Active Chest Tube Clearance Added to an Enhanced Recovery After Cardiac Surgery (ERAS) Program Improves Outcomes and Reduces Resource Utilization
Bibliographic record
Abstract
Objective: We initiated a cardiac enhanced recovery after cardiac surgery (ERAS) program in early 2019, protocolized it and applied it to all patients in 2020, and added the use of active chest tube clearance (ATC) in 2022. Prospective data collection of ATC patients was compared with historical controls to determine the impact of the device on outcomes. Methods: The study comprised 1,334 patients with 650 in the control group (group 1) and 684 in the ATC intervention group (group 2). Group 1 (historical control) consisted of 650 patients from January 1, 2020, to October 31, 2020, and January 1, 2021, to October 31, 2021. From October 31, 2021, to December 31, 2021, we introduced ATC use per protocol. Group 2 (ATC) consisted of 684 patients treated consecutively from January 1, 2022, to August 31, 2023, with ATC. The preoperative characteristics and operative procedures between groups were similar. Results: Patients in the ATC intervention (group 2) experienced a 41% reduction in the composite of retained blood syndrome (8.2% in group 1 vs 4.8% in group 2, P = 0.014). Postoperative atrial fibrillation was 17% reduced for group 2 (178 [33.8%] in group 1 vs 158 [28.1%] in group 2, P = 0.049). Group 2 had a 30% reduction in median intensive care unit (ICU) hours (51.6 [30.1 to 76.9] h in group 1 vs 36.3 [20.7 to 687] h in group 2, P < 0.001). Twenty-one patients (3.2%) were readmitted to the ICU after initial discharge to the step-down unit in group 1 and only 8 (1.17%) in group 2 ( P = 0.013). Conclusions: The addition of the ATC intervention to an established ERAS program in a high-volume private practice setting decreased complications, improved outcomes, and decreased resource utilization.
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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 | 0.003 |
| 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 | 0.001 |
| 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".