Advertisement

Critical Appraisal of Multivariable Prognostic Scores in Heart Failure: Development, Validation and Clinical Utility

  • Andrea PassantinoEmail author
  • Pietro Guida
  • Giuseppe Parisi
  • Massimo Iacoviello
  • Domenico Scrutinio
Chapter
Part of the Advances in Experimental Medicine and Biology book series (AEMB, volume 1067)

Abstract

Optimal management of heart failure requires accurate risk assessment. Many prognostic risk models have been proposed for patient with chronic and acute heart failure. Methodological critical issues are the data source, the outcome of interest, the choice of variables entering the model, the validation of the model in external population. Up to now, the proposed risk models can be a useful tool to help physician in the clinical decision-making. The availability of big data and of new methods of analysis may lead to developing new models in the future.

Keywords

Heart failure Risk model Prognosis 

References

  1. Aaronson KD, Schwartz JS, Chen TM et al (1997) Development and prospective validation of a clinical index to predict survival in ambulatory patients referred for cardiac transplant evaluation. Circulation 95(12):2660–2667CrossRefPubMedGoogle Scholar
  2. Abraham WT, Fonarow GC, Albert NM et al (2008) 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 52(5):347–356CrossRefPubMedGoogle Scholar
  3. Agostoni PG, Corrà U, Cattadori G et al (2013) Metabolic exercise test data combined with cardiac and kidney indexes, the MECKI score: a multiparametric approach to heart failure prognosis. Int J Cardiol 167(6):2710–2718CrossRefPubMedGoogle Scholar
  4. Alba AC, Agoritsas T, Jankowski M et al (2013) Risk prediction models for mortality in ambulatory patients with heart failure: a systematic review. Circ Heart Fail 6(5):881–889CrossRefPubMedGoogle Scholar
  5. Auble TE, Hsieh M, Gardner W et al (2005) A prediction rule to identify low-risk patients with heart failure. Acad Emerg Med 12(6):514–521CrossRefPubMedGoogle Scholar
  6. Barlera S, Tavazzi L, Franzosi MG et al (2013) Predictors of mortality in 6975 patients with chronic heart failure in the Gruppo Italiano per lo Studio della Streptochinasi nell’Infarto Miocardico-Heart Failure trial: proposal for a nomogram. Circ Heart Fail 6(1):31–39CrossRefPubMedGoogle Scholar
  7. Bilchick KC, Stukenborg GJ, Kamath S et al (2012) Prediction of mortality in clinical practice for medicare patients undergoing defibrillator implantation for primary prevention of sudden cardiac death. J Am Coll Cardiol 60(17):1647–1655CrossRefPubMedPubMedCentralGoogle Scholar
  8. Bilchick KC, Wang Y, Cheng A et al (2017) Seattle heartfFailure and proportional risk models predict benefit from implantable cardioverter-defibrillators. J Am Coll Cardiol 69(21):2606–2618CrossRefPubMedPubMedCentralGoogle Scholar
  9. Binanay C, Califf RM, Hasselblad V et al (2005) Evaluation study of congestive heart failure and pulmonary artery catheterization effectiveness: the ESCAPE trial. JAMA 294(13):1625–1633CrossRefPubMedGoogle Scholar
  10. Bradburn MJ, Clark TG, Love SB et al (2003) Survival analysis part II: multivariate data analysis – an introduction to concepts and methods. Br J Cancer 89(4):431–436CrossRefPubMedPubMedCentralGoogle Scholar
  11. Califf RM, Adams KF, McKenna WJ et al (1997) A randomized controlled trial of epoprostenol therapy for severe congestive heart failure: the Flolan International Randomized Survival Trial (FIRST). Am Heart J 134(1):44–54CrossRefPubMedGoogle Scholar
  12. Clark TG, Bradburn MJ, Love SB et al (2003a) Survival analysis part IV: further concepts and methods in survival analysis. Br J Cancer 89(5):781–786CrossRefPubMedPubMedCentralGoogle Scholar
  13. Clark TG, Bradburn MJ, Love SB et al (2003b) Survival analysis part I: basic concepts and first analyses. Br J Cancer 89(2):232–238CrossRefPubMedPubMedCentralGoogle Scholar
