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Diagnostic accuracy of clinical prediction rules to exclude acute coronary syndrome in the emergency department setting: a systematic review

Published online by Cambridge University Press:  21 May 2015

Erik P. Hess ,
Venkatesh Thiruganasambandamoorthy ,
George A. Wells ,
Patricia Erwin ,
Allan S. Jaffe ,
Judd E. Hollander ,
Victor M. Montori  and
Ian G. Stiell
Erik P. Hess*
Affiliation:
Department of Emergency Medicine, University of Ottawa, Ottawa, Ont.
Venkatesh Thiruganasambandamoorthy
Affiliation:
Department of Emergency Medicine, University of Ottawa, Ottawa, Ont.
George A. Wells
Affiliation:
Department of Epidemiology and Community Medicine, University of Ottawa, Ottawa, Ont.
Patricia Erwin
Affiliation:
Mayo Medical Libraries, Department of Internal Medicine, Mayo Clinic College of Medicine, Rochester, Minn.
Allan S. Jaffe
Affiliation:
Division of Cardiology, Department of Internal Medicine, Mayo Clinic College of Medicine, Rochester, Minn.
Judd E. Hollander
Affiliation:
Department of Emergency Medicine, Hospital of the University of Pennsylvania, Philadelphia, Penn.
Victor M. Montori
Affiliation:
Knowledge and Encounter Research Unit, Department of Internal Medicine, Mayo Clinic College of Medicine, Rochester, Minn.
Ian G. Stiell
Affiliation:
Department of Emergency Medicine, University of Ottawa, Ottawa, Ont. Department of Epidemiology and Community Medicine, University of Ottawa, Ottawa, Ont.
*
Clinical Epidemiology Unit, Office F657, Ottawa Health Research Institute, The Ottawa Hospital, Civic Campus, 1053 Carling Ave., Ottawa ON K1Y 4E9; hess.erik@mayo.edu

Abstract

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Objective:

We sought to determine the diagnostic accuracy of clinical prediction rules to exclude acute coronary syndrome (ACS) in the emergency department (ED) setting.

Methods:

We searched MEDLINE, EMBASE, Web of Science and the Cochrane Database of Systematic Reviews. We contacted content experts to identify additional articles for review. Reference lists of included studies were hand searched. We selected articles for review based on the following criteria: 1) enrolled consecutive ED patients; 2) incorporated variables from the history or physical examination, electrocardiogram and cardiac biomarkers; 3) did not incorporate cardiac stress testing or coronary angiography into prediction rule; 4) based on original research; 5) prospectively derived or validated; 6) did not require use of a computer; and 7) reported sufficient data to construct a 2 ∞ 2 contingency table. We assessed study quality and extracted data independently and in duplicate using a standardized data extraction form.

Results:

Eight studies met inclusion criteria, encompassing 7937 patients. None of the studies verified the prediction rule with a reference standard on all or a random sample of patients. Six studies did not report blinding prediction rule assessors to reference standard results, and vice versa. Three prediction rules were prospectively validated. Sensitivities and specificities ranged from 94% to 100% and 13% to 57%, and positive and negative likelihood ratios from 1.1 to 2.2 and 0.01 to 0.17, respectively.

Conclusion:

Current prediction rules for ACS have substantial methodological limitations and have not been successfully implemented in the clinical setting. Future methodologically sound studies are needed to guide clinical practice.

Keywords

acute coronary syndromemyocardial infarctionunstable anginadiagnosisemergency medical services
Type
State of the Art • À la fine pointe
Information
Canadian Journal of Emergency Medicine , Volume 10 , Issue 4 , July 2008 , pp. 373 - 382
Copyright
Copyright © Canadian Association of Emergency Physicians 2008

