Artificial Intelligence-Assisted Signal Detection of Medtronic Onyx Coronary Stent Complications: A Post-Market Analysis of the Manufacturer and User Facility Device Experience (MAUDE) Database

From Top Italian Scientists Journal
Published
January 12, 2026
Title
Artificial Intelligence-Assisted Signal Detection of Medtronic Onyx Coronary Stent Complications: A Post-Market Analysis of the Manufacturer and User Facility Device Experience (MAUDE) Database
Authors
Giuseppe Biondi-Zoccai, MD, MStat; Marco Failla Mulone, MD; Mattia Gallone; Edoardo Roberto Ginghina; Francesca Murri; Francesco Pezzullo; Ludovica Ruggiu; Giulia Testa; Sibilla Marie Tirabosco , Laura Vacciano, Anna Sirignano, Marco Bernardi, Luigi Spadafora, Salvatore Giordano, Arturo Giordano
DOI
10.62684/PMEF6950
Downloads
Download PDF
Download PDF

Giuseppe Biondi-Zoccai, MD, MStat; Marco Failla Mulone, MD3; Mattia Gallone3; Edoardo Roberto Ginghina3; Francesca Murri3; Francesco Pezzullo3; Ludovica Ruggiu3; Giulia Testa3; Sibilla Marie Tirabosco3, Laura Vacciano3, Anna Sirignano4, Marco Bernardi4, Luigi Spadafora4, Salvatore Giordano5, Arturo Giordano6

1Department of Medico-Surgical Sciences and Biotechnologies, Sapienza University of Rome, Latina, Italy;

2Maria Cecilia Hospital, GVM Care & Research, Cotignola, Italy;

3Faculty of Pharmacy and Medicine, Sapienza University of Rome, Latina, Italy;

4Department of Cardiology, Santa Maria Goretti, Latina, Italy;

5Division of Cardiology, Department of Medical and Surgical Sciences, Magna Graecia University, Catanzaro, Italy;

6Cardiovascular Interventional Unit, Pineta Grande Hospital, Castel Volturno, Caserta, Italy


Correspondence to: Prof. Giuseppe Biondi-Zoccai, Department of Medical-Surgical Sciences and Biotechnologies, Sapienza University of Rome, Latina, Italy. Email: giuseppe.biondizoccai@uniroma1.it

Abstract

Background

Medtronic Onyx zotarolimus-eluting stents (ZES) are widely used in complex percutaneous coronary interventions (PCI), yet real-world safety signals remain underexplored outside of clinical trials. We conducted a focused case study leveraging the Food and Drug Administration (FDA) Manufacturer and User Facility Device Experience (MAUDE) database to characterize complications and technical failure modes specifically associated with Onyx platforms.

Methods

We applied a ChatGPT 5-assisted extraction and curation workflow to the 500 most recent MAUDE reports retrieved using the query Manufacturer = “Medtronic” AND Product = “Onyx” (as of September 30, 2025). The model accurately parsed free-text narratives to identify device problems, clinical outcomes, and event severity, guided by a standardized taxonomy, which were then summarized providing row counts and prevalence rates, with exact 95% confidence intervals. Notably, out of 30 randomly selected reports manually verified by two independent reviewers, no inaccuracies were evident for death (0 [0-11.6%]) and two for non-fatal events (6.7% [0.8%-22.1%]).

Results

We identified 499 reports (one was excluded because focusing on a non-coronary device), with death occurring in 21 cases (4.2% [2.8%-6.3%]). Devices were predominantly Onyx Frontier (n=419, 84.0% [80.5%-86.9%]), with Trucor (n=68, 13.6% [10.9%-16.9%]) and Trustar (n=12, 2.4% [1.4%-4.2%]). Leading issues included stent dislodgement/displacement (n=400, 80.2% [76.4%-83.4%]) and positioning failure (n=183, 36.7% [32.6%-41.0%]); balloon breakage/burst and balloon malfunction occurred in 58 (11.6% [9.1%-14.7%]) and 42 (8.4% [6.3%-11.2%]), respectively. Other reported outcomes included myocardial infarction in 19 (3.8% [2.5%-5.9%]), bleeding in 10 (2.0% [1.1%-3.6%]), and stroke in 3 (0.6% [0.2%-1.8%]).

Conclusions

This case study demonstrates the feasibility and accuracy of ChatGPT 5 in extracting structured safety signals from unstructured MAUDE narratives. Complications linked to Medtronic Onyx stents were predominantly procedural, centering on delivery and positioning challenges, with case fatality of 4.2%. These findings are promising and insightful, albeit hypothesis-generating. They underscore the need for procedural optimization and enriched linkage with clinical registries to contextualize risk.

Declarations

Acknowledgements

This manuscript was drafted and illustrated with the assistance of artificial intelligence tools, such as ChatGPT 5 (OpenAI, San Francisco, CA, USA), Mage (Mage, New York, NY, USA), and Napkin AI (Napkin AI, Palo Alto, CA, USA), in keeping with established best practices (Biondi-Zoccai G, editor. ChatGPT for Medical Research. Torino: Edizioni Minerva Medica; 2024). The final content, including all conclusions and opinions, has been thoroughly revised, edited, and approved by the authors. The authors take full responsibility for the integrity and accuracy of the work and retain full credit for all intellectual contributions. Compliance with ethical standards and guidelines for the use of artificial intelligence in research has been ensured.

Conflict of Interest

The authors declare there is no conflict of interest.

Disclosure

Giuseppe Biondi-Zoccai has consulted, lectured and/or served as advisory board member for Abiomed, Advanced Nanotherapies, Aleph, Amarin, AstraZeneca, Balmed, Cardionovum, Cepton, Crannmedical, Endocore Lab, Eukon, Guidotti, Innovheart, Meditrial, Menarini, Microport, Opsens Medical, Synthesa, Terumo, and Translumina, outside the present work. All other authors report no conflict of interest.

