Mitral regurgitation has been recently recognized as a rapidly growing epidemic due to the increase of older populations who generally present prolapse of a single area of the valve. The challenge in Clinics is the identification of the best indexes and the correlation with severe conditions in asymptomatic patients affected by functional diseases.
We planned to investigate the LV-LA pair using both image-based clinical studies, and simplified mechanical models, with the intention to start from a new mechanical modeling which looks at the pair LV-LA as a system whose coupling changes due to MR, as mitral valve is never completely closed, and thus determines a change in the mechanical function of the left heart.
The expected impact of the research is sought in terms of the take up of new research and innovation practices for making diagnosis more effective. The challenge to identify a significant parameter from mechanical analysis, which allows to identify the onset of the disease also when it is asymptomatic.
The innovative characters of the research are both in the topic and the investigation methods set up by the group.
A research based on the analysis of the LV-LA pair mechanics, determined by the form and the size of LA and by the internal stiffened structure of the LV walls, is unique and new and represents a clear progress with respect to the state of art in mechanical modeling of the left heart. The mechanical modeling of the LA-LV pair as an in series system is original in the scientific scenario; moreover, the characterization of the mechanics of the left heart when, due to mitral valve dysfunction, the systolic uncoupling of the two chambers is missing is a further challenge itself. Our mechanical is starting from the mechanical approach already used for LV [26], a sort of lumped 0-D model simple enough to enlighten the important features of the pumping function and to look at the pressure-volume loops of the two chambers, and to their evolution under diseased conditions, through few key variables: blood pressure, LA and LV volumes, and LV wall contraction. All the changes in LA-LV function which play a critical role in diseases induced by mitral valve regurgitation can be visualized in terms of these variables. Details of the modeling will be described in the Section devoted to the description of the tasks of the components.
The expected impact of our research is the improvement of the knowledge of the mechanical behavior of the system during functional diseases as well as of the capability to deliver innovative functional indexes concerning the pair LA-LV which can be of clinical utility in patients with mitral valve dysfunction. And it is exactly what the scientific community is asking [22]; so, it would represent also a clear progress with respect to the state of art in Clinics.
About the methods, the global mechanical analysis at the basis of Modern Shape Analysis has been recently applied by our group to echocardiographic data corresponding to patients with hypertrophic cardiomyopathy diseases [6,13]. It allowed us to deliver original results and a new way to look at the differences between physio and physio-pathological conditions through non-invasive methods such as echocardiography. Our expectations in the present case of interest are similar and are supported by the first preliminary results already published [2].
Papers of the Sapienza Research Group in the last 5 years
[1] Varano et al., Medical Image Analysis 46, 35-56, 2018.
[2] Piras et al., Scientific Report 7, 6257, 2018.
[3] Varano et al., Lecture Notes in Computational Vision and Biomechanics 27, pg. 1125-1134, Springer International Publishing AG, 2018.
[4] Evangelista et al., Computational Vision and Medical Image Processing V, pg. 267-271, Taylor and Francis Group, London, 2016.
[5] Piras et al., Statistical Atlases and Computational Models of the Heart. Imaging and Modelling Challenges, 119-129, Springer International Publishing Switzerland, 2016.
[6] Piras et al., Scientific Report 6, 34906, 2016.
[7] Evangelista et al., Computer Methods in Biomechanics and Biomedical Engineering/ Imaging \& Visualization 4(3-4), 164-173, 2016.
[8] Lanconelli et al., Journal of Mechanics in Medicine and Biology 15(2), 1540012, 2015.
[9] Evangelista et al., Lecture Notes in Computational Vision and Biomechanics 19, pp. 41-54, 2015.
[10] Gabriele et al., Computational Vision and Medical Image Processing IV, pg. 19-24, CRC Press Taylor and Francis Group, 2014. Awarded as Best Paper of the Conference.
[11] Evangelista et al., Journal of Biomechanics 48, 465-471, 2015.
[12] Gabriele et al., Computer Methods in Biomechanics and Biomedical Engineering 18(7), 790-798, 2015.
[13]Madeo et al., 2015. PLoS ONE 10(4): e0122376.
[14] Torromeo et al., International Journal of Applied Science and Technology 4(4), 11-23, 2014.
[15] Piras et al., PLoS ONE 9(1): e86896, 2014.
Other References
[16] Nardinocchi et al., European Journal of Mechanics A/Solids 36, 173-179, 2012.
[17] Evangelista et al., Progress in Biophysics and Molecular Biology 107(1), 112-121, 2011.
[18] Dryden, I.L., Mardia, K.V., Statistical Shape Analysis: With Applications in R, 2nd Edition, Wiley, 2016.
[19] Bonow et al., JACC 48, 1-148, 2006.
[20] Vahanian et al., European Heart Journal 28, 230-268, 2007.
[21] Lisi et al., Journal of Clinical Ultrasound 46(1), 32-40, 2018.
[22] Bonow, R.O., JACC 7(3), 233-235, 2014.
[23] Cameli et al., The International Journal of Cardiovascular Imaging, June 2018. DOI:10.1007/s10554-018-1391-4
[24] Rausch. Et al., Biomechanics and modeling in Mechanobiology 12(5), 1053-1071, 2013.
[25] Morgan et al., Journal of Biomechanical Engineering 138(2), 021009, 2016.
[26] Nardinocchi, P., Teresi, L., Varano, V., Mechanics Research Communications 38, 532¿535, 2011.
[27] Nardinocchi, P., Teresi, L., Journal of Elasticity 88, 27-39, 2007.