The Role of Muscle Density in Predicting the Amputation Risk in Peripheral Arterial Disease: A Tissue Composition Study Using Lower Extremity CT Angiography
Bibliographic record
Abstract
Objectives: Peripheral arterial disease (PAD) is a common vascular condition with amputation as a major complication. While muscle volume and vascular severity is often considered in risk prediction, the prognostic value of muscle density remains underexplored. Methods: In this exploratory study, we retrospectively analyzed 134 patients undergoing lower-limb computed tomography angiography between January 2018 and December 2023. Muscle density (MD), muscle volume, intermuscular adipose tissue (IMAT), and vascular severity scores were quantified using deep learning software. Patients were stratified into non-PAD, mild PAD, and critical limb ischemia (CLI) groups. Multivariate Cox regression assessed associations with amputation risk. Results: PAD patients, especially those with CLI, had lower muscle volumes (e.g., total leg: 7945.3 ± 2012.5 cm3 in CLI vs. 11,161.6 ± 4670.4 cm3 in non-PAD), lower muscle densities (e.g., lower leg: 34.0 ± 10.5 HU in CLI vs. 44.1 ± 6.9 HU in non-PAD), higher intermuscular adipose tissue volume percentage (e.g., total leg: 15.6 ± 5.4% in CLI vs. 10.5 ± 3.6% in non-PAD), and higher vascular severity scores (e.g., total leg: 12.6 ± 5.0 in CLI vs. 0.1 ± 0.3 in non-PAD), compared to non-PAD individuals. Only muscle density (MD) including that of abdominal muscle, thigh muscle, and lower leg muscle remained significant predictors of amputation risk after adjusting for confounders. Multivariate Cox regression models, adjusted for demographics and comorbidities, revealed that lower MD of abdomen (<18.7 HU; HR, 6.50, 95% CI, 1.95–21.77), thigh (<27.8 HU; HR, 5.00, 95% CI, 1.60–15.66), and lower leg (<32.4 HU; HR, 6.89, 95% CI, 2.17–21.93) muscles were independently associated with increased amputation risk. Conclusions: Muscle density, reflecting muscle quality rather than quantity, was an independent predictor of amputation risk in PAD. These findings highlight the prognostic value of muscle quality and support the integration of advanced imaging techniques, such as automated CTA-based body composition analysis, for risk stratification in PAD patients.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".