Machine learning model for predicting 1-year and 3-year all-cause mortality in ischemic heart failure patients

References

• Bragazzi NL, Zhong W, Shu J, et al. Burden of heart failure and underlying causes in 195 countries and territories from 1990 to 2017. Eur J Prev Cardiol. 2021;28(15):1682–1690.
   PubMed Web of Science ®Google Scholar
• Groenewegen A, Rutten FH, Mosterd A, et al. Epidemiology of heart failure. Eur J Heart Fail. 2020;22(8):1342–1356.
   PubMed Web of Science ®Google Scholar
• Ibrahim NE, Burnett JCsJr., Butler J, et al. Natriuretic peptides as inclusion criteria in clinical trials: a JACC: heart failure position paper. JACC Heart Fail. 2020;8(5):347–358.
   PubMed Web of Science ®Google Scholar
• Fonarow GC, Adams KFsJr., Abraham WT, et al. Risk stratification for in-hospital mortality in acutely decompensated heart failure: classification and regression tree analysis. Jama. 2005;293(5):572–580.
   PubMed Web of Science ®Google Scholar
• Lee DS, Austin PC, Rouleau JL, et al. Predicting mortality among patients hospitalized for heart failure: derivation and validation of a clinical model. Jama. 2003;290(19):2581–2587.
   PubMed Web of Science ®Google Scholar
• Abraham WT, Fonarow GC, Albert NM, et al. Predictors of in-hospital mortality in patients hospitalized for heart failure: insights from the organized program to initiate lifesaving treatment in hospitalized patients with heart failure (OPTIMIZE-HF). J Am Coll Cardiol. 2008;52(5):347–356.
   PubMed Web of Science ®Google Scholar
• Peterson PN, Rumsfeld JS, Liang L, et al. A validated risk score for in-hospital mortality in patients with heart failure from the American heart association get with the guidelines program. Circ Cardiovasc Qual Outcomes. 2010;3(1):25–32.
   PubMed Web of Science ®Google Scholar
• Pocock SJ, Ariti CA, McMurray JJ, et al. Predicting survival in heart failure: a risk score based on 39 372 patients from 30 studies. Eur Heart J. 2013;34(19):1404–1413.
   PubMed Web of Science ®Google Scholar
• Canepa M, Fonseca C, Chioncel O, et al. Performance of prognostic risk scores in chronic heart failure patients enrolled in the European society of cardiology heart failure long-term registry. JACC Heart Fail. 2018;6(6):452–462.
   PubMed Web of Science ®Google Scholar
• Sartipy U, Dahlström U, Edner M, et al. Predicting survival in heart failure: validation of the MAGGIC heart failure risk score in 51,043 patients from the Swedish heart failure registry. Eur J Heart Fail. 2014;16(2):173–179.
   PubMed Web of Science ®Google Scholar
• Rajkomar A, Dean J, Kohane I. Machine learning in medicine. N Engl J Med. 2019;380(14):1347–1358.
   PubMed Web of Science ®Google Scholar
• Quer G, Arnaout R, Henne M, et al. Machine Learning and the future of cardiovascular care: JACC state-of-the-art review. J Am Coll Cardiol. 2021;77(3):300–313.
   PubMed Web of Science ®Google Scholar
• Angraal S, Mortazavi BJ, Gupta A, et al. Machine learning prediction of mortality and hospitalization in heart failure with preserved ejection fraction. JACC Heart Fail. 2020;8(1):12–21.
   PubMed Web of Science ®Google Scholar
• Adler ED, Voors AA, Klein L, et al. Improving risk prediction in heart failure using machine learning. Eur J Heart Fail. 2020;22(1):139–147.
   PubMed Web of Science ®Google Scholar
• Ahmad T, Lund LH, Rao P, et al. 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(8). DOI:10.1161/JAHA.117.008081.
