nach oben

Quality of Life Research

Erschienen in:

26.05.2018

Engaging patients and caregivers in prioritizing symptoms impacting quality of life for Duchenne and Becker muscular dystrophy

verfasst von: Ilene L. Hollin, Holly Peay, Ryan Fischer, Ellen M. Janssen, John F. P. Bridges

Erschienen in: Quality of Life Research | Ausgabe 9/2018

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

Purpose

Patient preference information (PPI) have an increasing role in regulatory decision-making, especially in benefit–risk assessment. PPI can also facilitate prioritization of symptoms to treat and inform meaningful selection of clinical trial endpoints. We engaged patients and caregivers to prioritize symptoms of Duchenne and Becker muscular dystrophy (DBMD) and explored preference heterogeneity.

Methods

Best–worst scaling (object case) was used to assess priorities across 11 symptoms of DBMD that impact quality of life and for which there is unmet need. Respondents selected the most and least important symptoms to treat among a subset of five. Relative importance scores were estimated for each symptom, and preference heterogeneity was identified using mixed logit and latent class analysis.

Results

Respondents included patients (n = 59) and caregivers (n = 96) affected by DBMD. Results indicated that respondents prioritized “weaker heart pumping” [score = 5.13; 95% CI (4.67, 5.59)] and pulmonary symptoms: “lung infections” [3.15; (2.80, 3.50)] and “weaker ability to cough” [2.65; (2.33, 2.97)] as the most important symptoms to treat and “poor attention span” as the least important symptom to treat [− 5.23; (− 5.93, − 4.54)]. Statistically significant preference heterogeneity existed (p value < 0.001). At least two classes existed with different priorities. Priorities of the majority latent class (80%) reflected the aggregate results, whereas the minority latent class (20%) did not distinguish among pulmonary and other symptoms.

Conclusions

Estimates of the relative importance for symptoms of Duchenne muscular dystrophy indicated that symptoms with direct links to morbidity and mortality were prioritized above other non-skeletal muscle symptoms. Findings suggested the existence of preference heterogeneity for symptoms, which may be related to symptom experience.

Vorheriger Artikel Trajectory of health-related quality of life and its determinants in patients who underwent lumbar spine surgery: a 1-year longitudinal study

Nächster Artikel Anxiety in the orthopedic patient: using PROMIS to assess mental health

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Nur mit Berechtigung zugänglich

Emery, A. E. (2002). The muscular dystrophies. The Lancet, 359(9307), 687–695.

Emery, A. E. (1991). Population frequencies of inherited neuromuscular diseases: A world survey. Neuromuscular Disorders, 1(1), 19–29.PubMed

Mah, J. K., Korngut, L., Dykeman, J., Day, L., Pringsheim, T., & Jette, N. (2014). A systematic review and meta-analysis on the epidemiology of Duchenne and Becker muscular dystrophy. Neuromuscular Disorders, 24(6), 482–491.PubMed

Bushby, K., Finkel, R., Birnkrant, D. J., Case, L. E., Clemens, P. R., Cripe, L., et al. (2010). Diagnosis and management of Duchenne muscular dystrophy, part 1: Diagnosis, and pharmacological and psychosocial management. The Lancet. Neurology, 9(1), 77–93.PubMed

Cyrulnik, S. E., Fee, R. J., Batchelder, A., Kiefel, J., Goldstein, E., & Hinton, V. J. (2008). Cognitive and adaptive deficits in young children with Duchenne muscular dystrophy (DMD). Journal of the International Neuropsychological Society, 14(5), 853–861.PubMed

Guglieri, M., Bushby, K., McDermott, M. P., Hart, K. A., Tawil, R., & Martens, W. B. (2017). Developing standardized corticosteroid treatment for Duchenne muscular dystrophy. Contemporary Clinical Trials, 58, 34–39.PubMedPubMedCentral

Traynor, K. (2017). Deflazacort approved for Duchenne muscular dystrophy. American Journal of Health-System Pharmacy, 74(6), 368.PubMed

Aartsma-Rus, A., & Krieg, A. M. (2017). FDA approves Eteplirsen for Duchenne muscular dystrophy: The next chapter in the Eteplirsen saga. Nucleic Acid Therapeutics, 27(1), 1–3.PubMedPubMedCentral

Unger, E. F., & Califf, R. M. (2017). Regarding “Eteplirsen for the treatment of Duchenne muscular dystrophy”. Annals of Neurology, 81(1), 162–164.PubMed

10.

