Abstract
Predicting potential complications after aortic valve replacement (AVR) is a crucial task that would help pre-planning procedures. The goal of this work is to generate data-driven models based on logistic regression, where the probability of developing transvalvular pressure gradient (DP) that exceeds 20 mmHg under different physiological conditions can be estimated without running extensive experimental or computational methods. The hemodynamic assessment of a 26 mm SAPIEN 3 transcatheter aortic valve and a 25 mm Magna Ease surgical aortic valve was performed under pulsatile conditions of a large range of systolic blood pressures (SBP; 100–180 mmHg), diastolic blood pressures (DBP; 40–100 mmHg), and heart rates of 60, 90 and 120 bpm. Logistic regression modeling was used to generate a predictive model for the probability of having a DP > 20 mmHg for both valves under different conditions. Experiments on different pressure conditions were conducted to compare the probabilities of the generated model and those obtained experimentally. To test the accuracy of the predictive model, the receiver operation characteristics curves were generated, and the areas under the curve (AUC) were calculated. The probabilistic predictive model of DP > 20 mmHg was generated with parameters specific to each valve. The AUC obtained for the SAPIEN 3 DP model was 0.9465 and that for Magna Ease was 0.9054 indicating a high model accuracy. Agreement between the DP probabilities obtained between experiments and predictive model was found. This model is a first step towards developing a larger statistical and data-driven model that can inform on certain valves reliability during AVR pre-procedural planning.
Similar content being viewed by others
References
Balomenos, G. P., S. Kameshwar, and J. E. Padgett. Parameterized fragility models for multi-bridge classes subjected to hurricane loads. Eng. Struct.208110213, 2020
Bapat, V. N., R. Attia, and M. Thomas. Effect of valve design on the stent internal diameter of a bioprosthetic valve: a concept of true internal diameter and its implications for the valve-in-valve procedure. JACC. 7(2):115–127, 2014.
Baumgartner, H., J. Hung, J. Bermejo, J. B. Chambers, T. Edvardsen, S. Goldstein, et al. Recommendations on the echocardiographic assessment of aortic valve stenosis: a focused update from the European Association of Cardiovascular Imaging and the American Society of Echocardiography. Eur. Heart J.-Cardiovasc. Imaging. 18(3):254–275, 2017.
Coylewright, M., J. K. Forrest, J. M. McCabe, and T. M. Nazif. TAVR in low-risk patients: FDA approval, the new NCD, and shared decision-making. J. Am. Coll. Cardiol. 75(10):1208–1211, 2020.
Darestani, Y. M, B. Webb, J. E. Padgett, G. Pennison, and E. Fereshtehnejad. Fragility analysis of coastal roadways and performance assessment of coastal transportation systems subjected to storm hazards. J. Perform. Construct. Facil. 2021.
Dasi, L. P., H. Hatoum, A. Kheradvar, R. Zareian, S. H. Alavi, W. Sun, et al. On the mechanics of transcatheter aortic valve replacement. Ann. Biomed. Eng. 45(2):310–331, 2017.
Hahn, R. T., P. Pibarot, W. J. Stewart, N. J. Weissman, D. Gopalakrishnan, M. G. Keane, et al. Comparison of transcatheter and surgical aortic valve replacement in severe aortic stenosis: a longitudinal study of echocardiography parameters in cohort A of the PARTNER trial (placement of aortic transcatheter valves). J. Am. Coll. Cardiol. 61(25):2514–2521, 2013.
Hatoum, H., J. Dollery, S. M. Lilly, J. Crestanello, and L. P. Dasi. Impact of patient-specific morphologies on sinus flow stasis in transcatheter aortic valve replacement: an in vitro study. J. Thoracic. Cardiovasc. Surg. 157(2):540–549, 2019.
Hatoum, H., S. Singh-Gryzbon, P. Ruile, F.-J. Neumann, P. Blanke, V. Thourani, et al. Novel predictive model for leaflet thrombosis in transcatheter aortic valve replacement. J. Am. Coll. Cardiol. 77(18):3405, 2021.
Hatoum, H., S. Vallabhuneni, A. K. Kota, D. L. Bark, K. C. Popat, and L. P. Dasi. Impact of superhydrophobicity on the fluid dynamics of a bileaflet mechanical heart valve. J. Mech. Behav. Biomed. Mater.110:103895, 2020.
Heitkemper, M., H. Hatoum, A. Azimian, B. Yeats, J. Dollery, B. Whitson, et al. Modeling risk of coronary obstruction during transcatheter aortic valve replacement. J. Thoracic Cardiovasc. Surg. 159(3):829–838, 2020.
Lalkhen, A. G., and A. McCluskey. Clinical tests: sensitivity and specificity. Contin. Educ. Anaesth. Crit. Care Pain. 8(6):221–223, 2008.
Namasivayam, M., W. He, T. W. Churchill, R. Capoulade, S. Liu, H. Lee, et al. Transvalvular flow rate determines prognostic value of aortic valve area in aortic stenosis. J. Am. Coll. Cardiol. 75(15):1758–1769, 2020.
Ribeiro, H. B., J. Rodés-Cabau, P. Blanke, J. Leipsic, J. Kwan Park, V. Bapat, et al. Incidence, predictors, and clinical outcomes of coronary obstruction following transcatheter aortic valve replacement for degenerative bioprosthetic surgical valves: insights from the VIVID registry. Eur. Heart J. 39(8):687–695, 2018.
Simonato, M., J. Webb, R. Kornowski, A. Vahanian, C. Frerker, H. Nissen, et al. Transcatheter replacement of failed bioprosthetic valves: large multicenter assessment of the effect of implantation depth on hemodynamics after aortic valve-in-valve. Circulation. 9(6):e003651, 2016.
Vogl, B. J, Y. M. Darestani, S. M Lilly, V. H. Thourani, M. A Alkhouli, and B. R. Lindman et al. Impact of blood pressure on coronary perfusion and valvular hemodynamics after aortic valve replacement. Catheterization Cardiovascular Interventions. 2021.
Vogl, B. J, A. El-Shaer, J. A Crestanello, M. Alkhouli, and H. Hatoum. Flow dynamics in the sinus and downstream of a third and fourth generation balloon expandable transcatheter aortic valve. J. Mech. Behav. Biomed. Mater.105092, 2022.
Yanagisawa, R., K. Hayashida, Y. Yamada, M. Tanaka, F. Yashima, T. Inohara, et al. Incidence, predictors, and mid-term outcomes of possible leaflet thrombosis after TAVR. JACC. 10(1):1–11, 2017.
Funding
This research received no external funding.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Disclosures
Dr. Hatoum filed a patent application on computational predictive modeling of thrombosis in heart valves and on a novel implantable vascular shunt with real-time precise flow control. Dr. Crestanello reports personal fees from Medtronic. Dr. Lindman has received investigator-initiated research grant support from Edwards Lifesciences. The other authors report no conflict of interest.
Additional information
Associate Editor Lakshmi Prasad Dasi oversaw the review of this article.
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary Information
Below is the link to the electronic supplementary material.
Rights and permissions
About this article
Cite this article
Vogl, B.J., Darestani, Y.M., Crestanello, J.A. et al. A Preliminary Study on the Usage of a Data-Driven Probabilistic Approach to Predict Valve Performance Under Different Physiological Conditions. Ann Biomed Eng 50, 941–950 (2022). https://doi.org/10.1007/s10439-022-02971-8
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10439-022-02971-8