Current Geriatrics Reports

, Volume 4, Issue 4, pp 338–346 | Cite as

A Decision-Making Framework for Objective Risk Assessment in Older Adults with Severe Symptomatic Aortic Stenosis

Decision-Making Framework in Severe AS
  • Ashok KrishnaswamiEmail author
  • Daniel E. Forman
  • Mathew S. Maurer
  • Sei J. Lee
Cardiovascular Disease in the Elderly (DE Forman, Section Editor)
Part of the following topical collections:
  1. Topical Collection on Cardiovascular Disease in the Elderly


The increasing prevalence of severe symptomatic aortic stenosis (AS) in older adults is now considered a major public health concern. Since medical therapy has not been shown to improve prognosis, surgical aortic valve replacement (SAVR) and transcatheter aortic valve replacement (TAVR) are the best options currently available, yet not all patients benefit. Objective assessment of risk versus benefit for SAVR and TAVR is essential. Clinical prediction models (CPM) have been created to augment subjective physician estimates of risk and have been shown to improve the accuracy of risk predictions. This manuscript presents the rationale for a framework of objective evaluation of risk assessment and decision making by linking clinically relevant CPM (life expectancy, Society of Thoracic Surgery, and TAVR risk calculators) with two additional concepts of lag time to benefit and competing risks that are relatively novel to the clinical arena. We believe that such aggregate framework can improve the assessment of risk and benefit and thereby facilitate a more informed and standardized shared decision-making process in the care of older adults with severe symptomatic AS.


Age Older adult Risk stratification Clinical prediction models Life expectancy Standard aortic valve replacement Transcatheter aortic valve replacement Lag time to benefit Competing risks Shared decision making 



Dr. Forman is supported in part by the NIA grant P30 AG024827 and VA Office of Rehabilitation Research and Development grant F0834-R. Other authors were not funded for this project.

Compliance with Ethics Guidelines

Conflict of Interest

Ashok Krishnaswami, Daniel E. Forman, Mathew S. Maurer, and Sei J. Lee declare that they have no conflict of interest.

Human and Animal Rights and Informed Consent

This article does not contain any studies with human or animal subjects performed by any of the authors.


Papers of particular interest, published recently, have been highlighted as: • Of importance •• Of major importance