Funding

None

References

  1. Blum M, Cao D, Mehran R. Device profile of the Resolute Onyx Zotarolimus eluting coronary stent system for the treatment of coronary artery disease: overview of its safety and efficacy. Expert Rev Med Devices 2020;17:257-265.
  2. Toth GG, Kandzari DE, Kirtane AJ, Windecker S, Latib A, Kedhi E, Mehran R, Price MJ, Choi JW, Caputo R, Troquay R, Diderholm E, Singh S, Brar SS, Loussararian A, Chetcuti S, Tulli M, Stone GW, Lung TH, Mylotte D. Two-year results from Onyx ONE clear in patients with high bleeding risk on one-month DAPT with and without intracoronary imaging. Cardiovasc Revasc Med 2024;58:60-67.
  3. U.S. Food and Drug Administation (FDA). Manufacturer and User Facility Device Experience (MAUDE) Database. Available at : https://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfmaude/search.cfm (last accessed on October 26, 2025).
  4. Everhart AO, Karaca-Mandic P, Redberg RF, Ross JS, Dhruva SS. Late adverse event reporting from medical device manufacturers to the US Food and Drug Administration: cross sectional study. BMJ 2025;388:e081518.
  5. Lawal OD, Mohanty M, Elder H, Skeer M, Erpelding N, Lanier R, Katz N. The nature, magnitude, and reporting compliance of device-related events for intravenous patient-controlled analgesia in the FDA Manufacturer and User Facility Device Experience (MAUDE) database. Expert Opin Drug Saf 2018;17:347-357.
  6. Sawaya J, Champlain A, Cohen J, Avram M. Barriers to Reporting: Limitations of the Maude Database. Dermatol Surg 2021;47:424-425.
  7. Biondi-Zoccai G, editor. ChatGPT for Medical Research. Torino: Edizioni Minerva Medica; 2024.
  8. Shi Y, Yang E, Gong Y. Unveiling Endoscopic Procedures in MAUDE: An ETL-LLM Method. Stud Health Technol Inform 2025;328:96-100.
  9. Shah F, Yu Y, Shi Y, Gong Y. Leveraging Data Pipeline and LLM to Advance Patient Safety Event Studies. Stud Health Technol Inform 2025;327:253-257.
  10. Yu Y, Shi Y, Feng Y, Gong Y. Developing a Generative AI-Powered Chatbot for Analyzing MAUDE Database. Stud Health Technol Inform 2024;316:1255-1259.
  11. Biondi-Zoccai G. Medical Statistics: A User-Friendly Handbook. Torino: Edizioni Minerva Medica; 2025.
  12. Murff HJ, FitzHenry F, Matheny ME, Gentry N, Kotter KL, Crimin K, Dittus RS, Rosen AK, Elkin PL, Brown SH, Speroff T. Automated identification of postoperative complications within an electronic medical record using natural language processing. JAMA 2011;306:848-55.
  13. Gao Y, Chen Y, Wang M, Wu J, Kim Y, Zhou K, Li M, Liu X, Fu X, Wu J, Wu H. Optimising the paradigms of human AI collaborative clinical coding. NPJ Digit Med 2024;7:368.
  14. Kıyak YS, Kononowicz AA. Using a Hybrid of AI and Template-Based Method in Automatic Item Generation to Create Multiple-Choice Questions in Medical Education: Hybrid AIG. JMIR Form Res 2025;9:e65726.
  15. Chawla R, Ahamad W, Sharma V. Techniques to Overcome Difficulty in Device Deliverability to Lesion in Complex PCI. Curr Cardiol Rev 2020;16:117-124.
  16. Biondi-Zoccai G. Medical Writing: A User-Friendly Handbook. Torino: Edizioni Minerva Medica; 2025.
  17. Biondi-Zoccai GG, Moretti C, Lotrionte M, Sheiban I. Safety of drug-coated stents. Expert Opin Drug Saf 2008;7:597-606.
  18. D'Ascenzo F, Iannaccone M, Saint-Hilary G, Bertaina M, Schulz-Schüpke S, Wahn Lee C, Chieffo A, Helft G, Gili S, Barbero U, Biondi Zoccai G, Moretti C, Ugo F, D'Amico M, Garbo R, Stone G, Rettegno S, Omedè P, Conrotto F, Templin C, Colombo A, Park SJ, Kastrati A, Hildick-Smith D, Gasparini M, Gaita F. Impact of design of coronary stents and length of dual antiplatelet therapies on ischaemic and bleeding events: a network meta-analysis of 64 randomized controlled trials and 102 735 patients. Eur Heart J 2017;38:3160-3172.
  19. Corcione N, Ferraro P, Morello A, Cimmino M, Albanese M, Pepe M, Nestola PL, Giordano S, Biondi-Zoccai G, Giordano A. Alex Plus Versus Xience Drug-Eluting Stents for Percutaneous Coronary Intervention in Routine Clinical Practice: A Propensity Score-Matched Analysis. J Invasive Cardiol 2022;34:E319-E325.
  20. Liao TJ, Crosby L, Cross K, Chen M, Elespuru R. Medical device report analyses from MAUDE: Device and patient outcomes, adverse events, and sex-based differential effects. Regul Toxicol Pharmacol 2024;149:105591.
Retrieved from ‘https://journal.topitalianscientists.org/index.php?title=Artificial_Intelligence-Assisted_Signal_Detection_of_Medtronic_Onyx_Coronary_Stent_Complications&oldid=2481’