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Clinical Research in Cardiology

, Volume 107, Issue 9, pp 824–835 | Cite as

Automatically computed ECG algorithm for the quantification of myocardial scar and the prediction of mortality

  • Patrick Badertscher
  • Ivo Strebel
  • Ursina Honegger
  • Nicolas Schaerli
  • Deborah Mueller
  • Christian Puelacher
  • Max Wagener
  • Roger Abächerli
  • Joan Walter
  • Zaid Sabti
  • Lorraine Sazgary
  • Stella Marbot
  • Jeanne du Fay de Lavallaz
  • Raphael Twerenbold
  • Jasper Boeddinghaus
  • Thomas Nestelberger
  • Nikola Kozhuharov
  • Tobias Breidthardt
  • Samyut Shrestha
  • Dayana Flores
  • Carmela Schumacher
  • Damian Wild
  • Stefan Osswald
  • Michael J. Zellweger
  • Christian Mueller
  • Tobias Reichlin
Original Paper

Abstract

Background

Myocardial scar is associated with adverse cardiac outcomes. The Selvester QRS-score was developed to estimate myocardial scar from the 12-lead ECG, but its manual calculation is difficult. An automatically computed QRS-score would allow identification of patients with myocardial scar and an increased risk of mortality.

Objectives

To assess the diagnostic and prognostic value of the automatically computed QRS-score.

Methods

The diagnostic value of the QRS-score computed automatically from a standard digital 12-lead was prospectively assessed in 2742 patients with suspected myocardial ischemia referred for myocardial perfusion imaging (MPI). The prognostic value of the QRS-score was then prospectively tested in 1151 consecutive patients presenting to the emergency department (ED) with suspected acute heart failure (AHF).

Results

Overall, the QRS-score was significantly higher in patients with more extensive myocardial scar: the median QRS-score was 3 (IQR 2–5), 4 (IQR 2–6), and 7 (IQR 4–10) for patients with 0, 5–20 and > 20% myocardial scar as quantified by MPI (p < 0.001 for all pairwise comparisons). A QRS-score ≥ 9 (n = 284, 10%) predicted a large scar defined as > 20% of the LV with a specificity of 91% (95% CI 90–92%). Regarding clinical outcomes in patients presenting to the ED with symptoms suggestive of AHF, mortality after 1 year was 28% in patients with a QRS-score ≥ 3 as opposed to 20% in patients with a QRS-score < 3 (p = 0.001).

Conclusions

The QRS-score can be computed automatically from the 12-lead ECG for simple, non-invasive and inexpensive detection and quantification of myocardial scar and for the prediction of mortality.

Trial-registration

http://www.clinicaltrials.gov. Identifier, NCT01838148 and NCT01831115.

Keywords

Selvester QRS-score ECG Myocardial scar Heart failure Cardiac imaging 

Notes

Acknowledgements

The authors thank the patients who participated in the study and the staff of the Department of Nuclear Medicine.

Funding

This study was supported by research Grants from the Swiss National Science Foundation, the Swiss Heart Foundation, the Cardiovascular Research Foundation Basel, the University Hospital Basel, Abbott and BRAHMS.

Compliance with ethical standards

Conflict of interest

Dr. Mueller has received research support from the Swiss National Science Foundation, the Swiss Heart Foundation, the Cardiovascular Research Foundation Basel, Abbott, Beckman Coulter, BRAHMS, Roche, Siemens. and the University Hospital Basel, as well as speaker honoraria from Abbott, ALERE, Astra Zeneca, BG Medicine, Biomerieux, Brahms, Cardiorentis, Lilly, Novartis, Roche, and Siemens. Dr. Reichlin has received research grants from the Goldschmidt-Jacobson Foundation, the Swiss National Science Foundation (PASMP3-136995) the Swiss Heart Foundation, the Professor Max Cloëtta Foundation, the Cardiovascular Research Foundation Basel, the University of Basel and the University Hospital Basel as well as speaker honoraria from Brahms and Roche. Dr. Twerenbold has received research support from the Swiss National Science Foundation (P300PB-167803/1) and speaker honoraria/consulting honoraria from Roche, Abbott, Siemens and Brahms. Dr. Boeddinghaus has received speaker honoraria from Siemens. All other authors declare that they have no conflict of interest with this study.

