1024. First Episode Infective Endocarditis in Persons Who Inject Drugs (PWIDs); A Retrospective Cohort Study
Bibliographic record
Abstract
Abstract Background Persons who inject drugs (PWID) represent a distinct demographic of patients with infective endocarditis (IE). Many centers do not perform valvular surgery on these patients due to concerns about poor outcomes. Methods Retrospective cohort study comparing PWID patients to non-PWID patients presenting between February 2007 and March 2016 in London, Ontario, among adult (>18) inpatients with first episode IE. Results In 370 first episode IE cases, 53.9% occurred in PWIDs. PWID patients were younger (35.4 SD 10.0 vs. 59.4 SD 14.9) (P < 0.001), more likely to have right-sided infection [125/202 (62%), vs. 16/168 (9.5%) (P < 0.001)], and more often due to S. aureus (156/202 (77.3%) vs. 54/168 (32.1%), P < 0.001). Myocardial and aortic root abscesses were less common in PWIDs [17/202 (8.4%) vs. 50/168 (30%) (P < 0.01)]. There was no difference in the frequency of noncardiac complications. In total, 36.5% of patients were treated surgically with PWID patients less likely to undergo surgery [39/202 (19.3%) vs. 98/168 (58%) P < 0.001]. Cox regression analysis identified the protective effect of cardiac surgery with regards to survival in all patients, with a hazard ratio of 0.49 (95% CI 0.31–0.76, P < 0.001), as well as among PWIDs (HR 0.39, 95% CI 0.17–0.87, P = 0.02). Among all patients, lower survival was associated with older age (HR 1.03, 95% CI 1.00–1.05, P < 0.001), injection drug use (HR 2.72, 95% CI 1.52–4.88, P < 0.001), left-sided infection (HR 3.48, 95% CI 2.01–6.03, P < 0.001), and bilateral infection (HR 3.19, 95% CI 1.45–7.01, P = 0.004). The lower survival of left-sided infection (HR 4.01, 95% CI 1.97–8.18, P < 0.001) or bilateral infection (HR 6.94, 95% CI 2.39–20.2,P < 0.001) was re-demonstrated in PWIDs. Conclusion This study identifies important clinical differences between PWIDs and nondrug users with respect to valve involvement, causative organism, complications, and management strategies. Our results highlight the important role of surgical treatment in a carefully selected PWID patient population. Disclosures All authors: No reported disclosures.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".