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Predicting survival in heart failure: a risk score based on machine-learning and change point algorithm

A Correction to this article was published on 29 November 2021

This article has been updated

Abstract

Objective

Machine learning (ML) algorithm can improve risk prediction because ML can select features and segment continuous variables effectively unbiased. We generated a risk score model for mortality with ML algorithms in East-Asian patients with heart failure (HF).

Methods

From the Korean Acute Heart Failure (KorAHF) registry, we used the data of 3683 patients with 27 continuous and 44 categorical variables. Grouped Lasso algorithm was used for the feature selection, and a novel continuous variable segmentation algorithm which is based on change-point analysis was developed for effectively segmenting the ranges of the continuous variables. Then, a risk score was assigned to each feature reflecting nonlinear relationship between features and survival times, and an integer score of maximum 100 was calculated for each patient.

Results

During 3-year follow-up time, 32.8% patients died. Using grouped Lasso, we identified 15 highly significant independent clinical features. The calculated risk score of each patient ranged between 1 and 71 points with a median of 36 (interquartile range: 27–45). The 3-year survival differed according to the quintiles of the risk score, being 80% and 17% in the 1st and 5th quintile, respectively. In addition, ML risk score had higher AUCs than MAGGIC-HF score to predict 1-year mortality (0.751 vs. 0.711, P < 0.001).

Conclusions

In East-Asian patients with HF, a novel risk score model based on ML and the new continuous variable segmentation algorithm performs better for mortality prediction than conventional prediction models.

Clinical Trial Registration

Unique identifier: INCT01389843 https://clinicaltrials.gov/ct2/show/NCT01389843.

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References

  1. Lee SE, Lee HY, Cho HJ, Choe WS, Kim H, Choi JO, Jeon ES, Kim MS, Kim JJ, Hwang KK, Chae SC, Baek SH, Kang SM, Choi DJ, Yoo BS, Kim KH, Park HY, Cho MC, Oh BH (2017) Clinical characteristics and outcome of acute heart failure in Korea: results from the Korean Acute Heart Failure Registry (KorAHF). Korean Circ J 47(3):341–353. https://doi.org/10.4070/kcj.2016.0419

    Article  PubMed  PubMed Central  Google Scholar 

  2. Levy WC, Mozaffarian D, Linker DT, Sutradhar SC, Anker SD, Cropp AB, Anand I, Maggioni A, Burton P, Sullivan MD, Pitt B, Poole-Wilson PA, Mann DL, Packer M (2006) The Seattle Heart Failure Model: prediction of survival in heart failure. Circulation 113(11):1424–1433. https://doi.org/10.1161/CIRCULATIONAHA.105.584102

    Article  PubMed  Google Scholar 

  3. Pocock SJ, Wang D, Pfeffer MA, Yusuf S, McMurray JJ, Swedberg KB, Ostergren J, Michelson EL, Pieper KS, Granger CB (2006) Predictors of mortality and morbidity in patients with chronic heart failure. Eur Heart J 27(1):65–75. https://doi.org/10.1093/eurheartj/ehi555

    Article  PubMed  Google Scholar 

  4. O’Connor CM, Hasselblad V, Mehta RH, Tasissa G, Califf RM, Fiuzat M, Rogers JG, Leier CV, Stevenson LW (2010) Triage after hospitalization with advanced heart failure: the ESCAPE (evaluation study of congestive heart failure and pulmonary artery catheterization effectiveness) risk model and discharge score. J Am Coll Cardiol 55(9):872–878. https://doi.org/10.1016/j.jacc.2009.08.083

    Article  PubMed  Google Scholar 

  5. O’Connor CM, Abraham WT, Albert NM, Clare R, Gattis Stough W, Gheorghiade M, Greenberg BH, Yancy CW, Young JB, Fonarow GC (2008) Predictors of mortality after discharge in patients hospitalized with heart failure: an analysis from the Organized Program to Initiate Lifesaving Treatment in Hospitalized Patients with Heart Failure (OPTIMIZE-HF). Am Heart J 156(4):662–673. https://doi.org/10.1016/j.ahj.2008.04.030

