{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,10]],"date-time":"2026-06-10T18:07:34Z","timestamp":1781114854777,"version":"3.54.1"},"reference-count":25,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2021,8,28]],"date-time":"2021-08-28T00:00:00Z","timestamp":1630108800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2021,8,28]],"date-time":"2021-08-28T00:00:00Z","timestamp":1630108800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100000266","name":"Engineering and Physical Sciences Research Council","doi-asserted-by":"publisher","award":["EP\/L015803\/1"],"award-info":[{"award-number":["EP\/L015803\/1"]}],"id":[{"id":"10.13039\/501100000266","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100006370","name":"Small Business Innovation Research","doi-asserted-by":"publisher","award":["1950994"],"award-info":[{"award-number":["1950994"]}],"id":[{"id":"10.13039\/100006370","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J. of Cardiovasc. Trans. Res."],"published-print":{"date-parts":[[2022,2]]},"abstract":"<jats:sec>\n                <jats:title>Abstract<\/jats:title>\n                <jats:p>Inadequate at-home management and self-awareness of heart failure (HF) exacerbations are known to be leading causes of the greater than 1 million estimated HF-related hospitalizations in the USA alone. Most current at-home HF management protocols include paper guidelines or exploratory health applications that lack rigor and validation at the level of the individual patient. We report on a novel triage methodology that uses machine learning predictions for real-time detection and assessment of exacerbations. Medical specialist opinions on statistically and clinically comprehensive, simulated patient cases were used to train and validate prediction algorithms. Model performance was assessed by comparison to physician panel consensus in a representative, out-of-sample validation set of 100 vignettes. Algorithm prediction accuracy and safety indicators surpassed all individual specialists in identifying consensus opinion on existence\/severity of exacerbations and appropriate treatment response. The algorithms also scored the highest sensitivity, specificity, and PPV when assessing the need for emergency care.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Lay summary<\/jats:title>\n                <jats:p>Here we develop a machine-learning approach for providing real-time decision support to adults diagnosed with congestive heart failure. The algorithm achieves higher exacerbation and triage classification performance than any individual physician when compared to physician consensus opinion.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Graphical abstract<\/jats:title>\n                \n              <\/jats:sec>","DOI":"10.1007\/s12265-021-10151-7","type":"journal-article","created":{"date-parts":[[2021,8,28]],"date-time":"2021-08-28T07:06:45Z","timestamp":1630134405000},"page":"103-115","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["A Machine Learning Methodology for Identification and Triage of Heart Failure Exacerbations"],"prefix":"10.1007","volume":"15","author":[{"given":"James","family":"Morrill","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Klajdi","family":"Qirko","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jacob","family":"Kelly","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andrew","family":"Ambrosy","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Botros","family":"Toro","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ted","family":"Smith","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nicholas","family":"Wysham","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marat","family":"Fudim","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2931-691X","authenticated-orcid":false,"given":"Sumanth","family":"Swaminathan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2021,8,28]]},"reference":[{"issue":"12","key":"10151_CR1","doi-asserted-by":"publisher","first-page":"1123","DOI":"10.1016\/j.jacc.2013.11.053","volume":"63","author":"AP Ambrosy","year":"2014","unstructured":"Ambrosy, A. P., et al. (2014). The global health and economic burden of hospitalizations for heart failure: lessons learned from hospitalized heart failure registries. Journal of the American College of Cardiology, 63(12), 1123\u20131133.","journal-title":"Journal of the American College of Cardiology"},{"issue":"1","key":"10151_CR2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12872-018-0815-3","volume":"18","author":"W Lesyuk","year":"2018","unstructured":"Lesyuk, W., Kriza, C., & Kolominsky-Rabas, P. (2018). Cost-of-illness studies in heart failure: a systematic review 2004\u20132016. BMC Cardiovascular Disorders, 18(1), 1\u201311.","journal-title":"BMC Cardiovascular Disorders"},{"issue":"4","key":"10151_CR3","doi-asserted-by":"publisher","first-page":"391","DOI":"10.1016\/j.jacc.2012.09.038","volume":"61","author":"M Gheorghiade","year":"2013","unstructured":"Gheorghiade, M., Vaduganathan, M., Fonarow, G. C., & Bonow, R. O. (2013). Rehospitalization for heart failure: problems and perspectives. Journal of the American College of Cardiology, 61(4), 391\u2013403.","journal-title":"Journal of the American College of Cardiology"},{"issue":"2","key":"10151_CR4","doi-asserted-by":"publisher","first-page":"128","DOI":"10.15420\/cfr.2015.1.2.128","volume":"1","author":"SR Toukhsati","year":"2015","unstructured":"Toukhsati, S. R., Driscoll, A., & Hare, D. L. (2015). Patient self-management in chronic heart failure\u2013establishing concordance between guidelines and practice. Cardiac Failure Review, 1(2), 128.","journal-title":"Cardiac Failure Review"},{"issue":"2","key":"10151_CR5","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1093\/eurjhf\/hfq219","volume":"13","author":"M Lainscak","year":"2011","unstructured":"Lainscak, M., et al. (2011). Self-care management of heart failure: practical recommendations from the Patient Care Committee of the Heart Failure Association of the European Society of Cardiology. European Journal of Heart Failure, 13(2), 115\u2013126.","journal-title":"European Journal of Heart Failure"},{"key":"10151_CR6","unstructured":"Mayo Clinic. (2021). What is a heart failure action plan? https:\/\/www.mayoclinic.org\/diseases-conditions\/heart-failure\/expert-answers\/heart-failure-action-plan\/faq-20433731. Accessed 7 Jan 2020."},{"key":"10151_CR7","unstructured":"Penn Medicine. (2021). Heart failure action plan. https:\/\/www.pennmedicine.org\/~\/media\/documents%20and%20audio\/patient%20guides%20and%20instructions\/heart%20and%20vascular\/penn_heart_failure_action_plan.ashx. Accessed 7 Jan 2020."},{"issue":"1","key":"10151_CR8","doi-asserted-by":"publisher","first-page":"153","DOI":"10.1111\/nyas.13218","volume":"1387","author":"J Finkelstein","year":"2017","unstructured":"Finkelstein, J., & Jeong, I. C. (2017). Machine learning approaches to personalize early prediction of asthma exacerbations. 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Dr. Andrew Ambrosy declares that he is supported by a Mentored Patient-Oriented Research Career Development Award (K23HL150159) through the National Heart, Lung and Blood institute and he has received relevant research support through grants to his institution from Amarin Pharma, Inc., Abbott, and Novartis, including modest travel reimb from Novartis. Dr. Marat Fudim declares that he is supported by The National Heart, Lung, and Blood Institute grant K23HL151744; The American Heart Association grant 20IPA35310955; Mario Family Award; Duke Chair\u2019s Award; Translating Duke Health Award; Bayer; and BTG Specialty Pharmaceuticals. He further receives consulting fees from AstraZeneca, AxonTherapies, CVRx, Daxor, Edwards LifeSciences, Galvani, NXT Biomedical, Zoll, Viscardia. Dr. Sumanth Swaminathan and Dr. Botros Toro both declare that they receive partial funding from the National Science Foundation Small Business Innovation Research Grant (no. 1950994) and that they are both shareholders of Vironix Health, Inc. Dr. Nicholas Wysham declares that he is a consultant for and shareholder of Vironix Health. Dr. Klajdi Qirko, Dr. Ted Smith, and Dr. Jacob Kelly declare that they have no conflicts of interest.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of Interest"}}]}}