Statistical Modeling Adoption on the Late-Life Function and Disability Instrument Compared to Kansas City Cardiomyopathy Questionnaire

  • Yunkai LiuEmail author
  • A. Kate MacPhedran
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10038)


Transcatheter aortic valve replacement (TAVR) has become a more utilized procedure to perform on patients deemed too frail to handle the demands of a standard open heart approach. How to determine frailty in cardiac patients, particularly TAVR candidates, has been difficult to objectively quantify. The purpose of this research was to statistically measure which outcome tool most accurately depicted frailty in patients who underwent TAVR. Our study was performed based on the comparison between two approaches: the Kansas City Cardiomyopathy Questionnaire (KCCQ), which is the current national assessment standard conducted, and the Fried scale, which tests five frailty domains: gait speed, grip strength, low physical activity, exhaustion, and weight loss. Each domain of the Fried scale was explored and compared alongside the KCCQ with that frail/not frail to the TAVR patients with complications and deaths. Low physical activity was the strongest single-frailty domain predictor. Three statistical models – Logistic Regression, Support Vector Machines (SVM), and Artificial Neural Network (ANN), were used to build classification systems to predict complication conditions. Comparing static numbers, such as Sensitivity, Specificity and Area under Curve (AUC), it is believed that composed models based on the five domains of the Fried scale were able to demonstrate more accurate results than the traditional KCCQ approach. Both SVM and ANN showed significant performance, but further research is necessary to confirm specificity for the Fried scale with the TAVR population.


Late-Life Function and Disability Instrument Kansas City Cardiomyopathy Questionnaire Fried’s frailty phenotype Logistic Regression Artificial Neural Networks Model Support Vector Machines 


