Hostname: page-component-586b7cd67f-g8jcs Total loading time: 0 Render date: 2024-11-28T10:11:59.231Z Has data issue: false hasContentIssue false
  • English
  • Français

Value of Information of a clinical prediction rule: Informing the efficient use of healthcare and health research resources

Published online by Cambridge University Press:  24 January 2008

Sonia Singh ,
Bohdan Nosyk ,
Huiying Sun ,
James Malcolm Christenson ,
Grant Innes  and
Aslam Hayat Anis
Sonia Singh
Affiliation:
University of British Columbia and Peace Arch Hospital
Bohdan Nosyk
Affiliation:
St. Paul's Hospital
Huiying Sun
Affiliation:
St. Paul's Hospital
James Malcolm Christenson
Affiliation:
University of British Columbia and St. Paul's Hospital
Grant Innes
Affiliation:
University of British Columbia and St. Paul's Hospital
Aslam Hayat Anis
Affiliation:
University of British Columbia and St. Paul's Hospital
Get access Rights & Permissions [Opens in a new window]

Abstract

Objectives: The aim of this study was to estimate the potential cost-effectiveness and expected value of perfect information of a recently derived clinical prediction rule for patients presenting to emergency departments with chest discomfort.

Methods: A decision analytic model was constructed to compare the Early Disposition Prediction Rule (EDPR) with the current standard of care. Results were used to calculate the potential cost-effectiveness of the EDPR, as well as the Value of Information in conducting further research. Study subjects were adults presenting with chest discomfort to two urban emergency departments in Vancouver, British Columbia, Canada. The clinical prediction rule identifies patients who are eligible for early discharge within 3 hours of presentation to the emergency department. The outcome measure used was inappropriate emergency department discharge of patients with acute coronary syndrome (ACS).

Results: The incremental cost-effectiveness ratio of the EDPR in comparison to usual care was (negative) $2,999 per inappropriate ACS discharge prevented, indicating a potential cost-savings in introducing the intervention. The expected value of perfect information was $16.3 million in the first year of implementation, suggesting a high benefit from conducting further research to validate the decision rule.

Conclusions: The EDPR is likely to be cost-effective; however, given the high degree of uncertainty in the estimates of costs and patient outcomes, further research is required to inform the decision to implement the intervention. The potential health and monetary benefits of this clinical prediction rule outweigh the costs of doing further research.

Keywords

Expected value of perfect informationValue of InformationAcute coronary syndromeChest discomfortCost-effectiveness analysis
Type
RESEARCH REPORTS
Information
International Journal of Technology Assessment in Health Care , Volume 24 , Issue 1 , January 2008 , pp. 112 - 119
Copyright
Copyright © Cambridge University Press 2008