Supplementary material

392_2018_1253_MOESM1_ESM.docx (1.1 mb)
Supplementary material 1 (DOCX 1175 KB)

References

  1. 1.
    Levy D, Kenchaiah S, Larson MG et al (2002) Long-term trends in the incidence of and survival with heart failure. N Engl J Med 347(18):1397–1402.  https://doi.org/10.1056/NEJMoa020265 CrossRefPubMedGoogle Scholar
  2. 2.
    de Bakker JM, van Capelle FJ, Janse MJ et al (1988) Reentry as a cause of ventricular tachycardia in patients with chronic ischemic heart disease: electrophysiologic and anatomic correlation. Circulation 77(3):589–606CrossRefPubMedGoogle Scholar
  3. 3.
    Ponikowski P, Voors AA, Anker SD et al (2016) 2016 ESC Guidelines for the diagnosis and treatment of acute and chronic heart failure: The Task Force for the diagnosis and treatment of acute and chronic heart failure of the European Society of Cardiology (ESC) developed with the special contribution of. Eur Heart J 37(27):2129–2200.  https://doi.org/10.1093/eurheartj/ehw128 CrossRefPubMedGoogle Scholar
  4. 4.
    Schmidt A, Azevedo CF, Cheng A et al (2007) Infarct tissue heterogeneity by magnetic resonance imaging identifies enhanced cardiac arrhythmia susceptibility in patients with left ventricular dysfunction. Circulation 115(15):2006–2014.  https://doi.org/10.1161/CIRCULATIONAHA.106.653568 CrossRefPubMedPubMedCentralGoogle Scholar
  5. 5.
    Barbagelata A, Di Carli MF, Califf RM et al (2005) Electrocardiographic infarct size assessment after thrombolysis: insights from the Acute Myocardial Infarction STudy ADenosine (AMISTAD) trial. Am Heart J 150(4):659–665.  https://doi.org/10.1016/j.ahj.2004.10.014 CrossRefPubMedGoogle Scholar
  6. 6.
    Horan LG, Flowers NC, Johnson JC (1971) Significance of the diagnostic Q wave of myocardial infarction. Circulation 43(3):428–436CrossRefPubMedGoogle Scholar
  7. 7.
    Selvester RH, Kalaba R, Collier CR, Bellman R, Kagiwada H (1967) A digital computer model of the vectorcardiogram with distance and boundary effects: Simulated myocardial infarction. Am Heart J 74(6):792–808.  https://doi.org/10.1016/0002-8703(67)90098-1 CrossRefPubMedGoogle Scholar
  8. 8.
    Wagner GS, Freye CJ, Palmeri ST et al (1982) Evaluation of a QRS scoring system for estimating myocardial infarct size. I. Specificity and observer agreement. Circulation 65(2):342–347.  https://doi.org/10.1161/01.CIR.65.2.342 CrossRefPubMedGoogle Scholar
  9. 9.
    Strauss DG, Selvester RH (2009) The QRS complex—a biomarker that “images” the heart: QRS scores to quantify myocardial scar in the presence of normal and abnormal ventricular conduction. J Electrocardiol 42(1):85–96.  https://doi.org/10.1016/j.jelectrocard.2008.07.011 CrossRefPubMedGoogle Scholar
  10. 10.
    Stevenson WG, Friedman PL, Sager PT et al (1997) Exploring postinfarction reentrant ventricular tachycardia with entrainment mapping. J Am Coll Cardiol 29(6):1180–1189.  https://doi.org/10.1016/S0735-1097(97)00065-X CrossRefPubMedGoogle Scholar
  11. 11.
    Yokota H, Heidary S, Katikireddy CK et al (2008) Quantitative characterization of myocardial infarction by cardiovascular magnetic resonance predicts future cardiovascular events in patients with ischemic cardiomyopathy. J Cardiovasc Magn Reson 10(1):17.  https://doi.org/10.1186/1532-429X-10-17 CrossRefPubMedPubMedCentralGoogle Scholar
  12. 12.
    Kojodjojo P, Tokuda M, Bohnen M et al (2013) Electrocardiographic left ventricular scar burden predicts clinical outcomes following infarct-related ventricular tachycardia ablation. Hear Rhythm 10(8):1119–1124.  https://doi.org/10.1016/j.hrthm.2013.04.011 CrossRefGoogle Scholar
  13. 13.
    Strauss DG, Selvester RH, Lima JAC et al (2008) ECG quantification of myocardial scar in cardiomyopathy patients with or without conduction defects. Circ Arrhythm Electrophysiol 1(5):327–336CrossRefPubMedPubMedCentralGoogle Scholar
  14. 14.
