Device-related patient outcomes for coronary stents: A MAUDE database analysis
Bibliographic record
Abstract
The growing prevalence of coronary artery diseases in the US corresponds to the increasing use of minimally invasive techniques that require coronary stents. Although extensive research is available on the perioperative outcomes of the 3 stent options - bare-metal stents (BMS), drug-eluting stents (DES), and bioresorbable drug-eluting stents (BVS), a knowledge gap exists in the longitudinal monitoring of patient outcomes due to device-related causes. Therefore, our study examines the device-related patient outcome and the relative performance for BMS, DES, and BVS. Data on 3 device outcomes (deaths, injuries, and malfunction) for each stent type was obtained from the January 2011 to February 2020 Manufacturer and User Facility Device Experience (MAUDE) database. Statistical visualizations and analysis were used to identify trends and significant differences between groups. Of a total of 68,618 adverse event reports, DES, BMS, and BVS each accounted for 88.5 %, 10.2 %, and 1.25 % of the cases, respectively. Device malfunctions were the most reported event (47.2 %), followed by injuries (44.1 %) and deaths (8.66 %). Over time, BMS malfunction rates showed a steady decrease (R = -0.87), while DES malfunction rates increased significantly (R = 0.79). An inversely proportional relationship between DES injuries and malfunctions was observed. The increase in DES malfunctions was 4 times greater than the decrease in BMS malfunctions. Approximately 7 % of reported adverse events were classified as misreported, with most involving DES. These results suggest 2 plausible interpretations: 1) reporting categorization for devices shifted from injuries to malfunction, and 2) stents choice is transitioning from BMS to DES. Our findings also highlight the need to improve reporting accuracy for MAUDE database data.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".