The Impact of Left Ventricular Assist Device Infections on Postcardiac Transplant Outcomes: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Left ventricular assist devices (LVADs) are associated with numerous short- and long-term complications, including infection. The impact LVAD infections have on clinical outcomes after transplantation is not well established. We sought to determine whether the presence of infection while on LVAD support negatively influences outcomes after cardiac transplantation. We searched electronic databases and bibliographies for full text studies that identified LVAD infections during support and also reported on posttransplant outcomes. A meta-analysis of posttransplant survival was conducted using a random effects model. Of 2,373 records, 13 bridge to transplant (BTT) cohort studies were selected (n = 6,631, 82% male, mean age 50.7 ± 2.7 years). A total of 6,067 records (91.5%) received transplant. There were 3,718 (56.1%) continuous-flow LVADs (CF-LVADs), 1,752 (26.4%) pulsatile LVADs, and 1,161 (17.5%) unknown type records. A total of 2,586 records (39.0%) developed LVAD infections. Patients with LVAD infections were younger (50.5 ± 1.5 vs. 51.3 ± 1.5, p = 0.02), had higher body mass indeices (BMIs) (28.4 ± 0.7 vs. 26.8 ± 0.4, p < 0.01), and longer LVAD support times (347.0 ± 157.6 days vs. 180.2 ± 106.0 days, p < 0.01). Meta-analysis demonstrated increased posttransplant mortality in those patients who had an LVAD infection (hazard ratio [HR] 1.30, 95% CI: 1.16-1.46, p < 0.001). Subgroup meta-analyses by continuous-flow and pulsatile device type demonstrated significant increased risk of death for both types of devices (HR 1.47, 95% CI: 1.22-1.76, p < 0.001 and 1.71, 95% CI: 1.19-2.45, p = 0.004, respectively). Patients who develop LVAD infections are younger, have higher BMIs and longer LVAD support times. Our data suggests that LVAD-related infections result in a 30% increase in postcardiac transplantation mortality. Strategies to prevent LVAD infections should be implemented to improve posttransplant outcomes in this high-risk population.
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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 | 0.010 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.032 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".