Cardiovascular Research

Instantaneous detection of acute myocardial infarction and ischaemia from a single carotid pressure waveform in rats.

Authors: Rashid Alavi|||Wangde Dai|||Ray V Matthews|||Robert A Kloner|||Niema M Pahlevan

Affiliations: + Expand

Abstract

Myocardial infarction (MI) is one of the leading causes of death worldwide. It is well accepted that early diagnosis followed by early reperfusion therapy significantly increases the MI survival. Diagnosis of acute MI is traditionally based on the presence of chest pain and electrocardiogram (ECG) criteria. However, around 50% of the MIs are without chest pain, and ECG is neither completely specific nor definitive. Therefore, there is an unmet need for methods that allow detection of acute MI or ischaemia without using ECG. Our hypothesis is that a hybrid physics-based machine learning (ML) method can detect the occurrence of acute MI or ischaemia from a single carotid pressure waveform.

---

Reference List

Benjamin EJ, Blaha MJ, Chiuve SE, Cushman M, Das SR, Deo R, de Ferranti SD, Floyd J, Fornage M, Gillespie C. Heart disease and stroke statistics—2017 update: a report from the American heart association. Circulation 2017:e146–e603.|||Members WG, Roger VL, Go AS, Lloyd-Jones DM, Benjamin EJ, Berry JD, Borden WB, Bravata DM, Dai S, Ford ES. Executive summary: heart disease and stroke statistics—2012 update: a report from the American heart association. Circulation 2012;125:188–197.|||Braunwald E. Reduction of myocardial-infarct size. New England Journal of Medicine 1974;291:525–526.|||Cannon CP, Gibson CM, Lambrew CT, Shoultz DA, Levy D, French WJ, Gore JM, Weaver WD, Rogers WJ, Tiefenbrunn AJ. Relationship of symptom-onset-to-balloon time and door-to-balloon time with mortality in patients undergoing angioplasty for acute myocardial infarction. JAMA 2000;283:2941–2947.|||Maseri A. Pathogenetic mechanisms of angina pectoris: expanding views. Br Heart J 1980;43:648–660.|||Khan MG. Heart disease diagnosis and therapy: a practical approach: Springer Science & Business Media; 2005.|||Stephens JF III. The diagnostic value of a frequency analysis of the isovolumic contraction phase of the first heart sound: University of Cincinnati; 1969.|||Hershberg P. First aid therapy: a new concept in the treatment of myocardial infarction. Med Times 1968;96:575–591.|||McCain FH, Kline EM, Gilson JS. A clinical study of 281 autopsy reports on patients with myocardial infarction. Am Heart J 1950;39:263–272.|||Distante A, Picano E, Moscarelli E, Palombo C, Benassi A, L'abbate A. Echocardiographic versus hemodynamic monitoring during attacks of variant angina pectoris. Am J Cardiol 1985;55:1319–1322.|||Sigwart U, Grbic M, Payot M, Goy J-J, Essinger A, Fischer A. Ischemic events during coronary artery balloon obstruction (ed.), Silent myocardial ischemia: Springer; 1984. p29–36.|||Bristow JD, Arai AE, Anselone CG, Pantely GA. Response to myocardial ischemia as a regulated process. Circulation 1991;84:2580–2587.|||Alavi R, Dai W, Amlani F, Rinderknecht DG, Kloner RA, Pahlevan NM. Scalability of cardiovascular intrinsic frequencies: validations in preclinical models and non-invasive clinical studies. Life Sci 2021;284:119880.|||Cooper LL, Rong J, Pahlevan NM, Rinderknecht DG, Benjamin EJ, Hamburg NM, Vasan RS, Larson MG, Gharib M, Mitchell GF. Intrinsic frequencies of carotid pressure waveforms predict heart failure events: the Framingham heart study. Hypertension 2021;77:338–346.|||Pahlevan NM, Rinderknecht DG, Tavallali P, Razavi M, Tran TT, Fong MW, Kloner RA, Csete M, Gharib M. Noninvasive iphone measurement of left ventricular ejection fraction using intrinsic frequency methodology. Crit Care Med 2017;45:1115–1120.