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A Novel Algorithm for Improving the Diagnostic Accuracy of Prehospital ST-Elevation Myocardial Infarction

Published online by Cambridge University Press:  11 September 2019

Mat Goebel*
University of Massachusetts Medical School – Baystate, Springfield, Massachusetts, USA
Florin Vaida
HIV Neurobehavioral Research Program, University of California, San Diego, California, USA
Christopher Kahn
UC San Diego School of Medicine, Department of Emergency Medicine, San Diego, California, USA San Diego Fire-Rescue Department, Division of EMS, San Diego, California, USA
J. Joelle Donofrio
UC San Diego School of Medicine, Department of Emergency Medicine, San Diego, California, USA San Diego Fire-Rescue Department, Division of EMS, San Diego, California, USA Rady Children’s Hospital of San Diego, San Diego, California, USA
Correspondence: Mat Goebel, MD, MAS, NR-P Baystate Medical Center Dept of Emergency Medicine 759 Chestnut St. Springfield, Massachusetts 01199 USA E-mail:



ST-segment elevation myocardial infarction (STEMI) is a time-sensitive entity that has been shown to benefit from prehospital diagnosis by electrocardiogram (ECG). Current computer algorithms with binary decision making are not accurate enough to be relied on for cardiac catheterization lab (CCL) activation.


An algorithmic approach is proposed to stratify binary STEMI computerized ECG interpretations into low, intermediate, and high STEMI probability tiers.


Based on previous literature, a four-criteria algorithm was developed to rule out/in common causes of prehospital STEMI false-positive computer interpretations: heart rate, QRS width, ST elevation criteria, and artifact. Prehospital STEMI cases were prospectively collected at a single academic center in Salt Lake City, Utah (USA) from May 2012 through October 2013. The prehospital ECGs were applied to the algorithm and compared against activation of the CCL by an emergency department (ED) physician as the outcome of interest. In addition to calculating test characteristics, linear regression was used to look for an association between number of criteria used and accuracy, and logistic regression was used to test if any single criterion performed better than another.


There were 63 ECGs available for review, 39 high probability and 24 intermediate probability. The high probability STEMI tier had excellent test characteristics for ruling in STEMI when all four criteria were used, specificity 1.00 (95% CI, 0.59-1.00), positive predictive value 1.00 (0.91-1.00). Linear regression showed a strong correlation demonstrating that false-positives increased as fewer criteria were used (adjusted r-square 0.51; P <.01). Logistic regression showed no significant predictive value for any one criterion over another (P = .80). Limiting physician overread to the intermediate tier only would reduce the number of ECGs requiring physician overread by a factor of 0.62 (95% CI, 0.48-0.75; P <.01).


Prehospital STEMI ECGs can be accurately stratified to high, intermediate, and low probabilities for STEMI using the four criteria. While additional study is required, using this tiered algorithmic approach in prehospital ECGs could lead to changes in CCL activation and decreased requirements for physician overread. This may have significant clinical and quality implications.