    Article  PubMed  Google Scholar 

  6. Lee SE, Cho HJ, Lee HY, Yang HM, Choi JO, Jeon ES, Kim MS, Kim JJ, Hwang KK, Chae SC, Seo SM, Baek SH, Kang SM, Oh IY, Choi DJ, Yoo BS, Ahn Y, Park HY, Cho MC, Oh BH (2014) A multicentre cohort study of acute heart failure syndromes in Korea: rationale, design, and interim observations of the Korean Acute Heart Failure (KorAHF) registry. Eur J Heart Fail 16(6):700–708. https://doi.org/10.1002/ejhf.91

    Article  PubMed  Google Scholar 

  7. Pocock SJ, Ariti CA, McMurray JJ, Maggioni A, Kober L, Squire IB, Swedberg K, Dobson J, Poppe KK, Whalley GA, Doughty RN, Meta-Analysis Global Group in Chronic Heart F (2013) Predicting survival in heart failure: a risk score based on 39,372 patients from 30 studies. Eur Heart J 34(19):1404–1413. https://doi.org/10.1093/eurheartj/ehs337

    Article  PubMed  Google Scholar 

  8. Sartipy U, Dahlstrom U, Edner M, Lund LH (2014) 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 16(2):173–179. https://doi.org/10.1111/ejhf.32

    Article  PubMed  Google Scholar 

  9. Khanam SS, Choi E, Son JW, Lee JW, Youn YJ, Yoon J, Lee SH, Kim JY, Ahn SG, Ahn MS, Kang SM, Baek SH, Jeon ES, Kim JJ, Cho MC, Chae SC, Oh BH, Choi DJ, Yoo BS (2018) Validation of the MAGGIC (Meta-Analysis Global Group in Chronic Heart Failure) heart failure risk score and the effect of adding natriuretic peptide for predicting mortality after discharge in hospitalized patients with heart failure. PLoS ONE 13(11):e0206380. https://doi.org/10.1371/journal.pone.0206380

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  10. Johnson KW, Torres Soto J, Glicksberg BS, Shameer K, Miotto R, Ali M, Ashley E, Dudley JT (2018) Artificial Intelligence in Cardiology. J Am Coll Cardiol 71(23):2668–2679. https://doi.org/10.1016/j.jacc.2018.03.521

    Article  PubMed  Google Scholar 

  11. Shortliffe EH, Sepulveda MJ (2018) Clinical decision support in the era of artificial intelligence. JAMA 320(21):2199–2200. https://doi.org/10.1001/jama.2018.17163

    Article  PubMed  Google Scholar 

  12. Bühlmann P, Van De Geer S (2011) Statistics for high-dimensional data: methods, theory and applications. Springer Science & Business Media, Berlin

    Book  Google Scholar 

  13. Hanushek EA, Jackson JE (2013) Statistical methods for social scientists. Academic Press, Cambridge

    Google Scholar 

  14. Yuan M, Lin Y (2006) Model selection and estimation in regression with grouped variables. J R Stat Soc Series B (Stat Methodol) 68(1):49–67

    Article  Google Scholar 

  15. Breheny P, Huang J (2009) Penalized methods for bi-level variable selection. Stat Interface 2(3):369

    Article  Google Scholar 

  16. Chen J, Gupta A (2000) Parametric statistical change point analysis (Oberwolfach seminars). Birkhäuser, Basel

    Book  Google Scholar 

  17. Blanche P, Dartigues JF, Jacqmin-Gadda H (2013) Estimating and comparing time-dependent areas under receiver operating characteristic curves for censored event times with competing risks. Stat Med 32(30):5381–5397

    Article  Google Scholar 

  18. Pepe MS, Longton G, Janes H (2009) Estimation and comparison of receiver operating characteristic curves. Stand J 9(1):1–16

