B-type peptides to predict post–liver transplant mortality: systematic review and meta-analysis
Bibliographic record
Abstract
Background: Cirrhotic patients undergoing liver transplantation are at risk of cardiac complications. Brain natriuretic peptide (BNP) and amino terminal brain natriuretic peptide (NT-BNP) are used in cardiac risk stratification. Their significance in predicting mortality risk in cirrhotic patients during or after liver transplantation is unknown. We conducted a systematic review and meta-analysis to answer this question. Methods: An electronic search of EMBASE, MEDLINE, Cochrane Central Register of Controlled Trials, Cochrane Database of Systematic Reviews (2005-September 2016), Google Scholar, and study bibliographies was conducted. Study quality was determined, and demographic and outcome data were gathered. Random effects meta-analyses of mortality-based BNP and NT-BNP level or presence of post-transplant heart failure were conducted. Results: Seven studies including 2,010 patients were identified. Demographics were similar between patients with high or low BNP or NT-BNP levels. Hepatitis C was the most prevalent etiology of cirrhosis (38%). Meta-analysis revealed a pooled relative risk of 3.1 (95% CI 1.9% to 5.0%) for post-transplant mortality based on elevated BNP or NT-BNP level. Meta-analysis also revealed a pooled relative risk of 1.6 (95% CI 1.3% to 2.1%) for post-transplant mortality if patients had demonstrated post-transplant heart failure. Conclusions: Our analysis suggests that BNP or NT-BNP measurement may help in risk stratification and provides data on post-operative mortality in cirrhotic patients undergoing liver transplantation. Discriminatory thresholds are higher in cirrhotic patients relative to prior studies with non-cirrhotic patients. However, the number of analyzed studies is limited, and our findings should be validated further through larger, prospective studies.
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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 | 0.010 | 0.024 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.039 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".