Poor performance of psoas muscle index for identification of patients with higher waitlist mortality risk in cirrhosis
Bibliographic record
Abstract
Abstract Background Sarcopenia, characterized by low muscle mass, associates with mortality in patients with cirrhosis. Skeletal muscle area in a single computed tomography image at the level of the third lumbar vertebrate (L3) is a valid representative of whole body muscle mass. Controversy remains regarding applicability of psoas muscle to identify patients at greater risk of mortality. We aimed to determine psoas muscle index (PMI) association with skeletal muscle index (SMI) and to evaluate the capacity of PMI to predict liver transplant waitlist mortality. Methods We evaluated listed adult patients with cirrhosis from 2012 to 2013 at four North American liver transplant centres. From L3 computed tomography images within 3 months of listing, we determined SMI and PMI expressed by cm2/m2. Low SMI was defined as SMI <39 cm2/m2 in women and <50 cm2/m2 in men as published by us earlier. Cut‐offs for PMI to predict mortality were established using a receiver‐operating characteristic analysis. Mortality predictors were determined using competing‐risk analysis with reported results as subdistribution hazard ratios (sHRs). Results Of 353 waitlist candidates, 68% were men, mean age 56 ± 9 years, and Model for End‐stage Liver Disease of 16 ± 8 points. Low SMI was present more frequently in men than women (51 vs. 36%, P = 0.02). Moderately strong correlation between SMI and PMI was observed (r > 0.7, P < 0.001). Low PMI (males < 5.1 cm2/m2; females < 4.3 cm2/m2) yielded poor and moderate concordance with low SMI in men and women, respectively (Kappa coefficient 0.31 and 0.63). SMI (39 ± 9 vs. 43 ± 7 cm2/m2; P = 0.009) and PMI (4.4 ± 1.3 vs. 5.2 ± 1.1 cm2/m2; P = 0.001) were lower in women who died and/or were delisted (compared with non‐deceased patients) whereas men who died and/or were delisted had only lower SMI (47 ± 7 vs. 51 ± 9 cm2/m2; P = 0.003), but not PMI compared with non‐deceased patients. In women, both SMI (sHR 0.94, P = 0.048) and PMI (sHR 0.58, P = 0.002) were predictors of mortality, while in men, SMI was significant (sHR 0.95, P = 0.001) and PMI showed a trend to be (sHR 0.85, P = 0.09) associated with mortality. Overall, 104 patients (29%) were misclassified between SMI and PMI categories. Using PMI cut‐offs, 66% and 28% of low SMI men and women, who have a higher risk of mortality, were incorrectly classified as low risk. Conclusions Skeletal muscle index is a more complete and robust measurement than PMI, especially in men with cirrhosis. Low PMI identifies an incomplete subset of patients at increased risk of mortality indicated by low SMI. Given the poor performance of PMI, SMI should not be substituted by PMI.
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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 | 0.000 |
| 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".