  1. 1.
    Nkomo VT, Gardin JM, Skelton TN, Gottdiener JS, Scott CG, Enriquez-Sarano M. Burden of valvular heart diseases: a population-based study. Lancet. 2006;368(9540):1005–11.CrossRefPubMedGoogle Scholar
  2. 2.
    Iung B, Vahanian A. Epidemiology of valvular heart disease in the adult. Nat Rev Cardiol. 2011;8(3):162–72.CrossRefPubMedGoogle Scholar
  3. 3.
    Iung B. Management of the elderly patient with aortic stenosis. Heart. 2008;94(4):519–24.CrossRefPubMedGoogle Scholar
  4. 4.•
    Osnabrugge RL, Mylotte D, Head SJ, Van Mieghem NM, Nkomo VT, LeReun CM, et al. Aortic stenosis in the elderly: disease prevalence and number of candidates for transcatheter aortic valve replacement: a meta-analysis and modeling study. J Am Coll Cardiol. 2013;62(11):1002–12. This is an important article that address the global burden of aortic stenosis.CrossRefPubMedGoogle Scholar
  5. 5.
    Bach DS, Siao D, Girard SE, Duvernoy C, McCallister Jr BD, Gualano SK. Evaluation of patients with severe symptomatic aortic stenosis who do not undergo aortic valve replacement: the potential role of subjectively overestimated operative risk. Circ Cardiovasc Qual Outcomes. 2009;2(6):533–9.CrossRefPubMedGoogle Scholar
  6. 6.
    Iung B, Cachier A, Baron G, Messika-Zeitoun D, Delahaye F, Tornos P, et al. Decision-making in elderly patients with severe aortic stenosis: why are so many denied surgery? Eur Heart J. 2005;26(24):2714–20.CrossRefPubMedGoogle Scholar
  7. 7.
    Kapadia SR, Goel SS, Svensson L, Roselli E, Savage RM, Wallace L, et al. Characterization and outcome of patients with severe symptomatic aortic stenosis referred for percutaneous aortic valve replacement. J Thorac Cardiovasc Surg. 2009;137(6):1430–5.CrossRefPubMedGoogle Scholar
  8. 8.•
    Jain R, Duval S, Adabag S. How accurate is the eyeball test?: A comparison of physician’s subjective assessment versus statistical methods in estimating mortality risk after cardiac surgery. Circ Cardiovasc Qual Outcomes. 2014;7(1):151–6. An important study that attempts to address the question of whether subjective evaluation is comparable to clinical prediction models.CrossRefPubMedGoogle Scholar
  9. 9.
    Pons JM, Borras JM, Espinas JA, Moreno V, Cardona M, Granados A. Subjective versus statistical model assessment of mortality risk in open heart surgical procedures. Ann Thorac Surg. 1999;67(3):635–40.CrossRefPubMedGoogle Scholar
  10. 10.
    Lee SJ, Lindquist K, Segal MR, Covinsky KE. Development and validation of a prognostic index for 4-year mortality in older adults. JAMA. 2006;295(7):801–8.CrossRefPubMedGoogle Scholar
  11. 11.
    Lee SJ, Boscardin WJ, Kirby KA, Covinsky KE. Individualizing life expectancy estimates for older adults using the Gompertz Law of Human Mortality. PLoS One. 2014;9(9):e108540.PubMedCentralCrossRefPubMedGoogle Scholar
  12. 12.
    Dewey TM, Brown D, Ryan WH, Herbert MA, Prince SL, Mack MJ. Reliability of risk algorithms in predicting early and late operative outcomes in high-risk patients undergoing aortic valve replacement. J Thorac Cardiovasc Surg. 2008;135(1):180–7.CrossRefPubMedGoogle Scholar
  13. 13.••
    Afilalo J, Alexander KP, Mack MJ, Maurer MS, Green P, Allen LA, et al. Frailty assessment in the cardiovascular care of older adults. J Am Coll Cardiol. 2013. This article provides a comprehensive review of frailty within the framework of cardiovascular disease. Google Scholar
  14. 14.
    Fried LP, Tangen CM, Walston J, Newman AB, Hirsch C, Gottdiener J, et al. Frailty in older adults: evidence for a phenotype. J Gerontol A Biol Sci Med Sci. 2001;56(3):M146–56.CrossRefPubMedGoogle Scholar
  15. 15.
