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Contemporary Applications of Machine Learning for Device Therapy in Heart FailureGET ACCESS

State-of-the-Art Review

J Am Coll Cardiol HF, 10 (9) 603–622
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Abstract

Despite a better understanding of the underlying pathogenesis of heart failure (HF), pharmacotherapy, surgical, and percutaneous interventions do not prevent disease progression in all patients, and a significant proportion of patients end up requiring advanced therapies. Machine learning (ML) is gaining wider acceptance in cardiovascular medicine because of its ability to incorporate large, complex, and multidimensional data and to potentially facilitate the creation of predictive models not constrained by many of the limitations of traditional statistical approaches. With the coexistence of “big data” and novel advanced analytic techniques using ML, there is ever-increasing research into applying ML in the context of HF with the goal of improving patient outcomes. Through this review, the authors describe the basics of ML and summarize the existing published reports regarding contemporary applications of ML in device therapy for HF while highlighting the limitations to widespread implementation and its future promises.

Highlights

While actuating exemplary changes, clinical gaps exist in the domain of device therapy in heart failure.

Machine learning has been postulated to improve candidate selection and outcomes for device therapy.

Integration of machine learning with current clinical practices can further the idea of precision medicine.

Data integration and model interpretability are the major challenges preventing widespread assimilation into clinical practice.

  • 1. Virani S.S., Alonso A., Aparicio H.J., et al. "Heart Disease and Stroke Statistics—2021 Update: a report from the American Heart Association". Circulation 2021;143:8: e254-e743.

    CrossrefMedlineGoogle Scholar
  • 2. Urbich M., Globe G., Pantiri K., et al. "A systematic review of medical costs associated with heart failure in the USA (2014–2020)". PharmacoEconomics 2020;38:11: 1219-1236.

    CrossrefMedlineGoogle Scholar
  • 3. Birnie D.H., Tang A.S. "The problem of non-response to cardiac resynchronization therapy". Curr Opin Cardiol 2006;21:1: 20-26.

    CrossrefMedlineGoogle Scholar
  • 4. Bzdok D., Altman N., Krzywinski M. "Statistics versus machine learning". Nat Methods 2018;15:4: 233-234.

    CrossrefMedlineGoogle Scholar
  • 5. Quer G., Arnaout R., Henne M., Arnaout R. "Machine learning and the future of cardiovascular care: JACC state-of-the-art review". J Am Coll Cardiol 2021;77:3: 300-313.

    View ArticleGoogle Scholar
  • 6. Ayodele T.O. "Types of machine learning algorithms". New Advances in Machine Learning 2010;3:19-48.

    Google Scholar
  • 7. Voss A., Witt K., Fischer C., et al. "Smelling heart failure from human skin odor with an electronic nose". Annu Int Conf IEEE Eng Med Biol Soc 2012;2012:4034-4037.

    MedlineGoogle Scholar
  • 8. Cikes M., Sanchez-Martinez S., Claggett B., et al. "Machine learning-based phenogrouping in heart failure to identify responders to cardiac resynchronization therapy". Eur J Heart Fail 2019;21:1: 74-85.

    CrossrefMedlineGoogle Scholar
  • 9. Feeny A.K., Rickard J., Trulock K.M., et al. "Machine learning of 12-lead QRS waveforms to identify cardiac resynchronization therapy patients with differential outcomes". Circ Arrhythm Electrophysiol 2020;13:7: e008210.

    CrossrefGoogle Scholar
  • 10. Usama M., Qadir J., Raza A., et al. "Unsupervised machine learning for networking: Techniques, applications and research challenges". IEEE Access 2019;7:65579-65615.

    CrossrefGoogle Scholar
  • 11. Lee J.-G., Jun S., Cho Y.-W., et al. "Deep learning in medical imaging: General overview". Korean J Radiol 2017;18:4: 570-584.

    CrossrefMedlineGoogle Scholar
  • 12. Redfield M.M., Anstrom K.J., Levine J.A., et al. "Isosorbide mononitrate in heart failure with preserved ejection fraction". N Engl J Med 2015;373:24: 2314-2324.

    CrossrefMedlineGoogle Scholar
  • 13. Stehlik J., Schmalfuss C., Bozkurt B., et al. "Continuous wearable monitoring analytics predict heart failure hospitalization". Circ Heart Fail 2020;13:3: e006513.