    Strauss DG, Mewton N, Verrier RL et al (2013) Screening entire health system ecg databases to identify patients at increased risk of death. Circ Arrhythm Electrophysiol 6(6):1156–1162.  https://doi.org/10.1161/CIRCEP.113.000411 CrossRefPubMedPubMedCentralGoogle Scholar
  15. 15.
    Tanglay Y, Twerenbold R, Lee G et al (2015) Incremental value of a single high-sensitivity cardiac troponin i measurement to rule out myocardial ischemia. Am J Med 128(6):638–646.  https://doi.org/10.1016/j.amjmed.2015.01.009 CrossRefPubMedGoogle Scholar
  16. 16.
    Wagener M, Abächerli R, Honegger U et al (2017) Diagnostic and prognostic value of lead aVR during exercise testing in patients suspected of having myocardial ischemia. Am J Cardiol.  https://doi.org/10.1016/j.amjcard.2016.11.056 PubMedGoogle Scholar
  17. 17.
    Buechel RR, Kaufmann BA, Tobler D, Wild D, Zellweger MJ (2015) Non-invasive nuclear myocardial perfusion imaging improves the diagnostic yield of invasive coronary angiography. Eur Heart J Cardiovasc Imaging 16(8):842–847.  https://doi.org/10.1093/ehjci/jev095 CrossRefPubMedGoogle Scholar
  18. 18.
    Zellweger MJ, Maraun M, Osterhues HH et al (2014) Progression to overt or silent CAD in asymptomatic patients with diabetes mellitus at high coronary risk: main findings of the prospective multicenter BARDOT trial with a pilot randomized treatment substudy. JACC Cardiovasc Imaging 7(10):1001–1010.  https://doi.org/10.1016/j.jcmg.2014.07.010 CrossRefPubMedGoogle Scholar
  19. 19.
    Arenja N, Mueller C, Ehl NF et al (2013) Prevalence, extent, and independent predictors of silent myocardial infarction. Am J Med 126(6):515–522.  https://doi.org/10.1016/j.amjmed.2012.11.028 CrossRefPubMedGoogle Scholar
  20. 20.
    Reichlin T, Potocki M, Breidthardt T et al (2009) Diagnostic and prognostic value of uric acid in patients with acute dyspnea. Am J Med 122(11):1054.  https://doi.org/10.1016/j.amjmed.2009.04.023 CrossRefPubMedGoogle Scholar
  21. 21.
    Breidthardt T, Irfan A, Klima T et al (2012) Pathophysiology of lower extremity edema in acute heart failure revisited. Am J Med 125(11):1124.e1–1124.e8.  https://doi.org/10.1016/j.amjmed.2011.12.015 CrossRefGoogle Scholar
  22. 22.
    Loring Z, Chelliah S, Selvester RH, Wagner G, Strauss DG (2011) A detailed guide for quantification of myocardial scar with the Selvester QRS score in the presence of electrocardiogram confounders. J Electrocardiol 44(5):544–554.  https://doi.org/10.1016/j.jelectrocard.2011.06.008 CrossRefPubMedPubMedCentralGoogle Scholar
  23. 23.
    Rosengarten JA, Scott PA, Chiu OKH, Shambrook JS, Curzen NP, Morgan JM (2013) Can QRS scoring predict left ventricular scar and clinical outcomes? Europace 15(7):1034–1041.  https://doi.org/10.1093/europace/eut014 CrossRefPubMedGoogle Scholar
  24. 24.
    Engblom H, Wagner GS, Setser RM et al (2003) Quantitative clinical assessment of chronic anterior myocardial infarction with delayed enhancement magnetic resonance imaging and QRS scoring. Am Heart J 146(2):359–366.  https://doi.org/10.1016/S0002-8703(03)00187-X CrossRefPubMedGoogle Scholar
  25. 25.
    Geerse DA, Wu KC, Gorgels AP, Zimmet J, Wagner GS, Miller JM (2009) Comparison between contrast-enhanced magnetic resonance imaging and selvester qrs scoring system in estimating changes in infarct size between the acute and chronic phases of myocardial infarction. Ann Noninvasive Electrocardiol 14(4):360–365.  https://doi.org/10.1111/j.1542-474X.2009.00327.x CrossRefPubMedGoogle Scholar
  26. 26.
    Strauss DG, Poole JE, Wagner GS et al (2011) An ECG index of myocardial scar enhances prediction of defibrillator shocks: an analysis of the sudden cardiac death in heart failure trial. Hear Rhythm 8(1):38–45.  https://doi.org/10.1016/j.hrthm.2010.09.071 CrossRefGoogle Scholar
  27. 27.
    Wieslander B, Nijveldt R, Klem I et al (2015) Evaluation of Selvester QRS score for use in presence of conduction abnormalities in a broad population. Am Heart J 170(2):346–352.  https://doi.org/10.1016/j.ahj.2015.05.005 CrossRefPubMedGoogle Scholar
