Hostname: page-component-78c5997874-s2hrs Total loading time: 0 Render date: 2024-11-11T08:59:19.307Z Has data issue: false hasContentIssue false

TREATMENT AFTER ACUTE CORONARY SYNDROME: ANALYSIS OF PATIENT'S PRIORITIES WITH ANALYTIC HIERARCHY PROCESS

Published online by Cambridge University Press:  18 October 2016

Axel C. Mühlbacher
Affiliation:
Hochschule Neubrandenburg, Neubrandenburg, GermanyCommonwealth Fund Harkness Fellow, Duke University, Durham, USAmuehlbacher@hs-nb.de
Susanne Bethge
Affiliation:
Hochschule Neubrandenburg, Neubrandenburg, Germany
Anika Kaczynski
Affiliation:
Hochschule Neubrandenburg, Neubrandenburg, Germany

Abstract

Background: Cardiovascular disease is one of the most common causes of death worldwide, with many individuals having experienced acute coronary syndrome (ACS). How patients with a history of ACS value aspects of their medical treatment have been evaluated rarely. The aim of this study was to determine patient priorities for long-term drug therapy after experiencing ACS.

Methods: To identify patient-relevant treatment characteristics, a systematic literature review and qualitative patient interviews were conducted. A questionnaire was developed to elicit patient's priorities for different characteristics of ACS treatment using Analytic Hierarchy Process (AHP). To evaluate the patient-relevant outcomes, the eigenvector method was applied.

Results: Six-hundred twenty-three patients participated in the computer-assisted personal interviews and were included in the final analysis. Patients showed a clear priority for the attribute “reduction of mortality risk” (weight: 0.402). The second most preferred attribute was the “prevention of a new myocardial infarction” (weight: 0.272), followed by “side effect: dyspnea” (weight: 0.165) and “side effect: bleeding” (weight: 0.117). The “frequency of intake” was the least important attribute (weight: 0.044).

Conclusion: In conclusion, this study shows that patients strongly value a reduction of the mortality risk in post-ACS treatment. Formal consideration of patient preferences and priorities can help to inform a patient-centered approach, clinical practice, development of future effective therapies, and health policy for decision makers that best represents the needs and goals of the patient.

Type
Methods
Copyright
Copyright © Cambridge University Press 2016 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