Original Research
© World Association for Disaster and Emergency Medicine 2019 

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Sanchis-Gomar, F, Perez-Quilis, C, Leischik, R, Lucia, A. Epidemiology of coronary heart disease and acute coronary syndrome. Ann Transl Med. 2016;4(13):256.CrossRefGoogle ScholarPubMed
Utah Department of Public Health. Complete Health Indicator Report ‐ Heart Attack: Hospitalizations. Public Health Indicator Based Information System. Accessed May 8, 2018.Google Scholar
Bossaert, L, O’Connor, RE, Arntz, H-R, et al. Part 9: acute coronary syndromes. Resuscitation. 2010;81(1):e175e212.CrossRefGoogle ScholarPubMed
Brown, JP, Mahmud, E, Dunford, J V, Ben-Yehuda, O. Effect of prehospital 12-lead electrocardiogram on activation of the cardiac catheterization laboratory and door-to-balloon time in ST-segment elevation acute myocardial infarction. Am J Cardiol. 2008;101(2):158161.CrossRefGoogle ScholarPubMed
Rao, A, Kardouh, Y, Darda, S, et al. Impact of the prehospital ECG on door-to-balloon time in ST elevation myocardial infarction. Catheter Cardiovasc Interv. 2010;75(2):174178.CrossRefGoogle ScholarPubMed
Hutchison, AW, Malaiapan, Y, Jarvie, I, et al. Prehospital 12-lead ECG to triage ST-elevation myocardial infarction and emergency department activation of the infarct team significantly improves door-to-balloon times. Circ Cardiovasc Interv. 2009;2(6).CrossRefGoogle Scholar
Davis, M, Lewell, M, McLeod, S, Dukelow, A. A prospective evaluation of the utility of the prehospital 12-lead electrocardiogram to change patient management in the emergency department. Prehosp Emerg Care. 2014;18(1):914.CrossRefGoogle ScholarPubMed
Cheskes, S, Turner, L, Foggett, R, et al. Paramedic contact to balloon in less than 90 minutes: a successful strategy for ST-segment elevation myocardial infarction bypass to primary percutaneous coronary intervention in a Canadian emergency medical system. Prehosp Emerg Care. 2011;15(4):490498.CrossRefGoogle Scholar
Clark, E, Sejersten, M, Clemmensen, P, Macfarlane, PW. Effectiveness of electrocardiogram interpretation programs in the ambulance setting. Comput Cardiol 2009. 2009:117120.Google Scholar
American Heart Association. Recommendations for Criteria for STEMI Systems of Care. Accessed December 21, 2016.Google Scholar
Feldman, JA, Brinsfield, K, Bernard, S, White, D, Maciejko, T. Real-time paramedic compared with blinded physician identification of ST-segment elevation myocardial infarction: results of an observational study. Am J Emerg Med. 2005;23(4):443448.CrossRefGoogle ScholarPubMed
Al-Akchar, M, Aguirre, FV, Mahmaljy, H, et al. Abstract 18253: reliance on electrocardiogram computer algorithm interpretation to activate ST elevation myocardial infarction processes of care and initiate reperfusion therapy: impact on false activation. Circulation. 2017;136(Suppl 1).Google Scholar
Garvey, JL, Zegre-Hemsey, J, Gregg, R, Studnek, JR. Electrocardiographic diagnosis of ST segment elevation myocardial infarction: an evaluation of three automated interpretation algorithms. J Electrocardiol. 2016;49(5):728732.CrossRefGoogle ScholarPubMed
Sanko, S, Eckstein, M, Bosson, N, et al. Accuracy of out-of-hospital automated ST segment elevation myocardial infarction detection by LIFEPAK 12 and 15 devices: the Los Angeles experience. Ann Emerg Med. 2015;66(4):S6S7.CrossRefGoogle Scholar
de Champlain, F, Boothroyd, LJ, Vadeboncoeur, A, et al. Computerized interpretation of the prehospital electrocardiogram: predictive value for ST segment elevation myocardial infarction and impact on on-scene time. Can J Emerg Med. 2014;16(2):94105.Google ScholarPubMed
Wilson, RE, Kado, HS, Percy, RF, et al. An algorithm for identification of ST-elevation myocardial infarction patients by emergency medicine services. Am J Emerg Med. 2013;31(7):10981102.CrossRefGoogle ScholarPubMed
Kado, HS, Wilson, RE, Strom, JA, Box, LC. Retrospective validation of pre-hospital electrocardiogram with ZOLL e-series monitoring system for field identification of ST elevation myocardial infarction patients. Circ Cardiovasc Qual Outcomes. 2012;5(3).CrossRefGoogle Scholar
Bhalla, MC, Mencl, F, Gist, MA, Wilber, S, Zalewski, J. Prehospital electrocardiographic computer identification of ST-segment elevation myocardial infarction. Prehosp Emerg Care. 2012;17(2):121015065524007.Google ScholarPubMed
