Electrical Stimulation for Pressure Injuries: A Health Technology Assessment.
Bibliographic record
Abstract
BACKGROUND: Pressure injuries (bedsores) are common and reduce quality of life. They are also costly and difficult to treat. This health technology assessment evaluates the effectiveness, cost-effectiveness, budget impact, and lived experience of adding electrical stimulation to standard wound care for pressure injuries. METHODS: We conducted a systematic search for studies published to December 7, 2016, limited to randomized and non-randomized controlled trials examining the effectiveness of electrical stimulation plus standard wound care versus standard wound care alone for patients with pressure injuries. We assessed the quality of evidence through Grading of Recommendations Assessment, Development, and Evaluation (GRADE). In addition, we conducted an economic literature review and a budget impact analysis to assess the cost-effectiveness and affordability of electrical stimulation for treatment of pressure ulcers in Ontario. Given uncertainties in clinical evidence and resource use, we did not conduct a primary economic evaluation. Finally, we conducted qualitative interviews with patients and caregivers about their experiences with pressure injuries, currently available treatments, and (if applicable) electrical stimulation. RESULTS: Nine randomized controlled trials and two non-randomized controlled trials were found from the systematic search. There was no significant difference in complete pressure injury healing between adjunct electrical stimulation and standard wound care. There was a significant difference in wound surface area reduction favouring electrical stimulation compared with standard wound care.The only study on cost-effectiveness of electrical stimulation was partially applicable to the patient population of interest. Therefore, the cost-effectiveness of electrical stimulation cannot be determined. We estimate that the cost of publicly funding electrical stimulation for pressure injuries would be $0.77 to $3.85 million yearly for the next 5 years.Patients and caregivers reported that pressure injuries were burdensome and reduced their quality of life. Patients and caregivers also noted that electrical stimulation seemed to reduce the time it took the wounds to heal. CONCLUSIONS: While electrical stimulation is safe to use (GRADE quality of evidence: high) there is uncertainty about whether it improves wound healing (GRADE quality of evidence: low). In Ontario, publicly funding electrical stimulation for pressure injuries could result in extra costs of $0.77 to $3.85 million yearly for the next 5 years.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".