Prevalence and Analysis of Medical Device-Related Pressure Injuries: Results from the International Pressure Ulcer Prevalence Survey
Bibliographic record
Abstract
OBJECTIVE: To examine the prevalence and characteristics of medical device-related pressure injuries (MDR PIs) in a large, generalizable database. METHODS: This study is a retrospective analysis of the 2016 International Pressure Ulcer Prevalence data. Data were limited to US and Canadian facilities. Facilities included acute care, long-term care, rehabilitation, long-term acute care hospitals, and hospice. Analysis included 102,865 adult patients; 99,876 had complete data and were the focus of the analysis and are reported in the results below. RESULTS: The overall PI prevalence was 7.2% (n = 7189), and the facility-acquired prevalence was 3.1% (n = 3113). The prevalence of MDR PIs was 0.60% (n = 601), which included both mucosal and nonmucosal MDR PIs. In this study, 75% of MDR PIs were facility acquired, whereas non-MDR PIs were most commonly present on admission. Facility-acquired MDR PIs formed 3 days faster than facility-acquired non-MDR PIs (12 vs 15 days; P < .05). By stage, most MDR PIs were superficial (58% were Stage 1 or 2), 15% were deep-tissue PIs, and 22% were full-thickness PIs (Stage 3 or 4 or unstageable). The most common anatomic locations for MDR PIs were the ears (29%) and the feet (12%). The most common devices associated with MDR PIs were nasal oxygen tubes, 26%; other, 19%; cast/splints, 12%; and continuous positive airway pressure/bilevel positive airway pressure masks, 9%. CONCLUSIONS: Because MDR PIs form faster than non-MDR PIs, timely proactive assessment and prevention measures are critical. Most MDR PIs occurred at the face and head region, and the ears specifically. The most common devices linked with MDR PIs were oxygen tubing and masks, making assessment and prevention efforts critical for patients who require those devices.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".