    Bell SP, Saraf A. Risk stratification in very old adults: how to best gauge risk as the basis of management choices for patients aged over 80. Prog Cardiovasc Dis. 2014;57(2):197–203.PubMedCentralCrossRefPubMedGoogle Scholar
  16. 16.
    Rockwood K, Song X, MacKnight C, Bergman H, Hogan DB, McDowell I, et al. A global clinical measure of fitness and frailty in elderly people. CMAJ. 2005;173(5):489–95.PubMedCentralCrossRefPubMedGoogle Scholar
  17. 17.
    Rockwood K, Mitnitski A, Song X, Steen B, Skoog I. Long-term risks of death and institutionalization of elderly people in relation to deficit accumulation at age 70. J Am Geriatr Soc. 2006;54(6):975–9.CrossRefPubMedGoogle Scholar
  18. 18.
    Song X, Mitnitski A, Rockwood K. Prevalence and 10-year outcomes of frailty in older adults in relation to deficit accumulation. J Am Geriatr Soc. 2010;58(4):681–7.CrossRefPubMedGoogle Scholar
  19. 19.•
    Lee SJ, Leipzig RM, Walter LC. Incorporating lag time to benefit into prevention decisions for older adults. JAMA. 2013;310(24):2609–10. This is a short viewpoint on how accounting for life expectancy could improve decisions in preventive cardiology.PubMedCentralCrossRefPubMedGoogle Scholar
  20. 20.•
    Berry SD, Ngo L, Samelson EJ, Kiel DP. Competing risk of death: an important consideration in studies of older adults. J Am Geriatr Soc. 2010;58(4):783–7. This article provides a framework of the importance of understanding competing risks in the geriatric population.PubMedCentralCrossRefPubMedGoogle Scholar
  21. 21.••
    Lin GA, Fagerlin A. Shared decision making: state of the science. Circ Cardiovasc Qual Outcomes. 2014;7(2):328–34. An important article that brings to light the importance and science of sharted decision making.CrossRefPubMedGoogle Scholar
  22. 22.••
    Spatz ES, Spertus JA. Shared decision making: a path toward improved patient-centered outcomes. Circ Cardiovasc Qual Outcomes. 2012;5(6):e75–7. An important article that brings to light the importance and science of sharted decision making.CrossRefPubMedGoogle Scholar
  23. 23.••
    Ting HH, Brito JP, Montori VM. Shared decision making: science and action. Circ Cardiovasc Qual Outcomes. 2014;7(2):323–7. An important article that brings to light the importance and science of sharted decision making.CrossRefPubMedGoogle Scholar
  24. 24.••
    Hess EP, Coylewright M, Frosch DL, Shah ND. Implementation of shared decision making in cardiovascular care: past, present, and future. Circ Cardiovasc Qual Outcomes. 2014;7(5):797–803. An important article that brings to light the importance and science of sharted decision making.CrossRefPubMedGoogle Scholar
  25. 25.
    Ross Jr J, Braunwald E. Aortic stenosis. Circulation. 1968;38(1 Suppl):61–7.PubMedGoogle Scholar
  26. 26.
    Varadarajan P, Kapoor N, Bansal RC, Pai RG. Clinical profile and natural history of 453 nonsurgically managed patients with severe aortic stenosis. Ann Thorac Surg. 2006;82(6):2111–5.CrossRefPubMedGoogle Scholar
  27. 27.
    Leon MB, Smith CR, Mack M, Miller DC, Moses JW, Svensson LG, et al. Transcatheter aortic-valve implantation for aortic stenosis in patients who cannot undergo surgery. N Engl J Med. 2010;363(17):1597–607.CrossRefPubMedGoogle Scholar
  28. 28.
    ePrognosis. Estimating prognosis for elders []
  29. 29.
    Piette JD, Kerr EA. The impact of comorbid chronic conditions on diabetes care. Diabetes Care. 2006;29(3):725–31.CrossRefPubMedGoogle Scholar
  30. 30.
    Altman DG, Vergouwe Y, Royston P, Moons KG. Prognosis and prognostic research: validating a prognostic model. BMJ. 2009;338:b605.CrossRefPubMedGoogle Scholar
  31. 31.
    TVT Registry manuscripts in progress []
  32. 32.