  14. Cleland JG, Chiswell K, Teerlink JR et al (2014) Predictors of postdischarge outcomes from information acquired shortly after admission for acute heart failure: a report from the Placebo-controlled randomized study of the selective A1 adenosine receptor antagonist rolofylline for patients hospitalized with acute decompensated heart failure and volume overload to assess treatment effect on congestion and renal function (PROTECT) study. Circ Heart Fail 7(1):76–87CrossRefPubMedGoogle Scholar
  15. Cuffe MS, Califf RM, Adams KF Jr, Outcomes of a Prospective Trial of Intravenous Milrinone for Exacerbations of Chronic Heart Failure (OPTIME-CHF) Investigators et al (2002) Short-term intravenous milrinone for acute exacerbation of chronic heart failure: a randomized controlled trial. JAMA 287(12):1541–1547CrossRefGoogle Scholar
  16. Felker GM, Leimberger JD, Califf RM et al (2004) Risk stratification after hospitalization for decompensated heart failure. J Card Fail 10(6):460–466CrossRefPubMedGoogle Scholar
  17. Fonarow GC, Adams KF Jr, Abraham W et al (2005) Risk stratification for in-hospital mortality in acutely decompensated heart failure classification and regression tree analysis. JAMA 293(5):572–580CrossRefPubMedGoogle Scholar
  18. Freitas P, Aguiar C, Ferreira A et al (2017) Comparative analysis of four scores to stratify patients with heart failure and reduced ejection fraction. Am J Cardiol 120(3):443–449CrossRefPubMedGoogle Scholar
  19. Giamouzis G, Kalogeropoulos A, Georgiopoulou V et al (2011) Hospitalization epidemic in patients with heart failure: risk factors, risk prediction, knowledge gaps, and future directions. J Card Fail 17(1):54–75CrossRefPubMedGoogle Scholar
  20. Harrelll FE Jr, Lee KL, Mark DB (1996) Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors. Stat Med 15(4):361–387CrossRefGoogle Scholar
  21. Hauptman PJ, Swindle J, Hussain Z et al (2008) Physician attitudes toward end-stage heart failure: a national survey. Am J Med 121(2):127–135CrossRefPubMedGoogle Scholar
  22. Hosmer DW, Lemeshow S (2000) The multiple regression model. In: Applied logistic regression, 2nd edn. Wiley, New YorkCrossRefGoogle Scholar
  23. Howlett JG (2013) Should we perform a heart failure risk score? Circ Heart Fail 6(1):4–5CrossRefPubMedGoogle Scholar
  24. Hsiao J, Motta M, Wyer P (2011) Validating the acute heart failure index for patients presenting to the emergency department with decompesated heart failure. Emerg Med J 29(12):e5CrossRefPubMedPubMedCentralGoogle Scholar
  25. Hsieh M, Auble TE, Yealy DM (2008) Validation of the acute heart failure index. Ann Emerg Med 51(1):37–44CrossRefPubMedGoogle Scholar
  26. Januzzi JL Jr, Sakhuja R, O’Donoghue M et al (2006) Utility of amino-terminal pro-brain natriuretic peptide testing for prediction of 1-year mortality in patients with dyspnea treated in the emergency department. Arch Intern Med 166(3):315–320CrossRefPubMedGoogle Scholar
  27. Januzzi JL Jr, Rehman S, Mueller T et al (2010) Importance of biomarkers for long-term mortality prediction in acutely dyspneic patients. Clin Chem 56(12):1814–1821CrossRefPubMedGoogle Scholar
  28. Koelling TM, Joseph S, Aaronson KD (2004) Heart failure survival score continues to predict clinical outcomes in patients with heart failure receiving beta-blockers. J Heart Lung Transplant 23(12):1414–1422CrossRefPubMedGoogle Scholar
  29. Krittanawong C, Zhang H, Wang Z et al (2017) Artificial Intelligence in precision cardiovascular medicine. J Am Coll Cardiol 69(21):2657–2664CrossRefPubMedGoogle Scholar
  30. LaValley MP (2008) Logistic regression. Circulation 117(18):2395–2399CrossRefPubMedGoogle Scholar
  31. Lee DS, Austin PC, Rouleau JL et al (2003) Predicting mortality among patients hospitalized for heart failure: derivation and validation of a clinical model. JAMA 290(19):2581–2587CrossRefPubMedGoogle Scholar
  32. Lee DS, Stitt A, Austin PC, Stukel TA et al (2012) Prediction of heart failure mortality in emergent care: a cohort study. Ann Intern Med 156(11):767–775, W-261, W-262CrossRefPubMedGoogle Scholar