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. Anis, AH, Sun, HS, Singh, S, et al. . A cost-utility analysis of Losartan versus Atenolol in the treatment of hypertension with left ventricular hypertrophy. Pharmacoeconomics. 2006;24:387–400.CrossRefGoogle ScholarPubMed
2. Briggs, AH. Handling uncertainty in cost-effectiveness models. Pharmacoeconomics. 2000;17:479–500.CrossRefGoogle ScholarPubMed
3. Briggs, AH, Ades, AE, Price, MJ. Probabilistic sensitivity analysis for decision trees with multiple branches: Use of the Dirichlet distribution in a Bayesian framework. Med Decis Making. 2003;23:341–350.CrossRefGoogle Scholar
4. British Columbia Medical Association. BCMA guide to fees 2002. Vancouver, BC: British Columbia Medical Association.Google Scholar
5. Christenson, J, Innes, G, McKnight, D, et al. Safety and efficiency of emergency department assessment of chest discomfort. CMAJ. 2004;170:1803–1807.CrossRefGoogle ScholarPubMed
6. Christenson, J, Innes, G, McKnight, D, et al. A clinical prediction rule for early discharge of patients with chest pain. Ann Emerg Med. 2006;47:1–10.CrossRefGoogle ScholarPubMed
7. Claxton, K. The irrelevance of inference: a decision-making approach to the stochastic evaluation of health care technologies. J Health Econ. 1999;8:341–364.CrossRefGoogle Scholar
8. Claxton, K, Sculpher, M, Drummond, M. A rational framework for decision making by the National Institute for Clinical Excellence (NICE). Lancet. 2002;360:711–715.CrossRefGoogle ScholarPubMed
9. Dong, H, Coyle, D, Buxton, M. Value of information analysis for a new technology: computer-assisted total knee replacement. Int J Technol Assess Health Care. 2007;23:337–342.CrossRefGoogle ScholarPubMed
10. Doubilet, P, Begg, CB, Weinstein, MC, et al. . Probabilistic sensitivity analysis using Monte Carlo simulation: A practical approach. Med Decis Making. 1983;5:157–177.CrossRefGoogle Scholar
11. Drummond, MF, Stoddart, GL, Torrance, GW. Methods for the economic evaluation of health care programs. Oxford: Oxford University Press; 1987.Google Scholar
12. Fesmire, FM, Hughes, AD, Fody, EP, et al. . The Erlanger chest pain evaluation protocol: A one-year experience with serial 12-lead ECG monitoring, two-hour delta serum marker measurements, and selective nuclear stress testing to identify and exclude acute coronary syndromes. Ann Emerg Med. 2002;40:584–94.CrossRefGoogle ScholarPubMed
13. Gafni, A, Birch, S. Incremental cost-effectiveness ratios (ICERs): The silence of the lambda. Soc Sci Med. 2006;62:2091–2100.CrossRefGoogle ScholarPubMed
14. Gibler, WB, Hoekstra, JW, Weaver, WD, et al. . A randomized trial of the effects of early cardiac marker availability on reperfusion therapy in patients with acute myocardial infarction: The serial markers in acute myocardial infarction rapid treatment trial. J Am Coll Cardiol. 2000;36:1500–1506.CrossRefGoogle ScholarPubMed
15. Gibson, GL, Martin, DK, Singer, PA. Evidence, economics and ethics: resource allocation in health services organizations. Healthc Q. 2005;8:50–59.CrossRefGoogle ScholarPubMed
16. Goldman, L, Cook, E, Brand, D. A computer protocol to predict myocardial infarction in emergency department patients with chest pain. N Engl J Med. 1988;318:797–803.CrossRefGoogle ScholarPubMed
17. Goldman, L, Weinberg, M, Weisberg, M, et al. A computer-derived protocol to aid in the diagnosis of emergency room patients with acute chest pain. N Engl J Med. 1982;307:588–596.CrossRefGoogle ScholarPubMed
18. Griebsch, I, Knowles, RL, Brown, J, et al. . Comparing the clinical and economic effects of clinical examination, pulse oximetry, and echocardiography in newborn screening for congenital heart defects: A probabilistic cost-effectiveness model and value of information analysis. Int J Technol Assess Health Care. 2007;23:192–204.CrossRefGoogle ScholarPubMed
19. Groot, Koerkamp B, Hunink, MG, Stijnen, T, et al. . Identifying key parameters in cost-effectiveness analysis using value of information: A comparison of methods. Health Econ. 2006;15:382–392.Google Scholar
20. Heart and Stroke Foundation of Canada. The changing face of heart disease and stroke in Canada, Ottawa. Ottawa: Heart and Stroke Foundation of Canada; 2000.Google Scholar
21. Jonsbu, J, Aase, O, Rollag, A, et al. Prospective evaluation of an EDB-based diagnostic program to be used in patients admitted to hospital with acute chest pain. Eur Heart J. 1993;14:441–446.CrossRefGoogle ScholarPubMed
22. Laupacis, A, Feeny, D, Detsky, A, et al. How attractive does a new technology have to be to warrant adoption and utilization? Tentative guidelines for using clinical and economic evaluations. Can Med Assoc J. 1992;146:473–481.Google ScholarPubMed
23. Limkakeng, A Jr, Gibler, WB, Pollack, C, et al. . Combination of Goldman risk and initial cardiac troponin I for emergency department chest pain patient risk stratification. Acad Emerg Med. 2001;8:696–702.CrossRefGoogle ScholarPubMed
24. Management Information Systems Project Steering Committee. Guidelines for management information systems in Canadian healthcare facilities. Ottawa, Ontario: The MIS Group; 2002.Google Scholar
25. Mitton, C, Patten, S, Waldner, H, et al. . Priority setting in health authorities: A novel approach to a historical activity. Soc Sci Med. 2003;57:1653–1663.CrossRefGoogle ScholarPubMed
26. Pope, JH, Aufderheide, TP, Ruthazer, R, et al. . Missed diagnosis of acute cardiac ischemia in the emergency department. N Engl J Med. 2000;342:1163–1170.CrossRefGoogle ScholarPubMed
27. Pozen, MW, D'Agostino, RB, Mitchell, JB, et al. . The usefulness of a predictive instrument to reduce inappropriate admissions to the coronary care unit. Ann Intern Med. 1980;92 (Pt 1):238–242.CrossRefGoogle Scholar
28. Pozen, MW, D'Agostino, RB, Selker, HP, et al. . A predictive instrument to improve coronary-care-unit admission practices in acute ischemic heart disease. A prospective multicenter clinical trial. N Engl J Med. 1984;310:1273–1278.CrossRefGoogle ScholarPubMed
29. Selker, H, Beshansky, JR, Griffith, JL, et al. . Use of the acute cardiac ischemia time-insensitive predictive instrument (ACI-TIPI) to assist with triage of patients with chest pain or other symptoms suggestive of acute cardiac ischemia. A multicenter, controlled clinical trial. Ann Intern Med. 1998;129:845–855.CrossRefGoogle ScholarPubMed
30. Selker, H, D'Agostino, R, Laks, M. A predictive instrument for acute ischemic heart disease to improve coronary care unit admission practices: a potential on-line tool in a computerized electrocardiograph. J Electrocardiol. 1988;(Suppl):S11–S17.CrossRefGoogle Scholar
31. Selker, H, Griffith, J, Patil, S, et al. . A comparison of performance of mathematical predictive methods for medical diagnosis: identifying acute cardiac ischemia among emergency department patients. J Invest Med. 1995;43:468–476.Google ScholarPubMed
32. Stinnett, AA, Mullahy, J. Net health benefits: A new framework for the analysis of uncertainty in cost-effectiveness analysis. Med Decis Making. 1998;18 (Suppl):S68–S80.CrossRefGoogle ScholarPubMed
33. Thomas, KC, Nosyk, B, Fisher, CG, et al. . Cost-effectiveness of surgery plus radiotherapy vs. radiotherapy alone for treatment of metastatic epidural spinal cord compression. Int J Radiat Oncol Biol Phys. 2006;66:1212–1218.CrossRefGoogle Scholar
34. Ubel, PA, Hirth, RA, Chernew, ME, et al. . What is the price of life and why doesn't it increase at the rate of inflation? Arch Intern Med. 2003;163:1637–1641.CrossRefGoogle Scholar
Supplementary material: File

Singh supplementary material

Singh supplementary material

Download Singh supplementary material(File)
File 91.6 KB