    CrossrefGoogle Scholar
  • 14. Shandhi M.M.H., Fan J., Heller J., Etemadi M., Klein L., Inan O. "Estimation of changes in intracardiac hemodynamics using wearable seismocardiography and machine learning in patients with heart failure: a feasibility study". IEEE Trans Biomed Eng 2022;69:8: 2443-2455. https://doi.org/10.1109/TBME.2022.3147066.

    CrossrefMedlineGoogle Scholar
  • 15. Inan O.T., Baran Pouyan M., Javaid A.Q., et al. "Novel wearable seismocardiography and machine learning algorithms can assess clinical status of heart failure patients". Circ Heart Fail 2018;11:1: e004313.

    CrossrefMedlineGoogle Scholar
  • 16. Boehmer J.P., Hariharan R., Devecchi F.G., et al. "A multisensor algorithm predicts heart failure events in patients with implanted devices: results from the MultiSENSE study". J Am Coll Cardiol HF 2017;5:3: 216-225.

    Google Scholar
  • 17. Angermann C.E., Assmus B., Anker S.D., et al. "Pulmonary artery pressure-guided therapy in ambulatory patients with symptomatic heart failure: the CardioMEMS European monitoring study for heart failure (MEMS-HF)". Eur J Heart Fail 2020;22:10: 1891-1901.

    CrossrefMedlineGoogle Scholar
  • 18. Vahanian A., Beyersdorf F., Praz F., et al. "2021 ESC/EACTS guidelines for the management of valvular heart disease". Eur J Cardiothorac Surg 2021;60:727-800.

    CrossrefMedlineGoogle Scholar
  • 19. Thomas N., Unsworth B., Ferenczi E.A., Davies J.E., Mayet J., Francis D.P. "Intraobserver variability in grading severity of repeated identical cases of mitral regurgitation". Am Heart J 2008;156:6: 1089-1094.

    CrossrefMedlineGoogle Scholar
  • 20. Biner S., Rafique A., Rafii F., et al. "Reproducibility of proximal isovelocity surface area, vena contracta, and regurgitant jet area for assessment of mitral regurgitation severity". J Am Coll Cardiol Img 2010;3:3: 235-243.

    View ArticleGoogle Scholar
  • 21. Zhang J., Gajjala S., Agrawal P., et al. "Fully automated echocardiogram interpretation in clinical practice". Circulation 2018;138:16: 1623-1635.

    CrossrefMedlineGoogle Scholar
  • 22. Jin C.-N., Salgo I.S., Schneider R.J., et al. "Automated quantification of mitral valve anatomy using anatomical intelligence in three-dimensional echocardiography". Int J Cardiol 2015;199:232-238.

    CrossrefMedlineGoogle Scholar
  • 23. Jin C.-N., Salgo I.S., Schneider R.J., et al. "Using anatomic intelligence to localize mitral valve prolapse on three-dimensional echocardiography". J Am Soc Echocardiogr 2016;29:10: 938-945.

    CrossrefMedlineGoogle Scholar
  • 24. Andreassen B.S., Veronesi F., Gerard O., Solberg A.H.S., Samset E. "Mitral annulus segmentation using deep learning in 3-D transesophageal echocardiography". IEEE J Biomed Health Inform 2020;24:4: 994-1003.

    CrossrefMedlineGoogle Scholar
  • 25. Hernandez-Suarez D.F., Ranka S., Kim Y., et al. "Machine-learning-based in-hospital mortality prediction for transcatheter mitral valve repair in the United States". Cardiovasc Revasc Med 2021;22:22-28.

    CrossrefMedlineGoogle Scholar
  • 26. Zweck E., Spieker M., Horn P., et al. "Machine learning identifies clinical parameters to predict mortality in patients undergoing transcatheter mitral valve repair". J Am Coll Cardiol Intv 2021;14:18: 2027-2036.

    View ArticleGoogle Scholar
  • 27. Cai C., Tafti A.P., Ngufor C., et al. "Using ensemble of ensemble machine learning methods to predict outcomes of cardiac resynchronization". J Cardiovasc Electrophysiol 2021;32:9: 2504-2514.

    CrossrefMedlineGoogle Scholar
  • 28. Galli E., Le Rolle V., Smiseth O.A., et al. "Importance of systematic right ventricular assessment in cardiac resynchronization therapy candidates: a machine learning approach". J Am Soc Echocardiogr 2021;34:5: 494-502.

    CrossrefMedlineGoogle Scholar
  • 29. Liang Y., Ding R., Wang J., et al. "Prediction of response after cardiac resynchronization therapy with machine learning". Int J Cardiol 2021;344:120-126.

