Impact of sarcopenia on outcomes in surgical patients: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Surgeons have historically used age as a preoperative predictor of postoperative outcomes. Sarcopenia, the loss of skeletal muscle mass due to disease or biological age, has been proposed as a more accurate risk predictor. The prognostic value of sarcopenia assessment in surgical patients remains poorly understood. Therefore, the authors aimed to synthesize the available literature and investigate the impact of sarcopenia on perioperative and postoperative outcomes across all surgical specialties. METHODS: The authors systematically assessed the prognostic value of sarcopenia on postoperative outcomes by conducting a systematic review and meta-analysis according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, searching the PubMed/MEDLINE and EMBASE databases from inception to 1st October 2022. Their primary outcomes were complication occurrence, mortality, length of operation and hospital stay, discharge to home, and postdischarge survival rate at 1, 3, and 5 years. Subgroup analysis was performed by stratifying complications according to the Clavien-Dindo classification system. Sensitivity analysis was performed by focusing on studies with an oncological, cardiovascular, emergency, or transplant surgery population and on those of higher quality or prospective study design. RESULTS: A total of 294 studies comprising 97 643 patients, of which 33 070 had sarcopenia, were included in our analysis. Sarcopenia was associated with significantly poorer postoperative outcomes, including greater mortality, complication occurrence, length of hospital stay, and lower rates of discharge to home (all P <0.00001). A significantly lower survival rate in patients with sarcopenia was noted at 1, 3, and 5 years (all P <0.00001) after surgery. Subgroup analysis confirmed higher rates of complications and mortality in oncological (both P <0.00001), cardiovascular (both P <0.00001), and emergency ( P =0.03 and P =0.04, respectively) patients with sarcopenia. In the transplant surgery cohort, mortality was significantly higher in patients with sarcopenia ( P <0.00001). Among all patients undergoing surgery for inflammatory bowel disease, the frequency of complications was significantly increased among sarcopenic patients ( P =0.007). Sensitivity analysis based on higher quality studies and prospective studies showed that sarcopenia remained a significant predictor of mortality and complication occurrence (all P <0.00001). CONCLUSION: Sarcopenia is a significant predictor of poorer outcomes in surgical patients. Preoperative assessment of sarcopenia can help surgeons identify patients at risk, critically balance eligibility, and refine perioperative management. Large-scale studies are required to further validate the importance of sarcopenia as a prognostic indicator of perioperative risk, especially in surgical subspecialties.
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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 | 0.027 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.038 |
| Bibliometrics | 0.007 | 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".