023
Statut:
epublish
Résumé
A previously developed decision model to prioritize surgical procedures in times of scarce surgical capacity used quality of life (QoL) primarily derived from experts in one center. These estimates are key input of the model, and might be more context-dependent than the other input parameters (age, survival). The aim of this study was to validate our model by replicating these QoL estimates. The original study estimated QoL of patients in need of commonly performed procedures in live expert-panel meetings. This study replicated this procedure using a web-based Delphi approach in a different hospital. The new QoL scores were compared with the original scores using mixed effects linear regression. The ranking of surgical procedures based on combined QoL values from the validation and original study was compared to the ranking based solely on the original QoL values. The overall mean difference in QoL estimates between the validation study and the original study was - 0.11 (95% CI: -0.12 - -0.10). The model output (DALY/month delay) based on QoL data from both studies was similar to the model output based on the original data only: The Spearman's correlation coefficient between the ranking of all procedures before and after including the new QoL estimates was 0.988. Even though the new QoL estimates were systematically lower than the values from the original study, the ranking for urgency based on health loss per unit of time delay of procedures was consistent. This underscores the robustness and generalizability of the decision model for prioritization of surgical procedures.
Identifiants
pubmed: 36721106
doi: 10.1186/s12874-022-01818-z
pii: 10.1186/s12874-022-01818-z
pmc: PMC9887555
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
31Informations de copyright
© 2023. The Author(s).
Références
Sud A, Jones ME, Broggio J, Loveday C, Torr B, Garrett A, et al. Collateral damage: the impact on outcomes from cancer surgery of the COVID-19 pandemic. Ann Oncol [Internet]. 2020;31(8):1065–74 Available from: https://linkinghub.elsevier.com/retrieve/pii/S0923753420398252 .
doi: 10.1016/j.annonc.2020.05.009
pubmed: 32442581
Shiau S, Krause KD, Valera P, Swaminathan S, Halkitis PN. The burden of COVID-19 in people living with HIV: a Syndemic perspective. AIDS Behav [Internet]. 2020;24(8):2244–9 Available from: https://pubmed.ncbi.nlm.nih.gov/32303925/ .
doi: 10.1007/s10461-020-02871-9
pubmed: 32303925
Tan W, Aboulhosn J. The cardiovascular burden of coronavirus disease 2019 (COVID-19) with a focus on congenital heart disease. Int J Cardiol. 2020 Jun;309:70–7 Available from: https://pubmed.ncbi.nlm.nih.gov/32248966/ .
Powell SN, Mullen T, Young L, Morgan C, Heald D, Powell ET. Experiences from the SARS-CoV-2 pandemic. J Bone Jt Surg [Internet]. 2020;102(13):1123–5 Available from: https://journals.lww.com/10.2106/JBJS.20.00690 .
doi: 10.2106/JBJS.20.00690
Emanuel EJ, Persad G, Upshur R, Thome B, Parker M, Glickman A, et al. Fair allocation of scarce medical resources in the time of Covid-19. N Engl J Med. 2020;382(21):2049–55.
doi: 10.1056/NEJMsb2005114
pubmed: 32202722
Gravesteijn B, Krijkamp E, Busschbach J, Geleijnse G, Helmrich IR, Bruinsma S, et al. Minimizing population health loss in times of scarce surgical capacity during the coronavirus disease 2019 crisis and beyond: a modeling study. Value Health. 2021;24:648–57. https://doi.org/10.1016/j.jval.2020.12.010 .
MacCormick AD, Parry BR. Judgment Analysis of Surgeons’ Prioritization of Patients for Elective General Surgery. Med Decis Mak. 2006;26(3):255–64 Available from: http://journals.sagepub.com/doi/10.1177/0272989X06288680 .
doi: 10.1177/0272989X06288680
Salomon JA, Haagsma JA, Davis A, Maertens De Noordhout C, Polinder S, Havelaar AH, et al. Disability weights for the Global Burden of Disease 2013 study. Lancet Glob Health. 2015. Available from: www.thelancet.com/lancetgh ;3:e712–23.
Stouthard EA, Essink-Bot M-L, Bonsel GJ. Disability weights for diseases a modified protocol and results for a Western European region. Eur J Public Health [Internet]. 2000;10(1):24 Available from: https://academic.oup.com/eurpub/article-abstract/0/1//490779 .
doi: 10.1093/eurpub/10.1.24
Welphi - Applications2020. Available from: https://www.welphi.com/en/Applications.html
Martin Bland J, Altman DG. Statistical methods for assessing agreement between two methods of clinical measurement. Lancet. 1986;327(8476):307–10 Available from: http://www.thelancet.com/article/S0140673686908378/fulltext .
doi: 10.1016/S0140-6736(86)90837-8
R Core Team. R: a language and environment for statistical computing. Vienna, Austria; 2013.
Bates D, Mächler M, Bolker B, Walker S. Fitting linear mixed-effects models using lme4. J Stat Softw [Internet]. 2015;67(1):1–48 Available from: http://www.jstatsoft.org/v67/i01/ .
Linstone HA, Turoff M. The delphi method. MA: Addison-Wesley Reading; 1975.
Brüggen E, Willems P. A critical comparison of offline focus groups, online focus groups and E-Delphi. Int J Mark Res [Internet]. 2009;51(3):1–15 Available from: http://journals.sagepub.com/doi/10.1177/147078530905100301 .
doi: 10.1177/147078530905100301
Abrams KM, Wang Z, Song YJ, Galindo-Gonzalez S. Data richness trade-offs between face-to-face, online audiovisual, and online text-only focus groups. Social Science computer review. 2015;33(1):80–96 Available from: http://journals.sagepub.com/doi/10.1177/0894439313519733 .
doi: 10.1177/0894439313519733
Chapman RH, Berger M, Weinstein MC, Weeks JC, Goldie S, Neumann PJ. When does quality-adjusting life-years matter in cost-effectiveness analysis? Health Econ. 2004;13(5):429–36.
doi: 10.1002/hec.853
pubmed: 15127423
Feng X, Kim DD, Cohen JT, Neumann PJ, Ollendorf DA. Using QALYs versus DALYs to measure cost-effectiveness: how much does it matter? Int J Technol Assess Health Care. 2020;36(2):96–103.
doi: 10.1017/S0266462320000124
pubmed: 32340631
Birko S, Dove ES, Özdemir V, Dalal K. Evaluation of nine consensus indices in delphi foresight research and their dependency on delphi survey characteristics: a simulation study and debate on delphi design and interpretation. PLoS One. 2015;10(8):1–14.
doi: 10.1371/journal.pone.0135162