    Google Scholar 

  19. Venkatraman E (2000) A permutation test to compare receiver operating characteristic curves. Biometrics 56(4):1134–1138

    CAS  Article  Google Scholar 

  20. Venkatraman ES, Begg CB (1996) A distribution-free procedure for comparing receiver operating characteristic curves from a paired experiment. Biometrika 83(4):835–848

    Article  Google Scholar 

  21. Robin X, Turck N, Hainard A, Tiberti N, Lisacek F, Sanchez J-C, Müller M (2011) pROC: an open-source package for R and S+ to analyze and compare ROC curves. BMC Bioinform 12(1):1–8

    Article  Google Scholar 

  22. Adler ED, Voors AA, Klein L, Macheret F, Braun OO, Urey MA, Zhu W, Sama I, Tadel M, Campagnari C (2020) Improving risk prediction in heart failure using machine learning. Eur J Heart Fail 22(1):139–147

    Article  Google Scholar 

  23. Park JJ, Park JB, Park JH, Cho GY (2018) Global longitudinal strain to predict mortality in patients with acute heart failure. J Am Coll Cardiol 71(18):1947–1957. https://doi.org/10.1016/j.jacc.2018.02.064

    Article  PubMed  Google Scholar 

  24. Kang SH, Park JJ, Choi DJ, Yoon CH, Oh IY, Kang SM, Yoo BS, Jeon ES, Kim JJ, Cho MC, Chae SC, Ryu KH, Oh BH, Kor HFR (2015) Prognostic value of NT-proBNP in heart failure with preserved versus reduced EF. Heart 101(23):1881–1888. https://doi.org/10.1136/heartjnl-2015-307782

    CAS  Article  PubMed  Google Scholar 

  25. Hastie T, Tibshirani R, Friedman J, Franklin J (2005) The elements of statistical learning: data mining, inference and prediction. Math Intell 27(2):83–85

    Google Scholar 

  26. Chen J, Gupta AK (2011) Parametric statistical change point analysis: with applications to genetics, medicine, and finance. Springer Science & Business Media, Berlin

    Google Scholar 

  27. Géron A (2019) Hands-on machine learning with Scikit-learn, Keras, and tensorflow: concepts, tools, and techniques to build intelligent systems. O’Reilly Media, Newton

    Google Scholar 

  28. Zaya M, Phan A, Schwarz ER (2012) Predictors of re-hospitalization in patients with chronic heart failure. World J Cardiol 4(2):23–30. https://doi.org/10.4330/wjc.v4.i2.23

    Article  PubMed  PubMed Central  Google Scholar 

  29. Vivo RP, Krim SR, Liang L, Neely M, Hernandez AF, Eapen ZJ, Peterson ED, Bhatt DL, Heidenreich PA, Yancy CW, Fonarow GC (2014) Short- and long-term rehospitalization and mortality for heart failure in 4 racial/ethnic populations. J Am Heart Assoc 3(5):e001134. https://doi.org/10.1161/JAHA.114.001134

    Article  PubMed  PubMed Central  Google Scholar 

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Funding

This work was supported by Research of Korea Centers for Disease Control and Prevention [2010-E63003-00, 2011-E63002-00, 2012-E63005-00, 2013-E63003-00, 2013-E63003-01, 2013-E63003-02, and 2016-ER6303-00]. This work was also supported by the National Research Foundation of Korea [2017R1A5A1015626, 2018R1A2A3075511, 2020R1I1A1A01073151].

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Correspondence to Woong Kook or Dong-Ju Choi.

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The original online version of this article was revised: The Funding section has been revised.

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Kim, W., Park, J.J., Lee, HY. et al. Predicting survival in heart failure: a risk score based on machine-learning and change point algorithm. Clin Res Cardiol 110, 1321–1333 (2021). https://doi.org/10.1007/s00392-021-01870-7

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  • DOI: https://doi.org/10.1007/s00392-021-01870-7

Keywords

  • Heart failure
  • Machine learning
  • Grouped Lasso
  • Prognostic model
  • Mortality
  • Change-point analysis