Active clearance of chest tubes is associated with reduced postoperative complications and costs after cardiac surgery: a propensity matched analysis
Bibliographic record
Abstract
BACKGROUND: Chest tubes are routinely used to evacuate shed mediastinal blood in the critical care setting in the early hours after heart surgery. Inadequate evacuation of shed mediastinal blood due to chest tube clogging may result in retained blood around the heart and lungs after cardiac surgery. The objective of this study was to compare if active chest tube clearance reduces the incidence of retained blood complications and associated hospital resource utilization after cardiac surgery. METHODS: Propensity matched analysis of 697 consecutive patients who underwent cardiac surgery at a single center. 302 patients served as a baseline control (Phase 0), 58 patients in a training and compliance verification period (Phase 1) and 337 were treated prospectively using active tube clearance (Phase 2). The need to drain retained blood, pleural effusions, postoperative atrial fibrillation, ICU resource utilization and hospital costs were assessed. RESULTS: Propensity matched patients in Phase 2 had a reduced need for drainage procedures for pleural effusions (22% vs. 8.1%, p < 0.001) and reduced postoperative atrial fibrillation (37 to 25%, P = 0.011). This corresponded with fewer hours in the ICU (43.5 [24-79] vs 30 [24-49], p = < 0.001), reduced median postoperative length of stay (6 [4-8] vs 5 [4-6.25], p < 0.001) median costs reduced by $1831.45 (- 3580.52;82.38, p = 0.04) and the mean costs reduced by an average of $2696 (- 6027.59;880.93, 0.116). CONCLUSIONS: This evidence supports the concept that efforts to actively maintain chest tube patency in early recovery is useful in improving outcomes and reducing resource utilization and costs after cardiac surgery. TRIAL REGISTRATION: Clinicaltrial.gov, NCT02145858, Registered: May 23, 2014.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".