    CrossrefMedlineGoogle Scholar
  • 30. Hu S.-Y., Santus E., Forsyth A.W., et al. "Can machine learning improve patient selection for cardiac resynchronization therapy?". PloS One 2019;14:10: e0222397.

    CrossrefGoogle Scholar
  • 31. Lei J., Wang Y.G., Bhatta L., et al. "Ventricular geometry-regularized QRSD predicts cardiac resynchronization therapy response: machine learning from crosstalk between electrocardiography and echocardiography". Int J Cardiovasc Imaging 2019;35:7: 1221-1229.

    CrossrefMedlineGoogle Scholar
  • 32. Feeny A.K., Rickard J., Patel D., et al. "Machine learning prediction of response to cardiac resynchronization therapy: improvement versus current guidelines". Circ Arrhythm Electrophysiol 2019;12:7: e007316.

    CrossrefMedlineGoogle Scholar
  • 33. Damy T., Ghio S., Rigby A.S., et al. "Interplay between right ventricular function and cardiac resynchronization therapy: an analysis of the CARE-HF Trial (cardiac resynchronization–heart failure)". J Am Coll Cardiol 2013;61:21: 2153-2160.

    View ArticleGoogle Scholar
  • 34. Sharma A., Bax J.J., Vallakati A., et al. "Meta-analysis of the relation of baseline right ventricular function to response to cardiac resynchronization therapy". Am J Cardiol 2016;117:8: 1315-1321.

    CrossrefMedlineGoogle Scholar
  • 35. Gold M.R., Birgersdotter-Green U., Singh J.P., et al. "The relationship between ventricular electrical delay and left ventricular remodelling with cardiac resynchronization therapy". Eur Heart J 2011;32:20: 2516-2524.

    CrossrefMedlineGoogle Scholar
  • 36. Singh J.P., Fan D., Heist E.K., et al. "Left ventricular lead electrical delay predicts response to cardiac resynchronization therapy". Heart Rhythm 2006;3:11: 1285-1292.

    CrossrefMedlineGoogle Scholar
  • 37. Field M.E., Yu N., Wold N., Gold M.R. "Comparison of measures of ventricular delay on cardiac resynchronization therapy response". Heart Rhythm 2020;17:4: 615-620.

    CrossrefMedlineGoogle Scholar
  • 38. Altman R.K., Parks K.A., Schlett C.L., et al. "Multidisciplinary care of patients receiving cardiac resynchronization therapy is associated with improved clinical outcomes". Eur Heart J 2012;33:17: 2181-2188.

    CrossrefMedlineGoogle Scholar
  • 39. Howell S.J., Stivland T., Stein K., Ellenbogen K.A., Tereshchenko L.G. "Using machine-learning for prediction of the response to cardiac resynchronization therapy: the SMART-AV study". J Am Coll Cardiol EP 2021;7:12: 1505-1515.

    Google Scholar
  • 40. Kalscheur M.M., Kipp R.T., Tattersall M.C., et al. "Machine learning algorithm predicts cardiac resynchronization therapy outcomes: lessons from the COMPANION trial". Circ Arrhythm Electrophysiol 2018;11:1: e005499.

    CrossrefMedlineGoogle Scholar
  • 41. Tokodi M., Schwertner W.R., Kovács A., et al. "Machine learning-based mortality prediction of patients undergoing cardiac resynchronization therapy: the SEMMELWEIS-CRT score". Eur Heart J 2020;41:18: 1747-1756.

    CrossrefMedlineGoogle Scholar
  • 42. Taye G.T., Shim E.B., Hwang H.-J., Lim K.M. "Machine learning approach to predict ventricular fibrillation based on QRS complex shape". Front Physiol 2019;10:1193.

    CrossrefMedlineGoogle Scholar
  • 43. Lee H., Shin S.-Y., Seo M., Nam G.-B., Joo S. "Prediction of ventricular tachycardia one hour before occurrence using artificial neural networks". Sci Rep 2016;6:1: 1-7.

    MedlineGoogle Scholar
  • 44. Joo S., Choi K.-J., Huh S.-J. "Prediction of spontaneous ventricular tachyarrhythmia by an artificial neural network using parameters gleaned from short-term heart rate variability". Expert Systems with Applications 2012;39:3: 3862-3866.

    CrossrefGoogle Scholar
  • 45. Au-Yeung W.-T.M., Reinhall P.G., Bardy G.H., Brunton S.L. "Development and validation of warning system of ventricular tachyarrhythmia in patients with heart failure with heart rate variability data". PloS One 2018;13:11: e0207215-e.

