Reduction of Chest Drain Overuse Through Implementation of a Pleural Drainage Order Set
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Small chest drains are used in many centers as the default drainage strategy for various pleural effusions. This can lead to drain overuse, which may be harmful. This study aimed to reduce chest drain overuse. METHODS: We studied consecutive pleural procedures performed in the radiology department before (August 1, 2015, to July 31, 2016) and after intervention (September 1, 2019, to January 31, 2020). Chest drains were deemed indicated or not based on criteria established by a local interdisciplinary work group. The intervention consisted of a pleural drainage order set embedded in electronic medical records. It included indications for chest drain insertion, prespecified drain sizes for each indication, fluid analyses, and postprocedure radiography orders. Overall chest drain use and proportion of nonindicated drains were the outcomes of interest. RESULTS: We reviewed a total of 288 procedures (pre-intervention) and 155 procedures (post-intervention) (thoracentesis and drains). Order-set implementation led to a reduction in drain use (86.5% vs 54.8% of all procedures, P < .001) and reduction in drain insertions in the absence of an indication (from 45.4% to 29.4% of drains, P = .01). The need for repeat procedures did not increase after order-set implementation (22.0% pre vs 17.7% post, P = .40). Complication rates and length of hospital stay did not differ significantly after the intervention. More pleural infections were treated with drain sizes of 12Fr and greater (31 vs 70%, P < .001) after order-set deployment, and direct procedural costs were reduced by 27 CAN$ per procedure. CONCLUSION: Implementation of a pleural drainage order-set reduced chest drain use, improved procedure selection according to clinical needs, and reduced direct procedural costs. In institutions where small chest drains are used as the default drainage strategy for pleural effusions, this order set can reduce chest drain overuse.
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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 | 0.003 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".