    Lindman BR, Alexander KP, O’Gara PT, Afilalo J. Futility, benefit, and transcatheter aortic valve replacement. J Am Coll Cardiol Intv. 2014;7(7):707–16.CrossRefGoogle Scholar
  33. 33.••
    Afilalo J, Mottillo S, Eisenberg MJ, Alexander KP, Noiseux N, Perrault LP, et al. Addition of frailty and disability to cardiac surgery risk scores identifies elderly patients at high risk of mortality or major morbidity. Circ Cardiovasc Qual Outcomes. 2012;5(2):222–8. An important study comparing the discriminative ability of frailty markers cardiac surgery risk scores.CrossRefPubMedGoogle Scholar
  34. 34.
    Sepehri A, Beggs T, Hassan A, Rigatto C, Shaw-Daigle C, Tangri N, et al. The impact of frailty on outcomes after cardiac surgery: a systematic review. J Thorac Cardiovasc Surg. 2014;148(6):3110–7.CrossRefPubMedGoogle Scholar
  35. 35.•
    Green P, Woglom AE, Genereux P, Daneault B, Paradis JM, Schnell S, et al. The impact of frailty status on survival after transcatheter aortic valve replacement in older adults with severe aortic stenosis: a single-center experience. J Am Coll Cardiol Intv. 2012;5(9):974–81. Addressed the importance of frailty assessment prior to TAVR.CrossRefGoogle Scholar
  36. 36.
    Bernstein AD, Parsonnet V. Bedside estimation of risk as an aid for decision-making in cardiac surgery. Ann Thorac Surg. 2000;69(3):823–8.CrossRefPubMedGoogle Scholar
  37. 37.
    Makkar RR, Fontana GP, Jilaihawi H, Kapadia S, Pichard AD, Douglas PS, et al. Transcatheter aortic-valve replacement for inoperable severe aortic stenosis. N Engl J Med. 2012;366(18):1696–704.CrossRefPubMedGoogle Scholar
  38. 38.
    Bendayan M, Bibas L, Levi M, Mullie L, Forman DE, Afilalo J. Therapeutic interventions for frail elderly patients: part II. Ongoing and unpublished randomized trials. Prog Cardiovasc Dis. 2014;57(2):144–51.CrossRefPubMedGoogle Scholar
  39. 39.
    Bibas L, Levi M, Bendayan M, Mullie L, Forman DE, Afilalo J. Therapeutic interventions for frail elderly patients: part I. Published randomized trials. Prog Cardiovasc Dis. 2014;57(2):134–43.CrossRefPubMedGoogle Scholar
  40. 40.
    Fried TR, McGraw S, Agostini JV, Tinetti ME. Views of older persons with multiple morbidities on competing outcomes and clinical decision-making. J Am Geriatr Soc. 2008;56(10):1839–44.PubMedCentralCrossRefPubMedGoogle Scholar
  41. 41.
    Gooley TA, Leisenring W, Crowley J, Storer BE. Estimation of failure probabilities in the presence of competing risks: new representations of old estimators. Stat Med. 1999;18(6):695–706.CrossRefPubMedGoogle Scholar
  42. 42.
    Southern DA, Faris PD, Brant R, Galbraith PD, Norris CM, Knudtson ML, et al. Kaplan-Meier methods yielded misleading results in competing risk scenarios. J Clin Epidemiol. 2006;59(10):1110–4.CrossRefPubMedGoogle Scholar
  43. 43.
    Holmes Jr DR, Brennan JM, Rumsfeld JS, Dai D, O’Brien SM, Vemulapalli S, et al. Clinical outcomes at 1 year following transcatheter aortic valve replacement. JAMA. 2015;313(10):1019–28.CrossRefPubMedGoogle Scholar
  44. 44.
    Kent DM, Alsheikh-Ali A, Hayward RA. Competing risk and heterogeneity of treatment effect in clinical trials. Trials. 2008;9:30.PubMedCentralCrossRefPubMedGoogle Scholar
  45. 45.
    Sepucha KR. Shared decision-making and patient decision AIDS: is it time? Circ Cardiovasc Qual Outcomes. 2012;5(3):247–8.CrossRefPubMedGoogle Scholar
  46. 46.
    Hess EP, Knoedler MA, Shah ND, Kline JA, Breslin M, Branda ME, et al. The chest pain choice decision aid: a randomized trial. Circ Cardiovasc Qual Outcomes. 2012;5(3):251–9.CrossRefPubMedGoogle Scholar
  47. 47.
    Schwalm JD, Stacey D, Pericak D, Natarajan MK. Radial artery versus femoral artery access options in coronary angiogram procedures: randomized controlled trial of a patient-decision aid. Circ Cardiovasc Qual Outcomes. 2012;5(3):260–6.CrossRefPubMedGoogle Scholar
