Clinical Applications of Patient-Specific Models: The Case for a Simple Approach

  • Jeffrey W. Holmes
  • Joost Lumens


Over the past several decades, increasingly sophisticated models of the heart have provided important insights into cardiac physiology and are increasingly used to predict the impact of diseases and therapies on the heart. In an era of personalized medicine, many envision patient-specific computational models as a powerful tool for personalizing therapy. Yet the complexity of current models poses important challenges, including identifying model parameters and completing calculations quickly enough for routine clinical use. We propose that early clinical successes are likely to arise from an alternative approach: relatively simple, fast, phenomenologic models with a small number of parameters that can be easily (and automatically) customized. We discuss examples of simple yet foundational models that have already made a tremendous impact on clinical education and practice, and make the case that reducing rather than increasing model complexity may be the key to realizing the promise of patient-specific modeling for clinical applications.


Cardiac mechanics Growth and remodeling Computational modeling Cardiology Biomechanics 



This study was funded by the National Institutes of Health (U01 127654, JWH), the Seraph Foundation (JWH), the Dutch Heart Foundation (2015T082, JL), and the Netherlands Organization for Scientific Research (016.176.340, JL).

Compliance with Ethical Standards

Conflict of Interest

J.W.H. declares that he has no conflict of interest. J.L. has served as a consultant to Medtronic.