Niks, E. H., & Aartsma-Rus, A. (2017). Exon skipping: A first in class strategy for Duchenne muscular dystrophy. Expert Opinion on Biological Therapy, 17(2), 225–236.PubMed

11.

Medical Device Innovation Consortium (MDIC). (2015). Patient centered benefit-risk project report: A framework for incorporating information on patient preferences regarding benefit and risk into regulatory assessments of new medical technology. Arlington: Medical Device Innovation Consortium (MDIC). Retrieved November 3, 2017, from http://mdic.org/wp-content/uploads/2015/05/MDIC_PCBR_Framework_Proof5_Web.pdf.

12.

US Food and Drug Administration. (2016) Patient preference information—voluntary submission, review in premarket approval applications, humanitarian device exemption applications, and de novo requests, and inclusion in decision summaries and device labeling: Guidance for industry, food and drug administration staff, and other stakeholders. Silver Spring: Center for Devices and Radiological Health, Center for Biologics Evaluation and Research, Food and Drug Administration. Retrieved November 3, 2017, from https://www.fda.gov/downloads/medicaldevices/deviceregulationandguidance/guidancedocuments/ucm446680.pdf.

13.

Woodward, A. T. (2013). A latent class analysis of age differences in choosing service providers to treat mental and substance use disorders. Psychiatric Services, 64(11), 1087–1094.PubMed

14.

Wong, Y. N., Egleston, B. L., Sachdeva, K., Eghan, N., Pirollo, M., Stump, T. K., et al. (2013). Cancer patients’ trade-offs among efficacy, toxicity and out-of-pocket cost in the curative and noncurative setting. Medical Care, 51(9), 838–845.PubMed

15.

Whitty, J. A., Stewart, S., Carrington, M. J., Calderone, A., Marwick, T., Horowitz, J. D., et al. (2013). Patient preferences and willingness-to-pay for a home or clinic based program of chronic heart failure management: Findings from the which? trial. PLoS ONE, 8(3), e58347.PubMedPubMedCentral

16.

Waschbusch, D. A., Cunningham, C. E., Pelham, W. E. Jr., Rimas, H. L., Greiner, A. R., Gnagy, E. M., et al. (2011). A discrete choice conjoint experiment to evaluate parent preferences for treatment of young, medication naive children with ADHD. Journal of Clinical Child and Adolescent Psychology, 40(4), 546–561.PubMedPubMedCentral

17.

Naik-Panvelkar, M. P., Armour, C., Rose, J.,M., & Saini, B. (2012). Patient preferences for community pharmacy asthma services. PharmacoEconomics, 30(10), 961–976.PubMed

18.

Lagarde, M. (2013). Investigating attribute non-attendance and its consequences in choice experiments with latent class models. Health Economics, 22(5), 554–567.PubMed

19.

Guo, N., Marra, C. A., FitzGerald, J. M., Elwood, R. K., Anis, A. H., & Marra, F. (2011). Patient preference for latent tuberculosis infection preventive treatment: A discrete choice experiment. Value in Health, 14(6), 937–943.PubMed

20.

Goossens, L. M., Utens, C. M., Smeenk, F. W., Donkers, B., van Schayck, O. C., & Rutten-van Mölken, M. P. (2014). Should I stay or should I go home? A latent class analysis of a discrete choice experiment on hospital-at-home. Value in Health, 17(5), 588–596.PubMed

21.

Fraenkel, L., Suter, L., Cunningham, C. E., & Hawker, G. (2014). Understanding preferences for disease-modifying drugs in osteoarthritis. Arthritis Care and Research, 66(8), 1186–1192.PubMed

22.