    CrossrefMedlineGoogle Scholar
  • 46. Wu K.C., Wongvibulsin S., Tao S., et al. "Baseline and dynamic risk predictors of appropriate implantable cardioverter defibrillator therapy". J Am Heart Assoc 2020;9:20: e017002.

    CrossrefGoogle Scholar
  • 47. Kim U.H., Kim M.Y., Park E.A., et al. "Deep learning-based algorithm for the detection and characterization of MRI safety of cardiac implantable electronic devices on chest radiographs". Korean J Radiol 2021;22:11: 1918-1928.

    CrossrefMedlineGoogle Scholar
  • 48. Tarakji K.G., Krahn A.D., Poole J.E., et al. "Risk factors for CIED infection after secondary procedures: insights from the WRAP-IT trial". J Am Coll Cardiol EP 2022;8:1: 101-111.

    Google Scholar
  • 49. Chudow J.J., Jones D., Weinreich M., et al. "A head-to head comparison of machine learning algorithms for identification of implanted cardiac devices". Am J Cardiol 2021;144:77-82.

    CrossrefMedlineGoogle Scholar
  • 50. Birnie D.H., Wang J., Alings M., et al. "Risk factors for infections involving cardiac implanted electronic devices". J Am Coll Cardiol 2019;74:23: 2845-2854.

    View ArticleGoogle Scholar
  • 51. Beecy A.N., Bratt A., Yum B., et al. "Development of novel machine learning model for right ventricular quantification on echocardiography: a multimodality validation study". Echocardiography 2020;37:5: 688-697.

    CrossrefMedlineGoogle Scholar
  • 52. Genovese D., Rashedi N., Weinert L., et al. "Machine learning–based three-dimensional echocardiographic quantification of right ventricular size and function: validation against cardiac magnetic resonance". J Am Soc Echocardiogr 2019;32:8: 969-977.

    CrossrefMedlineGoogle Scholar
  • 53. Bellavia D., Iacovoni A., Agnese V., et al. "Usefulness of regional right ventricular and right atrial strain for prediction of early and late right ventricular failure following a left ventricular assist device implant: a machine learning approach". Int J Artif Organs 2020;43:5: 297-314.

    CrossrefMedlineGoogle Scholar
  • 54. Kanwar M.K., Lohmueller L.C., Kormos R.L., et al. "A Bayesian model to predict survival after left ventricular assist device implantation". J Am Coll Cardiol HF 2018;6:9: 771-779.

    Google Scholar
  • 55. Kilic A., Dochtermann D., Padman R., Miller J.K., Dubrawski A. "Using machine learning to improve risk prediction in durable left ventricular assist devices". PLoS One 2021;16:3: e0247866.

    CrossrefGoogle Scholar
  • 56. Kilic A., Macickova J., Duan L., et al. "Machine learning approaches to analyzing adverse events following durable LVAD implantation". Ann Thorac Surg 2021;112:3: 770-777.

    CrossrefMedlineGoogle Scholar
  • 57. Loghmanpour N.A., Kormos R.L., Kanwar M.K., Teuteberg J.J., Murali S., Antaki J.F. "A Bayesian model to predict right ventricular failure following left ventricular assist device therapy". J Am Coll Cardiol HF 2016;4:9: 711-721.

    Google Scholar
  • 58. Misumi Y., Miyagawa S., Yoshioka D., et al. "Prediction of aortic valve regurgitation after continuous-flow left ventricular assist device implantation using artificial intelligence trained on acoustic spectra". J Artif Organs 2021;24:2: 164-172.

    CrossrefMedlineGoogle Scholar
  • 59. Shad R., Quach N., Fong R., et al. "Predicting post-operative right ventricular failure using video-based deep learning". Nat Commun 2021;12:1: 5192.

    CrossrefMedlineGoogle Scholar
  • 60. Simkowski J.M., Wehbe R.M., Goergen J., et al. "Unsupervised machine learning of LGE patterns on cardiac MRI identifies patients at risk for right ventricular failure after LVAD". J Card Fail 2020;26:10: S146.

    CrossrefGoogle Scholar
  • 61. Topkara V.K., Elias P., Jain R., Sayer G., Burkhoff D., Uriel N. "Machine learning-based prediction of myocardial recovery in patients with left ventricular assist device support". Circ Heart Fail 2022;15:1: e008711.

