The Feasibility of a Completely Automated Total IV Anesthesia Drug Delivery System for Cardiac Surgery
Bibliographic record
Abstract
BACKGROUND: In this pilot study, we tested a novel automatic anesthesia system for closed-loop administration of IV anesthesia drugs for cardiac surgical procedures with cardiopulmonary bypass. This anesthesia drug delivery robot integrates all 3 components of general anesthesia: hypnosis, analgesia, and muscle relaxation. METHODS: Twenty patients scheduled for elective cardiac surgery with cardiopulmonary bypass were enrolled. Propofol, remifentanil, and rocuronium were administered using closed-loop feedback control. The main objective was the feasibility of closed-loop anesthesia defined as successful automated cardiac anesthesia without manual override by the attending anesthesiologist. Secondary qualitative observations were clinical and controller performances. The clinical performance of hypnosis control was the efficacy to maintain a bispectral index (BIS) of 45. To evaluate the hypnosis performance, BIS values were stratified into 4 categories: "excellent," "good," "poor," and "inadequate" hypnosis control defined as BIS values within 10%, ranging from 11% to 20%, ranging from 21% to 30%, or >30% of the target value, respectively. The clinical performance of analgesia was the efficacy to maintain NociMap values close to 0. The analgesia performance was assessed classifying the NociMap values in 3 pain control groups: -33 to +33 representing excellent pain control, -34 to -66 and +34 to +66 representing good pain control, and -67 to -100 and +67 to +100 representing insufficient pain control. The controller performance was calculated using the Varvel parameters. RESULTS: Robotic anesthesia was successful in 16 patients, which is equivalent to 80% (97.5% confidence interval [CI], 53%-95%) of the patients undergoing cardiac surgery. Four patients were excluded from the final analysis because of technical problems with the automated anesthesia delivery system. The secondary qualitative observations revealed that the clinical performance of hypnosis allowed an excellent and good control during 70% (97.5% CI, 63%-76%) of maintenance time and an insufficient clinical performance of analgesia for only 3% (97.5% CI, 1%-6%) of maintenance time. CONCLUSIONS: The completely automated closed-loop system tested in this investigation could be used successfully and safely for cardiac surgery necessitating cardiopulmonary bypass. The results of the present trial showed satisfactory clinical performance of anesthesia control.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".