Abstract 13941: CT-derived Skeletal Muscle Index: A Novel Predictor of Frailty and Hospital Length of Stay After Transcatheter Aortic Valve Replacement
Bibliographic record
Abstract
Introduction: We determined the prevalence of sarcopenia in patients undergoing transcatheter aortic valve replacement (TAVR) and whether skeletal muscle mass measured from preoperative computed tomography (CT) images provides value in predicting post-operative length of stay. Background: There is limited data on the use of body composition as a frailty measure in TAVR patients and no studies have determined if this measure predicts length of stay. Methods: We studied 104 consecutive patients who underwent TAVR at Tallahassee Memorial Hospital from 2012 to 2016. Patient demographics, frailty measures (hand grip, albumin, and 5m walk test), clinical comorbidities and echocardiographic data were recorded. Skeletal muscle index (SMI) [skeletal muscle mass cross-sectional area/height 2 ] was measured from CT images using Slice-O-Matic software (Tomovision, Montreal, Quebec, Canada) (Figure 1). Clinical outcomes were assessed and multivariate methods used to determine predictors of LOS. Results: Sarcopenia was prevalent in men (83%) and women (56%). Only SMI and mitral regurgitation showed a univariate relationship with LOS, while none of the established frailty measures predicted LOS. SMI was correlated with age, gender, BMI, handgrip strength, previous PCI and previous CABG. A multivariate model including age, gender, major complications, BMI, SMI, mitral regurgitation, grip strength and walk test showed only SMI, MR, and major complications as independent predictors of LOS. For every 8.6 cm 2 /m 2 increase in SMI, there was a 1 day reduction in LOS. Conclusions: SMI, a measure of sarcopenia readily determined from pre-TAVR CT scans, independently predicts TAVR LOS better than standard frailty testing. Further evaluation of SMI as a frailty measure after TAVR is warranted.
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 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".