Outcomes of patients with infection related to a ventricular assist device after heart transplantation
Bibliographic record
Abstract
BACKGROUND: Despite significant advances in durable mechanical support survival, infectious complications remain the most common adverse event after ventricular assist device (VAD) implantation and the leading cause of early death after transplantation. In this study, we aim to describe our local infectious epidemiology and review short-term survival and infectious incidence rates in the post-transplantation period and assess risk factors for infectious episodes after transplantation. METHODS: Retrospective single-center study of all consecutive adult heart transplant patients from 2008 to 2017. Survival data were estimated and summarized using the Kaplan-Meier method. We quantified and evaluated the difference in the incidence rate between patients with and without infection using a Fine-Gray model. The outcome of interest is the time to first infection diagnosis with post-transplant death as the competing event. RESULTS: , P = .03). Median follow-up post-transplantation was 3.01 years. Significant risk factors for the competing risk regression for infection after heart transplantation include LVAD infection (HR 1.94, [95% CI] 1.11-3.39, P = .020) and recipient COPD (HR 2.14, [95% CI] 1.39-3.32, P = .001) when adjusted for recipient age, gender, hypertension, diabetes mellitus, and body mass index. CONCLUSIONS: Patients with LVAD-related infection had a significantly increased risk of infectious complications after heart transplantation. Further research on the avoidance of induction agents and reduced maintenance immunosuppression in this patient population is warranted.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".