Non-Invasive Respiratory Volume Monitoring to Detect Apnea in Post-Operative Patients: Case Series
Bibliographic record
Abstract
Obstructive sleep apnea (OSA) is a potential independent risk factor for postoperative complications, adverse surgical outcomes, and longer hospital stays. Obese patients with OSA have increased post-operative complications. An estimated 25-30% of pre-operative patients are at a high risk for OSA. A novel, non-invasive respiratory volume monitor (RVM) has been developed to provide a real time respiratory curve demonstrating lung volumes as well as a continuous, display of minute ventilation, tidal volume and respiratory rate. Clinical application of this device in the post-anesthesia care unit (PACU) can "unmask" post-operative apneic events resulting from partial or complete airway collapse due to the residual effects of narcotic administration and volatile and/or intravenous anesthetics. Clinical examples from two patients, one with known OSA and one without a previous diagnosis of OSA, monitored in the PACU with RVM are presented here. Post-operatively both patients had an increase in apneic episodes with significant decreases in their MV during apneic episodes after opioid administration as compared to pre-op baseline. In addition, oxygen saturation, for both patients, which is an essential component of current respiratory monitoring remained normal in the cases presented, despite the significant decreases in MV. Continuous RVM monitoring demonstrates both changes in respiratory patterns and overall adequacy of ventilation, and allows practitioners to quantify the increase in the number and duration apneic episodes as a response to narcotic administration. These case studies demonstrate that a non-invasive respiratory volume monitoring system can detect and quantify respiratory disturbances that currently go undetected.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".