1. World Health Organization (WHO). The top 10 causes of death. Fact sheet 310 [updated May 2014]. 2015. http://www.who.int/mediacentre/factsheets/fs310_2008.pdf (accessed July 2016).Google Scholar
2. Kelm, M, Strauer, BE. Das akute Koronarsyndrom. Internist (Berl). 2005;46:265274.Google Scholar
3. Mühlbacher, AC, Bethge, S. Reduce mortality risk above all else: A discrete-choice experiment in acute coronary syndrome patients. Pharmacoeconomics. 2015;33:7181.Google Scholar
4. Mühlbacher, AC, Juhnke, C. Patient preferences versus physicians’ judgement: Does it make a difference in healthcare decision making? Appl Health Econ Health Policy. 2013;11:163180.Google Scholar
5. Hummel, MJ, Volz, F, van Manen, JG, et al. Using the analytic hierarchy process to elicit patient preferences. Patient. 2012;5:225237.CrossRefGoogle ScholarPubMed
6. Mühlbacher, AC, Bridges, JF, Bethge, S, et al. Preferences for antiviral therapy of chronic hepatitis C: A discrete choice experiment. Eur J Health Econ. 2016 [Epub ahead of print].Google Scholar
7. Ho, MP, Gonzalez, JM, Lerner, HP, et al. Incorporating patient-preference evidence into regulatory decision making. Surg Endosc. 2015;29:28942993.Google Scholar
8. U.S. Department of Health and Human Services - Food and Drug Administration (FDA). Patient preference information – submission, review in PMAs, HDE applications, and de novo requests, and inclusion in device labeling (draft). 2015. http://www.fda.gov/downloads/medicaldevices/deviceregulationandguidance/guidancedocuments/ucm446680.pdf (accessed July 2016).Google Scholar
9. Hunter, NL, O'Callaghan, KM, Califf, RM. Engaging patients across the spectrum of medical product development: View from the US Food and Drug Administration. JAMA. 2015;314:24992500.CrossRefGoogle ScholarPubMed
10. Glaeske, G. The dilemma between efficacy as defined by regulatory bodies and effectiveness in clinical practice. Dtsch Arzteblatt Int. 2012;109:115.Google Scholar
11. International Society for Pharmacoeconomics and Outcomes Research (ISPOR). Multi-criteria decision analysis in health care decision making emerging good practices task force. http://www.ispor.org/TaskForces/Multi-Criteria-Decision-Analysis-Bgr.asp. 2015. (accessed July 2016)Google Scholar
12. Danner, M, Hummel, JM, Volz, F, et al. Integrating patients' views into health technology assessment: Analytic hierarchy process (AHP) as a method to elicit patient preferences. Int J Technol Assess Health Care. 2011;27:369375.Google Scholar
13. Mühlbacher, AC, Kaczynski, A. Making good decisions in healthcare with multi-criteria decision analysis: The use, current research and future development of MCDA. Appl Health Econ Health Policy. 2016;14:2940.Google Scholar
14. Diaby, V, Goeree, R, Hoch, J, Siebert, U. Multi-criteria decision analysis for health technology assessment in Canada: Insights from an expert panel discussion. Expert Rev Pharmacoecon Outcomes Res. 2015;15:1319.Google Scholar
15. Post, F, Münzel, T. Das akute Koronarsyndrom. Internist. 2010;51:953962.Google Scholar
16. Van de Werf, F, Bax, J, Betriu, A, et al. Management of acute myocardial infarction in patients presenting with persistent ST-segment elevation. Eur Heart J. 2008;29:2909.Google Scholar
17. Hamm, CW. Leitlinien: Akutes Koronarsyndrom (ACS). Z Kardiol. 2004;93:7290.Google Scholar
18. Johansson, G, Stallberg, B, Tornling, G, et al. Asthma treatment preference study: A conjoint analysis of preferred drug treatments. Chest. 2004;125:916923.Google Scholar
19. Saaty, TL. A scaling method for priorities in hierarchical structures. J Math Psychol. 1977;15:234281.Google Scholar
20. Meixner, O, Haas, R. Computergestützte Entscheidungsfindung: Expert choice und AHP-innovative Werkzeuge zur Lösung komplexer Probleme. Frankfurt: Redline Wirtschaft bei Ueberreuter; 2002.Google Scholar
21. Dolan, JG. Medical decision making using the analytic hierarchy process choice of initial antimicrobial therapy for acute pyelonephritis. Med Decis Making. 1989;9:5156.Google Scholar
22. Saaty, TL. How to make a decision: The analytic hierarchy process. Eur J Oper Res. 1990;48:926.CrossRefGoogle Scholar
23. Helm, R, Manthey, L, Scholl, A, Steiner, M. Empirical evaluation of preference elicitation techniques from marketing and decision analysis. Jenaer Schriften zur Wirtschaftswissenschaft. 2003.Google Scholar
24. Belton, V. A comparison of the analytic hierarchy process and a simple multi-attribute value function. Eur J Oper Res. 1986;26:721.Google Scholar
25. Helm, R, Steiner, M. Präferenzmessung: Methodengestützte Entwicklung zielgruppenspezifischer Produktinnovationen. Stuttgart: W. Kohlhammer Verlag; 2008.Google Scholar
26. Saaty, TL. Decision making with the analytic hierarchy process. Int J Serv Sci. 2008;1:8398.Google Scholar
27. Saaty, TL. Fundamentals of decision making and priority theory with the analytic hierarchy process. Pittsburgh: RWS Publications; 2000. XIV, 478 S p.Google Scholar
28. Ijzerman, MJ, van Til, JA, Snoek, GJ. Comparison of two multi-criteria decision techniques for eliciting treatment preferences in people with neurological disorders. Patient. 2008;1:265272.Google Scholar
29. Saaty, TL. The analytic hierarchy process. Planning, priority setting, resource allocation, 2nd ed. New York: McGraw-Hill; 1980. XIII, 287 S p.Google Scholar
30. U.S. Department of Health and Human Services, Office of Human Research Protection. Guidance on reviewing and reporting unanticipated problems involving risks to subjects or others and adverse events. 2007. http://www.hhs.gov/ohrp/regulations-and-policy/guidance/reviewing-unanticipated-problems/index.html (accessed August 2016).Google Scholar
31. Hayen, A, Herigstad, M, Pattinson, KTS. Understanding dyspnea as a complex individual experience. Maturitas. 2013;76:4550.Google Scholar
32. Johnson, RF, Lancsar, E, Marshall, D, et al. Constructing experimental designs for discrete-choice experiments: Report of the ISPOR conjoint analysis experimental design good research practices task force. Value Health. 2013;16:313.Google Scholar
33. Federspiel, JJ, Stearns, SC, van Domburg, RT, et al. Risk-benefit trade-offs in revascularisation choices. EuroIntervention. 2011;6:936941.Google Scholar
34. Louviere, JJ, Islam, T, Wasi, N, Street, D, Burgess, L. Designing discrete choice experiments: Do optimal designs come at a price? J Cons Res. 2008;35:360375.Google Scholar
35. Johnson, MM. Age differences in decision making: A process methodology for examining strategic information processing. J Gerontol. 1990;45:P75P78.Google Scholar
36. Hensher, DA. Revealing differences in willingness to pay due to the dimensionality of stated choice designs: An initial assessment. Environ Resour Econ. 2006;34:744.Google Scholar
37. Mühlbacher, AC, Kaczynski, A. Der Analytic Hierarchy Process (AHP): Eine Methode zur Entscheidungsunterstützung im Gesundheitswesen. Pharmacoeconomics. 2013;11:119132.Google Scholar
38. Dolan, JG, Isselhardt, BJ, Cappuccio, JD. The analytic hierarchy process in medical decision making: A tutorial. Med Decis Making. 1989;9:4050.Google Scholar
Supplementary material: Image

Mühlbacher supplementary material

Supplementary Figure 1

Download Mühlbacher supplementary material(Image)
Image 119.3 KB
Supplementary material: File

Mühlbacher supplementary material

Supplementary Table 1

Download Mühlbacher supplementary material(File)
File 38.1 KB