Abstract

Background. Understanding the severity of the disease from the parents’ perspective can lead to better patient outcomes, improving both the child’s health-related quality of life and the family’s quality of life. The implementation of 3-dimensional (3D) modeling technology in care is critical from a translational science perspective.

Aim. The purpose of this study is to determine the effect of 3D modeling on family quality of life, surgical success, and patient outcomes in congenital heart diseases. Additionally, we aim to identify challenges and potential solutions related to this innovative technology.

Methods. The study is a two-group pretest-posttest randomized controlled trial protocol. The sample size is 15 in the experimental group and 15 in the control group. The experimental group’s heart models will be made from their own computed tomography (CT) images and printed using a 3D printer. The experimental group will receive surgical simulation and preoperative parent education with their 3D heart model. The control group will receive the same parent education using the standard anatomical model. Both groups will complete the Sociodemographic Information Form, the Surgical Simulation Evaluation Form - Part I-II, and the Pediatric Quality of Life Inventory (PedsQL) Family Impacts Module. The primary outcome of the research is the average PedsQL Family Impacts Module score. Secondary outcome measurement includes surgical success and patient outcomes. Separate analyses will be conducted for each outcome and compared between the intervention and control groups.

Conclusions. Anomalies that can be clearly understood by parents according to the actual size and dimensions of the child’s heart will affect the preoperative preparation of the surgical procedure and the recovery rate in the postoperative period.

Keywords: congenital heart diseases, 3D printing, heart modeling, family quality of life, surgical simulation

Copyright and license

Copyright © 2024 The Author(s). This is an open access article distributed under the Creative Commons Attribution License (CC BY), which permits unrestricted use, distribution, and reproduction in any medium or format, provided the original work is properly cited.

How to cite

1.
Akça Sümengen A, İsmailoğlu AV, İsmailoğlu P, et al. The effect of 3D modeling on family quality of life, surgical success, and patient outcomes in congenital heart diseases: objectives and design of a randomized controlled trial. Turk J Pediatr 2024; 66: 237-250. https://doi.org/10.24953/turkjpediatr.2024.4574