Ethical Approval

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


  1. 1.
    Gray, R.A., & Pathmanathan, P. (2018). Patient-specific cardiovascular computational modeling: diversity of personalization and challenges. Journal of Cardiovascular Translational Research.
  2. 2.
    Trusty, P. M., Slesnick, T. C., Wei, Z. A., Rossignac, J., Kanter, K. R., Fogel, M. A., & Yoganathan, A. P. (2018). Fontan surgical planning: previous accomplishments, current challenges and future directions. Journal of Cardiovascular Translational Research.
  3. 3.
    Frank, O. (1899). Die Grundform des arteriellen Pulses. Zeitschrift für Biologie, 37, 483–526.Google Scholar
  4. 4.
    Frank, O. (1990). The basic shape of the arterial pulse. First treatise: mathematical analysis. Journal of Molecular and Cellular Cardiology, 22(3), 255–277. CrossRefPubMedGoogle Scholar
  5. 5.
    Westerhof, N., Lankhaar, J. W., & Westerhof, B. E. (2009). The arterial Windkessel. Medical and Biological Engineering and Computing, 47(2), 131–141. CrossRefPubMedGoogle Scholar
  6. 6.
    Benetos, A., Safar, M., Rudnichi, A., Smulyan, H., Richard, J. L., Ducimetieère, P., & Guize, L. (1997). Pulse pressure: A predictor of long-term cardiovascular mortality in a French male population. Hypertension, 30(6), 1410–1415.CrossRefPubMedGoogle Scholar
  7. 7.
    Mitchell, G. F., Moyé, L. A., Braunwald, E., Rouleau, J. L., Bernstein, V., Geltman, E. M., et al. (1997). Sphygmomanometrically determined pulse pressure is a powerful independent predictor of recurrent events after myocardial infarction in patients with impaired left ventricular function. SAVE investigators. Survival and ventricular enlargement. Circulation, 96(12), 4254–4260. CrossRefPubMedGoogle Scholar
  8. 8.
    Truijen, J., Van Lieshout, J. J., Wesselink, W. A., & Westerhof, B. E. (2012). Noninvasive continuous hemodynamic monitoring. Journal of Clinical Monitoring and Computing, 26(4), 267–278. CrossRefPubMedPubMedCentralGoogle Scholar
  9. 9.
    Kerckhoffs, R. C. P., Neal, M. L., Gu, Q., Bassingthwaighte, J. B., Omens, J. H., & McCulloch, A. D. (2007). Coupling of a 3D finite element model of cardiac ventricular mechanics to lumped systems models of the systemic and pulmonic circulation. Annals of Biomedical Engineering, 35(1), 1–18. CrossRefPubMedGoogle Scholar
  10. 10.
    Dillon-Murphy, D., Noorani, A., Nordsletten, D., & Figueroa, C. A. (2016). Multi-modality image-based computational analysis of haemodynamics in aortic dissection. Biomechanics and Modeling in Mechanobiology, 15(4), 857–876. CrossRefPubMedGoogle Scholar
  11. 11.
    Taylor, C. A., Fonte, T. A., & Min, J. K. (2013). Computational fluid dynamics applied to cardiac computed tomography for noninvasive quantification of fractional flow reserve: Scientific basis. Journal of the American College of Cardiology, 61(22), 2233–2241. CrossRefPubMedGoogle Scholar
  12. 12.
    Chiastra, C., Migliori, S., Burzotta, F., Dubini, G., & Migliavacca, F. (2018). Patient-specific modeling of stented coronary arteries reconstructed from optical coherence tomography: towards a widespread clinical use of fluid dynamics analyses. Journal of Cardiovascular Translational Research.
  13. 13.
    van Bakel, T. M. J., Lau, K. D., Hirsch-Romano, J., Trimarchi, S., Dorfman, A. L., & Figueroa, C. A. (2018). Patient-specific modeling of hemodynamics: supporting surgical planning in a fontan circulation correction. Journal of Cardiovascular Translational Research.
  14. 14.
    Suga, H., Sagawa, K., & Shoukas, A. A. (1973). Load independence of the instantaneous pressure–volume ratio of the canine left ventricle and effects of epinephrine and heart rate on the ratio. Circulation Research, 32(3), 314–322. CrossRefPubMedGoogle Scholar
  15. 15.
    Suga, H., & Sagawa, K. (1974). Instantaneous pressure–volume relationships and their ratio in the excised, supported canine left ventricle. Circulation Research, 35(1), 117–126. CrossRefPubMedGoogle Scholar
  16. 16.
    Grossman, W., Braunwald, E., Mann, T., McLaurin, L. P., & Green, L. H. (1977). Contractile state of the left ventricle in man as evaluated from end-systolic pressure–volume relations. Circulation, 56(5), 845–852. CrossRefPubMedGoogle Scholar