  33. Levy WC, Kenchaiah S, Larson MG et al (2002) Long-term trends in the incidence of and survival with heart failure. N Engl J Med 347(18):1397–1402CrossRefPubMedGoogle Scholar
  34. Levy WC, Mozaffarian D, Linker DT et al (2006) The Seattle Heart Failure Model: prediction of survival in heart failure. Circulation 113(11):1424–1433CrossRefPubMedGoogle Scholar
  35. Lim E, Ali Z, Ali A et al (2005) Comparison of survival by allocation to medical therapy, surgery, or heart transplantation for ischemic advanced heart failure. J Heart Lung Transplant 24(8):983–989CrossRefPubMedGoogle Scholar
  36. Lund LH, Mancini DM (2010) Comparison across races of peak oxygen consumption and heart failure survival score for selection for cardiac transplantation. Am J Cardiol 105(10):1439–1444CrossRefPubMedGoogle Scholar
  37. Massie BM, O’Connor CM, Metra M et al (2010) Rolofylline, an adenosine A1-receptor antagonist, in acute heart failure. N Engl J Med 363(15):1419–1428CrossRefPubMedGoogle Scholar
  38. May HT, Horne BD, Levy WC et al (2007) Validation of the Seattle Heart Failure Model in a community-based heart failure population and enhancement by adding B-type natriuretic peptide. Am J Cardiol 100(4):697–670CrossRefPubMedGoogle Scholar
  39. Mehra MR, Canter CE, Hannan MM et al (2016) The 2016 International Society for Heart Lung Transplantation listing criteria for heart transplantation: A 10-year update. J Heart Lung Transplant 35(1):1–23CrossRefPubMedGoogle Scholar
  40. Moons KG, Kengne AP, Grobbee DE et al (2012a) Risk prediction models: II. External validation, model updating, and impact assessment. Heart 98(9):691–698CrossRefPubMedGoogle Scholar
  41. Moons KG, Kengne AP, Woodward M et al (2012b) Risk prediction models: I. Development, internal validation, and assessing the incremental value of a new (bio)marker. Heart 98(9):683–690CrossRefPubMedGoogle Scholar
  42. Nutter AL, Tanawuttiwat T, Silver MA (2010) Evaluation of 6 prognostic models used to calculate mortality rates in elderly heart failure patients with a fatal heart failure admission. Congest Heart Fail 16(5):196–201CrossRefPubMedGoogle Scholar
  43. O’Connor CM, Abraham WT, Albert NM et al (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):662–667CrossRefPubMedGoogle Scholar
  44. O’Connor CM, Whellan DJ, Lee KL et al (2009) HF-ACTION Investigators. Efficacy and safety of exercise training in patients with chronic heart failure: HF-ACTION randomized controlled trial. JAMA 301(14):1439–1450CrossRefPubMedPubMedCentralGoogle Scholar
  45. O’Connor CM, Hasselblad V, Mehta RH et al (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):872–878CrossRefPubMedGoogle Scholar
  46. O’Connor CM, Whellan DJ, Wojdyla D et al (2012) Factors related to morbidity and mortality in patients with chronic heart failure with systolic dysfunction: the HFACTION predictive risk score model. Circ Heart Fail 5(1):63–71CrossRefPubMedGoogle Scholar
  47. Ourwerkerk W, Voors AA, Zwinderman AH (2014) Factors influencing the predictive power of models for predicting mortality and/or heart failure hospitalization in patient with heart failure. J Am Coll Cardiol Heart Fail 2(5):429–436Google Scholar
  48. Packer M, O’Connor CM, Ghali JK et al (1996) Effect of amlodipine on morbidity and mortality in severe chronic heart failure. Prospective Randomized Amlodipine Survival Evaluation Study Group. N Engl J Med 335(15):1107–1114CrossRefPubMedGoogle Scholar
  49. Parikh MN, Lund LH, Goda A et al (2009) Usefulness of peak exercise oxygen consumption and the heart failure survival score to predict survival in patients >65 years of age with heart failure. Am J Cardiol 103(7):998–1002CrossRefPubMedGoogle Scholar
  50. Pencina MJ, D’Agostino RB Sr et al (2008) Evaluating the added predictive ability of a new marker: from area under the ROC curve to reclassification and beyond. Stat Med 27(2):157–172CrossRefPubMedGoogle Scholar