References

  1. Yoo SJ, Hussein N, Peel B, et al. 3D modeling and printing in congenital heart surgery: entering the stage of maturation. Front Pediatr 2021; 9: 621672. https://doi.org/10.3389/fped.2021.621672
  2. Center for Disease Control and Prevention Center (CDC). What are CHDs. Available at: https://www.cdc.gov/ncbddd/heartdefects/facts.html#References (Accessed on January 9, 2023).
  3. Center for Disease Control and Prevention Center (CDC). Congenital heart disease data and statistics. Number of U.S. babies born with CHDs. Available at: https://www.cdc.gov/ncbddd/heartdefects/data.html (Accessed on January 8, 2023).
  4. Bouma BJ, Mulder BJ. Changing landscape of congenital heart disease. Circ Res 2017; 120: 908-922. https://doi.org/10.1161/CIRCRESAHA.116.309302
  5. van der Linde D, Konings EE, Slager MA, et al. Birth prevalence of congenital heart disease worldwide: a systematic review and meta-analysis. J Am Coll Cardiol 2011; 58: 2241-2247. https://doi.org/10.1016/j.jacc.2011.08.025
  6. Wu W, He J, Shao X. Incidence and mortality trend of congenital heart disease at the global, regional, and national level, 1990-2017. Medicine (Baltimore) 2020; 99: e20593. https://doi.org/10.1097/MD.0000000000020593
  7. American Academy of Pediatrics. Congenital heart defects fact sheet. Available at: https://www.aap.org/en/patient-care/congenital-heart-defects/congenital-heart-defect-fact-sheets/ (Accessed on January 11, 2023).
  8. Sachdeva R, Armstrong AK, Arnaout R, et al. Novel techniques in imaging congenital heart disease: JACC scientific statement. J Am Coll Cardiol 2024; 83: 63-81. https://doi.org/10.1016/j.jacc.2023.10.025
  9. Puranik R, Muthurangu V, Celermajer DS, Taylor AM. Congenital heart disease and multi-modality imaging. Heart Lung Circ 2010; 19: 133-144. https://doi.org/10.1016/j.hlc.2010.01.001
  10. Anwar S, Singh GK, Miller J, et al. 3D printing is a transformative technology in congenital heart disease. JACC Basic Transl Sci 2018; 3: 294-312. https://doi.org/10.1016/j.jacbts.2017.10.003
  11. Vettukattil JJ, Bennett PS, Jordan MG, Harikrishnan KN. Creation of a 3D printed model: from virtual to physical. In: Farooqi KM, editor. Rapid Prototyping in Cardiac Disease. Springer Cham; 2017: 9-19. https://doi.org/10.1007/978-3-319-53523-4_2
  12. Awori J, Friedman SD, Chan T, et al. 3D models improve understanding of congenital heart disease. 3D Print Med 2021; 7: 26. https://doi.org/10.1186/s41205-021-00115-7
  13. Bhatla P, Tretter JT, Ludomirsky A, et al. Utility and scope of rapid prototyping in patients with complex muscular ventricular septal defects or double-outlet right ventricle: does it alter management decisions? Pediatr Cardiol 2017; 38: 103-114. https://doi.org/10.1007/s00246-016-1489-1
  14. Tenhoff AC, Aggarwal V, Ameduri R, et al. Patient-specific three-dimensional computational heart modeling and printing to enhance clinical understandings and treatment planning: congenital recurrent pulmonary artery stenosis and transcatheter pulmonary valve replacement. Proceedings of the 2021 Design of Medical Devices Conference. 2021 Design of Medical Devices Conference. https://doi.org/10.1115/dmd2021-1059
  15. Valverde I, Gomez-Ciriza G, Hussain T, et al. Three-dimensional printed models for surgical planning of complex congenital heart defects: an international multicentre study. Eur J Cardiothorac Surg 2017; 52: 1139-1148. https://doi.org/10.1093/ejcts/ezx208
  16. Ngan EM, Rebeyka IM, Ross DB, et al. The rapid prototyping of anatomic models in pulmonary atresia. J Thorac Cardiovasc Surg 2006; 132: 264-269. https://doi.org/10.1016/j.jtcvs.2006.02.047
  17. Ryan JR, Moe TG, Richardson R, Frakes DH, Nigro JJ, Pophal S. A novel approach to neonatal management of tetralogy of Fallot, with pulmonary atresia, and multiple aortopulmonary collaterals. JACC Cardiovasc Imaging 2015; 8: 103-104. https://doi.org/10.1016/j.jcmg.2014.04.030
  18. Biber S, Andonian C, Beckmann J, et al. Current research status on the psychological situation of parents of children with congenital heart disease. Cardiovasc Diagn Ther 2019; 9: S369-S376. https://doi.org/10.21037/cdt.2019.07.07
