Long-term Health Outcomes and Health System Costs Associated With Surgical Site Infections
Bibliographic record
Abstract
OBJECTIVES: To examine the association between surgical site infections (SSIs) and hospital readmissions and all-cause mortality, and to estimate the attributable health care costs of SSIs 1 year following surgery. BACKGROUND: SSIs are a common postoperative complication; the long-term impact of SSI on health outcomes and costs has not been formally evaluated. METHODS: This retrospective cohort study included all adult patients who underwent surgery at the 1202-bed teaching hospital in Ottawa, Ontario, Canada, and were included in the National Surgical Quality Improvement Program database between 2010 and 2015. The study exposure was postoperative SSI. The study outcomes included hospital readmission, all-cause mortality, and health care costs at 1 year (primary) and at 30 days and 90 days (secondary) following surgery. RESULTS: We identified 14,351 patients, including 795 patients with SSIs. Our multivariable analyses that accounted for competing risks demonstrated that at 1-year following the index date, superficial and deep/organ space SSIs were significantly associated with an increase in hospital readmission [hazard ratio (HR) = 1.63, 95% confidence interval (95% CI) 1.39-1.92 and HR = 3.49, (95% CI 2.76-4.17, respectively) and all-cause mortality (HR = 1.35, 95% CI 1.10-1.98 and HR = 2.21, 95% CI 1.44-2.78, respectively]. At 1 year after surgery, patients with superficial and deep/organ space SSIs incurred higher health care costs C$20,648 (95% CI) C$16,980- C$24,112and C$53,075 (95% CI) C$44,628- C$60,936), than non-SSI patients. CONCLUSION: SSIs, especially deep/organ space SSI, contribute to adverse health outcomes and health care costs across the entire year after surgery. Our findings highlight the importance of effective prevention/monitoring strategies targeting both short- and long-term consequences of SSI.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".