3-Dimensional Printing in Transcatheter Aortic Valve Replacement: Periprocedural Value and Training Applications
DOI:
https://doi.org/10.59958/hsf.7177Keywords:
transcatheter aortic valve replacement, 3D printing, models, teaching, trainingAbstract
Transcatheter aortic valve replacement (TAVR) is a rapidly developing, cutting-edge technology. The skills to perform such procedures are difficult to acquire, and the learning curve is steep. In recent years, structural heart diseases, particularly valvular disease, have become one of the main areas to which 3-dimensional (3D) printing has been applied because it facilitates visualization and exploration of complex cardiovascular anatomical structures. 3D printing also addresses some of the challenges of these interventions, such as patient selection, prosthesis sizing, and of course, teaching and training. 3D printing can provide a valuable resource for teaching and training because it can produce educational models for all types of valvular diseases. A pulsatile platform for the simulation of TAVR with 3D printed models could be used for comprehensive training of young clinicians as part of the overall TAVR teaching and training program. In this review, we introduced the 3D printed model and TAVR simulator, illustrate its training applications in morphology teaching, surgical simulations and preprocedural planning. Additionally, we reviewed studies on 3D printing in predicting periprocedural complications of TAVR, discussed the current limitations and prospected future directions of 3D printing.
References
Wang C, Zhang L, Qin T, Xi Z, Sun L, Wu H, et al. 3D printing in adult cardiovascular surgery and interventions: a systematic review. Journal of Thoracic Disease. 2020; 12: 3227–3237.
Jack G, Arora S, Strassle PD, Sitammagari K, Gangani K, Yeung M, et al. Differences in Inpatient Outcomes After Surgical Aortic Valve Replacement at Transcatheter Aortic Valve Replacement (TAVR) and Non-TAVR Centers. Journal of the American Heart Association. 2019; 8: e013794.
Mao J, Redberg RF, Carroll JD, Marinac-Dabic D, Laschinger J, Thourani V, et al. Association Between Hospital Surgical Aortic Valve Replacement Volume and Transcatheter Aortic Valve Replacement Outcomes. JAMA Cardiology. 2018; 3: 1070–1078.
Claessen BE, Tang GHL, Kini AS, Sharma SK. Considerations for Optimal Device Selection in Transcatheter Aortic Valve Replacement: A Review. JAMA Cardiology. 2021; 6: 102–112.
Corrigan FE, 3rd, Gleason PT, Condado JF, Lisko JC, Chen JH, Kamioka N, et al. Imaging for Predicting, Detecting, and Managing Complications After Transcatheter Aortic Valve Replacement. JACC. Cardiovascular Imaging. 2019; 12: 904–920.
Alasnag MA, Al-Nasser IM, Porqueddu MM, Ahmed WH, Al-Shaibi KF. 3D Model Guiding Transcatheter Aortic Valve Replacement in a Patient with Aortic Coarctation. JACC. Case Reports. 2020; 2: 352–357.
Bompotis G, Meletidou M, Karakanas A, Sotiriou S, Sachpekidis V, Konstantinidou M, et al. Transcatheter Aortic Valve Implantation using 3-D printing modeling assistance. A single-center experience. Hellenic Journal of Cardiology: HJC = Hellenike Kardiologike Epitheorese. 2020; 61: 131–132.
Abd Alamir M, Nazir S, Alani A, Golub I, Gilchrist IC, Jr, Aslam F, et al. Multidetector computed tomography in transcatheter aortic valve replacement: an update on technological developments and clinical applications. Expert Review of Cardiovascular Therapy. 2020; 18: 709–722.
Levin D, Mackensen GB, Reisman M, McCabe JM, Dvir D, Ripley B. 3D Printing Applications for Transcatheter Aortic Valve Replacement. Current Cardiology Reports. 2020; 22: 23.
Zelis JM, Meiburg R, Roijen JJD, Janssens KLPM, van 't Veer M, Pijls NHJ, et al. 3D-printed stenotic aortic valve model to simulate physiology before, during, and after transcatheter aortic valve implantation. International Journal of Cardiology. 2020; 313: 32–34.
Gardin C, Ferroni L, Latremouille C, Chachques JC, Mitrečić D, Zavan B. Recent Applications of Three Dimensional Printing in Cardiovascular Medicine. Cells. 2020; 9: 742.
