Machine learning in the prevention of heart failure

Savarese G, Becher PM, Lund LH, Seferovic P, Rosano GMC, Coats AJS (2022) Global burden of heart failure: a comprehensive and updated review of epidemiology. Cardiovasc Res 118:3272–3287. https://doi.org/10.1093/cvr/cvac013

Article  Google Scholar 

Bozkurt B, Ahmad T, Alexander KM, Baker WL, Bosak K, Breathett K, Fonarow GC, Heidenreich P, Ho JE, Hsich E, Ibrahim NE, Jones LM, Khan SS, Khazanie P, Koelling T, Krumholz HM, Khush KK, Lee C, Morris AA, Page RL 2nd, Pandey A, Piano MR, Stehlik J, Stevenson LW, Teerlink JR, Vaduganathan M, Ziaeian B (2023) Heart failure epidemiology and outcomes statistics: a report of the Heart Failure Society of America. J Card Fail 29:1412–1451. https://doi.org/10.1016/j.cardfail.2023.07.006

Article  PubMed  PubMed Central  Google Scholar 

Chioncel O, Lainscak M, Seferovic PM, Anker SD, Crespo-Leiro MG, Harjola V-P, Parissis J, Laroche C, Piepoli MF, Fonseca C, Mebazaa A, Lund L, Ambrosio GA, Coats AJ, Ferrari R, Ruschitzka F, Maggioni AP, Filippatos G (2017) Epidemiology and one-year outcomes in patients with chronic heart failure and preserved, mid-range and reduced ejection fraction: an analysis of the ESC Heart Failure Long-Term Registry. Eur J Heart Fail 19:1574–1585. https://doi.org/10.1002/ejhf.813

Article  PubMed  Google Scholar 

McMurray JJV, Solomon SD, Inzucchi SE, Køber L, Kosiborod MN, Martinez FA, Ponikowski P, Sabatine MS, Anand IS, Bělohlávek J, Böhm M, Chiang C-E, Chopra VK, Boer RAd, Desai AS, Diez M, Drozdz J, Dukát A, Ge J, Howlett JG, Katova T, Kitakaze M, Ljungman CEA, Merkely B, Nicolau JC, O’Meara E, Petrie MC, Vinh PN, Schou M, Tereshchenko S, Verma S, Held C, DeMets DL, Docherty KF, Jhund PS, Bengtsson O, Sjöstrand M, Langkilde A-M (2019) Dapagliflozin in patients with heart failure and reduced ejection fraction. N Engl J Med 381:1995–2008. https://doi.org/10.1056/NEJMoa1911303

Article  PubMed  Google Scholar 

Heidenreich PA, Bozkurt B, Aguilar D, Allen LA, Byun JJ, Colvin MM, Deswal A, Drazner MH, Dunlay SM, Evers LR, Fang JC, Fedson SE, Fonarow GC, Hayek SS, Hernandez AF, Khazanie P, Kittleson MM, Lee CS, Link MS, Milano CA, Nnacheta LC, Sandhu AT, Stevenson LW, Vardeny O, Vest AR, Yancy CW (2022) 2022 AHA/ACC/HFSA guideline for the management of heart failure: a report of the American College of Cardiology/American Heart Association Joint Committee on Clinical Practice Guidelines. Circulation 145:e895–e1032. https://doi.org/10.1161/CIR.0000000000001063

Article  PubMed  Google Scholar 

Yasmin F, Shah SMI, Naeem A, Shujauddin SM, Jabeen A, Kazmi S, Siddiqui SA, Kumar P, Salman S, Hassan SA, Dasari C, Choudhry AS, Mustafa A, Chawla S, Lak HM (2021) Artificial intelligence in the diagnosis and detection of heart failure: the past, present, and future. RCM 22:1095–1113. https://doi.org/10.31083/j.rcm2204121

Article  Google Scholar 

Ruchi P, Tejasvi P, Vaishnavi K, Jane W, Lisa W, Sadiya SK (2024) Prioritizing the primary prevention of heart failure: measuring, modifying and monitoring risk. Prog Cardiovasc Dis 82:2–14. https://doi.org/10.1016/j.pcad.2024.01.001

Article  Google Scholar 

Randall JE, Ryan MS, Alfonso L (2022) Twelve key challenges in medical machine learning and solutions. Intell-Based Med 6:100068. https://doi.org/10.1016/j.ibmed.2022.100068

