A cross-sectional analysis of 284 complications for lumbar disc replacements from medical device reports maintained by the United States Food and Drug Administration

An Analysis of the Food and Drug Administration Manufacturer and User Facility Device Experience Database for MAGnetic Expansion Control Spinal Rods

BACKGROUND

MAGnetic Expansion Control (MAGEC) rods can prevent repeated lengthening operations for scoliosis patients. However, there have been several Field Safety Notices issued, including a worldwide product recall due to actuator endcap separation. We aimed to review adverse events reported to the Food and Drug Administration (FDA) regarding MAGEC rods, focusing on MAGEC X.

METHODS

Reports submitted to the Manufacturer and User Facility Device Experience database in relation to MAGEC devices were accessed and analysed using R Statistical Software. Exclusion criteria included duplicate and literature review reports (n = 54). Free-text data were analysed using inductive content analysis.

RESULTS

1016 adverse events were reported to 11/30/2023. 99.0% (1006) were submitted by the manufacturer. Reports primarily arose from the UK (465, 45.8%) or US (421, 41.4%). From free-text data the most frequent adverse events were distraction mechanism failure (573), device wear (272), and actuator seal damage (180). Rod fracture (n = 48) was not significantly associated with rod diameter (≤ 5.0 mm or > 5.0 mm), p = 0.736. 234 reports referenced MAGEC X devices; actuator endcap separation was identified in 41.9% (99). Other events include failure of distraction (63), surface damage (31), and rod fracture (15). On 06/30/2020 MAGEC X2 received FDA approval. Twenty reports reference devices manufactured after this date, seven describe distraction mechanism failure; notably there are no reports of endcap separation.

CONCLUSION

These data represent the largest series of adverse events reported for MAGEC rods, including significant new data regarding MAGEC X. As well as endcap separation, failure of distraction, surface damage, and rod fracture were reported.

Assessing the Reproducibility of Research Based on the Food and Drug Administration Manufacturer and User Facility Device Experience Data

OBJECTIVE

This article aims to assess the reproducibility of Manufacturer and User Facility Device Experience (MAUDE) data-driven studies by analyzing the data queries used in their research processes.

METHODS

Studies using MAUDE data were sourced from PubMed by searching for "MAUDE" or "Manufacturer and User Facility Device Experience" in titles or abstracts. We manually chose articles with executable queries. The reproducibility of each query was assessed by replicating it in the MAUDE Application Programming Interface. The reproducibility of a query is determined by a reproducibility coefficient that ranges from 0.95 to 1.05. This coefficient is calculated by comparing the number of medical device reports (MDRs) returned by the reproduced queries to the number of reported MDRs in the original studies. We also computed the reproducibility ratio, which is the fraction of reproducible queries in subgroups divided by the query complexity, the device category, and the presence of a data processing flow.

RESULTS

As of August 8, 2022, we identified 523 articles from which 336 contained queries, and 60 of these were executable. Among these, 14 queries were reproducible. Queries using a single field like product code, product class, or brand name showed higher reproducibility (50%, 33.3%, 31.3%) compared with other fields (8.3%, P = 0.037). Single-category device queries exhibited a higher reproducibility ratio than multicategory ones, but without statistical significance (27.1% versus 8.3%, P = 0.321). Studies including a data processing flow had a higher reproducibility ratio than those without, although this difference was not statistically significant (42.9% versus 17.4%, P = 0.107).

CONCLUSIONS

Our findings indicate that the reproducibility of queries in MAUDE data-driven studies is limited. Enhancing this requires the development of more effective MAUDE data query strategies and improved application programming interfaces.