Stress Index Can Be Accurately and Reliably Assessed by Visually Inspecting Ventilator Waveforms
Bibliographic record
Abstract
BACKGROUND: Stress index provides a noninvasive approach to detect injurious ventilation patterns and to personalize ventilator settings. Obtaining the stress index (SI), however, requires quantitatively analyzing the shape of pressure-time curve with dedicated instruments or a specific ventilator, which may encumber its clinical implementation. We hypothesized that the SI could be qualitatively determined through a visual inspection of ventilator waveforms. METHODS: Thirty-six adult subjects undergoing volume controlled ventilation without spontaneous breathing were enrolled. For each subject, 2 trained clinicians visually inspected the pressure-time curve directly from the ventilator screen. They then qualitatively categorized the shape of pressure-time curve as linear, a downward concavity, or an upward concavity at the bedside. We simultaneously recorded airway pressure and flow signals using a dedicated instrument. A quantitative off-line analysis was performed to calculate the SI using specific research software. This quantitative analysis of the SI served as the reference method for classifying the shape of the pressure-time curve (ie, linear, a downward concavity, or an upward concavity). We compared the SI categorized by visual inspection with that by the reference. RESULTS: We obtained 200 SI assessments of pressure-time curves, among which 125 (63%) were linear, 55 (27%) were a downward concavity, and 20 (10%) were an upward concavity as determined by the reference method. The overall accuracy of visual inspection and weighted kappa statistic (95% CI) was 93% (88-96%) and 0.88 (0.82-0.94), respectively. The sensitivity and specificity to distinguish a downward concavity from a linear shape were 91% and 98%, respectively. The respective sensitivity and specificity to distinguish an upward concavity from a linear shape were 95% and 95%. CONCLUSIONS: Visual inspection of the pressure-time curve on the ventilator screen is a simple and reliable approach to assess SI at the bedside. This simplification may facilitate the implementation of SI in clinical practice to personalize mechanical ventilation. (ClinicalTrials.gov registration NCT03096106.).
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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".