References

1.Pope, JH, Aufderheide, TP, Ruthazer, R, et al. Missed diagnoses of acute cardiac ischemia in the emergency department. N Engl J Med 2000;342:1163–70.CrossRefGoogle ScholarPubMed
2.Christenson, J, Innes, G, McKnight, D, et al. Safety and efficiency of emergency department assessment of chest discomfort. CMAJ 2004;170:1803–7.Google Scholar
3.Swap, CJ, Nagurney, JT. Value and limitations of chest pain history in the evaluation of patients with suspected acute coronary syndromes. JAMA 2005;294:2623–9.Google Scholar
4.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;2001:696–702.CrossRefGoogle ScholarPubMed
5.Heidenreich, PA, Go, A, Melsop, KA, et al. Prediction of risk for patients with unstable angina. Evid Rep Technol Assess (Summ) 2000;(31):1–3.Google Scholar
6.Anderson, JL, Adams, CD, Antman, EM, et al. ACC/AHA 2007 guidelines for the management of patients with unstable angina/non ST-elevation myocardial infarction: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines (Writing Committee to Revise the 2002 Guidelines for the Management of Patients With Unstable Angina/Non ST-Elevation Myocardial Infarction): developed in collaboration with the American College of Emergency Physicians, the Society for Cardiovascular Angiography and Interventions, and the Society of Thoracic Surgeons: endorsed by the American Association of Cardiovascular and Pulmonary Rehabilitation and the Society for Academic Emergency Medicine. Circulation 2007;116:e148–304.Google Scholar
7.Gibler, WB, Cannon, CP, Blomkalns, AL, et al. Practical implementation of the guidelines for unstable angina/non-ST-segment elevation myocardial infarction in the emergency department: a scientific statement from the American Heart Association Council on Clinical Cardiology (Subcommittee on Acute Cardiac Care), Council on Cardiovascular Nursing, and Quality of Care and Outcomes Research Interdisciplinary Working Group, in Collaboration With the Society of Chest Pain Centers. Circulation 2005;111:2699–710.Google Scholar
8.McGinn, TG, Guyatt, GH, Wyer, PC, et al. Users’ guides to the medical literature: XXII: how to use articles about clinical decision rules. Evidence-Based Medicine Working Group. JAMA 2000;284:79–84.Google Scholar
9.Stiell, IG, Wells, GA. Methodologic standards for the development of clinical decision rules in emergency medicine. Ann Emerg Med 1999;33:437–47.Google Scholar
10.Moher, D, Cook, DJ, Eastwood, S, et al. Improving the quality of reports of meta-analyses of randomised controlled trials: the QUOROM statement. Quality of Reporting of Meta-analyses. Lancet 1999; 27;354:1896–1900.Google Scholar
11.Wong, SS, Wilczynski, NL, Haynes, RB, et al. Developing optimal search strategies for detecting sound clinical prediction studies in MEDLINE. AMIA Annu Symp Proc 2003;728–32.Google Scholar
12.Swap, CJ, Nagurney, JT. Value and limitations of chest pain history in the evaluation of patients with suspected acute coronary syndromes. JAMA 2005;294:2623–9.CrossRefGoogle ScholarPubMed
13.Whiting, P, Rutjes, AW, Reitsma, JB, et al. The development of QUADAS: a tool for the quality assessment of studies of diagnostic accuracy included in systematic reviews. BMC Med Res Methodol 2006;6:6–9.Google Scholar
14.Zamora, J, Abraira, V, Muriel, A, et al. Meta-DiSc: a software for meta-analysis of test accuracy data. BMC Med Res Methodol. 2006;12;6:31.Google Scholar
15.Bassan, R, Pimenta, L, Scofano, M, et al. Accuracy of a neural diagnostic tree for the identification of acute coronary syndrome in patients with chest pain and no ST-segment elevation. Crit Pathw Cardiol 2004;3:72–8.Google Scholar
16.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.Google Scholar
17.Chase, M, Robey, JL, Zogby, KE, et al. Prospective validation of the Thrombolysis in Myocardial Infarction Risk Score in the emergency department chest pain population. Ann Emerg Med 2006;48:252–9.Google Scholar
18.Conway Morris, A, Caesar, D, Gray, S, et al. TIMI risk score accurately risk stratifies patients with undifferentiated chest pain presenting to an emergency department. Heart 2006;92:1333–4.Google Scholar
19.Marsan, RJ Jr, Shaver, KJ, Sease, KL, et al. Evaluation of a clinical decision rule for young adult patients with chest pain. Acad Emerg Med 2005;12:26–31.Google Scholar
20.Lyon, R, Morris, AC, Caesar, D, et al. Chest pain presenting to the emergency department — to stratify risk with GRACE or TIMI? Resuscitation 2007;74:90–3.Google Scholar
21.Fernandez Portales, J, Perez Reyes, F, Garcia Robles, JA, et al. Risk stratification using combined ECG, clinical, and biochemical assessment in patients with chest pain without ST-segment elevation. How long should we wait? Rev Esp Cardiol 2003;56:338–45.Google Scholar
