{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,19]],"date-time":"2026-08-19T18:43:56Z","timestamp":1787165036328,"version":"build-2736575974"},"reference-count":17,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,5,14]],"date-time":"2026-05-14T00:00:00Z","timestamp":1778716800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2026,7,15]],"date-time":"2026-07-15T00:00:00Z","timestamp":1784073600000},"content-version":"vor","delay-in-days":62,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Cardiovasc Disord"],"DOI":"10.1186\/s12872-026-05976-6","type":"journal-article","created":{"date-parts":[[2026,5,14]],"date-time":"2026-05-14T06:52:56Z","timestamp":1778741576000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Comparative accuracy of a predictive algorithm versus cardiologist assessment for 90-day readmission in decompensated chronic heart failure"],"prefix":"10.1186","volume":"26","author":[{"given":"Gr\u00e9goire","family":"Mercier","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cl\u00e9ment","family":"Delmas","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fabien","family":"Huet","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fran\u00e7ois","family":"Roubille","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,5,14]]},"reference":[{"key":"5976_CR1","doi-asserted-by":"publisher","first-page":"3272","DOI":"10.1093\/cvr\/cvac013","volume":"118","author":"G Savarese","year":"2023","unstructured":"Savarese G, Becher PM, Lund LH, Seferovic P, Rosano GMC, Coats AJS. Global burden of heart failure: a comprehensive and updated review of epidemiology. Cardiovasc Res. 2023;118:3272\u201387.","journal-title":"Cardiovasc Res"},{"issue":"12","key":"5976_CR2","doi-asserted-by":"publisher","first-page":"705","DOI":"10.1016\/j.acvd.2024.10.004","volume":"117","author":"A Gabet","year":"2024","unstructured":"Gabet A, Blacher J, Pousset F, Grave C, Lailler G, Tuppin P, et al. Epidemiology of heart failure in France. Arch Cardiovasc Dis. 2024;117(12):705\u201314.","journal-title":"Arch Cardiovasc Dis"},{"key":"5976_CR3","doi-asserted-by":"publisher","first-page":"927","DOI":"10.1007\/s41669-023-00431-0","volume":"7","author":"F Chemouni","year":"2023","unstructured":"Chemouni F, Nishikawa TC, Groyer H, Diaby O, Chollet J, Ittah D. Hospital resource utilization and costs in patients with heart failure in France. Pharmacoecon Open. 2023;7:927\u201340.","journal-title":"Pharmacoecon Open"},{"issue":"1","key":"5976_CR4","doi-asserted-by":"publisher","first-page":"74","DOI":"10.1186\/s12872-018-0815-3","volume":"18","author":"W Lesyuk","year":"2018","unstructured":"Lesyuk W, Kriza C, Kolominsky-Rabas P. Cost-of-illness studies in heart failure: a systematic review 2004\u20132016. BMC Cardiovasc Disord. 2018;18(1):74.","journal-title":"BMC Cardiovasc Disord"},{"issue":"7","key":"5976_CR5","doi-asserted-by":"publisher","first-page":"1115","DOI":"10.1002\/ejhf.2888","volume":"25","author":"M Metra","year":"2023","unstructured":"Metra M, Adamo M, Tomasoni D, et al. Pre-discharge and early post-discharge management of patients hospitalized for acute heart failure: A scientific statement by the Heart Failure Association of the ESC. Eur J Heart Fail. 2023;25(7):1115\u201331.","journal-title":"Eur J Heart Fail"},{"issue":"3","key":"5976_CR6","doi-asserted-by":"publisher","first-page":"415","DOI":"10.1093\/ehjdh\/ztac035","volume":"3","author":"PM Croon","year":"2022","unstructured":"Croon PM, Selder JL, Allaart CP, et al. Current state of artificial intelligence-based algorithms for hospital admission prediction in patients with heart failure: a scoping review. Eur Heart J Digit Health. 2022;3(3):415\u201325.","journal-title":"Eur Heart J Digit Health"},{"issue":"6","key":"5976_CR7","doi-asserted-by":"crossref","first-page":"618","DOI":"10.1016\/j.jacc.2019.12.017","volume":"75","author":"T Ahmad","year":"2020","unstructured":"Ahmad T, Lund LH, Rao P, et al. Machine learning to predict early readmission after heart failure hospitalization. J Am Coll Cardiol. 2020;75(6):618\u201329.","journal-title":"J Am Coll Cardiol"},{"issue":"6","key":"5976_CR8","doi-asserted-by":"publisher","first-page":"629","DOI":"10.1161\/CIRCOUTCOMES.116.003039","volume":"9","author":"BJ Mortazavi","year":"2016","unstructured":"Mortazavi BJ, Downing NS, Bucholz EM, et al. Analysis of machine learning techniques for heart failure readmissions. Circ Cardiovasc Qual Outcomes. 2016;9(6):629\u201340.","journal-title":"Circ Cardiovasc Qual Outcomes"},{"issue":"24","key":"5976_CR9","first-page":"e014566","volume":"9","author":"JM Kwon","year":"2020","unstructured":"Kwon JM, Lee SY, Jeon KH, et al. Deep learning-based algorithm for detecting heart failure using electrocardiography. J Am Heart Assoc. 