Article  Google Scholar 

Sidey-Gibbons JAM, Sidey-Gibbons CJ (2019) Machine learning in medicine: a practical introduction. BMC Med Res Methodol 19:64. https://doi.org/10.1186/s12874-019-0681-4

Article  PubMed  PubMed Central  Google Scholar 

Meijs C, Handoko ML, Savarese G, Vernooij RWM, Vaartjes I, Banerjee A, Koudstaal S, Brugts JJ, Asselbergs FW, Uijl A (2023) Discovering distinct phenotypical clusters in heart failure across the ejection fraction spectrum: a systematic review. Curr Heart Fail Rep 20:333–349. https://doi.org/10.1007/s11897-023-00615-z

Article  PubMed  PubMed Central  Google Scholar 

Bourazana A, Xanthopoulos A, Briasoulis A, Magouliotis D, Spiliopoulos K, Athanasiou T, Vassilopoulos G, Skoularigis J, Triposkiadis F (2024) Artificial intelligence in heart failure: friend or foe? Life 14:145

Article  PubMed  PubMed Central  Google Scholar 

Huff DT, Weisman AJ, Jeraj R (2021) Interpretation and visualization techniques for deep learning models in medical imaging. Phys Med Biol 66:04tr01. https://doi.org/10.1088/1361-6560/abcd17

Article  PubMed  PubMed Central  Google Scholar 

Segar MW. Predict the 10-year risk of incident heart failure. Available at: https://cvriskscores.shinyapps.io/HFrisk/. Accessed on: 9/20/2024.

Segar MW, Jaeger BC, Patel KV, Nambi V, Ndumele CE, Correa A, Butler J, Chandra A, Ayers C, Rao S, Lewis AA, Raffield LM, Rodriguez CJ, Michos ED, Ballantyne CM, Hall ME, Mentz RJ, de Lemos JA, Pandey A (2021) Development and validation of machine learning-based race-specific models to predict 10-year risk of heart failure: a multicohort analysis. Circulation 143:2370–2383. https://doi.org/10.1161/circulationaha.120.053134

Article  PubMed  PubMed Central  Google Scholar 

Khan MS, Arshad MS, Greene SJ, Van Spall HGC, Pandey A, Vemulapalli S, Perakslis E, Butler J (2023) Artificial intelligence and heart failure: a state-of-the-art review. Eur J Heart Fail 25:1507–1525. https://doi.org/10.1002/ejhf.2994

Article  PubMed  Google Scholar 

Marx N, Federici M, Schütt K, Müller-Wieland D, Ajjan RA, Antunes MJ, Christodorescu RM, Crawford C, Di Angelantonio E, Eliasson B, Espinola-Klein C, Fauchier L, Halle M, Herrington WG, Kautzky-Willer A, Lambrinou E, Lesiak M, Lettino M, McGuire DK, Mullens W, Rocca B, Sattar N, Group ESD (2023) 2023 ESC guidelines for the management of cardiovascular disease in patients with diabetes: developed by the task force on the management of cardiovascular disease in patients with diabetes of the European Society of Cardiology (ESC). Eur Heart J 44:4043–4140. https://doi.org/10.1093/eurheartj/ehad192

Article  PubMed  Google Scholar 

Segar MW, Vaduganathan M, Patel KV, McGuire DK, Butler J, Fonarow GC, Basit M, Kannan V, Grodin JL, Everett B, Willett D, Berry J, Pandey A (2019) Machine learning to predict the risk of incident heart failure hospitalization among patients with diabetes: the WATCH-DM Risk score. Diabetes Care 42:2298–2306. https://doi.org/10.2337/dc19-0587

Article  PubMed  PubMed Central  Google Scholar 

Segar MW, Khan MS, Patel KV, Vaduganathan M, Kannan V, Willett D, Peterson E, Tang WHW, Butler J, Everett BM, Fonarow GC, Wang TJ, McGuire DK, Pandey A (2022) Incorporation of natriuretic peptides with clinical risk scores to predict heart failure among individuals with dysglycaemia. Eur J Heart Fail 24:169–180. https://doi.org/10.1002/ejhf.2375

Article  PubMed  Google Scholar 

Sanjay B, Jeremy BS, Seth AB, Rodney AH, John SY (2017) Development and validation of Risk Equations for Complications Of type 2 Diabetes (RECODe) using individual participant data from randomised trials. Lancet Diabetes Endocrinol 5:788–798. https://doi.org/10.1016/S2213-8587(17)30221-8