Bosson, N, Sanko, S, Stickney, RE, et al. Causes of prehospital misinterpretations of ST elevation myocardial infarction. Prehosp Emerg Care. 2017;21(3):283290.CrossRefGoogle ScholarPubMed
Swan, PY, Nighswonger, B, Boswell, GL, Stratton, SJ. Factors associated with false-positive emergency medical services triage for percutaneous coronary intervention. West J Emerg Med. 2009;10(4):208212.Google ScholarPubMed
Simel, DL, Matchar, DB, Feussner, JR. Diagnostic tests are not always black or white: or, all that glitters is not [a] gold [standard]. J Clin Epidemiol. 1991;44(9):967970; discussion 970-971.CrossRefGoogle Scholar
Sackett, DL. Clinical reality, binary models, babies and bath water. J Clin Epidemiol. 1991;44(2):217219.CrossRefGoogle ScholarPubMed
Jamart, J. Chance-corrected sensitivity and specificity for three-zone diagnostic tests. J Clin Epidemiol. 1992;45(9):10351039.CrossRefGoogle ScholarPubMed
Feinstein, AR. The clinical reality of three-zone. J Clin Epidemiol. 1990;43(1):109113.CrossRefGoogle ScholarPubMed
Coste, J, Pouchot, J. A grey zone for quantitative diagnostic and screening tests. Int J Epidemiol. 2003;32(2):304313.CrossRefGoogle ScholarPubMed
Cannesson, M. The “grey zone” or how to avoid the binary constraint of decision-making. Can J Anesth Can d’anesthésie. 2015;62(11):11391142.CrossRefGoogle ScholarPubMed
Cannesson, M, Le Manach, Y, Hofer, CK, et al. Assessing the diagnostic accuracy of pulse pressure variations for the prediction of fluid responsiveness: a “gray zone” approach. Anesthesiology. 2011;115(2):231241.CrossRefGoogle ScholarPubMed
Pilbery, R, Teare, MD, Goodacre, S, Morris, F. The recognition of STEMI by paramedics and the effect of computer interpretation (RESPECT): a randomized crossover feasibility study. Emerg Med J. 2016;33(7):471476.CrossRefGoogle ScholarPubMed
Coffey, C, Serra, J, Goebel, M, Espinoza, S, Castillo, E, Dunford, J. Prehospital acute ST-elevation myocardial infarction identification in San Diego: a retrospective analysis of the effect of a new software algorithm. J Emerg Med. 2018;55(1):7177.CrossRefGoogle Scholar
Verbeek, PR, Ryan, D, Turner, L, Craig, AM. Serial prehospital 12-lead electrocardiograms increase identification of ST-segment elevation myocardial infarction. Prehosp Emerg Care. 2012;16(1):109114.CrossRefGoogle ScholarPubMed
Tanguay, A, Lebon, J, Lau, L, Hébert, D, Bégin, F. Detection of STEMI using prehospital serial 12-lead electrocardiograms. Prehosp Emerg Care. 2018;22(4):419426.CrossRefGoogle ScholarPubMed
Westbrook, JI, Raban, MZ, Walter, SR, Douglas, H. Task errors by emergency physicians are associated with interruptions, multitasking, fatigue and working memory capacity: a prospective, direct observation study. BMJ Qual Saf. January 2018;27(8):655663.CrossRefGoogle ScholarPubMed
Berg, LM, Källberg, A-S, Göransson, KE, Östergren, J, Florin, J, Ehrenberg, A. Interruptions in emergency department work: an observational and interview study. BMJ Qual Saf. 2013;22(8):656663.CrossRefGoogle ScholarPubMed
Blocker, RC, Heaton, HA, Forsyth, KL, et al. Physician, interrupted: workflow interruptions and patient care in the emergency department. J Emerg Med. 2017;53(6):798804.CrossRefGoogle ScholarPubMed
Chisholm, CD, Dornfeld, AM, Nelson, DR, Cordell, WH. Work interrupted: a comparison of workplace interruptions in emergency departments and primary care offices. Ann Emerg Med. 2001;38(2):146151.CrossRefGoogle ScholarPubMed
Menees, DS, Peterson, ED, Wang, Y, et al. Door-to-balloon time and mortality among patients undergoing primary PCI. N Engl J Med. 2013;369(10):901909.CrossRefGoogle ScholarPubMed
Chen, FC, Lin, YR, Kung, CT, Cheng, CI, Li, CJ. The association between door-to-balloon time of less than 60 minutes and prognosis of patients developing ST segment elevation myocardial infarction and undergoing primary percutaneous coronary intervention. Biomed Res Int. 2017;2017:1910934.Google ScholarPubMed
Fanari, Z, Abraham, N, Kolm, P, et al. Aggressive measures to decrease “door to balloon” time and incidence of unnecessary cardiac catheterization: potential risks and role of quality improvement. Mayo Clin Proc. 2015;90(12):16141622.CrossRefGoogle ScholarPubMed
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