2020;9(24):e014566.","journal-title":"J Am Heart Assoc"},{"issue":"14","key":"5976_CR10","doi-asserted-by":"publisher","first-page":"1347","DOI":"10.1056\/NEJMra1814259","volume":"380","author":"A Rajkomar","year":"2019","unstructured":"Rajkomar A, Dean J, Kohane I. Machine learning in medicine. N Engl J Med. 2019;380(14):1347\u201358.","journal-title":"N Engl J Med"},{"key":"5976_CR11","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1038\/s41746-020-0253-3","volume":"3","author":"MP Sendak","year":"2020","unstructured":"Sendak MP, D\u2019Arcy J, Kashyap S, et al. A path for translation of machine learning products into healthcare delivery. NPJ Digit Med. 2020;3:106.","journal-title":"NPJ Digit Med"},{"key":"5976_CR12","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1016\/j.jclinepi.2018.07.003","volume":"103","author":"P Constantinou","year":"2018","unstructured":"Constantinou P, Tuppin P, Fagot-Campagna A, Gastaldi-M\u00e9nager C, Schellevis FG, Pelletier-Fleury N. Two morbidity indices developed in a nationwide population permitted performant outcome-specific severity adjustment. J Clin Epidemiol. 2018;103:60\u201370.","journal-title":"J Clin Epidemiol"},{"issue":"3","key":"5976_CR13","doi-asserted-by":"publisher","first-page":"266","DOI":"10.1016\/j.cardfail.2020.07.018","volume":"27","author":"P Constantinou","year":"2021","unstructured":"Constantinou P, Pelletier-Fleury N, Oli\u00e9 V, Gastaldi-M\u00e9nager C, Juill\u00c8re Y, Tuppin P. Patient stratification for risk of readmission due to heart failure by using nationwide administrative data. J Card Fail. 2021;27(3):266\u201376.","journal-title":"J Card Fail"},{"issue":"1","key":"5976_CR14","doi-asserted-by":"publisher","first-page":"e100617","DOI":"10.1136\/bmjhci-2022-100617","volume":"29","author":"S Parbhoo","year":"2022","unstructured":"Parbhoo S, Wawira Gichoya J, Celi LA, de la Hoz M\u00c1A, for MIT Critical Data. Operationalising fairness in medical algorithms. BMJ Health Care Inf. 2022;29(1):e100617.","journal-title":"BMJ Health Care Inf"},{"key":"5976_CR15","doi-asserted-by":"publisher","first-page":"e078378","DOI":"10.1136\/bmj-2023-078378","volume":"385","author":"GS Collins","year":"2024","unstructured":"Collins GS, Moons KGM, Dhiman P, Riley RD, Beam AL, Van Calster B, Ghassemi M, Liu X, Reitsma JB, van Smeden M, Boulesteix AL, Camaradou JC, Celi LA, Denaxas S, Denniston AK, Glocker B, Golub RM, Harvey H, Heinze G, Hoffman MM, Kengne AP, Lam E, Lee N, Loder EW, Maier-Hein L, Mateen BA, McCradden MD, Oakden-Rayner L, Ordish J, Parnell R, Rose S, Singh K, Wynants L, Logullo P. TRIPOD + AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods. BMJ. 2024;385:e078378.","journal-title":"BMJ"},{"issue":"20","key":"5976_CR16","doi-asserted-by":"publisher","first-page":"e009594","DOI":"10.1161\/JAHA.118.009594","volume":"7","author":"JD Rich","year":"2018","unstructured":"Rich JD, Burns J, Freed BH, Maurer MS, Burkhoff D, Shah SJ. Meta-Analysis Global Group in Chronic (MAGGIC) Heart Failure Risk Score: Validation of a Simple Tool for the Prediction of Morbidity and Mortality in Heart Failure With Preserved Ejection Fraction. J Am Heart Assoc. 2018;7(20):e009594.","journal-title":"J Am Heart Assoc"},{"issue":"1","key":"5976_CR17","first-page":"84","volume":"36","author":"TA Buchan","year":"2020","unstructured":"Buchan TA, Ross HJ, McDonald M, Billia F, Delgado D, Duero Posada JG, Luk A, Guyatt GH, Alba AC. Physician Judgement vs Model-Predicted Prognosis in Patients With Heart Failure. Can J Cardiol. 2020;36(1):84\u201391.","journal-title":"Can J Cardiol"}],"container-title":["BMC Cardiovascular Disorders"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12872-026-05976-6","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12872-026-05976-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12872-026-05976-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,15]],"date-time":"2026-07-15T02:45:18Z","timestamp":1784083518000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1186\/s12872-026-05976-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5,14]]},"references-count":17,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["5976"],"URL":"https:\/\/doi.org\/10.1186\/s12872-026-05976-6","relation":{},"ISSN":["1471-2261"],"issn-type":[{"value":"1471-2261","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,5,14]]},"assertion":[{"value":"31 December 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 May 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 May 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"Ethics approval was granted by the Institutional Review Board of Montpellier University Hospital (approval number: IRB-MTP_2021_07_202100899).Following French regulation on retrospective studies, all participants were informed and were given the possibility to withdraw their personal data from the study.","order":1,"name":"Ethics","label":"Ethics approval and consent to participate","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","label":"Consent for publication","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"GM is a shareholder of KanopyMed.","order":3,"name":"Ethics","label":"Competing interests","group":{"name":"EthicsHeading","label":"Declarations"}}],"article-number":"602"}}