Sarcopenia Predicts Adverse Prognosis in Patients with Heart Failure: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background: This study aims to assess whether sarcopenia can be used to predict prognosis in patients with heart failure (HF) and if different diagnostic criteria for sarcopenia and diverse regions where studies were conducted could affect prognostic outcomes, thus providing a preliminary basis for early identification and prediction of poor prognosis in HF. Methods: The PubMed, Cochrane, Embase, and CNKI (China National Knowledge Infrastructure) databases were searched from inception until March 2023. Cohort studies evaluating the prognostic effect of sarcopenia in patients with HF were included. Two authors independently assessed the studies according to the Newcastle-Ottawa Scale. The meta-analyses were performed using RevMan 5.3 software. The study results were reported using a checklist of Preferred Reporting Items for Systematic Reviews and Meta-analyses were used to report the study results. Results: A total of 12 studies with 3696 HF patients were included. The results showed that the sarcopenia population had a higher risk of all-cause mortality (HR (hazard ratio) = 1.98, 95% CI (confidence interval): 1.61-2.44) and major adverse cardiovascular events (MACE) (HR = 1.24, 95% CI: 1.06-1.45) compared to the non-sarcopenia population. Moreover, the subgroup analysis reported that different diagnostic criteria for sarcopenia and diverse regions were statistically significant for all-cause mortality, except for the Europe subgroup (HR = 1.34, 95% CI: 0.89-2.02). In the subgroup analysis of MACE, all subgroups were statistically significant except for the European Working Group on Sarcopenia in Older People (EWGSOP) (HR = 1.39, 95% CI: 0.86-2.25) and European subgroups (HR = 1.39, 95% CI: 0.86-2.25). Conclusions: Sarcopenia is associated with poor prognosis, including all-cause mortality and MACE, in patients with HF. However, due to the adoption of various diagnostic criteria in different regions of the world, these results need further validation.
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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 | 0.014 | 0.032 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.036 |
| Bibliometrics | 0.007 | 0.008 |
| 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".