  19. Barsella R. Pilot study: educational tool reduces parental stress at home post pediatric cardiac surgery [DNP Thesis]. DePaul University, College of Science and Health; 2020.
  20. Atalay B, Güler R, Haylı ÇM. Investigation of preoperative anxiety levels in pediatric groups. Turkish Journal of Health Science and Life 2021; 4: 24-26.
  21. Kain A, Mueller C, Golianu BJ, Jenkins BN, Fortier MA. The impact of parental health mindset on postoperative recovery in children. Paediatr Anaesth 2021; 31: 298-308. https://doi.org/10.1111/pan.14071
  22. Boyer PJ, Yell JA, Andrews JG, Seckeler MD. Anxiety reduction after pre-procedure meetings in patients with CHD. Cardiol Young 2020; 30: 991-994. https://doi.org/10.1017/S1047951120001407
  23. Lau IWW, Liu D, Xu L, Fan Z, Sun Z. Clinical value of patient-specific three-dimensional printing of congenital heart disease: quantitative and qualitative assessments. PLoS One 2018; 13: e0194333. https://doi.org/10.1371/journal.pone.0194333
  24. Liddle D, Balsara S, Hamann K, Christopher A, Olivieri L, Loke YH. Combining patient-specific, digital 3D models with tele-education for adolescents with CHD. Cardiol Young 2022; 32: 912-917. https://doi.org/10.1017/S1047951121003243
  25. Ruggiero KM, Hickey PA, Leger RR, Vessey JA, Hayman LL. Parental perceptions of disease-severity and health-related quality of life in school-age children with congenital heart disease. J Spec Pediatr Nurs 2018; 23: 12204. https://doi.org/10.1111/jspn.12204
  26. Azhar AS, AlShammasi ZH, Higgi RE. The impact of congenital heart diseases on the quality of life of patients and their families in Saudi Arabia: biological, psychological, and social dimensions. Saudi Med J 2016; 37: 392-402. https://doi.org/10.15537/smj.2016.4.13626
  27. Boutron I, Altman DG, Moher D, Schulz KF, Ravaud P; CONSORT NPT Group. CONSORT statement for randomized trials of nonpharmacologic treatments: a 2017 update and a CONSORT extension for nonpharmacologic trial abstracts. Ann Intern Med 2017; 167: 40-47. https://doi.org/10.7326/M17-0046
  28. Chan AW, Tetzlaff JM, Altman DG, et al. SPIRIT 2013 statement: defining standard protocol items for clinical trials. Ann Intern Med 2013; 158: 200-207. https://doi.org/10.7326/0003-4819-158-3-201302050-00583
  29. Ladak LA, Hasan BS, Gullick J, Awais K, Abdullah A, Gallagher R. Health-related quality of life in surgical children and adolescents with congenital heart disease compared with their age-matched healthy sibling: a cross-sectional study from a lower middle-income country, Pakistan. Arch Dis Child 2019; 104: 419-425. https://doi.org/10.1136/archdischild-2018-315594
  30. Burkhart HM. Simulation in congenital cardiac surgical education: we have arrived. J Thorac Cardiovasc Surg 2017; 153: 1528-1529. https://doi.org/10.1016/j.jtcvs.2017.03.012
  31. Feins RH. Expert commentary: cardiothoracic surgical simulation. J Thorac Cardiovasc Surg 2008; 135: 485-486. https://doi.org/10.1016/j.jtcvs.2008.01.001
  32. Shiraishi I, Yamagishi M, Hamaoka K, Fukuzawa M, Yagihara T. Simulative operation on congenital heart disease using rubber-like urethane stereolithographic biomodels based on 3D datasets of multislice computed tomography. Eur J Cardiothorac Surg 2010, 37: 302-306. https://doi.org/10.1016/j.ejcts.2009.07.046
  33. Yoo SJ, Spray T, Austin EH, Yun TJ, van Arsdell GS. Hands-on surgical training of congenital heart surgery using 3-dimensional print models. J Thorac Cardiovasc Surg 2017; 153: 1530-1540. https://doi.org/10.1016/j.jtcvs.2016.12.054
  34. Varni JW, Sherman SA, Burwinkle TM, Dickinson PE, Dixon P. The PedsQL family impact module: preliminary reliability and validity. Health Qual Life Outcomes 2004; 2: 55. https://doi.org/10.1186/1477-7525-2-55
  35. Gürkan KP, Bahar Z, Çapık C, Aydoğdu NG, Beşer A. Psychometric properties of the Turkish version of the pediatric quality of life: the family impact module in parents of children with type 1 diabetes. Children’s Health Care 2019; 49: 87–99. https://doi.org/10.1080/02739615.2019.1570464