Cunningham, C. E., Chen, Y., Deal, K., Rimas, H., McGrath, P., Reid, G., et al. (2013). The interim service preferences of parents waiting for children’s mental health treatment: A discrete choice conjoint experiment. Journal of Abnormal Child Psychology, 41(6), 865–877.PubMed

23.

Carroll, F. E., Al-Janabi, H., Flynn, T., & Montgomery, A. A. (2013). Women and their partners’ preferences for Down’s syndrome screening tests: A discrete choice experiment. Prenatal Diagnosis, 33(5), 449–456.PubMed

24.

Brown, D. S., Poulos, C., Johnson, F. R., Chamiec-Case, L., & Messonnier, M. L. (2014). Adolescent girls’ preferences for HPV vaccines: A discrete choice experiment. Advances in Health Economics and Health Services Research, 24, 93–121.PubMed

25.

Yan, K., Bridges, J. F., Augustin, S., Laine, L., Garcia-Tsao, G., & Fraenkel, L. (2015). Factors impacting physicians decisions to prevent variceal hemorrhage. BMC Gastroenterology, 15, 55.PubMedPubMedCentral

26.

Fraenkel, L., Lim, J., Garcia-Tsao, G., Reyna, V., Monto, A., & Bridges, J. F. P. (2016). Variation in treatment priorities for chronic Hepatitis C: A latent class analysis. The Patient, 9(3), 241–249.PubMed

27.

US Food and Drug Administration. (2012) Guidance for industry and food and drug administration staff: Factors to consider when making benefit-risk determinations in medical device premarket approval and de novo classifications. Silver Spring: Center for Devices and Radiological Health, Center for Biologics Evaluation and Research, Food and Drug Administration. Retrieved November 3, 2017, from https://www.fda.gov/downloads/medicaldevices/deviceregulationandguidance/guidancedocuments/ucm517504.pdf.

28.

Hunter, N. L., O’Callaghan, K. M., & Califf, R. M. (2015). Engaging patients across the spectrum of medical product development: View from the US Food and Drug Administration. Journal of the American Medical Association, 314(23), 2499–2500.PubMed

29.

Ho, M. P., Gonzalez, J. M., Lerner, H. P., Neuland, C. Y., Whang, J. M., McMurry-Heath, M., et al. (2015). Incorporating patient-preference evidence into regulatory decision making. Surgical Endoscopy, 29(10), 2984–2993.PubMed

30.

Hauber, B. A., Fairchild, A. O., & Johnson, R. F. (2013). Quantifying benefit-risk preferences for medical interventions: An overview of a growing empirical literature. Applied Health Economics and Health Policy, 11(4), 319–329.

31.

van Til, J. A., & Ijzerman, M. J. (2014). Why should regulators consider using patient preferences in benefit-risk assessment? PharmacoEconomics, 32(1), 1–4.PubMed

32.

US Food and Drug Administration. (2018) Duchenne muscular dystrophy and related dystrophinopathies: Developing drugs for treatment Guidance for industry. Silver Spring: Center for Devices and Radiological Health, Center for Biologics Evaluation and Research, Food and Drug Administration. Retrieved March 17, 2018, from https://www.fda.gov/downloads/Drugs/GuidanceComplianceRegulatoryInformation/Guidances/UCM450229.pdf.

33.

McNeil, D. E., Davis, C., Jillapalli, D., Targum, S., Durmowicz, A., & Coté, T. R. (2010). Duchenne muscular dystrophy: Drug development and regulatory considerations. Muscle and Nerve, 41(6), 740–745.PubMed

34.

Peay, H. L., Hollin, I. L., Fischer, R., & Bridges, J. F. P. (2014). A community-engaged approach to quantifying caregiver preferences for the benefits and risks of emerging therapies for Duchenne muscular dystrophy. Clinical Therapeutics, 36(5), 624–637.PubMed

35.