  28. 28.
    Ward RM, White RD, Ideker RE et al (1984) Evaluation of a QRS scoring system for estimating myocardial infarct size. Am J Cardiol 53(6):706–714.  https://doi.org/10.1016/0002-9149(84)90390-4 CrossRefPubMedGoogle Scholar
  29. 29.
    Weinsaft JW, Kochav JD, Afroz A, Okin PM (2014) Q wave area for stratification of global left ventricular infarct size. Coron Artery Dis 25(2):138–144.  https://doi.org/10.1097/MCA.0000000000000062 CrossRefPubMedPubMedCentralGoogle Scholar
  30. 30.
    Horáček BM, Warren JW, Albano A et al (2006) Development of an automated Selvester scoring system for estimating the size of myocardial infarction from the electrocardiogram. J Electrocardiol 39(2):162–168.  https://doi.org/10.1016/j.jelectrocard.2005.08.013 CrossRefPubMedGoogle Scholar
  31. 31.
    Wagner GS, Freye CJ, Palmeri ST et al (1982) Evaluation of a QRS scoring system for estimating myocardial infarct size. I. Specificity and observer agreement. Circulation 65(2):342–357CrossRefPubMedGoogle Scholar
  32. 32.
    Kalogeropoulos AP, Chiladakis JA, Sihlimiris I, Koutsogiannis N, Alexopoulos D (2008) Predischarge QRS score and risk for heart failure after first ST-elevation myocardial infarction. J Card Fail 14(3):225–231.  https://doi.org/10.1016/j.cardfail.2007.11.004 CrossRefPubMedGoogle Scholar
  33. 33.
    Abächerli R, Twerenbold R, Boeddinghaus J et al (February 2017) Diagnostic and prognostic values of the V-index, a novel ECG marker quantifying spatial heterogeneity of ventricular repolarization, in patients with symptoms suggestive of non-ST-elevation myocardial infarction. Int J Cardiol.  https://doi.org/10.1016/j.ijcard.2017.01.151 Google Scholar
  34. 34.
    Wagner A, Mahrholdt H, Holly TA et al (2003) Contrast-enhanced MRI and routine single photon emission computed tomography (SPECT) perfusion imaging for detection of subendocardial myocardial infarcts: an imaging study. Lancet 361(9355):374–379.  https://doi.org/10.1016/S0140-6736(03)12389-6 CrossRefPubMedGoogle Scholar
  35. 35.
    Flett AS, Hasleton J, Cook C et al (2011) Evaluation of techniques for the quantification of myocardial scar of differing etiology using cardiac magnetic resonance. JACC Cardiovasc Imaging 4(2):150–156.  https://doi.org/10.1016/j.jcmg.2010.11.015 CrossRefPubMedGoogle Scholar
  36. 36.
    Strauss DG, Selvester RH, Wagner GS (2011) Defining Left bundle branch block in the era of cardiac resynchronization therapy. Am J Cardiol 107(6):927–934.  https://doi.org/10.1016/j.amjcard.2010.11.010 CrossRefPubMedGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Patrick Badertscher
    • 1
  • Ivo Strebel
    • 1
  • Ursina Honegger
    • 1
  • Nicolas Schaerli
    • 1
  • Deborah Mueller
    • 1
  • Christian Puelacher
    • 1
  • Max Wagener
    • 1
  • Roger Abächerli
    • 1
    • 2
  • Joan Walter
    • 1
  • Zaid Sabti
    • 1
  • Lorraine Sazgary
    • 1
  • Stella Marbot
    • 1
  • Jeanne du Fay de Lavallaz
    • 1
  • Raphael Twerenbold
    • 1
    • 3
  • Jasper Boeddinghaus
    • 1
  • Thomas Nestelberger
    • 1
  • Nikola Kozhuharov
    • 1
  • Tobias Breidthardt
    • 1
    • 4
  • Samyut Shrestha
    • 1
  • Dayana Flores
    • 1
  • Carmela Schumacher
    • 1
  • Damian Wild
    • 5
  • Stefan Osswald
    • 1
  • Michael J. Zellweger
    • 1
  • Christian Mueller
    • 1
  • Tobias Reichlin
    • 1
  1. 1.Cardiovascular Research Institute Basel (CRIB) and Department of CardiologyUniversity Hospital Basel, University of BaselBaselSwitzerland
  2. 2.Insitute for Medical Engineering (IMT)Lucerne University of Applied Sciences and ArtsHorwSwitzerland
  3. 3.Clinic for General and Interventional CardiologyUniversity Heart Center HamburgHamburgGermany
  4. 4.Department of Internal MedicineUniversity Hospital Basel, University of BaselBaselSwitzerland
  5. 5.Division of Nuclear MedicineUniversity Hospital Basel, University BaselBaselSwitzerland

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