  1. 1.
    Alfirevic, A., Mehta, A.R., Svensson, L.G.: Trans catheter aortic valve replacement. Anesthesiol. Clin. 31, 355–381 (2013)CrossRefGoogle Scholar
  2. 2.
    Puls, M., Sobisiak, B., Bleckmann, A., et al.: Impact of frailty on short- and long-term morbidity and mortality after transcatheter aortic valve implantation: risk assessment by Katz Index of activities of daily living. EuroIntervention 10, 609–619 (2014)CrossRefGoogle Scholar
  3. 3.
    Afilalo, J., Alexander, K., Mack, M., et al.: Frailty assessment in the cardiovascular care of older adults. J. Am. Coll. Cardiol. 63, 747–762 (2014)CrossRefGoogle Scholar
  4. 4.
    Bagnall, N., Faiz, O., Darzi, A., et al.: What is the utility of preoperative frailty assessment for risk stratification in cardiac surgery? Interact. Cardiovasc. Thoarc. Surg. 14, 398–402 (2013)CrossRefGoogle Scholar
  5. 5.
    de Vries, N.M., Staal, J.B., van Ravensberg, C.D., et al.: Outcome instruments to measure frailty: a systematic review. Ageing Res. Rev. 10, 104–114 (2010)CrossRefGoogle Scholar
  6. 6.
    Mack, M.: Frailty and aortic valve disease. J. Cardiovasc. Surg. 145, S7–10 (2013)Google Scholar
  7. 7.
    Makary, M., Segev, D., Pronovost, P., et al.: Frailty as a predictor of surgical outcomes in older patients. J. Am. Coll. Surg. 210, 901–908 (2010)CrossRefGoogle Scholar
  8. 8.
    Garin, O., Ferrer, M., Pont, A., et al.: Disease-specific health-related quality of life questionnaires for heart failure: a systematic review with meta-analyses. Qual. Life Res. 18, 71–85 (2009)CrossRefGoogle Scholar
  9. 9.
    Green, C., Porter, C., Bresnaham, D., et al.: Development and evaluation of the Kansas City Cardiomyopathy Questionnaire: a new health status measure for heart failure. J. Am. Coll. Cardiol. 35, 1245–1255 (2000)CrossRefGoogle Scholar
  10. 10.
    Reynolds, M., Magnuson, E., Wang, K., et al.: Health-related quality of life after transcatheter or surgical aortic valve replacement in high-risk patients with severe aortic stenosis: results from the PARTNER (Placement of AoRTic TraNscathetER Valve) Trial (cohort A). J. Am. Coll. Cardiol. 60, 548–558 (2012)CrossRefGoogle Scholar
  11. 11.
    Spertus, J., Peterson, E., Conard, M.W., et al.: Monitoring clinical changes in patients with heart failure: a comparison of methods. Am. Heart J. 150, 707–715 (2005)CrossRefGoogle Scholar
  12. 12.
    Schoenenberger, A.W., Stortecky, S., Neumann, S., et al.: Predictors of functional decline in elderly patients undergoing transcatheter aortic valve implantation (TAVI). Eur. Heart J. (2012). Epub ahead of printGoogle Scholar
  13. 13.
    Arnold, S., Spertus, J., Lei, Y., et al.: Use of the Kansas City Cardiomyopathy Questionnaire for monitoring health status in patients with aortic stenosis. Cir. Heart Fail. 6, 61–67 (2013)CrossRefGoogle Scholar
  14. 14.
    Afilalo, J., Eisenberg, M., Morin, J.F., et al.: Gait speed as an incremental predictor of mortality and major morbidity in elderly patients undergoing cardiac surgery. J. Am. Coll. Cardiol. 56(20), 1668–1676 (2010)CrossRefGoogle Scholar
  15. 15.
    Green, P.: Defining and measuring frailty: an important assessment feature of high risk TAVR patients. J. Am. Coll. Cardiol. Intv. 5, 974 (2012)CrossRefGoogle Scholar
  16. 16.
    Green, P., Woglom, A.E., Genereux, P., et al.: The impact of frailty status on survival after transcatheter aortic valve replacement in older adults with severe aortic stenosis. J. Am. Coll. Cardiol. Intv. 5, 974–981 (2012)CrossRefGoogle Scholar
  17. 17.
    Wilson, C.M., Kostsuca, S.R.K., Boura, J.A.: Utilization of a 5-Meter Walk Test in evaluating self-selected gait speed during preoperative screening of patients scheduled for cardiac surgery. Cardiopulm. Phys. Ther. J. 24, 36–43 (2013)Google Scholar
  18. 18.
    Bean, J.F., Olveczky, D.D., Kiely, D.K., et al.: Performance-based versus patient-reported physical function: what are the underlying predictors? Phys. Ther. 91, 1804–1811 (2011)CrossRefGoogle Scholar
  19. 19.
    Dineen, E., Brent, B.: Aortic valve stenosis: comparison of patient with to those without chronic congestive heart failure. Am. J. Cardiol. 57, 419–422 (1986)CrossRefGoogle Scholar
  20. 20.
    Fried, L.P., Tangen, C.M., Walston, J., et al.: Frailty in older adults: evidence for a phenotype. J. Gerontol. 56, 146–156 (2000)CrossRefGoogle Scholar
  21. 21.
    Bouillon, K., Kivimaki, M., Hamer, M., et al.: Measures of frailty in population based studies: an overview. BMC Geriatr. 13(64), 2–11 (2013)Google Scholar
  22. 22.
    Haley, S.M., Jette, A.M., Coster, W.J., et al.: Late life function and disability instrument: II. development and evaluation of the function component. J. Gerontol. Med. Sci. 57A, M217–M222 (2002)CrossRefGoogle Scholar
  23. 23.
    Kinney LaPier, T.K., Mizner, R.: Outcome measures in cardiopulmonary physical therapy: focus on the late life function and disability instrument (LLFDI). Cardiopulm. Phys. Ther. J. 20(2), 32–35 (2009)Google Scholar
  24. 24.
    Kinney, T.K.: LaPier: utility of the late life function and disability instrument as an outcome measure in patients participating in outpatient cardiac rehabilitation: a preliminary study. Physiother. Can. 64(1), 53–62 (2012)CrossRefGoogle Scholar
  25. 25.
    Sayers, S.P., Jette, A.M., Haley, S.M., et al.: Validation of the late-life function and disability instrument. J. Am. Geriatr. Soc. 52, 1554–1559 (2004)CrossRefGoogle Scholar
  26. 26.
    Dubuc, N., Haley, S.M., Ni, P., et al.: Function and disability in late life: comparision of the late-life function and disability instrument to the short-form-36 and the London handicap scale. Disabil. Rehabil. 26, 362–370 (2004)CrossRefGoogle Scholar
  27. 27.
    Holmes, D.R., Mack, M.J., Kaul, S., et al.: ACCF/ AATS/SCAI/STS expert consensus document on transcatheter aortic valve replacement. J. Am. Coll. Cardiol. 59(13), 1200–1254 (2012)CrossRefGoogle Scholar
  28. 28.
    Osnabrugge, R.L.J., Mylotte, D., Head, S.J., 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. 62(11), 1002–1012 (2013)CrossRefGoogle Scholar
  29. 29.
    Vapnik, V.N., Vapnik, V.: Statistical Learning Theory. Wiley, New York (1998)zbMATHGoogle Scholar
  30. 30.
    Dobson, A.J.: An Introduction to Generalized Linear Models. Chapman and Hall, London (1990)CrossRefzbMATHGoogle Scholar
  31. 31.
    Venables, W.N., Ripley, B.D.: Modern Applied Statistics with S. Springer, New York (2002)CrossRefzbMATHGoogle Scholar
  32. 32.
    Bennett, K.P., Campbell, C.: Support vector machines: hype or Hallelujah? SIGKDD Explor. 2(2) (2000)Google Scholar
  33. 33.
    Cortes, C., Vapnik, V.: Support-vector network. Mach. Learn. 20, 1–25 (1995)zbMATHGoogle Scholar
  34. 34.
    Murata, et al.: Network information criterion determining the number of hidden units for an artificial neural network model. IEEE Trans. Neural Netw. 5(6), 865–871 (1994)CrossRefGoogle Scholar
  35. 35.
    Anastasiadis, A., et al.: New globally convergent training scheme based on the resilient propagation algorithm. Neurocomputing 64, 253–270 (2005)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.Gannon UniversityErieUSA

Personalised recommendations