Patients’ Perspectives on Transplantation While Undergoing Left Ventricular Assist Device Support
Bibliographic record
Abstract
Left-ventricular assist device (LVAD) therapy is a growing mechanical circulatory support therapy used to treat patients with advanced heart failure. There is a general assumption among clinicians that patients would prefer to accept a heart to any other treatment were they eligible. However, little research has been done to clarify the nuances of patient treatment preferences for LVAD therapy versus transplantation. The objective of this study was to investigate this treatment preference assumption from patients' perspectives. In a single-site study, 15 LVAD patients, 15 LVAD candidates, 15 LVAD decliners, and 15 LVAD caregivers (n = 60) participated in structured, in-depth interviews to assess decisional processes and treatment preferences for advanced heart failure. The interview guides were consistent with the Ottawa framework on decision-making processes. All participants were identified by the LVAD coordination team and recruited consecutively between February and November 2014. The patient and candidate groups included both LVAD designations: destination therapy (n = 22), bridge-to-transplant (n = 4), as well as four participants who deferred designation pending worsening clinical status. The interviews were analyzed using qualitative description with constant comparisons, aided by ATLAS.ti. Although LVAD eligible patients generally show a preference for heart transplantation as the ideal treatment for heart failure, some patients begin to show ambivalence as they experience LVAD therapy and begin to prefer LVAD as a long-term, destination treatment. Some themes that emerged from the interviews concerning transplantation centered on the consequences of multiple major surgeries (i.e., LVAD placement followed by heart transplantation, n = 18), fears surrounding lifestyle changes of accepting a heart transplant (n = 14) and life satisfaction with an LVAD as a "new normal" (n = 18). Findings suggest that experience with an LVAD can sometimes cause ambivalence about transplant with some patients preferring LVAD as destination even when transplant is an option. We provide clinical and programmatic implications of this, concluding with practical recommendations for how to fully address patients' goals of treatment as their health and situation changes over time.
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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 | 0.009 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| 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".