Predictors of 1-year mortality in heart transplant recipients: a systematic review and meta-analysis
Bibliographic record
Abstract
OBJECTIVE: A systematic summary of the observational studies informing heart transplant guideline recommendations for selection of candidates and donors has thus far been unavailable. We performed a meta-analysis to better understand the impact of such known risk factors. METHODS: We systematically searched and meta-analysed the association between known pretransplant factor and 1-year mortality identified by multivariable regression models. Our review used the Grading of Recommendations, Assessment, Development and Evaluation for assessing the quality of assessment. We pooled risk estimates by using random effects models. RESULTS: Recipient variables including age (HR 1.16 per 10-year increase, 95% CI 1.10-1.22, high quality), congenital aetiology (HR 2.35, 95% CI 1.62 to 3.41, moderate quality), diabetes (HR 1.37, 95% CI 1.15 to 1.62, high quality), creatinine (HR 1.11 per 1 mg/dL increase, 95% CI 1.06 to 1.16, high quality), mechanical ventilation (HR 2.46, 95% CI 1.48 to 4.09, low quality) and short-term mechanical circulatory support (MCS) (HR 2.47, 95% CI 1.04 to 5.87, low quality) were significantly associated with 1-year mortality. Donor age (HR 1.20 per 10-year increase, 95% CI 1.14 to 1.26, high quality) and female donor to male recipient sex mismatch (HR 1.38, 95% CI 1.06 to 1.80, high quality) were significantly associated with 1-year mortality. None of the operative factors proved significant predictors. CONCLUSION: High-quality and moderate-quality evidence demonstrates that recipient age, congenital aetiology, creatinine, pulsatile MCS, donor age and female donor to male recipient sex mismatch are associated with 1-year mortality post heart transplant. The results of this study should inform future guideline and predictive model development.
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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 | 0.011 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.034 |
| Bibliometrics | 0.005 | 0.006 |
| 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".