  48. 48.
    Stacey D, Bennett CL, Barry MJ, Col NF, Eden KB, Holmes-Rovner M, et al. Decision aids for people facing health treatment or screening decisions. Cochrane Database Syst Rev. 2011;10:CD001431.PubMedGoogle Scholar
  49. 49.
    Olesen JB, Torp-Pedersen C, Hansen ML, Lip GY. The value of the CHA2DS2-VASc score for refining stroke risk stratification in patients with atrial fibrillation with a CHADS2 score 0–1: a nationwide cohort study. Thromb Haemost. 2012;107(6):1172–9.CrossRefPubMedGoogle Scholar
  50. 50.
    Menezes AR, Lavie CJ, Forman DE, Arena R, Milani RV, Franklin BA. Cardiac rehabilitation in the elderly. Prog Cardiovasc Dis. 2014;57(2):152–9.CrossRefPubMedGoogle Scholar
  51. 51.
    Forman DE, Sanderson BK, Josephson RA, Raikhelkar J, Bittner V, American College of Cardiology’s Prevention of Cardiovascular Disease S. Heart failure as a newly approved diagnosis for cardiac rehabilitation: challenges and opportunities. J Am Coll Cardiol. 2015;65(24):2652–9.CrossRefPubMedGoogle Scholar
  52. 52.••
    Wessler BS, Lai YhL, Kramer W, Cangelosi M, Raman G, Lutz JS, Kent DM. Clinical prediction models for cardiovascular disease: tufts predictive analytics and comparative effectiveness clinical prediction model database. Circ Cardiovasc Qual Outcomes. 2015. An important study that bringsto light the abundance of currently available clinical prediction models and compares the discrimination between them. Google Scholar
  53. 53.••
    Salisbury AC, Spertus JA. Realizing the Potential of clinical risk prediction models: where are we now and what needs to change to better personalize delivery of care? Circ Cardiovasc Qual Outcomes. 2015. An editorial of reference 53 with suggestions of how clinical prediction models can improve on its uptake by the cardiology community. Google Scholar
  54. 54.
    Moons KG, Royston P, Vergouwe Y, Grobbee DE, Altman DG. Prognosis and prognostic research: what, why, and how? BMJ. 2009;338:b375.CrossRefPubMedGoogle Scholar
  55. 55.
    Hemingway H, Croft P, Perel P, Hayden JA, Abrams K, Timmis A, et al. Prognosis research strategy (PROGRESS) 1: a framework for researching clinical outcomes. BMJ. 2013;346:e5595.PubMedCentralCrossRefPubMedGoogle Scholar
  56. 56.
    Steyerberg EW. Clinical prediction models. Springer. 2009.Google Scholar
  57. 57.
    Steyerberg EW, Moons KG, van der Windt DA, Hayden JA, Perel P, Schroter S, et al. Prognosis research strategy (PROGRESS) 3: prognostic model research. PLoS Med. 2013;10(2):e1001381.PubMedCentralCrossRefPubMedGoogle Scholar
  58. 58.
    Vittinghoff E, Glidden DV, Shiboski SC, McCulloch CE. Regression methods in biostatistics: linear, logistic, survival, and repeated measures models. Second Edition, Springer. 2012.Google Scholar
  59. 59.
    Moons KG, Altman DG, Vergouwe Y, Royston P. Prognosis and prognostic research: application and impact of prognostic models in clinical practice. BMJ. 2009;338:b606.CrossRefPubMedGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  • Ashok Krishnaswami
    • 1
    Email author
  • Daniel E. Forman
    • 2
    • 3
  • Mathew S. Maurer
    • 4
  • Sei J. Lee
    • 5
  1. 1.Division of CardiologyKaiser Permanente San Jose Medical CenterSan JoseUSA
  2. 2.Division of Geriatric CardiologyUniversity of PittsburghPittsburghUSA
  3. 3.Geriatric Research, Education, and Clinical CenterVA Pittsburgh Healthcare SystemPittsburghUSA
  4. 4.Division of CardiologyColumbia University Medical CenterNew YorkUSA
  5. 5.Division of GeriatricsUniversity of CaliforniaSan FranciscoUSA

Personalised recommendations