Sarcopenia and risk of postoperative pneumonia: a systematic review and meta-analysis
Bibliographic record
Abstract
• This study was the first study utilizing the quantitative analysis method to explore the impact of preoperative sarcopenia on postoperative pneumonia. • Meta-analysis revealed that individuals with sarcopenia faced a 2.62 times higher risk of postoperative pneumonia compared to those without sarcopenia. • This study holds significant clinical guidance for surgical patients: preventions and interventions targeting sarcopenia should be considered to improve outcomes. Identifying patients at risk for postoperative pneumonia and preventing it in advance is crucial for improving the prognoses of patients undergoing surgery. This review aimed to interpret the predictive value of sarcopenia on postoperative pneumonia. Science Citation Index Expanded (SCIE), Embase, Medline, and Cochrane Central Register of Controlled Trials were searched from inception to August 2nd, 2023 to retrieve eligible studies. The risk of bias was assessed by the Newcastle-Ottawa Scale (NOS). For each study, we computed the odds ratio (OR) and 95% confidence interval (CI) for postoperative pneumonia in patients with and without preoperative sarcopenia, and the I-squared (I 2 ) test was employed to estimate heterogeneity. The search identified 6530 studies, and 32 studies including 114,532 participants were analyzed in this review. In most of the studies included, the risk of bias was moderate. The most reported surgical site was the chest and abdomen, followed by the abdomen, chest, limbs and spine, and head and neck. Overall, patients with preoperative sarcopenia have a 2.62-fold increased risk of developing postoperative pneumonia compared to non-sarcopenic patients [OR 2.62 (I 2 = 67.5%, 95%CI 2.04–3.37). Subgroup analysis focusing on different surgical sites revealed that sarcopenia has the strongest predictive effect on postoperative pneumonia following abdominal surgery (OR 4.69, I 2 = 0, 95% CI 3.06–7.19). Subgroup analyses targeting different types of research revealed that sarcopenia has a stronger predictive effect on postoperative pneumonia in prospective studies (OR 5.84 vs. 2.22). Our research findings indicate that preoperative sarcopenia significantly increases the risk of postoperative pneumonia. Future high-quality prospective studies and intervention studies are needed to validate the relationship between sarcopenia and postoperative pneumonia and improve patient outcomes.
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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 | 0.035 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.038 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".