Peay, H. L., Sheffer, H., & Tibben, A. (2013). Expectations and decision making in clinical trials for Duchenne and Becker muscular dystrophy. In 18th international congress of the world muscle society, Asilomar.

36.

Finn, A., & Louviere, J. J. (1992). Determining the appropriate response to evidence of public concern: The case of food safety. Journal of Public Policy and Marketing, 11(2), 12–25.

37.

Marley, A. A., & Louviere, J. J. (2005). Some probabilistic models of best, worst, and best-worst choices. Journal of Mathematical Psychology, 49(6), 464–480.

38.

Flynn, T. N. (2010). Valuing citizen and patient preferences in health: Recent developments in three types of best-worst scaling. Expert Review of Pharmacoeconomics and Outcomes Research, 10(3), 259–267.PubMed

39.

Mühlbacher, A. C., Kaczynski, A., Zweifel, P., & Johnson, F. R. (2015). Experimental measurement of preferences in health and healthcare using best-worst scaling: An overview. Health Economics Review, 6(1), 1–14.

40.

Flynn, T. N., Louviere, J. J., Peters, T. J., & Coast, J. (2007). Best-worst scaling: What it can do for health care research and how to do it. Journal of Health Economics, 26(1), 171–189.PubMed

41.

Flynn, T. N., & Marley, A. (2014). Best-worst scaling: Theory and methods. In S. Hess & A. Daly (Eds.), Handbook of choice modelling (pp. 178–201). Cheltenham: Edward Elgar Publishing Limited.

42.

Hollin, I. L., Young, C., Hanson, C., Bridges, J., & Peay, H. (2016). Developing a patient-centered benefit-risk survey: A community-engaged process. Value in Health, 19, 751–757.PubMed

43.

Kuhfeld, W. (2010). Orthogonal arrays [TS-723]. Cary, NC: SAS.

44.

Youden, W. J. (1940). Experimental designs to increase accuracy of greenhouse studies. Contributions. Boyce Thompson Institute for Plant Research, 11, 219–228.

45.

Youden, W. J. (1937). Use of incomplete block replications in estimating tobacco-mosaic virus. Contributions from Boyce Thompson Institute, 9(1), 41–48.

46.

Hauber, A. B., González, J. M., Groothuis-Oudshoorn, C. G., Prior, T., Marshall, D. A., Cunningham, C. et al. (2016). Statistical methods for the analysis of discrete choice experiments: A report of the ISPOR Conjoint Analysis Good Research Practices Task Force. Value in Health, 19(4):300–315.PubMed

47.

Deal, K. (2014). Segmenting patients and physicians using preferences from discrete choice experiments. The Patient, 7(1), 5–21.PubMed

Titel: Engaging patients and caregivers in prioritizing symptoms impacting quality of life for Duchenne and Becker muscular dystrophy
verfasst von: Ilene L. Hollin
Holly Peay
Ryan Fischer
Ellen M. Janssen
John F. P. Bridges
Publikationsdatum: 26.05.2018
Verlag: Springer International Publishing
Erschienen in: Quality of Life Research / Ausgabe 9/2018
Print ISSN: 0962-9343
Elektronische ISSN: 1573-2649
DOI: https://doi.org/10.1007/s11136-018-1891-7

Springer Professional

Abstract

Purpose

Methods

Results

Conclusions

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Wirtschaft"

Weitere Artikel der Ausgabe 9/2018

Changes in health-related quality of life (EQ-5D) dimensions associated with community-based musculoskeletal physiotherapy: a multi-centre analysis

Cross-national health comparisons using the Rasch model: findings from the 2012 US Health and Retirement Study and the 2012 Mexican Health and Aging Study

Anxiety in the orthopedic patient: using PROMIS to assess mental health

What is the current evidence of the impact on quality of life whilst waiting for management/treatment of orthopaedic/musculoskeletal complaints? A systematic scoping review

Diabetes symptoms predictors of health-related quality of life in adolescents and young adults with type 1 or type 2 diabetes

Does anthropometric and fitness parameters mediate the effect of exercise on the HRQoL of overweight and obese children/adolescents?