Association of comorbid burden with clinical outcomes after transcatheter aortic valve implantation
Bibliographic record
Abstract
OBJECTIVES: To investigate the association of the CharlsonComorbidity Index (CCI) with clinical outcomes after transcatheter aortic valve implantation (TAVI). BACKGROUND: Patients undergoing TAVI have high comorbid burden; however, there is limited evidence of its impact on clinical outcomes. METHODS: Data from 1887 patients from the UK, Canada, Spain, Switzerland and Italy were collected between 2007 and 2016. The association of CCI with 30-day mortality, Valve Academic Research Consortium-2 (VARC-2) composite early safety, long-term survival and length of stay (LoS) was calculated using logistic regression and Cox proportional hazard models, as a whole cohort and at a country level, through a two-stage individual participant data (IPD) random effect meta-analysis. RESULTS: =0.16 to 0.29), and approximately 50% of patients classed as low risk from four risk prediction models still presented with a CCI ≥3. Per-unit increases in total CCI were not associated with increased odds of 30-day mortality (OR 1.09, 95% CI 0.96 to 1.24) or VARC-2 early safety (OR 1.04, 95% CI 0.96 to 1.14) but were associated with increased hazard of long-term mortality (HR 1.10, 95% CI 1.05 to 1.16). The two-stage IPD meta-analysis indicated that CCI was not associated with LoS (HR 0.97, 95% CI 0.93 to 1.02). CONCLUSION: In this multicentre international study, patients undergoing TAVI had significant comorbid burden. We found a weak correlation between the CCI and well-established preoperative risks scores. The CCI had a moderate association with long-term mortality up to 5 years post-TAVI.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.007 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".