Psoas Muscle Area as a Measure for Sarcopenia and a Predictor of Poor Health Outcomes in Chronic Kidney Disease
Bibliographic record
Abstract
Background: Sarcopenia ofen presents in parallel with chronic kidney disease (CKD), associated with a deconditioned state, chronic inflammation, and poor nutritional status. Psoas muscle area (PMA) measurements have been previously associated with mortality in CKD however, whether PMA can be useful in predicting other clinical outcomes such as hospitalizations, infections and other health events are less established.Methods: A retrospective non-matched cohort study was conducted in a community nephrology clinic in Quebec, Canada, and included laboratory, radiological, and demographic data collected from April 1, 2015, until December 31, 2021. PMA was measured by two independent readers using the DICOM RADiant Viewer Sofware.ObjecFve: The primary objective of this study is to report the association of PMA with hospitalizations, hemodialysis, mortality, and infections in patients with CKD.Main Findings: A total of 269 clinic charts were reviewed and included. Subjects had a median age of 74 years (IQR 31-92) with 58.3% reported as male. The prevalence of Grade 1 and 2, Grade 3, Grade 4 and Grade 5 CKD was 3%, 53%, 32%, and 10%. Median follow-up time was 1160 days (28-2516). Median measurement of PMA/height was 3.53 cm2/m (0.5-8.73).Statistically significant concurrent relationships were documented between PMA and event-free time for dialysis and hospitalization. The adjusted HR for PMA < 2.66 cm2/m are 0.39 (95% CI 0.16-0.94), p=0.0356. For hospital-free time, the adjusted HR for PMA quartiles showed a dose-dependent increase in effect size, with PMA about a 2-fold higher hazard risk ratio seen for a longer hospital admission-free period, (p=0.0273).The Cox hazard proportional multivariate analyses documented the concurrently negative relationships between PMA and length of survival and infection-free time, reaching a trend towards significance. For length of survival, PMA < 2.1 cm2/m had an adjusted Hazard risk reduction of 82 % (HR 0.18 (95%CI 0.024-1.38)) and similarly for infection-free time until the first event, the adjusted HR for PMA < 2 cm2/m are 0.38 (95% CI 0.17-1.0). Conclusion: Our study demonstrated an overall association between increased health complication rates with increased comorbidity and decreased psoas muscle area. Further prospective studies are needed to identify methods for muscle mass sustainability
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".