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Shoulder arthroplasty device clearance: an ancestral network analysis.

Publication ,  Journal Article
Zhu, A; Ying, X; Carey, EG; Pean, CA; Premkumar, A; Fu, MC; Taylor, SA
Published in: J Shoulder Elbow Surg
March 2023

BACKGROUND: The US Food and Drug Administration (FDA) oversees medical device regulation and oversight in the United States, and the majority of shoulder arthroplasty devices are cleared via the 510(k) pathway, in which a device demonstrates "substantial equivalence" to a previously cleared predicate. The purpose of this study was to determine an interconnected ancestral network of shoulder arthroplasty devices and determine equivalency ties to devices subsequently recalled by the FDA for design-related issues. METHODS: The FDA 510(k) database was used to identify all legally marketed shoulder arthroplasty devices from May 28, 1976, to July 1, 2021. Direct predicate information obtained via clearance summary documents associated with each device was used to generate an ancestral genealogy network for all shoulder arthroplasty devices cleared between July 1, 2020, and July 1, 2021. FDA design recalls were analyzed, and the number of descendant devices was calculated for each recalled device. RESULTS: An evaluation of all 476 510(k) premarket notification pathway-cleared shoulder devices since 1976 identified 0-313 descendant devices for each. Eighty of these devices (16.8%) have since been recalled, of which 10 recalls were directly related to implant design issues. Furthermore, among 29 of the most recently cleared devices (July 1, 2020-July 1, 2021), 16 (55.2%) claim predicates devices that have subsequently been withdrawn from the market because of design-related failures. CONCLUSIONS: Shoulder arthroplasty devices are linked together via an interconnected FDA 510(k) equivalency approval network dating back to 1976 despite substantive changes in material specifications and device design, many of which have since been recalled. Many of the cleared modern devices claim predicates based on subsequently recalled prostheses.

Duke Scholars

Published In

J Shoulder Elbow Surg

DOI

EISSN

1532-6500

Publication Date

March 2023

Volume

32

Issue

3

Start / End Page

671 / 676

Location

United States

Related Subject Headings

  • United States Food and Drug Administration
  • United States
  • Orthopedics
  • Humans
  • Device Approval
  • Databases, Factual
  • Arthroplasty, Replacement, Shoulder
  • Arthroplasty
  • 3202 Clinical sciences
  • 1103 Clinical Sciences
 

Citation

APA
Chicago
ICMJE
MLA
NLM
Zhu, A., Ying, X., Carey, E. G., Pean, C. A., Premkumar, A., Fu, M. C., & Taylor, S. A. (2023). Shoulder arthroplasty device clearance: an ancestral network analysis. J Shoulder Elbow Surg, 32(3), 671–676. https://doi.org/10.1016/j.jse.2022.09.017
Zhu, Andrew, Xiaohan Ying, Edward G. Carey, Christian A. Pean, Ajay Premkumar, Michael C. Fu, and Samuel A. Taylor. “Shoulder arthroplasty device clearance: an ancestral network analysis.J Shoulder Elbow Surg 32, no. 3 (March 2023): 671–76. https://doi.org/10.1016/j.jse.2022.09.017.
Zhu A, Ying X, Carey EG, Pean CA, Premkumar A, Fu MC, et al. Shoulder arthroplasty device clearance: an ancestral network analysis. J Shoulder Elbow Surg. 2023 Mar;32(3):671–6.
Zhu, Andrew, et al. “Shoulder arthroplasty device clearance: an ancestral network analysis.J Shoulder Elbow Surg, vol. 32, no. 3, Mar. 2023, pp. 671–76. Pubmed, doi:10.1016/j.jse.2022.09.017.
Zhu A, Ying X, Carey EG, Pean CA, Premkumar A, Fu MC, Taylor SA. Shoulder arthroplasty device clearance: an ancestral network analysis. J Shoulder Elbow Surg. 2023 Mar;32(3):671–676.
Journal cover image

Published In

J Shoulder Elbow Surg

DOI

EISSN

1532-6500

Publication Date

March 2023

Volume

32

Issue

3

Start / End Page

671 / 676

Location

United States

Related Subject Headings

  • United States Food and Drug Administration
  • United States
  • Orthopedics
  • Humans
  • Device Approval
  • Databases, Factual
  • Arthroplasty, Replacement, Shoulder
  • Arthroplasty
  • 3202 Clinical sciences
  • 1103 Clinical Sciences