Midthigh Muscle Cross-Sectional Area Is a Better Predictor of Mortality than Body Mass Index in Patients with Chronic Obstructive Pulmonary Disease
Bibliographic record
Abstract
This study was undertaken to test the hypothesis that a reduction in midthigh muscle cross-sectional area obtained by CT scan (MTCSA(CT)) is a better predictor of mortality in chronic obstructive pulmonary disease (COPD) than low body mass index (BMI). We also wished to evaluate whether anthropometric measurements could be used to estimate MTCSA(CT). One hundred forty-two patients with COPD (age = 65 +/- 9 years, mean +/- SD, 26 F, BMI = 26 +/- 6 kg/m(2), FEV(1) = 42 +/- 16% predicted) were recruited from September 1995 to April 2000 with a mean follow-up of 41 +/- 18 months. The primary end-point was all-cause mortality during the study period. A Cox proportional hazards regression model was used to predict mortality using the following independent variables: age, sex, daily use of corticosteroid, FEV(1), DL(CO), BMI, thigh circumference, MTCSA(CT), peak exercise workrate, Pa(O2), and Pa(CO2). Only MTCSA(CT) and FEV(1) were found to be significant predictors of mortality (p = 0.0008 and p = 0.01, respectively). A second analysis was also performed with FEV(1) and MTCSA(CT) dichotomized. Patients were divided into four subgroups based on FEV(1) (< or >or= 50% predicted) and MTCSA(CT) (< or >or= 70 cm(2)). Compared with patients with an FEV(1) >or= 50% predicted and a MTCSA(CT) >or= 70 cm(2), those with an FEV(1) < 50% predicted and a MTCSA(CT) >or= 70 cm(2) had a mortality odds ratio of 3.37 (95% confidence interval, 0.41-28.00), whereas patients with an FEV(1) < 50% predicted and a MTCSA(CT) < 70 cm(2) had a mortality odds ratio of 13.16 (95% confidence interval, 1.74-99.20). MTCSA(CT) could not be estimated with sufficient accuracy from anthropometric measurements. In summary, we found in this cohort of patients with COPD that (1) MTCSA(CT) was a better predictor of mortality than BMI, and (2) MTCSA had a strong impact on mortality in patients with an FEV(1) < 50% predicted. These findings suggest that the assessment of body composition may be useful in the clinical evaluation of these patients.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".