Index hospital cost of adverse events following thoracic surgery: a systematic review of economic literature
Bibliographic record
Abstract
OBJECTIVES: Adverse events (AEs) following thoracic surgery place considerable strain on healthcare systems. A rigorous evaluation of the economic impact of thoracic surgical AEs remains lacking and is required to understand the value of money of formal quality improvement initiatives. Our objective was to conduct a systematic review of all available literature focused on specific cost of postoperative AEs following thoracic surgery. DESIGN: Systematic review of the economic literature was performed, following recommendations from the Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement. DATA SOURCES: An economic search filter developed by the Canadian Agency for Drugs and Technologies in Health was applied, and MEDLINE, Embase and The Cochrane Library were searched from inception to January 2022. ELIGIBILITY CRITERIA: We included English articles involving adult patients who underwent a thoracic surgical procedure with estimated costs of postoperative complications. Eligible study designs included comparative observational studies, randomised control trials, decision analytic or cost-prediction models, cost analyses, cost or burden of illness studies, economic evaluation studies and systematic reviews and/or meta-analyses of cost analyses and cost of illness studies. DATA EXTRACTION AND SYNTHESIS: Two reviewers independently screened titles and abstracts in the first stage and full-text articles of included studies in the second stage. Disagreements during abstract and full-text screening stages were resolved via discussion until a consensus was reached. Studies were appraised for methodological quality using the Critical Appraisal Skills Program checklist. RESULTS: 3349 studies were identified: 20 met inclusion criteria. Most were conducted in the USA (12/20), evaluating AE impact on hospital expenditures (18/20). 68 procedure-specific AE mean costs were characterised (USD$). The most commonly described were anastomotic leak (mean:range) (USD$49 278:$6 176-$133 002) and pneumonia ($12 258:$2608-$34 591) following esophagectomy, and prolonged air leak ($2556:$571-$3573), respiratory failure ($19 062:$11 841-$37 812), empyema ($30 189:$23 784-$36 595), pneumonia ($15 362:$2542-$28 183), recurrent laryngeal nerve injury ($16 420:$4224-$28 616) and arrhythmia ($6835:$5833-$8659) following lobectomy. CONCLUSIONS: Hospital costs associated with AEs following thoracic surgery are substantial and varied. Quantifying costs of AEs enable future economic evaluation studies, which could help prioritising value-directed quality improvement to optimally improve outcomes and reduce costs.
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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 | 0.021 | 0.097 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.014 |
| Bibliometrics | 0.025 | 0.020 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".