Annales françaises de médecine d'urgence

, Volume 4, Issue 2, pp 75–81

Évaluation d’un algorithme de prise en charge des syndromes coronariens non ST+ aux urgences

  • A. Le Meur
  • D. Lauque
  • D. Carrié
  • M. Galinier
  • H. Juchet
  • S. Charpentier
Article Original / Original Article
  • 260 Downloads

Résumé

Objectif

Nous avons mis en place, aux urgences, un algorithme de prise en charge des patients présentant une douleur thoracique d’allure angineuse, fondé sur l’évaluation de la probabilité diagnostique de syndrome coronarien aigu (SCA). L’objectif principal de cette étude était d’évaluer les performances de cet algorithme pour le diagnostic de SCA.

Matériel et méthodes

Nous avons inclus, de février à avril 2012, 212 patients admis pour douleur thoracique d’allure angineuse. Ces patients ont été classés à probabilité forte, intermédiaire ou faible de SCA. Nous avons étudié les performances de ces trois groupes comparativement au diagnostic final de SCA, fondé sur les définitions internationales.

Résultats

L’incidence du SCA dans les groupes de forte, intermédiaire et faible probabilité était respectivement de 59, 24 et 3%. Tous les infarctus dumyocarde (IDM) se trouvaient dans le groupe de forte probabilité. La sensibilité et la spécificité du groupe forte probabilité pour le diagnostic de SCA étaient de 77 % (IC 95 %: [64–89]) et de 85 % (IC 95 %: [78–90]). La sensibilité et la spécificité du groupe faible probabilité pour l’exclusion du SCA étaient de 72 % (IC 95 %: [64–78]) et de 91 % (IC 95 %: [80–98]).

Conclusion

Notre algorithme permet de stratifier précocement et de façon efficace la probabilité de SCA. Cette classification permet de ne pas laisser rentrer à domicile les patients les plus graves et d’identifier les patients nécessitant la mise en oeuvre d’un traitement antithrombotique.

Mots clés

Syndrome coronarien aigu Médecine d’urgence Stratification du risque Score de risque Algorithme diagnostique 

An algorithm to manage acute coronary syndromes without ST elevation in emergency departments

Abstract

Aim

An algorithm was developed in emergency department to manage patients with chest pain based on the evaluation of the diagnostic likelihood of acute coronary syndrome (ACS). The main objective of this study was to evaluate the diagnostic accuracy of this algorithm.

Procedure

We prospectively included, from February to April 2012, 212 patients admitted for chest pain suspected of ACS. These patients were classified as high, intermediate or low likelihood of ACS. The accuracy of these three groups were assessed and compared to the final diagnosis of ACS, based on the international definitions.

Results

The incidence of ACS in groups of high, intermediate and low probability was respectively 59, 24 and 3%. All myocardial infarctions (MIs) were in the group of high likelihood. The sensitivity and specificity of high likelihood for the rule in of ACS were 77% (95% CI: [64–89]) and 85% (95% CI: [78–90]). The sensitivity and specificity of low likelihood group to rule out ACS were 72% (95% CI: [64–78]) and 91% (95% CI: [80–98]).

Conclusion

Our algorithm allows to stratify rapidly and in an effective way the likelihood of ACS. This classification allows to identify patients with ACS and requiring the implementation of an antithrombotic treatment.

Keywords

Acute coronary syndrome Emergency medicine Risk stratification Risk score Diagnostic algorithm 

