A Single-Centre Canadian Cost Analysis for the Care and Management of Injection Drug Use-Associated Infective Endocarditis Requiring Cardiac Surgery
Bibliographic record
Abstract
Background: No Canadian data are available on the economic impact of cardiac surgery hospitalizations for injection drug use-related infective endocarditis (IDU-IE) during the current opioid crisis. This study aimed to quantify the healthcare system cost of care for patients with IDU-IE who required cardiac surgery in Calgary, Alberta, Canada. Methods: A retrospective cohort analysis of the cost of care for patients diagnosed with IDU-IE who had cardiac surgery between 2013 and 2019 was performed. The overall and categorical costs to the Canadian healthcare system for this specific population were quantified using the Alberta Health Services patient coding system and the Alberta Financial General Ledger. Results: Eighteen patients were diagnosed with IDU-IE and had cardiac surgery. The majority of patients underwent isolated tricuspid valve surgery. The mean postoperative intensive care unit and total hospital stays were 5.2 days and 24.9 days, respectively. No 30-day or in-hospital mortalities occurred. The average cost per patient from diagnosis to discharge was CAD$131,072 ± $17,844. The average cost of surgical intervention and postoperative course was CAD$63,090 ± $7983. The highest cost contributor was the cost of in-hospital nursing care. Conclusions: Patients diagnosed with IDU-IE who have cardiac surgery have higher per-patient cost than the general cardiac surgery population, as shown by comparison to other available data. This specific patient population has unique care needs that should be optimized to improve patient outcomes and improve healthcare 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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.007 | 0.011 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".