  51. Pencina MJ, D’Agostino RB Sr, Steyerberg EW (2011) Extensions of net reclassification improvement calculations to measure usefulness of new biomarkers. Stat Med 30(1):11–21CrossRefPubMedGoogle Scholar
  52. Peterson PN, Rumsfeld JS, Liang L et al (2010) 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 3(1):25–32CrossRefPubMedGoogle Scholar
  53. Pocock SJ, Ariti CA, McMurray JJ et al (2013) Predicting survival in heart failure: a risk score based on 39,372 patients from 30 studies. Eur Heart J 34(19):1404–1413CrossRefPubMedGoogle Scholar
  54. Ponikowski P, Voors AA, Anker SD et al (2016) 2016 ESC Guidelines for the diagnosis and treatment of acute and chronic heart failure: the Task Force for the diagnosis and treatment of acute and chronic heart failure of the European Society of Cardiology (ESC). Eur J Heart Fail 18(8):891–975CrossRefPubMedGoogle Scholar
  55. Rahimi K, Bennett D, Conrad N et al (2014) Risk prediction in patients with heart failure: a systematic review and analysis. JACC Heart Fail 2(5):440–446CrossRefPubMedGoogle Scholar
  56. Rao SR, Schoenfeld DA (2007) Survival methods. Circulation 115(1):109–113CrossRefPubMedGoogle Scholar
  57. Roger VL, Weston SA, Redfield MM et al (2004) Trends in heart failure incidence and survival in a community-based population. JAMA 292(3):344–350CrossRefPubMedGoogle Scholar
  58. Ross JS, Mulvey GK, Stauffer B et al (2008) Statistical models and patient predictors of readmission for heart failure: a systematic review. Arch Intern Med 168(13):1371–1386CrossRefPubMedGoogle Scholar
  59. Royston P, Moons KG, Altman DG et al (2009) Prognosis and prognostic research: developing a prognostic model. BMJ 338:b604CrossRefPubMedGoogle Scholar
  60. Salah K, Kok WE, Eurlings LW et al (2014) A novel discharge risk model for patients hospitalised for acute decompensated heart failure incorporating N-terminal pro-B-type natriuretic peptide levels: a European coLlaboration on AcuteecompeNsated Heart Failure: ELAN-HF Score. Heart 100(2):115–125CrossRefPubMedGoogle Scholar
  61. Scrutinio D, Ammirati E, Guida P et al (2013) Clinical utility of N-terminal pro-B-type natriuretic peptide for risk stratification of patients with acute decompensated heart failure. Derivation and validation of the ADHF/NT-proBNP risk score. Int J Cardiol 168(3):2120–2126CrossRefPubMedGoogle Scholar
  62. Tavazzi L, Maggioni AP, Marchioli R et al (2008) Effect of rosuvastatin in patients with chronic heart failure (the GISSI-HF trial): a randomised, double-blind, placebo-controlled trial. Lancet 372(9645):1231–1239CrossRefPubMedGoogle Scholar
  63. Yamokoski LM, Hasselblad V, Moser DK et al (2007) Prediction of rehospitalization and death in severe heart failure by physicians and nurses of the ESCAPE trial. J Card Fail 13(1):8–13CrossRefPubMedGoogle Scholar
  64. Yancy CW, Jessup M, Bozkurt B et al (2013) Practice guidelines 2013 ACCF/AHA guideline for the management of heart failure: a report of the American College of Cardiology Foundation/American Heart Association Task Force on Practice Guidelines. J Am Coll Cardiol 62(16):e147–e239CrossRefPubMedGoogle Scholar
  65. Zugck C, Krüger C, Kell R et al (2001) Risk stratification in middle-aged patients with congestive heart failure: prospective comparison of the Heart Failure Survival Score (HFSS) and a simplified two-variable model. Eur J Heart Fail 3(5):577–558CrossRefPubMedGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Andrea Passantino
    • 1
    Email author
  • Pietro Guida
    • 1
  • Giuseppe Parisi
    • 2
  • Massimo Iacoviello
    • 3
  • Domenico Scrutinio
    • 1
  1. 1.Division of Cardiology and Cardiac RehabilitationScientific Clinical Institutes Maugeri, I.R.C.C.S., Institute of Cassano delle MurgeBariItaly
  2. 2.School of Cardiology, Aldo Moro University of BariBariItaly
  3. 3.Cardiology Unit, Cardiothoracic DepartmentPoliclinic University HospitalBariItaly

Personalised recommendations