Continuous On-Demand Diaphragm Neurostimulation to Prevent Diaphragm Inactivity During Mechanical Ventilation: A Phase 1 Clinical Trial (STIMULUS)
Bibliographic record
Abstract
Abstract Rationale Diaphragm inactivity during invasive mechanical ventilation may predispose the lung and diaphragm to injury and is associated with adverse clinical outcomes. Objectives Assess the feasibility of continuous on-demand diaphragm neurostimulation–assisted mechanical ventilation to maintain diaphragm activity in the absence of respiratory drive for at least 24 hours of mechanical ventilation. Methods In a single-center phase 1 clinical trial, patients receiving invasive mechanical ventilation for acute hypoxemic respiratory failure or after thoracic surgery underwent transvenous diaphragm neurostimulation delivered in synchrony with mechanical ventilation. Diaphragm neurostimulation was delivered when breaths were initiated by the ventilator and not by the patient until a successful spontaneous breathing trial was performed or for up to 7 days. The coprimary outcomes were safety and feasibility of maintaining diaphragm activity over the first 24 hours of intervention. Measurements and Main Results Twenty participants were enrolled and 19 underwent study procedures. Diaphragm neurostimulation was successfully initiated in all 19 patients (100%), and on-target diaphragm activity was maintained for ⩾50% of hours of passive mechanical ventilation over the initial 24-hour period in 18/19 (95%) patients. Diaphragm neurostimulation was well tolerated; one pneumothorax unrelated to the device occurred after subclavian catheter placement before surgery. Over the 7-day study period, diaphragm activity was maintained during a median of 100% (interquartile range, 95–100%) hours with absent respiratory drive. Conclusions Continuous on-demand diaphragm neurostimulation–assisted mechanical ventilation is feasible and can prevent diaphragm inactivity during mechanical ventilation. Clinical Trial registered with www.clinicaltrials.gov (NCT05465083).
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".