  17. 17.
    Kass, D. A., Midei, M., Graves, W., Brinker, J. A., & Maughan, W. L. (1988). Use of a conductance (volume) catheter and transient inferior vena caval occlusion for rapid determination of pressure–volume relationships in man. Catheterization and Cardiovascular Diagnosis, 15(3), 192–202. CrossRefPubMedGoogle Scholar
  18. 18.
    Kass, D. A., & Maughan, W. L. (1988). From “Emax” to pressure–volume relations: a broader view. Circulation, 77(6), 1203–1212.CrossRefPubMedGoogle Scholar
  19. 19.
    Ehsani, A. A., Biello, D. R., Schultz, J., Sobel, B. E., & Holloszy, J. O. (1986). Improvement of left ventricular contractile function by exercise training in patients with coronary artery disease. Circulation, 74(2), 350–358. CrossRefPubMedGoogle Scholar
  20. 20.
    Kass, D. A., Chen, C. H., Curry, C., Talbot, M., Berger, R., Fetics, B., & Nevo, E. (1999). Improved left ventricular mechanics from acute VDD pacing in patients with dilated cardiomyopathy and ventricular conduction delay. Circulation, 99(12), 1567–1573. CrossRefPubMedGoogle Scholar
  21. 21.
    Obokata, M., Kurosawa, K., Ishida, H., Ito, K., Ogawa, T., Ando, Y., et al. (2017). Incremental prognostic value of ventricular-arterial coupling over ejection fraction in patients with maintenance hemodialysis. Journal of the American Society of Echocardiography, 30(5), 444–453.e2. CrossRefPubMedGoogle Scholar
  22. 22.
    Santamore, W. P., & Burkhoff, D. (1991). Hemodynamic consequences of ventricular interaction as assessed by model analysis. The American Journal of Physiology, 260(1 Pt 2), H146–H157.PubMedGoogle Scholar
  23. 23.
    Dickstein, M. L., Spotnitz, H. M., Rose, E. A., & Burkhoff, D. (1997). Heart reduction surgery: an analysis of the impact on cardiac function. The Journal of Thoracic and Cardiovascular Surgery, 113(6), 1032–1040. CrossRefPubMedGoogle Scholar
  24. 24.
    Artrip, J. H., Oz, M. C., & Burkhoff, D. (2001). Left ventricular volume reduction surgery for heart failure: a physiologic perspective. The Journal of Thoracic and Cardiovascular Surgery, 122(4), 775–782. CrossRefPubMedGoogle Scholar
  25. 25.
    Gorcsan, J., Feldman, A. M., Kormos, R. L., Mandarino, W. A., Demetris, A. J., & Batista, R. J. (1998). Heterogeneous immediate effects of partial left ventriculectomy on cardiac performance. Circulation, 97(9), 839–842.CrossRefPubMedGoogle Scholar
  26. 26.
    Burkhoff, D., & Wechsler, A. S. (2006). Surgical ventricular remodeling: a balancing act on systolic and diastolic properties. Journal of Thoracic and Cardiovascular Surgery, 132(3), 459–463. CrossRefPubMedGoogle Scholar
  27. 27.
    Kelsey, R., Botello, M., Millard, B., & Zimmerman, J. (2002). An online heart simulator for augmenting first-year medical and dental education. Proceedings AMIA Symposium, 370–4.Google Scholar
  28. 28.
    Lumens, J. (2014). Creating your own virtual patient with CircAdapt simulator. European Heart Journal, 35(6), 335–337.PubMedGoogle Scholar
  29. 29.
    Harvi. (n.d.). Retrieved November 20, 2017, from
  30. 30.
    Just Physiology. (n.d.). Retrieved November 20, 2017, from
  31. 31.
    Sunagawa, K., Maughan, W. L., & Sagawa, K. (1983). Effect of regional ischemia on the left ventricular end-systolic pressure–volume relationship of isolated canine hearts. Circulation Research, 52(2), 170–178. CrossRefPubMedGoogle Scholar
  32. 32.
    Bogen, D. K., Rabinowitz, S. A., Needleman, A., McMahon, T. A., & Abelmann, W. H. (1980). An analysis of the mechanical disadvantage of myocardial infarction in the canine left ventricle. Circulation Research, 47(5), 728–741. CrossRefPubMedGoogle Scholar
  33. 33.
    Richardson, W. J., Clarke, S. A., Quinn, T. A., & Holmes, J. W. (2015). Physiological implications of myocardial scar structure. Comprehensive Physiology, 5(4), 1877–1909.
  34. 34.
    Clarke, S. A., Richardson, W. J., & Holmes, J. W. (2016). Modifying the mechanics of healing infarcts: is better the enemy of good? Journal of Molecular and Cellular Cardiology, 93, 115–124. CrossRefPubMedGoogle Scholar