Ferrari E, Piazza G, Scoglio M, Berdajs D, Tozzi P, Maisano F, et al. Suitability of 3D-Printed Root Models for the Development of Transcatheter Aortic Root Repair Technologies. ASAIO Journal (American Society for Artificial Internal Organs: 1992). 2019; 65: 874–881.
Ko CY, Escarce JJ, Baker L, Klein D, Guarino C. Predictors for medical students entering a general surgery residency: National survey results. Surgery. 2004; 136: 567–572.
Berman L, Rosenthal MS, Curry LA, Evans LV, Gusberg RJ. Attracting surgical clerks to surgical careers: role models, mentoring, and engagement in the operating room. Journal of the American College of Surgeons. 2008; 207: 793–800, 800.e1–800.e2.
Erzurum VZ, Obermeyer RJ, Fecher A, Thyagarajan P, Tan P, Koler AK, et al. What influences medical students' choice of surgical careers. Surgery. 2000; 128: 253–256.
O'Herrin JK, Lewis BJ, Rikkers LF, Chen H. Why do students choose careers in surgery? The Journal of Surgical Research. 2004; 119: 124–129.
Akaike M, Fukutomi M, Nagamune M, Fujimoto A, Tsuji A, Ishida K, et al. Simulation-based medical education in clinical skills laboratory. The Journal of Medical Investigation: JMI. 2012; 59: 28–35.
Hosny A, Dilley JD, Kelil T, Mathur M, Dean MN, Weaver JC, et al. Pre-procedural fit-testing of TAVR valves using parametric modeling and 3D printing. Journal of Cardiovascular Computed Tomography. 2019; 13: 21–30.
Nam JG, Lee W, Jeong B, Park EA, Lim JY, Kwak Y, et al. Three-Dimensional Printing of Congenital Heart Disease Models for Cardiac Surgery Simulation: Evaluation of Surgical Skill Improvement among Inexperienced Cardiothoracic Surgeons. Korean Journal of Radiology. 2021; 22: 706–713.
Greil GF, Wolf I, Kuettner A, Fenchel M, Miller S, Martirosian P, et al. Stereolithographic reproduction of complex cardiac morphology based on high spatial resolution imaging. Clinical Research in Cardiology: Official Journal of the German Cardiac Society. 2007; 96: 176–185.
Byrne N, Velasco Forte M, Tandon A, Valverde I, Hussain T. A systematic review of image segmentation methodology, used in the additive manufacture of patient-specific 3D printed models of the cardiovascular system. JRSM Cardiovascular Disease. 2016; 5: 2048004016645467.
Halliburton S, Arbab-Zadeh A, Dey D, Einstein AJ, Gentry R, George RT, et al. State-of-the-art in CT hardware and scan modes for cardiovascular CT. Journal of Cardiovascular Computed Tomography. 2012; 6: 154–163.
Vukicevic M, Mosadegh B, Min JK, Little SH. Cardiac 3D Printing and its Future Directions. JACC. Cardiovascular Imaging. 2017; 10: 171–184.
Li K, Kui C, Lee E, Ho C, Sunny Hei S, Wu W, et al. The Role of 3D Printing in Anatomy Education and Surgical Training: A Narrative Review. MedEdPublish: Dundee, UK. 2017.
Ma Y, Ding P, Li L, Liu Y, Jin P, Tang J, et al. Three-dimensional printing for heart diseases: clinical application review. Bio-design and Manufacturing. 2021; 4: 675–687.
Valverde I. Three-dimensional Printed Cardiac Models: Applications in the Field of Medical Education, Cardiovascular Surgery, and Structural Heart Interventions. Revista Espanola De Cardiologia (English Ed.). 2017; 70: 282–291.
Wang DD, Gheewala N, Shah R, Levin D, Myers E, Rollet M, et al. Three-Dimensional Printing for Planning of Structural Heart Interventions. Interventional Cardiology Clinics. 2018; 7: 415–423.
Maragiannis D, Jackson MS, Igo SR, Schutt RC, Connell P, Grande-Allen J, et al. Replicating Patient-Specific Severe Aortic Valve Stenosis with Functional 3D Modeling. Circulation. Cardiovascular Imaging. 2015; 8: e003626.
Ventola CL. Medical Applications for 3D Printing: Current and Projected Uses. P & T: a Peer-reviewed Journal for Formulary Management. 2014; 39: 704–711.
Petrini C. Ethical and legal considerations regarding the ownership and commercial use of human biological materials and their derivatives. Journal of Blood Medicine. 2012; 3: 87–96.