Article  Google Scholar 

Laila R, Yonghui W, Ningtao W, Xin G, Zheng WJ, Fei W, Hulin W, Hua X, Degui Z (2018) A study of generalizability of recurrent neural network-based predictive models for heart failure onset risk using a large and heterogeneous EHR data set. J Biomed Inform 84:11–16. https://doi.org/10.1016/j.jbi.2018.06.011

Article  Google Scholar 

Choi E, Schuetz A, Stewart WF, Sun J (2016) Using recurrent neural network models for early detection of heart failure onset. J Am Med Inform Assoc 24:361–370. https://doi.org/10.1093/jamia/ocw112

Article  PubMed Central  Google Scholar 

Sun Z, Dong W, Shi H, Ma H, Cheng L, Huang Z (2022) Comparing machine learning models and statistical models for predicting heart failure events: a systematic review and meta-analysis. Frontiers in Cardiovascular Medicine 9. https://doi.org/10.3389/fcvm.2022.812276

Wang TJ, Evans JC, Benjamin EJ, Levy D, LeRoy EC, Vasan RS (2003) Natural history of asymptomatic left ventricular systolic dysfunction in the community. Circulation 108:977–982. https://doi.org/10.1161/01.CIR.0000085166.44904.79

Article  PubMed  Google Scholar 

Attia ZI, Kapa S, Lopez-Jimenez F, McKie PM, Ladewig DJ, Satam G, Pellikka PA, Enriquez-Sarano M, Noseworthy PA, Munger TM, Asirvatham SJ, Scott CG, Carter RE, Friedman PA (2019) Screening for cardiac contractile dysfunction using an artificial intelligence–enabled electrocardiogram. Nat Med 25:70–74. https://doi.org/10.1038/s41591-018-0240-2

Article  PubMed  Google Scholar 

Attia ZI, Kapa S, Yao X, Lopez-Jimenez F, Mohan TL, Pellikka PA, Carter RE, Shah ND, Friedman PA, Noseworthy PA (2019) Prospective validation of a deep learning electrocardiogram algorithm for the detection of left ventricular systolic dysfunction. J Cardiovasc Electrophysiol 30:668–674. https://doi.org/10.1111/jce.13889

Article  PubMed  Google Scholar 

Dhingra LS, Aminorroaya A, Sangha V, Camargos AP, Asselbergs FW, Brant LC, Barreto SM, Ribeiro ALP, Krumholz HM, Oikonomou EK, Khera R (2024) Scalable risk stratification for heart failure using artificial intelligence applied to 12-lead electrocardiographic images: a multinational study. medRxiv. https://doi.org/10.1101/2024.04.02.24305232

Bhave S, Rodriguez V, Poterucha T, Mutasa S, Aberle D, Capaccione KM, Chen Y, Dsouza B, Dumeer S, Goldstein J, Hodes A, Leb J, Lungren M, Miller M, Monoky D, Navot B, Wattamwar K, Wattamwar A, Clerkin K, Ouyang D, Ashley E, Topkara VK, Maurer M, Einstein AJ, Uriel N, Homma S, Schwartz A, Jaramillo D, Perotte AJ, Elias P (2024) Deep learning to detect left ventricular structural abnormalities in chest X-rays. Eur Heart J 45:2002–2012. https://doi.org/10.1093/eurheartj/ehad782

Article  PubMed  PubMed Central  Google Scholar 

Tromp J, Sarra C, Nidhal B, Mejdi BM, Zouari F, Hummel Y, Mzoughi K, Kraiem S, Fehri W, Gamra H, Lam CSP, Mebazaa A, Addad F (2024) Nurse-led home-based detection of cardiac dysfunction by ultrasound: results of the CUMIN pilot study. Eur Heart J Digit Health 5:163–169. https://doi.org/10.1093/ehjdh/ztad079

Article  PubMed  Google Scholar 

Firima E, Gonzalez L, Manthabiseng M, Bane M, Lukau B, Leigh B, Kaufmann BA, Weisser M, Amstutz A, Tromp J, Labhardt ND, Burkard T (2024) Implementing focused echocardiography and AI-supported analysis in a population-based survey in Lesotho: implications for community-based cardiovascular disease care models. Hypertens Res 47:708–713. https://doi.org/10.1038/s41440-023-01559-6

Article  PubMed  PubMed Central  Google Scholar 

留言 (0)

沒有登入
gif