  35. 35.
    Burkhoff, D., & Tyberg, J. V. (1993). Why does pulmonary venous pressure rise after onset of LV dysfunction: a theoretical analysis. The American Journal of Physiology, 265(5 Pt 2), H1819–H1828.PubMedGoogle Scholar
  36. 36.
    Fallick, C., Sobotka, P. A., & Dunlap, M. E. (2011). Sympathetically mediated changes in capacitance redistribution of the venous reservoir as a cause of decompensation. Circulation: Heart Failure, 4(5), 669–675. PubMedGoogle Scholar
  37. 37.
    Pluijmert, M., Delhaas, T., de la Parra, A. F., Kroon, W., Prinzen, F. W., & Bovendeerd, P. H. M. (2017). Determinants of biventricular cardiac function: a mathematical model study on geometry and myofiber orientation. Biomechanics and Modeling in Mechanobiology, 16(2), 721–729. CrossRefPubMedGoogle Scholar
  38. 38.
    Holmes, J. W. (2004). Determinants of left ventricular shape change during filling. Journal of Biomechanical Engineering, 126(1), 98–103.CrossRefPubMedGoogle Scholar
  39. 39.
    Arts, T., Bovendeerd, P. H., Prinzen, F. W., & Reneman, R. S. (1991). Relation between left ventricular cavity pressure and volume and systolic fiber stress and strain in the wall. Biophysical Journal, 59(1), 93–102. CrossRefPubMedPubMedCentralGoogle Scholar
  40. 40.
    Chirinos, J. A., Segers, P., Rietzschel, E. R., De Buyzere, M. L., Raja, M. W., Claessens, T., et al. (2013). Early and late systolic wall stress differentially relate to myocardial contraction and relaxation in middle-aged adults: the Asklepios study. Hypertension, 61(2), 296–303. CrossRefPubMedGoogle Scholar
  41. 41.
    Chirinos, J. A., Phan, T. S., Syed, A. A., Hashmath, Z., Oldland, H. G., Koppula, M. R., et al. (2017). Late systolic myocardial loading is associated with left atrial dysfunction in hypertension. Circulation. Cardiovascular Imaging, 10(6), e006023. CrossRefPubMedGoogle Scholar
  42. 42.
    Gaemperli, O., Biaggi, P., Gugelmann, R., Osranek, M., Schreuder, J. J., Bühler, I., et al. (2013). Real-time left ventricular pressure–volume loops during percutaneous mitral valve repair with the MitraClip system. Circulation, 127(9), 1018–1027. CrossRefPubMedGoogle Scholar
  43. 43.
    Walmsley, J., Arts, T., Derval, N., Bordachar, P., Cochet, H., Ploux, S., et al. (2015). Fast simulation of mechanical heterogeneity in the electrically asynchronous heart using the MultiPatch module. PLoS Computational Biology, 11(7), 1–23. CrossRefGoogle Scholar
  44. 44.
    Lumens, J., Tayal, B., Walmsley, J., Delgado-Montero, A., Huntjens, P. R., Schwartzman, D., et al. (2015). Differentiating electromechanical from non-electrical substrates of mechanical discoordination to identify responders to cardiac resynchronization therapy. Circulation: Cardiovascular Imaging, 8(9):e003744.
  45. 45.
    Mast, T. P., Teske, A. J., Walmsley, J., van der Heijden, J. F., van Es, R., Prinzen, F. W., et al. (2016). Right ventricular imaging and computer simulation for electromechanical substrate characterization in arrhythmogenic right ventricular cardiomyopathy. Journal of the American College of Cardiology, 68(20), 2185–2197. CrossRefPubMedGoogle Scholar
  46. 46.
    te Riele, A. S. J. M., James, C. A., Rastegar, N., Bhonsale, A., Murray, B., Tichnell, C., et al. (2014). Yield of serial evaluation in at-risk family members of patients with ARVD/C. Journal of the American College of Cardiology, 64(3), 293–301. CrossRefGoogle Scholar
  47. 47.
    Wall, S. T., Walker, J. C., Healy, K. E., Ratcliffe, M. B., & Guccione, J. M. (2006). Theoretical impact of the injection of material into the myocardium: a finite element model simulation. Circulation, 114(24), 2627–2635. CrossRefPubMedGoogle Scholar
  48. 48.
    Niederer, S. A., Shetty, A. K., Plank, G., Bostock, J., Razavi, R., Smith, N. P., & Rinaldi, C. A. (2012). Biophysical modeling to simulate the response to multisite left ventricular stimulation using a quadripolar pacing lead. Pacing and Clinical Electrophysiology, 35(2), 204–214. CrossRefPubMedGoogle Scholar
  49. 49.
