Sarcopenia defined by multidimensional factors and its prognostic role in heart failure: a systematic review and meta-analysis
Bibliographic record
Abstract
Objective: To perform a systematic review and meta-analysis evaluating the impact of sarcopenia-defined by reductions in muscle mass, strength, and/or function-on clinical outcomes in patients with heart failure (HF), thereby informing more effective management strategies. Methods: A comprehensive literature search was conducted through 14 February 2025, using PubMed, Embase, Cochrane Library, and CNKI to identify prospective and retrospective cohort studies involving HF patients diagnosed with sarcopenia based on Asian Working Group for Sarcopenia (AWGS), European Working Group on Sarcopenia in Older People (EWGSOP2), or Ishii criteria. Data extraction was performed using standardized forms, and study quality was assessed using the Newcastle-Ottawa Scale (NOS). Meta-analytical procedures, including heterogeneity assessment and subgroup analyses, were carried out in Stata 18.0 and R 4.4.2. Results: Fifteen studies comprising 5,713 HF patients were included. Pooled analysis demonstrated that sarcopenia significantly increased the risk of adverse clinical outcomes [hazard ratio (HR) = 1.62, 95% confidence interval (CI): 1.35-1.89], including all-cause mortality (HR = 1.89, 95% CI: 1.63-2.15) and major adverse cardiovascular events (HR = 1.37, 95% CI: 1.11-1.64). Subgroup analyses revealed that sarcopenia defined by AWGS criteria and the Ishii score was significantly associated with worse outcomes (HR = 1.63, 95% CI: 1.33-1.94; HR = 1.78, 95% CI: 1.29-2.27, respectively), whereas definitions based on EWGSOP2 did not reach statistical significance (HR = 1.87, 95% CI: 0.70-3.05). Sarcopenia identified through DXA or BIA-based muscle mass assessments was also significantly correlated with adverse outcomes (DXA: HR = 1.53, 95% CI: 1.29-1.78; BIA: HR = 1.85, 95% CI: 1.10-2.61). Statistically significant associations were observed across all remaining subgroups. Conclusion: Sarcopenia, when defined using multidimensional criteria, is significantly associated with poor clinical outcomes in patients with HF. These findings underscore the importance of implementing comprehensive sarcopenia assessments to enhance prognostic evaluation and guide early intervention. Clinically, adopting multidimensional diagnostic approaches can improve risk stratification and optimize the management of HF patients. Systematic review registration: https://inplasy.com/inplasy-2025-3-0023/, identifier INPLASY202530023.
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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 | 0.016 | 0.032 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.023 | 0.039 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".