Long-term outcomes following infection of cardiac implantable electronic devices: a prospective matched cohort study
Bibliographic record
Abstract
OBJECTIVE: To assess long-term outcomes and predictors of mortality in patients treated according to current recommendations for cardiac implantable electronic device (CIED) infection. DESIGN: Two-group matched cohort study. SETTING: Tertiary-care institution. PATIENTS: Consecutive patients admitted for CIED infection between 2004 and 2008 were prospectively enrolled. Study subjects were matched to a cohort of uninfected CIED patients by age, sex and type of device. INTERVENTIONS: In all infected patients, the therapeutic approach consisted of complete hardware removal whenever possible, antimicrobial therapy, and implantation of a new device, if indicated. Patients were systematically followed, with standardised outcomes assessment. MAIN OUTCOME MEASURES: All-cause mortality and predictors of long-term mortality. RESULTS: 197 patients were included and matched 1:1 to controls. Pocket infections were present in 41.1% and definite or suspected infective endocarditis in 58.9%. Total or subtotal hardware removal was achieved in 98.5% of cases. Median follow up was 25 months (12-70). Mortality rates in the study group and controls were 14.3% vs 11.0% (NS) at 1 year and 35.4% vs 27.0% (p=NS) at 5 years. Independent predictors of long-term mortality were older age (HR=1.09, p<0.001), cardiac resynchronisation therapy (HR=3.70, p=0.001), thrombocytopenia (HR=5.10, p=0.003) and renal insufficiency (HR=2.66, p=0.006). In patients with reimplanted devices, epicardial right ventricular pacemakers were associated with higher mortality (HR=2.85, p=0.034). CONCLUSION: In patients with CIED infection managed by recommended therapy, long-term mortality rates are similar to comparable controls. Independent predictors include patient and disease-related factors, in addition to implantation of right ventricular epicardial pacemakers.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".