Abstract 18267: The Economic Impact of Healthcare Associated Infections in Cardiac Surgery
Bibliographic record
Abstract
Background: Healthcare-associated infections (HAIs) are the most common non-cardiac complication after cardiac surgery, and are associated with increased morbidity, mortality and resource use. Despite being the focus of quality improvement efforts, there is little information about their economic impact. This prospective cohort study examines the incremental costs associated with HAIs within 65 days of cardiac surgery. Methods: Clinical data on major and minor infections (CDC/NHSN definitions) from 9 academic centers were merged with related financial data routinely collected by the University Health Consortium. Incremental length of stay (LOS) and cost attributable to HAIs were estimated using generalized linear models, adjusting for patient demographics, clinical history, baseline labs and surgery type. Results: The most common procedures in 4313 cardiac surgery patients were isolated valve (31%), isolated CABG (29%), and CABG/valve (12%), with a mean age 64±13 years. During the index hospitalization, 3% of patients experienced major infections, including pneumonia, sepsis, C. Difficile and surgical site infections. The most common minor infection was UTI, which occurred in 2% of patients. The adjusted average incremental cost due to major infection was nearly $50,000 (table), with the additional ICU stay increasing the index hospitalization cost by $1094/day during the first 2 weeks after surgery. Patients with major index HAIs were nearly twice as likely to be readmitted as those without. Overall, there were 855 readmissions; 19 % due to HAIs, costing on average nearly twice as much as non-HAI related readmissions. Conclusions: In an era that emphasizes early discharge and the need to avert preventable readmissions, this study shows that both are heavily influenced by infection rates and that the costs of HAIs in cardiac surgery are substantial. These data provide critical insights about the potential economic impact of infection prevention programs.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".