Diagnostic performance of an acoustic-based system for coronary artery disease risk stratification
Simon Winther, Louise Nissen, Samuel Emil Schmidt, Jelmer Sybren Westra, Laust Dupont Rasmussen, Lars Lyhne Knudsen, Lene Helleskov Madsen, Jane Kirk Johansen, Bjarke Skogstad Larsen, Johannes Jan Struijk, Lars Frost, Niels Ramsing Holm, Evald Høj Christiansen, Hans Erik Botker, Morten Bøttcher, Simon Winther, Louise Nissen, Samuel Emil Schmidt, Jelmer Sybren Westra, Laust Dupont Rasmussen, Lars Lyhne Knudsen, Lene Helleskov Madsen, Jane Kirk Johansen, Bjarke Skogstad Larsen, Johannes Jan Struijk, Lars Frost, Niels Ramsing Holm, Evald Høj Christiansen, Hans Erik Botker, Morten Bøttcher
Abstract
Objective: Diagnosing coronary artery disease (CAD) continues to require substantial healthcare resources. Acoustic analysis of transcutaneous heart sounds of cardiac movement and intracoronary turbulence due to obstructive coronary disease could potentially change this. The aim of this study was thus to test the diagnostic accuracy of a new portable acoustic device for detection of CAD.
Methods: We included 1675 patients consecutively with low to intermediate likelihood of CAD who had been referred for cardiac CT angiography. If significant obstruction was suspected in any coronary segment, patients were referred to invasive angiography and fractional flow reserve (FFR) assessment. Heart sound analysis was performed in all patients. A predefined acoustic CAD-score algorithm was evaluated; subsequently, we developed and validated an updated CAD-score algorithm that included both acoustic features and clinical risk factors. Low risk is indicated by a CAD-score value ≤20.
Results: Haemodynamically significant CAD assessed from FFR was present in 145 (10.0%) patients. In the entire cohort, the predefined CAD-score had a sensitivity of 63% and a specificity of 44%. In total, 50% had an updated CAD-score value ≤20. At this cut-off, sensitivity was 81% (95% CI 73% to 87%), specificity 53% (95% CI 50% to 56%), positive predictive value 16% (95% CI 13% to 18%) and negative predictive value 96% (95% CI 95% to 98%) for diagnosing haemodynamically significant CAD.
Conclusion: Sound-based detection of CAD enables risk stratification superior to clinical risk scores. With a negative predictive value of 96%, this new acoustic rule-out system could potentially supplement clinical assessment to guide decisions on the need for further diagnostic investigation.
Trial registration number: ClinicalTrials.gov identifier NCT02264717; Results.
Keywords: cardiac imaging and diagnostics; coronary artery disease.
Conflict of interest statement
Competing interests: The current research is financed partly by Acarix A/S by an unrestricted grant. SES is a minor shareholder and part-time consultant in Acarix A/S. BSL is an industrial student at Acarix A/S. MB is part of the Acarix A/S advisory board. MB and SW received an unrestricted institutional research grant from Acarix A/S.
© Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2018. All rights reserved. No commercial use is permitted unless otherwise expressly granted.
Figures
References
- Levitt K, Guo H, Wijeysundera HC, et al. . Predictors of normal coronary arteries at coronary angiography. Am Heart J 2013;166:694–700. 10.1016/j.ahj.2013.07.030
- Nielsen LH, Nørgaard BL, Tilsted HH, et al. . The Western Denmark cardiac computed tomography registry: a review and validation study. Clin Epidemiol 2015;7:53–64. 10.2147/CLEP.S73728
- Patel MR, Peterson ED, Dai D, et al. . Low diagnostic yield of elective coronary angiography. N Engl J Med 2010;362:886–95. 10.1056/NEJMoa0907272
- Winther S, Schmidt SE, Holm NR, et al. . Diagnosing coronary artery disease by sound analysis from coronary stenosis induced turbulent blood flow: diagnostic performance in patients with stable angina pectoris. Int J Cardiovasc Imaging 2016;32:235–45. 10.1007/s10554-015-0753-4
- Schmidt SE, Holst-Hansen C, Hansen J, et al. . Acoustic features for the identification of coronary artery disease. IEEE Trans Biomed Eng 2015;62:2611–9. 10.1109/TBME.2015.2432129
- Schmidt SE, Hansen J, Zimmermann H, et al. . Coronary artery disease and low frequency heart sound signatures. Comput Cardiol 2011;38:481–4.
- Wang JZ, Tie B, Welkowitz W, et al. . Modeling sound generation in stenosed coronary arteries. IEEE Trans Biomed Eng 1990;37:1087–94. 10.1109/10.61034
- Thomas JL, Winther S, Wilson RF, et al. . A novel approach to diagnosing coronary artery disease: acoustic detection of coronary turbulence. Int J Cardiovasc Imaging 2017;33:129–36. 10.1007/s10554-016-0970-5
- Semmlow J, Rahalkar K. Acoustic detection of coronary artery disease. Annu Rev Biomed Eng 2007;9:449–69. 10.1146/annurev.bioeng.9.060906.151840
- Makaryus AN, Makaryus JN, Figgatt A, et al. . Utility of an advanced digital electronic stethoscope in the diagnosis of coronary artery disease compared with coronary computed tomographic angiography. Am J Cardiol 2013;111:786–92. 10.1016/j.amjcard.2012.11.039
- Azimpour F, Caldwell E, Tawfik P, et al. . Audible coronary artery stenosis. Am J Med 2016;129:515–21. 10.1016/j.amjmed.2016.01.015
- Montalescot G, Sechtem U, Achenbach S, et al. . 2013 ESC guidelines on the management of stable coronary artery disease: the Task Force on the management of stable coronary artery disease of the European Society of Cardiology. Eur Heart J 2013;34:2949–3003. 10.1093/eurheartj/eht296
- Genders TS, Steyerberg EW, Alkadhi H, et al. . A clinical prediction rule for the diagnosis of coronary artery disease: validation, updating, and extension. Eur Heart J 2011;32:1316–30. 10.1093/eurheartj/ehr014
- Nissen L, Winther S, Isaksen C, et al. . Danish study of Non-Invasive testing in Coronary Artery Disease (Dan-NICAD): study protocol for a randomised controlled trial. Trials 2016;17:262 10.1186/s13063-016-1388-z
- Schmidt SE, Holst-Hansen C, Graff C, et al. . Segmentation of heart sound recordings by a duration-dependent hidden Markov model. Physiol Meas 2010;31:513–29. 10.1088/0967-3334/31/4/004
- DeLong ER, DeLong DM, Clarke-Pearson DL. Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 1988;44:837–45. 10.2307/2531595
- Knapper JT, Khosa F, Blaha MJ, et al. . Coronary calcium scoring for long-term mortality prediction in patients with and without a family history of coronary disease. Heart 2016;102:204–8. 10.1136/heartjnl-2015-308429
- Chang SM, Nabi F, Xu J, et al. . Value of CACS compared with ETT and myocardial perfusion imaging for predicting long-term cardiac outcome in asymptomatic and symptomatic patients at low risk for coronary disease: clinical implications in a multimodality imaging world. JACC Cardiovasc Imaging 2015;8:134–44. 10.1016/j.jcmg.2014.11.008
Source: PubMed