Olivieri LJ, Krieger A, Loke YH, Nath DS, Kim PCW, Sable CA. Three-dimensional printing of intracardiac defects from three-dimensional echocardiographic images: feasibility and relative accuracy. Journal of the American Society of Echocardiography: Official Publication of the American Society of Echocardiography. 2015; 28: 392–397.
Hermsen JL, Burke TM, Seslar SP, Owens DS, Ripley BA, Mokadam NA, et al. Scan, plan, print, practice, perform: Development and use of a patient-specific 3-dimensional printed model in adult cardiac surgery. The Journal of Thoracic and Cardiovascular Surgery. 2017; 153: 132–140.
Bauer T, Linke A, Sievert H, Kahlert P, Hambrecht R, Nickenig G, et al. Comparison of the effectiveness of transcatheter aortic valve implantation in patients with stenotic bicuspid versus tricuspid aortic valves (from the German TAVI Registry). The American Journal of Cardiology. 2014; 113: 518–521.
Qian Z, Wang K, Liu S, Zhou X, Rajagopal V, Meduri C, et al. Quantitative Prediction of Paravalvular Leak in Transcatheter Aortic Valve Replacement Based on Tissue-Mimicking 3D Printing. JACC. Cardiovascular Imaging. 2017; 10: 719–731.
Reiff C, Zhingre Sanchez JD, Mattison LM, Iaizzo PA, Garcia S, Raveendran G, et al. 3-Dimensional printing to predict paravalvular regurgitation after transcatheter aortic valve replacement. Catheterization and Cardiovascular Interventions: Official Journal of the Society for Cardiac Angiography & Interventions. 2020; 96: E703–E710.
Thorburn C, Abdel-Razek O, Fagan S, Pearce N, Furey M, Harris S, et al. Three-dimensional printing for assessment of paravalvular leak in transcatheter aortic valve implantation. Journal of Cardiothoracic Surgery. 2020; 15: 211.
Rocatello G, El Faquir N, De Santis G, Iannaccone F, Bosmans J, De Backer O, et al. Patient-Specific Computer Simulation to Elucidate the Role of Contact Pressure in the Development of New Conduction Abnormalities After Catheter-Based Implantation of a Self-Expanding Aortic Valve. Circulation. Cardiovascular Interventions. 2018; 11: e005344.
Heitkemper M, Hatoum H, Azimian A, Yeats B, Dollery J, Whitson B, et al. Modeling risk of coronary obstruction during transcatheter aortic valve replacement. The Journal of Thoracic and Cardiovascular Surgery. 2020; 159: 829–838.e3.
Young L, Harb SC, Puri R, Khatri J. Percutaneous coronary intervention of an anomalous coronary chronic total occlusion: The added value of three-dimensional printing. Catheterization and Cardiovascular Interventions: Official Journal of the Society for Cardiac Angiography & Interventions. 2020; 96: 330–335.
Yokoyama Y, Sakata T, Mikami T, Misumida N, Scotti A, Takagi H, et al. Vascular access for transcatheter aortic valve replacement: A network meta-analysis. Journal of Cardiology. 2023; 82: 227–233.
Ovcharenko EA, Klyshnikov KU, Shilov AA, Kochergin NA, Rezvova MA, Belikov NV, et al. Mechanism of Vascular Injury in Transcatheter Aortic Valve Replacement. Sovremennye Tekhnologii V Meditsine. 2021; 13: 6–13.
Rotman OM, Kovarovic B, Sadasivan C, Gruberg L, Lieber BB, Bluestein D. Realistic Vascular Replicator for TAVR Procedures. Cardiovascular Engineering and Technology. 2018; 9: 339–350.
Vaporciyan AA, Reed CE, Erikson C, Dill MJ, Carpenter AJ, Guleserian KJ, et al. Factors affecting interest in cardiothoracic surgery: Survey of North American general surgery residents. The Journal of Thoracic and Cardiovascular Surgery. 2009; 137: 1054–1062.
Allen JG, Weiss ES, Patel ND, Alejo DE, Fitton TP, Williams JA, et al. Inspiring medical students to pursue surgical careers: outcomes from our cardiothoracic surgery research program. The Annals of Thoracic Surgery. 2009; 87: 1816–1819.
Cochran A, Melby S, Neumayer LA. An Internet-based survey of factors influencing medical student selection of a general surgery career. American Journal of Surgery. 2005; 189: 742–746.