22.Boersma, E, Pieper, KS, Steyerberg, EW, et al. Predictors of outcome in patients with acute coronary syndromes without persistent ST-segment elevation. Results from an international trial of 9461 patients. The PURSUIT Investigators. Circulation 2000;101:2557–67.Google Scholar
23.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;310:1273–8.Google Scholar
24.Henrikson, CA, Howell, EE, Bush, DE, et al. Prognostic usefulness of marginal troponin T elevation. Am J Cardiol 2004;93:275–9.CrossRefGoogle ScholarPubMed
25.Tong, KL, Kaul, S, Wang, XQ, et al. Myocardial contrast echocardiography versus Thrombolysis In Myocardial Infarction score in patients presenting to the emergency department with chest pain and a nondiagnostic electrocardiogram. J Am Coll Cardiol 2005;46:920–7.Google Scholar
26.Hollander, JE1, Blomkalns, AL, Brogan, GX, et al. Standardized reporting guidelines for studies evaluating risk stratification of emergency department patients with potential acute coronary syndromes. Ann Emerg Med 2004;44:589–98.Google ScholarPubMed
27.Jaeschke, R, Guyatt, G, Lijmer, J. Diagnostic tests. In: Guyatt, G, Rennie, D, editors. Users’ guides to the medical literature. Chicago (IL): AMA Press, 2002: 121–40.Google Scholar
28.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;307:588–96.CrossRefGoogle ScholarPubMed
29.Goldman, L, Cook, EF, Brand, DA, et al. A computer protocol to predict myocardial infarction in emergency department patients with chest pain. N Engl J Med 1988;318:797–803.CrossRefGoogle ScholarPubMed
30.Baxt, WG. Use of an artificial neural network for the diagnosis of myocardial infarction. Ann Intern Med 1991;115:843–8.CrossRefGoogle ScholarPubMed
31.Baxt, WG. A neural network trained to identify the presence of myocardial infarction bases some decisions on clinical associations that differ from accepted clinical teaching. Med Decis Making 1994;14:217–22.CrossRefGoogle ScholarPubMed
32.Baxt, WG, Shofer, FS, Sites, FD, et al. A neural network aid for the early diagnosis of cardiac ischemia in patients presenting to the emergency department with chest pain. Ann Emerg Med 2002;40:575–83.CrossRefGoogle Scholar
33.Baxt, WG, Shofer, FS, Sites, FD, et al. A neural computational aid to the diagnosis of acute myocardial infarction. Ann Emerg Med 2002;39:366–73.Google Scholar
34.Baxt, WG, Skora, J. Prospective validation of artificial neural network trained to identify acute myocardial infarction. Lancet 1996;347:12–5.Google Scholar
35.Lorenzoni, R, Ebert, AG, Lattanzi, F, et al. A computer protocol to evaluate subjects with chest pain in the emergency department: a multicenter study. J Cardiovasc Med 2006;7:203–9.CrossRefGoogle ScholarPubMed
36.Poretsky, L, Leibowitz, IH, Friedman, SA. The diagnosis of myocardial infarction by computer-derived protocol in a municipal hospital. Angiology 1985;36:165–70.CrossRefGoogle Scholar
37.Harrison, RF, Kennedy, RL. Artificial neural network models for prediction of acute coronary syndromes using clinical data from the time of presentation. Ann Emerg Med 2005;46:431–9.CrossRefGoogle ScholarPubMed
38.Mitchell, AM, Garvey, JL, Chandra, A. Prospective multicenter study of quantitative pretest probability assessment to exclude acute coronary syndrome for patients evaluated in emergency department chest pain units. Ann Emerg Med 2006;47:447.Google Scholar
39.Selker, HP, 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;129:845–55.Google Scholar
40.Green, M, Bjork, J, Forberg, J, et al. Comparison between neural networks and multiple logistic regression to predict acute coronary syndrome in the emergency room. Artif Intell Med 2006;38:305–18.Google Scholar
41.Qamar, A, McPherson, C, Babb, J, et al. The Goldman algorithm revisited: prospective evaluation of a computer-derived algorithm versus unaided physician judgment in suspected acute my-ocardial infarction. Am Heart J 1999;138:705–9.Google Scholar
42.Macgougan, CK, Christenson, JM, Innes, GD, et al. Emergency physicians’ attitudes toward a clinical prediction rule for the identification and early discharge of low risk patients with chest discomfort. CJEM 2001;3:89–94.Google Scholar
43.Alpert, JS, Thygesen, K, Antman, E, et al. Myocardial infarction redefined-a consensus document of The Joint European Society of Cardiology/American College of Cardiology Committee for the redefinition of myocardial infarction. J Am Coll Cardiol 2000;36:959–69.Google Scholar
44.Jaffe, AS. Chasing troponin: how low can you go if you can see the rise? J Am Coll Cardiol 2006;48:1763–4.CrossRefGoogle Scholar
45.Apple, FS, Wu, AH, Jaffe, AS. European Society of Cardiology and American College of Cardiology guidelines for redefinition of myocardial infarction: how to use existing assays clinically and for clinical trials. Am Heart J 2002;144:981–6.CrossRefGoogle Scholar