    Pluijmert, M., Bovendeerd, P. H. M., Lumens, J., Vernooy, K., Prinzen, F. W., & Delhaas, T. (2016). New insights from a computational model on the relation between pacing site and CRT response. Europace, 18(suppl 4), iv94–iv103. CrossRefPubMedGoogle Scholar
  50. 50.
    Phung, T. K. N., Moyer, C. B., Norton, P. T., Ferguson, J. D., & Holmes, J. W. (2017). Effect of ablation pattern on mechanical function in the atrium. Pacing and Clinical Electrophysiology, 40(6), 648–654. CrossRefPubMedGoogle Scholar
  51. 51.
    Witzenburg, C. M., & Holmes, J. W. (2017). A comparison of phenomenologic growth laws for myocardial hypertrophy. Journal of Elasticity, 129(1–2), 257–281. CrossRefPubMedGoogle Scholar
  52. 52.
    Taber, L. A. (1998). Biomechanical growth laws for muscle tissue. Journal of Theoretical Biology, 193(2), 201–213. CrossRefPubMedGoogle Scholar
  53. 53.
    Arts, T., Delhaas, T., Bovendeerd, P., Verbeek, X., & Prinzen, F. W. (2005). Adaptation to mechanical load determines shape and properties of heart and circulation: the CircAdapt model. American Journal of Physiology. Heart and Circulatory Physiology, 288(4), H1943–H1954. CrossRefPubMedGoogle Scholar
  54. 54.
    Kerckhoffs, R. C. P., Omens, J., & McCulloch, A. D. (2012). A single strain-based growth law predicts concentric and eccentric cardiac growth during pressure and volume overload. Mechanics Research Communications, 42, 40–50. CrossRefPubMedGoogle Scholar
  55. 55.
    Kerckhoffs, R. C. P., Omens, J. H., & McCulloch, A. D. (2012). Mechanical discoordination increases continuously after the onset of left bundle branch block despite constant electrical dyssynchrony in a computational model of cardiac electromechanics and growth. Europace, 14(SUPPL 5), 65–72. CrossRefGoogle Scholar
  56. 56.
    Witzenburg, C. M., & Holmes, J. W. (2018). Predicting the time course of ventricular dilation and thickening using a rapid compartmental model. Journal of Cardiovascular Translational Research.
  57. 57.
    Gilbert, K., Forsch, N., Hedge, S., Mauger, C., Omens, J.H., Perry, J.C., et al. (2018). Atlas based computational analysis of heart shape and function in congenital heart disease. Journal of Cardiovascular Translational Research.
  58. 58.
    Vadakkumpadan, F., Arevalo, H., Ceritoglu, C., Miller, M., & Trayanova, N. (2012). Image-based estimation of ventricular fiber orientations for personalized modeling of cardiac electrophysiology. IEEE Transactions on Medical Imaging, 31(5), 1051–1060. CrossRefPubMedPubMedCentralGoogle Scholar
  59. 59.
    Lee, A. W. C., Costa, C. M., Strocchi, M., Rinaldi, C. A., & Niederer, S. A. (2018). Computational modeling for cardiac resynchronization therapy. Journal of Cardiovascular Translational Research.
  60. 60.
    Arts, T., Prinzen, F. W., Snoeckx, L. H. E. H., Rijcken, J. M., & Reneman, R. S. (1994). Adaption of cardiac structure by mechanical feedback in the environment of the cell: a model study. Biophysical Journal, 66(4), 953–961. CrossRefPubMedPubMedCentralGoogle Scholar
  61. 61.
    Kroon, W., Delhaas, T., Bovendeerd, P., & Arts, T. (2008). Structure and torsion in the normal and situs inversus totalis cardiac left ventricle. II. Modeling cardiac adaptation to mechanical load. American Journal of Physiology. Heart and Circulatory Physiology, 295(1), H202–H210. CrossRefPubMedGoogle Scholar
  62. 62.
    Arts, T., Lumens, J., Kroon, W., & Delhaas, T. (2012). Control of whole heart geometry by intramyocardial mechano-feedback: a model study. PLoS Computational Biology, 8(2), e1002369. CrossRefPubMedPubMedCentralGoogle Scholar
  63. 63.
    Beard, D. A., Pettersen, K. H., Carlson, B. E., Omholt, S. W., & Bugenhagen, S. M. (2013). A computational analysis of the long-term regulation of arterial pressure. F1000Research, 2:208. PubMedPubMedCentralGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Jeffrey W. Holmes
    • 1
  • Joost Lumens
    • 2
  1. 1.Departments of Biomedical Engineering and MedicineUniversity of VirginiaCharlottesvilleUSA
  2. 2.Cardiovascular Research Institute Maastricht (CARIM)Maastricht University Medical CenterMaastrichtThe Netherlands

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