Lee JT, Qiu M, Teshome M, Raghavan SS, Tedesco MM, Dalman RL. The utility of endovascular simulation to improve technical performance and stimulate continued interest of preclinical medical students in vascular surgery. Journal of Surgical Education. 2009; 66: 367–373.
Cook DA, Hatala R, Brydges R, Zendejas B, Szostek JH, Wang AT, et al. Technology-enhanced simulation for health professions education: a systematic review and meta-analysis. JAMA. 2011; 306: 978–988.
Schmauss D, Schmitz C, Bigdeli AK, Weber S, Gerber N, Beiras-Fernandez A, et al. Three-dimensional printing of models for preoperative planning and simulation of transcatheter valve replacement. The Annals of Thoracic Surgery. 2012; 93: e31–e33.
Gordon JA, Wilkerson WM, Shaffer DW, Armstrong EG. "Practicing" medicine without risk: students' and educators' responses to high-fidelity patient simulation. Academic Medicine: Journal of the Association of American Medical Colleges. 2001; 76: 469–472.
Lee JT, Son JH, Chandra V, Lilo E, Dalman RL. Long-term impact of a preclinical endovascular skills course on medical student career choices. Journal of Vascular Surgery. 2011; 54: 1193–1200.
Tavakol M, Mohagheghi MA, Dennick R. Assessing the skills of surgical residents using simulation. Journal of Surgical Education. 2008; 65: 77–83.
Fann JI, Caffarelli AD, Georgette G, Howard SK, Gaba DM, Youngblood P, et al. Improvement in coronary anastomosis with cardiac surgery simulation. The Journal of Thoracic and Cardiovascular Surgery. 2008; 136: 1486–1491.
Yang J, Bai R, Chen B, Suo Z. Hydrogel adhesion: a supramolecular synergy of chemistry, topology, and mechanics. Advanced Functional Materials. 2020; 30, 1901693.
Zhao X. Multi-scale multi-mechanism design of tough hydrogels: building dissipation into stretchy networks. Soft Matter. 2014; 10: 672–687.
Yang H, Ji MK, Yang M, Shi MXZ, Pan YD, Zhou YF, et al. Fabricating hydrogels to mimic biological tissues of complex shapes and high fatigue resistance. Matter. 2021; 4: 1935–1946.
Votta E, Le TB, Stevanella M, Fusini L, Caiani EG, Redaelli A, et al. Toward patient-specific simulations of cardiac valves: state-of-the-art and future directions. Journal of Biomechanics. 2013; 46: 217–228.
de Jaegere P, Rocatello G, Prendergast BD, de Backer O, Van Mieghem NM, Rajani R. Patient-specific computer simulation for transcatheter cardiac interventions: what a clinician needs to know. Heart (British Cardiac Society). 2019; 105: s21–s27.
Marom G. Numerical methods for fluid–structure interaction models of aortic valves. Archives of Computational Methods in Engineering. 2015; 22: 595–620.
Butera G, Sturla F, Pluchinotta FR, Caimi A, Carminati M. Holographic Augmented Reality and 3D Printing for Advanced Planning of Sinus Venosus ASD/Partial Anomalous Pulmonary Venous Return Percutaneous Management. JACC. Cardiovascular Interventions. 2019; 12: 1389–1391.
Sugimoto M. Extended Reality (XR: VR/AR/MR), 3D Printing, Holography, AI, Radiomics, and Online VR Tele-Medicine for Precision Surgery. In Takenoshita S, Yasuhara H (eds.) Surgery and Operating Room Innovation (pp. 65–70). Springer: Singapore. 2021.
Vernikouskaya I, Rottbauer W, Seeger J, Gonska B, Rasche V, Wöhrle J. Patient-specific registration of 3D CT angiography (CTA) with X-ray fluoroscopy for image fusion during transcatheter aortic valve implantation (TAVI) increases performance of the procedure. Clinical Research in Cardiology: Official Journal of the German Cardiac Society. 2018; 107: 507–516.
Wang M, Niu G, Chen Y, Zhou Z, Feng D, Zhang Y, et al. Development and validation of a deep learning-based fully automated algorithm for pre-TAVR CT assessment of the aortic valvular complex and detection of anatomical risk factors: a retrospective, multicentre study. EBioMedicine. 2023; 96: 104794.
Meyer A, Kofler M, Montagner M, Unbehaun A, Sündermann S, Buz S, et al. Reliability and Influence on Decision Making of fully-automated vs. semi-automated Software Packages for Procedural Planning in TAVI. Scientific Reports. 2020; 10: 10746.