    CrossrefGoogle Scholar
  • 62. Wang Y., Simon M.A., Bonde P., et al. "Decision tree for adjuvant right ventricular support in patients receiving a left ventricular assist device". J Heart Lung Transplant 2012;31:2: 140-149.

    CrossrefMedlineGoogle Scholar
  • 63. Frankfurter C., Molinero M., Vishram-Nielsen J.K.K., et al. "Predicting the risk of right ventricular failure in patients undergoing left ventricular assist device implantation". Circ Heart Fail 2020;13:10: e006994.

    CrossrefMedlineGoogle Scholar
  • 64. Jaeger B.C., Cantor R.S., Sthanam V., Rudraraju R. "Improving mortality predictions for patients with mechanical circulatory support using follow-up data and machine learning". Circ Genom Precis Med 2020;13:2: e002877.

    CrossrefMedlineGoogle Scholar
  • 65. Birks E.J., Drakos S.G., Patel S.R., et al. "Prospective multicenter study of myocardial recovery using left ventricular assist devices (RESTAGE-HF [remission from stage D heart failure])". Circulation 2020;142:21: 2016-2028.

    CrossrefMedlineGoogle Scholar
  • 66. Lundberg S.M., Lee S.-I. "A unified approach to interpreting model predictions". Adv Neural Inf Process Syst 2017;30:4768-4777.

    Google Scholar
  • 67. "“Why should I trust you?” Explaining the predictions of any classifier". In: Ribeiro M.T., Singh S., Guestrin C. , eds. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 2016.

    CrossrefGoogle Scholar
  • 68. Luo Y. "Evaluating the state of the art in missing data imputation for clinical data". Brief Bioinform 2021;23:1: bbab489.

    CrossrefGoogle Scholar
  • 69. Borgman C.L. "The conundrum of sharing research data". J Am Soc Inf Sci Tech 2012;63:6: 1059-1078.

    CrossrefGoogle Scholar
  • 70. Tellam R.L., Rushton P., Schuerman P., Pala I., Anane D. "The primary reasons behind data sharing, its wider benefits and how to cope with the realities of commercial data". BMC Genomics 2015;16:1: 626.

    CrossrefMedlineGoogle Scholar
  • 71. Studer R., Sartini C., Suzart-Woischnik K., et al. "Identification and mapping real-world data sources for heart failure, acute coronary syndrome, and atrial fibrillation". Cardiology 2022;147:1: 98-106.

    CrossrefMedlineGoogle Scholar
  • 72. Tat E., Bhatt D.L., Rabbat M.G. "Addressing bias: artificial intelligence in cardiovascular medicine". Lancet Digit Health 2020;2:12: e635-e636.

    CrossrefMedlineGoogle Scholar
  • 73. Shin S., Austin P.C., Ross H.J., et al. "Machine learning vs. conventional statistical models for predicting heart failure readmission and mortality". ESC Heart Fail 2021;8:1: 106-115.

    CrossrefMedlineGoogle Scholar
  • 74. Liu X., Cruz Rivera S., Moher D., et al. "Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension". Nat Med 2020;26:9: 1364-1374.

    CrossrefMedlineGoogle Scholar
  • 75. Cruz Rivera S., Liu X., Chan A.-W., et al. "Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI extension". Lancet Digit Health 2020;2:10: e549-e560.

    CrossrefMedlineGoogle Scholar
  • 76. Pandey M., Xu Z., Sholle E., et al. "Extraction of radiographic findings from unstructured thoracoabdominal computed tomography reports using convolutional neural network based natural language processing". PLoS One 2020;15:7: e0236827.

    CrossrefGoogle Scholar
  • 77. Ambrosy A.P., Parikh R.V., Sung S.H., et al. "A natural language processing-based approach for identifying hospitalizations for worsening heart failure within an integrated health care delivery system". JAMA Netw Open 2021;4:11: e2135152.

    CrossrefMedlineGoogle Scholar
  • 78. Khezr S., Moniruzzaman M., Yassine A., Benlamri R. "Blockchain technology in healthcare: a comprehensive review and directions for future research". Appl Sci 2019;9:9: 1736.

    CrossrefGoogle Scholar
  • 79. Atluri P., Goldstone A.B., Fairman A.S., et al. "Predicting right ventricular failure in the modern, continuous flow left ventricular assist device era". Ann Thorac Surg 2013;96:3: 857-863. https://doi.org/10.1016/j.athoracsur.2013.03.099.

    CrossrefMedlineGoogle Scholar