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Références

  1. 1.
    Karlson BW, Herlitz J, Pettersson P, et al (1991) Patients admitted to the emergency room with symptoms indicative of acute myocardial infarction. J Intern Med 230:251–258PubMedCrossRefGoogle Scholar
  2. 2.
    Cochrane DG, Allegra JR, Graff LG (1993) Epidemiology of observation services. Observation Medicine services. Andover Medical Publisher, Boston, MA, pp 37–45Google Scholar
  3. 3.
    Yeh RW, Sidney S, Chandra M, et al (2010) Population trends in the incidence and outcomes of acute myocardial infarction. N Engl J Med 362:2155–2165PubMedCrossRefGoogle Scholar
  4. 4.
    Hamm CW, Bassand JP, Agewall S, et al (2011) ESC Guidelines for the management of acute coronary syndromes in patients presenting without persistent ST-segment elevation: The Task Force for the management of acute coronary syndromes (ACS) in patients presenting without persistent ST-segment elevation of the European Society of Cardiology (ESC). Eur Heart J 236: 2999–3054Google Scholar
  5. 5.
    Fox KA, Eagle KA, Lim MJ, et al (2006) Prediction of risk of death and myocardial infarction in the six month after presentation with acute coronary syndrome: prospective multinational observational study (GRACE). BMJ 333:1091PubMedCrossRefPubMedCentralGoogle Scholar
  6. 6.
    Antman E, Cohen M, Bernink PJ, et al (2000) The TIMI risk score for unstable angina/non ST elevation MI: a method for prognostication and therapeutic decision making. JAMA 284:835–842PubMedCrossRefGoogle Scholar
  7. 7.
    Goldman L, Cook EF, Johnson PA, et al (1996) Prediction of the need of intensive care in patients who come to the emergency departments with acute chest pain. N Engl J Med 334:1498–1504PubMedCrossRefGoogle Scholar
  8. 8.
    Sanchez M, Lopez B, Bragulat E, et al (2007) Triage flowchart to rule out acute coronary syndrome. Am J Emerg Med 25:865–872PubMedCrossRefGoogle Scholar
  9. 9.
    Björk J, Forberg JL, Ohlsson M, et al (2006) A simple statistical model for prediction of acute coronary syndrome in chest pain patients in the emergency department. BMC Med Inform Decis Mak 6:28PubMedCrossRefPubMedCentralGoogle Scholar
  10. 10.
    Gruseels E, Deckers J, Hoes A, et al (1995) Pre-hospital triage of patients with suspected myocardial infarction, evaluation of previously developed algorithms and new proposals. Eur Heart J 16:325–332Google Scholar
  11. 11.
    Thygesen K, Alpert JS, Jaffe AS, et al (2012) Third universal definition of myocardial infarction. Eur Heart J. 33:2551–2567PubMedCrossRefGoogle Scholar
  12. 12.
    Anderson JL, Adams CD, Antman EM, et al (2007) ACC/AHA 2007 guidelines for the management of patients with unstable angina/non-ST-Elevation myocardial infarction. J Am Coll Cardiol 50:e1–e157PubMedCrossRefGoogle Scholar
  13. 13.
    Chandra A, Lindsell JC, Limkaken A, et al (2009) Emergency physician high pretest probability for acute coronary syndrome correlates with adverse cardiovascular outcomes. Acad Emerg Med 16:740–748PubMedCrossRefGoogle Scholar
  14. 14.
    Six AJ, Backus BE, Kelder JC, et al (2008) Chest pain in the emergency room: value of the HEART score. Neth Heart J 16:191–196PubMedCrossRefPubMedCentralGoogle Scholar
  15. 15.
    Body R, Mcdowell G, Carley S, et al (2008) Do risk factors for chronic coronary heart disease help diagnose. Acute myocardial infarction in the emergency department?. Resuscitation 79:41–45PubMedCrossRefGoogle Scholar
  16. 16.
    Geleijnse ML, Elhendy A, Kasprzak JD, et al (2000) Safety and prognostic value of early dobutamine-atropine stress echocardiography in patients with spontaneous chest pain and a nondiagnostic electrocardiogram. Eur Heart J 21:397–406PubMedCrossRefGoogle Scholar
  17. 17.
    Sanchis J, Bodí V, Núñez J, et al (2005) New Risk Score for patients with acute chest pain, non-ST-segment deviation, and normal troponin concentrations. A comparison with the TIMI risk score. J Am Coll Cardiol 46:443–449PubMedCrossRefGoogle Scholar
  18. 18.
    Keller T, Post F, Tzikas S, et al (2010) Improved outcome in acute coronary syndrome by establishing a chest pain unit. Clin Res Cardiol 99:149–155PubMedCrossRefGoogle Scholar
  19. 19.
    Manini AF, Dannemann N, Brown DF, et al (2009) Limitations of risk score models in patients with acute chest pain. Am J Emerg Med 27:43–48PubMedCrossRefGoogle Scholar
  20. 20.
    Meune C, Reichlin T, Irfan A, et al (2012) How safe is the outpatient management of patients with acute chest pain and mildly increased cardiac troponin concentrations? Clin Chem 58:916–924PubMedCrossRefGoogle Scholar
  21. 21.
    Meune C, Balmelli C, Twerenbold R, et al (2011) Patients with acute coronary syndrome and normal high-sensitivity troponin. Am J Med 124:1151–1157PubMedCrossRefGoogle Scholar
  22. 22.
    Goldman L, Cook EF, Brand DA, et al (1998) A computer protocol to predict myocardial infarction in emergency department patients with chest pain. N Engl J Med 318:797–803CrossRefGoogle Scholar
  23. 23.
    Hollande JE, Chang AM, Shofer FS, et al (2009) Coronary computed tomographic angiography for rapid discharge of lowrisk patients with potential acute coronary syndromes. Ann Emerg Med 53:295–304CrossRefGoogle Scholar

Copyright information

© Société française de médecine d'urgence and Springer-Verlag France 2013

Authors and Affiliations

  • A. Le Meur
    • 1
  • D. Lauque
    • 1
    • 2
  • D. Carrié
    • 3
  • M. Galinier
    • 3
  • H. Juchet
    • 1
  • S. Charpentier
    • 1
    • 2
    • 4
  1. 1.Service des urgencesCHU Rangueil, 1ToulouseFrance
  2. 2.Université Paul-SabatierToulouseFrance
  3. 3.Service de cardiologieCHU RangueilToulouseFrance
  4. 4.InsermToulouseFrance

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