Outcomes of patients discharged home with a chest tube after lung resection: a multicentre cohort study
Bibliographic record
Abstract
Background: Prolonged air leaks are increasingly treated in the outpatient setting, with patients discharged with chest tubes in place. We evaluated the incidence and risk factors associated with readmission, empyema development and further interventions in this patient population. Methods: We undertook a retrospective cohort analysis of all patients from 4 tertiary academic centres (January 2014 to December 2017) who were discharged home with a chest tube after lung resection for a postoperative air leak lasting more than 5 days. We analyzed demographics, patient factors, surgical details, hospital readmission, reintervention, antibiotics at discharge, empyema and death. Results: Overall, 253 of 2794 patients were analyzed (9.0% of all resections), including 30 of 759 from centre 1 (4.0%), 67 of 857 from centre 2 (7.8%), 9 of 247 from centre 3 (3.6%) and 147 of 931 from centre 4 (15.8%) (p < 0.001). Our cohort consisted of 56.5% men, and had a median age of 69 (range 19–88) years. Despite similar initial lengths of stay (p = 0.588), 49 patients (19.4%) were readmitted (21%, 0%, 23% and 11% from centres 1 to 4, respectively, p = 0.029), with 18 (36.7%) developing empyema, 11 (22.4%) requiring surgery and 3 (6.1%) dying. Only chest tube duration was a significant predictor of readmission (p < 0.001) and empyema development (p = 0.003), with a nearly threefold increased odds of developing empyema when the chest tube remained in situ for more than 20 days. Conclusion: Discharge with chest tube after lung resection is associated with serious adverse events. Given the high risk of empyema development, removal of chest tubes should be considered, when appropriate, within 20 days of surgery. Our data suggest a potential need for proactive postdischarge outpatient management programs to diminish risk of morbidity and death.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".