|||Armenian SH, Rinderknecht D, Au K, Lindenfeld L, Mills G, Siyahian A, Herrera C, Wilson KD, Venkataraman K, Mascarenhas K, Tavallali P, Razavi M, Pahlevan N, Detterich J, Bhatia S, Gharib M. Accuracy of a novel handheld wireless platform for detection of cardiac dysfunction in anthracycline-exposed survivors of childhood cancer. Clin Cancer Res 2018;24:3119–3125.|||Tavallali P, Razavi M, Pahlevan NM. Artificial intelligence estimation of carotid-femoral pulse wave velocity using carotid waveform. Sci Rep 2018;8:1014.|||Mogadam E, Shavelle DM, Giesler GM, Economides C, Lidia SP, Duquette S, Matthews RV, Pahlevan NM. Intrinsic frequency method for instantaneous assessment of left ventricular-arterial coupling after transcatheter aortic valve replacement. Physiol Meas 2020;41:085002.|||Alavi R, Dai W, Kloner RA, Pahlevan NM. A hybrid artificial intelligence-intrinsic frequency method for instantaneous detection of acute myocardial infarction. Circulation 2019;140:A12573–A12573.|||Dai W, Amoedo ND, Perry J, Le Grand B, Boucard A, Carreno J, Zhao L, Brown DA, Rossignol R, Kloner RA. Effects of OP2113 on myocardial infarct size and No reflow in a rat myocardial ischemia/reperfusion model. Cardiovasc Drugs Ther 2022;36:217–227.|||Pahlevan NM, Tavallali P, Rinderknecht DG, Petrasek D, Matthews RV, Hou TY, Gharib M. Intrinsic frequency for a systems approach to haemodynamic waveform analysis with clinical applications. J R Soc Interface 2014;11:20140617.|||Alavi R, Wang Q, Gorji H, Pahlevan NM. Sequentially-reduced representation of artificial neural network to determine cardiovascular intrinsic frequencies. bioRxiv 2022;2022.2002. 2014.480311. 10.1101/2022.02.14.480311|||Hou TY, Shi Z. Adaptive data analysis via sparse time-frequency representation. Adv Adapt Data Anal 2011;3:1–28.|||Aghilinejad A, Alavi R, Rogers B, Amlani F, Pahlevan NM. Effects of vessel wall mechanics on non-invasive evaluation of cardiovascular intrinsic frequencies. J Biomech 2021;129:110852.|||Petrasek D, Pahlevan NM, Tavallali P, Rinderknecht DG, Gharib M. Intrinsic frequency and the single wave biopsy: implications for insulin resistance. J Diabetes Sci Technol 2015;9:1246–1252.|||Alavi R, Dai W, Arechavala RJ, Kleinman MT, Kloner RA, Pahlevan NM. Detection of the effect of nicotine delivered by E-cigarettes or standard cigarettes on cardiovascular system from a carotid waveform using a physics-based machine learning approach. Circulation 2022;146:A12922–A12922.|||Alavi R, Dai W, Arechavala RJ, Kleinman MT, Kloner RA, Pahlevan NM. Nicotine delivered by electronic cigarette vapor or standard cigarettes adversely affects left ventricular systolic function measured by cardiovascular intrinsic frequency in rats. Circulation 2021;144:A13745–A13745.|||Niroumandi S, Alavi R, Wolfson AM, Vaidya AS, Pahlevan NM. Assessment of aortic characteristic impedance and arterial compliance from non-invasive carotid pressure waveform in the Framingham heart study. Am J Cardiol 2023;204:195–199.|||Kloner RA, Fishbein MC, Hare CM, Maroko PR. Early ischemic ultrastructural and histochemical alterations in the myocardium of the rat following coronary artery occlusion. Exp Mol Pathol 1979;30:129–143.|||Abadi M, Agarwal A, Barham P, Brevdo E, Chen Z, Citro C, Corrado GS, Davis A, Dean J, Devin M. Tensorflow: large-scale machine learning on heterogeneous distributed systems. arXiv preprint arXiv:160304467 2016. 10.48550/arXiv.1603.04467|||Lindsey ML, Bolli R, Canty Jr JM, Du X-J, Frangogiannis NG, Frantz S, Gourdie RG, Holmes JW, Jones SP, Kloner RA. Guidelines for experimental models of myocardial ischemia and infarction. Am J Physiol Heart Circ Physiol 2018;314:H812–H838.|||Hachamovitch R, Berman DS, Shaw LJ, Kiat H, Cohen I, Cabico JA, Friedman J, Diamond GA. Incremental prognostic value of myocardial perfusion single photon emission computed tomography for the prediction of cardiac death: differential stratification for risk of cardiac death and myocardial infarction. Circulation 1998;97:535–543.