Quality of life metrics in LVAD patients after hemocompatibility‐related adverse events
Bibliographic record
Abstract
BACKGROUND: Hemocompatibility-related adverse events (HRAE) negatively influence survival. However, no study has examined the impact of these events on health-related quality of life (HRQOL) and functional outcomes following continuous-flow left ventricular assist device implantation (CF-LVAD). We assessed the impact of HRAE events on HRQOL and hypothesized that HRAE's adversely impact HRQOL and functional outcomes. METHODS: INTERMACS database identified patients undergoing primary CF-LVAD implantation from 2008 to 2017. HRAEs included stroke, non-surgical bleeding, hemolysis, and pump thrombosis and were identified as defined in the literature. HRAEs were further stratified as Tier 1-2 and disabling stroke events. Time-series analysis was executed for HRAE patients with values pre-HRAE, post-HRAE, and closest to 12 months follow up. Local polynomial regression curves modeling individual patients were superimposed into "spaghetti" plots. RESULTS: All HRQOL and functional metrics improved in patients over time, despite HRAE complications. However, these patient metrics were significantly reduced compared to the non-HRAE cohort. Advanced data visualization techniques noted a decline after experiencing an HRAE with a subsequent recovery to baseline levels or higher. Six-minute walk test (6MWT) was noted to be most affected in the post-HRAE period but recovered similar to other metrics. CONCLUSIONS: The burden of HRAE following CF-LVAD implantation did not negatively impact the quality of life. However, the 6MWT did not increase in the post-HRAE period in all HRAE patients. Improvement of heart failure symptoms after CF-LVAD coupled with optimal management following the HRAE act to preserve the enhanced quality of life.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".