   Web of Science ®Google Scholar
• Jing L, Ulloa Cerna AE, Good CW, et al. A machine learning approach to management of heart failure populations. JACC Heart Fail. 2020;8(7):578–587.
   PubMed Web of Science ®Google Scholar
• Piepoli MF, Adamo M, Barison A, et al. Preventing heart failure: a position paper of the heart failure association in collaboration with the European Association of preventive cardiology. Eur J Heart Fail. 2022;24(1):143–168.
   PubMed Web of Science ®Google Scholar
• Zhang Y, Zhang J, Butler J, et al. Contemporary epidemiology, management, and outcomes of patients hospitalized for heart failure in China: results from the China heart failure (China-HF) registry. J Card Fail. 2017;23(12):868–875.
   PubMed Web of Science ®Google Scholar
• McMurray JJ, Adamopoulos S, Anker SD, et al. ESC guidelines for the diagnosis and treatment of acute and chronic heart failure 2012: the task force for the diagnosis and treatment of acute and chronic heart failure 2012 of the European society of cardiology. Developed in collaboration with the Heart Failure Association (HFA) of the ESC. Eur J Heart Fail. 2012;14(8):803–869.
   PubMed Web of Science ®Google Scholar
• Yancy CW, Jessup M, Bozkurt B, et al. ACCF/AHA guideline for the management of heart failure: executive summary: a report of the American College of Cardiology Foundation/American Heart Association task force on practice guidelines. Circulation. 2013;128(16):1810–1852.
   PubMed Web of Science ®Google Scholar
• Pitt B, Pfeffer MA, Assmann SF, et al. Spironolactone for heart failure with preserved ejection fraction. N Engl J Med. 2014;370(15):1383–1392.
   PubMed Web of Science ®Google Scholar
• Lang RM, Badano LP, Mor-Avi V, et al. Recommendations for cardiac chamber quantification by echocardiography in adults: an update from the American society of echocardiography and the European association of cardiovascular imaging. Eur Heart J Cardiovasc Imaging. 2015;16(3):233–270.
   PubMed Web of Science ®Google Scholar
• Cai A, Wu Z, Xu L, et al. Association of anaemia and all-cause mortality in patients with ischaemic heart failure varies by renal function status. ESC Heart Fail. 2021;8(3):2270–2281.
   PubMed Web of Science ®Google Scholar
• Cai A, Qiu W, Xiao X, et al. All-Cause mortality in ischemic heart failure patients with functional mitral regurgitation undergoing percutaneous coronary intervention. Am J Cardiol. 2022;171:55–64.
   PubMed Web of Science ®Google Scholar
• Wang H, Chai K, Du M, et al. Prevalence and incidence of heart failure among Urban patients in China: a national population-based analysis. Circ Heart Fail. 2021;14(10):e008406.
   PubMed Web of Science ®Google Scholar
• Tromp J, Bamadhaj S, Cleland JGF, et al. Post-discharge prognosis of patients admitted to hospital for heart failure by world region, and national level of income and income disparity (REPORT-HF): a cohort study. Lancet Glob Health. 2020;8(3):e411–e422.
   PubMed Web of Science ®Google Scholar
• Gori M, Lam CSP, D’Elia E, et al. Integrating natriuretic peptides and diastolic dysfunction to predict adverse events in high-risk asymptomatic subjects. Eur J Prev Cardiol. 2021;28(9):937–945.
   PubMed Web of Science ®Google Scholar
• Krittanawong C, Rogers AJ, Johnson KW, et al. Integration of novel monitoring devices with machine learning technology for scalable cardiovascular management. Nat Rev Cardiol. 2021;18(2):75–91.
   PubMed Web of Science ®Google Scholar
• Hazelbag CM, Dushoff J, Dominic EM, et al. Calibration of individual-based models to epidemiological data: a systematic review. PLoS Comput Biol. 2020;16(5):e1007893.
   PubMed Web of Science ®Google Scholar