Quality Improvement Initiatives for Pleural Infection Managed with Intrapleural Therapy
Bibliographic record
Abstract
Abstract Rationale Pleural infection is associated with significant mortality, and its management is complex. Little attention has been given to care-process metrics such as management delays, pleural drainage practices, and adequacy of intrapleural therapy administration despite their potential impact on outcomes. Audits revealed gaps in those care processes in our institution. Objectives To assess the impact of quality-improvement initiatives on pleural effusion management in adults. Methods We performed a retrospective comparison of patients treated with intrapleural therapy for pleural infection at the McGill University Health Center before (April 2013 to April 2016; N = 109) and after interventions (June 2020 to June 2021; N = 44). Interventions included a pleural drainage policy and order set, an intrapleural therapy protocol and preprinted order, implementation of intrapleural therapy administration by nurses, local pleural infection guideline development, and an online learning module for physicians. Major outcomes (length of stay, mortality, surgical treatment) and care-process metrics (management delays, pleural drainage practices, intrapleural therapy administration) were compared between the two periods. Results After implementation of the interventions, in-hospital mortality and length of stay were unchanged, but the incidence of surgical management went from 14% to 0% (P = 0.01). Delays in drain insertion and intrapleural therapy initiation were not significantly different. Insertion of drains smaller than 12 F decreased from 51% to 7% (P < 0.001). Drain blockage decreased from 20% to 2% (P = 0.004). The incidence of additional drain insertion went from 62% to 48% (P = 0.12). After interventions, 70% of intrapleural therapy doses were given by nurses, the intrapleural therapy protocol was more often adequately followed, fewer doses were missed, and less extended therapy was prescribed. Complications related to drain insertion and intrapleural therapy were similar between the two periods. Conclusions After the implementation of multifaceted quality improvement interventions for pleural infection including the involvement of nurses in pleural drain flushing and intrapleural therapy, improvements were observed in intrapleural therapy administration, chest drainage practices, and need for surgery. However, length of stay, mortality, and management delays were unchanged.
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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 | 0.010 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".