|||Sabatine MS, Morrow DA, De Lemos JA, Omland T, Desai MY, Tanasijevic M, Hall C, McCabe CH, Braunwald E. Acute changes in circulating natriuretic peptide levels in relation to myocardial ischemia. J Am Coll Cardiol 2004;44:1988–1995.|||Ohman EM, Armstrong PW, Christenson RH, Granger CB, Katus HA, Hamm CW, O'Hanesian MA, Wagner GS, Kleiman NS, Harrell Jr FE. Cardiac troponin T levels for risk stratification in acute myocardial ischemia. N Engl J Med 1996;335:1333–1342.|||Leischik R, Dworrak B, Sanchis-Gomar F, Lucia A, Buck T, Erbel R. Echocardiographic assessment of myocardial ischemia. Ann Transl Med 2016;4:259–259.|||Kelly R. Non-invasive registration of the arterial pulse waveform using high fidelity applanation tonometry. J Vasc Med Biol 1989;1:142–149.|||Salvi P, Lio G, Labat C, Ricci E, Pannier B, Benetos A. Validation of a new non-invasive portable tonometer for determining arterial pressure wave and pulse wave velocity: the PulsePen device. J Hypertens 2004;22:2285–2293.|||Frizzell JD, Liang L, Schulte PJ, Yancy CW, Heidenreich PA, Hernandez AF, Bhatt DL, Fonarow GC, Laskey WK. Prediction of 30-day all-cause readmissions in patients hospitalized for heart failure: comparison of machine learning and other statistical approaches. JAMA Cardiol 2017;2:204–209.|||Ahmad T, Lund LH, Rao P, Ghosh R, Warier P, Vaccaro B, Dahlström U, O'Connor CM, Felker GM, Desai NR. Machine learning methods improve prognostication, identify clinically distinct phenotypes, and detect heterogeneity in response to therapy in a large cohort of heart failure patients. J Am Heart Assoc 2018;7:e008081.|||Hatib F, Jian Z, Buddi S, Lee C, Settels J, Sibert K, Rinehart J, Cannesson M. Machine-learning algorithm to predict hypotension based on high-fidelity arterial pressure waveform analysis. Anesthesiology 2018;129:663–674.|||Bikia V, Rovas G, Pagoulatou S, Stergiopulos N. Determination of aortic characteristic impedance and total arterial compliance from regional pulse wave velocities using machine learning: an in-silico study. Front Bioeng Biotechnol 2021;9:649866.|||Bikia V, Papaioannou TG, Pagoulatou S, Rovas G, Oikonomou E, Siasos G, Tousoulis D, Stergiopulos N. Noninvasive estimation of aortic hemodynamics and cardiac contractility using machine learning. Sci Rep 2020;10:1–17.|||Alavi R, Dai W, Kloner RA, Pahlevan NM. A hybrid artificial intelligence-intrinsic frequency method for instantaneous determination of myocardial infarct size. Circulation 2020;142:A15899–A15899.|||Distante A, Rovai D, Picano E, Moscarelli E, Morales MA, Palombo C, L'Abbate A. Transient changes in left ventricular mechanics during attacks of prinzmetal angina: A two-dimensional echocardiographic study. Am Heart J 1984;108:440–446.|||Alavi R, Dai W, Kloner RA, Pahlevan N. INSTANTANEOUS DETECTION OF MYOCARDIAL ISCHEMIA FROM A SINGLE CAROTID WAVEFORM USING A PHYSICS-BASED MACHINE LEARNING METHODOLOGY. J Am Coll Cardiol 2023;81:4012–4012.|||Alavi R, Dai W, Kloner RA, Pahlevan NM. A physics-based machine learning approach for instantaneous classification of myocardial infarct size. Circulation 2021;144:A12098–A12098.|||Pahlevan NM, Alavi R, Ramos M, Hindoyan A, Matthews RV. An artificial intelligence derived method for instantaneous detection of elevated left ventricular End diastolic pressure. Circulation 2020;142:A16334–A16334.|||Alavi R, Liu J, Ramos M, Hindoyan A, Matthews RV, Pahlevan NM. A hybrid machine learning method for instantaneous classification of left ventricular filling pressure using femoral waveforms. Circulation 2021;144:A14086–A14086.|||Alavi R, Aghilinejad A, Wei H, Niroumandi S, Wieman S, Pahlevan NM. A coupled atrioventricular-aortic setup for in-vitro hemodynamic study of the systemic circulation: design, fabrication, and physiological relevancy. PLoS One 2022;17:e0267765.|||Yellon DM, Hausenloy DJ. Myocardial reperfusion injury. N Engl J Med 2007;357:1121–1135.