  36. Abdullah KA, Reed W. 3D printing in medical imaging and healthcare services. J Med Radiat Sci 2018; 65: 237-239. https://doi.org/10.1002/jmrs.292
  37. Hounsfield GN. Computed medical imaging. J Comput Assist Tomogr 1980; 4: 665-674. https://doi.org/10.1097/00004728-198010000-00017
  38. Mahesh M. Search for isotropic resolution in CT from conventional through multiple-row detector. Radiographics 2002; 22: 949-962. https://doi.org/10.1148/radiographics.22.4.g02jl14949
  39. Raju TN. The Nobel chronicles. 1979: Allan MacLeod Cormack (b 1924); and Sir Godfrey Newbold Hounsfield (b 1919). Lancet 1999; 354: 1653. https://doi.org/10.1016/s0140-6736(05)77147-6
  40. Brüning J, Kramer P, Goubergrits L, et al. 3D modeling and printing for complex biventricular repair of double outlet right ventricle. Front Cardiovasc Med 2022; 9: 1024053. https://doi.org/10.3389/fcvm.2022.1024053
  41. Bibb R. Medical imaging. In: Bibb R, Eggbeer D, Paterson A, editors. Medical modelling: the application of advanced design and rapid prototyping techniques in medicine. 2nd ed. Woodhead Publishing; 2014: 7-13. https://doi.org/10.1016/B978-1-78242-300-3.00002-0
  42. Tabachnick BG, Fidell LS. Using multivariate statistics 6th edition. Pearson Education Limited; 2014.
  43. Staveski SL, Boulanger K, Erman L, et al. The impact of massage and reading on children’s pain and anxiety after cardiovascular surgery: a pilot study. Pediatr Crit Care Med 2018; 19: 725-732. https://doi.org/10.1097/PCC.0000000000001615
  44. Hartman DM, Medoff-Cooper B. Transition to home after neonatal surgery for congenital heart disease. MCN Am J Matern Child Nurs 2012; 37: 95-100. https://doi.org/10.1097/NMC.0b013e318241dac1
  45. Lopez C, Hanson CC, Yorke D, et al. Improving communication with families of patients undergoing pediatric cardiac surgery. Progress in Pediatric Cardiology 2017; 45: 83–90. https://doi.org/10.1016/j.ppedcard.2016.11.001
  46. Simeone S, Platone N, Perrone M, et al. The lived experience of parents whose children discharged to home after cardiac surgery for congenital heart disease. Acta Biomed 2018; 89: 71-77. https://doi.org/10.23750/abm.v89i4-s.7223
  47. Marella NT, Gil AM, Fan W, et al. 3D-printed cardiac models for fetal counseling: a pilot study and novel approach to improve communication. Pediatr Cardiol 2023; 44: 1800-1807. https://doi.org/10.1007/s00246-023-03177-y
  48. Osakwe O, Moore R, Divanovic A, et al. Improving patient experience and education on congenital heart defects: the evolving role of digital heart models, 3D-printing and mobile application. Pediatrics 2019; 144: 340–340. https://doi.org/10.1542/peds.144.2ma4.340.
  49. Karsenty C, Hadeed K, Djeddai C, et al. Impact of 3D-printed models in meetings with parents of children undergoing interventional cardiac catheterisation. Front Pediatr 2023; 10: 947340. https://doi.org/10.3389/fped.2022.947340
  50. Olivieri LJ, Su L, Hynes CF, et al. “Just-In-Time” simulation training using 3-D printed cardiac models after congenital cardiac surgery. World J Pediatr Congenit Heart Surg 2016; 7: 164-168. https://doi.org/10.1177/2150135115623961
  51. Chaudhuri A, Naseraldin H, Søberg PV, Kroll E, Librus M. Should hospitals invest in customised on-demand 3D printing for surgeries? International Journal of Operations & Production Management 2020; 41: 55–62. https://doi.org/10.1108/ijopm-05-2020-0277
  52. Tack P, Willems R, Annemans L. An early health technology assessment of 3D anatomic models in pediatric congenital heart surgery: potential cost-effectiveness and decision uncertainty. Expert Rev Pharmacoecon Outcomes Res 2021; 21: 1107-1115. https://doi.org/10.1080/14737167.2021.1879645
  53. American Heart Association (AHA). How 3D printing is impacting clinical care. Anatomical models. Available at: https://www.aha.org/aha-center-health-innovation-market-scan/2022-06-07-3-ways-3d-printing-revolutionizing-health-care (Accessed on June 17, 2023).
  54. Gómez-Ciriza G, Gómez-Cía T, Rivas-González JA, Velasco Forte MN, Valverde I. Affordable three-dimensional printed heart models. Front Cardiovasc Med 2021; 8: 642011. https://doi.org/10.3389/fcvm.2021.642011