Evidence map›Paper›PMID 34471999›Full record

Article3D printing in medicine2021

3D models improve understanding of congenital heart disease.

Jonathan Awori, Seth D Friedman, Titus Chan, Christopher Howard, Steve Seslar, Brian D Soriano, Sujatha Buddhe

Registry-linked trialOpen access · goldAbstract read
In one paragraph

Article in 3D printing in medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05852106 (The Effect of 3D Modeling on Family Quality of Life, Surgical Success and Patient Outcomes in Congenital Heart Diseases), which is not on this map. Cited by 16 papers.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed
1.6field-weighted citation impact, top 18% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

NCT05852106 naunknown statusnot on this mapstarted 2023, after this paper: background citation

The Effect of 3D Modeling on Family Quality of Life, Surgical Success and Patient Outcomes in Congenital Heart Diseases: A Randomized Controlled Trial Protocol

TypeinterventionalSponsorYeditepe UniversityRan2023 to 2024Enrolled30ConditionsCongenital Heart DiseaseArmsSurgical Simulation with 3D Heart Model and Parental Education with "Congenital Heart Disease Parent Education Booklet" and tailored 3D Heart Modeling
3 · Its place in the literature

Who cites it

16 citing papers in PubMed, 28 citations in OpenAlex.

  1. Trial
  2. Article
  3. Three-Dimensional Printing in Pediatric Orthopaedics: Cost Analysis and Emerging Applications.Journal of the Pediatric Orthopaedic Society of North America · 2026
    Review
  4. Simulation a new frontier in training in congenital heart disease.European heart journal. Imaging methods and practice · 2025
    Article
  5. Review
  6. Article
  7. Article
  8. Review
  9. Review
  10. Article
  11. Article
  12. Article
  13. Article
  14. Article
  15. Review
  16. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors at 1 institution in 1 country.

Jonathan AworiDivision of Pediatric Cardiology and Radiology, Seattle Children's Hospital, Seattle, WA, USA. Jonathan.awori@gmail.com.ORCID http://orcid.org/0000-0001-5491-2556
Seth D FriedmanDivision of Pediatric Cardiology and Radiology, Seattle Children's Hospital, Seattle, WA, USA.
Titus ChanDivision of Pediatric Cardiology and Radiology, Seattle Children's Hospital, Seattle, WA, USA.
Christopher HowardDivision of Pediatric Cardiology and Radiology, Seattle Children's Hospital, Seattle, WA, USA.
Steve SeslarDivision of Pediatric Cardiology and Radiology, Seattle Children's Hospital, Seattle, WA, USA.
Brian D SorianoDivision of Pediatric Cardiology and Radiology, Seattle Children's Hospital, Seattle, WA, USA.
Sujatha BuddheDivision of Pediatric Cardiology and Radiology, Seattle Children's Hospital, Seattle, WA, USA.
Seattle Children's Hospital · US

Funding

Seattle Children's Hospital Seattle Children's Hospital
6 · The paper itself

Abstract

introductionUnderstanding congenital heart disease (CHD) is vital for medical personnel and parents of affected children. While traditional 2D schematics serve as the typical approach used, several studies have shown these models to be limiting in understanding complex structures. Recent world-emphasis has shifted to 3D printed models as a complement to 2D imaging to bridge knowledge and create new opportunities for experiential learning. We sought to systematically compare 3D digital and physical models for medical personnel and parent education compared to traditional methods.

methods3D printed and digital models were made out of MRI and CT data for 20 common CHD. Fellows and nurse practitioners used these models to explore intra-cardiac pathologies following traditional teaching. The models were also used for parent education in outpatient settings after traditional education. The participants were then asked to fill out a Likert scale questionnaire to assess their understanding and satisfaction with different teaching techniques. These ratings were compared using paired t-tests and Pearson's correlation.

resultsTwenty-five medical personnel (18 fellows; 2 nurses; 4 nurse practitioners and one attending) and twenty parents participated in the study. The diagnosis varied from simple mitral valve pathology to complex single ventricle palliation. Parent and medical personnel perceived understanding with digital models was significantly higher than traditional (p = 0.01). Subjects also felt that physical models were overall more useful than digital ones (p = 0.001). Physicians using models for parent education also perceived the models to be useful, not significantly impacting their clinical workflow.

conclusions3D models, both digital and printed, enhance medical personnel and parental perceived understanding of CHD.

Indexed as

3D printed modelsCongenital heart diseaseDigital modelsEducation

Identifiers

PMID34471999
PMCPMC8411549
OpenAlexW3194634019

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.