Muscle Wasting in the Hospitalised COPD Patients—How Can it Be Prevented and Treated?

Cahill, Tara; Ahmed, Mohammed

doi:10.1007/s13670-018-0245-1

Cited by 1 publication

(1 citation statement)

References 34 publications

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“…Age, sex, BMI, comorbidities, hospitalization, and tobacco smoking were considered the general factors associated with muscle function and mass in patients with COPD (11,(18)(19)(20)(21)(22). There may be some differences in the structure of PM on MDCT among patients with COPD at different age stages, body mass index, and gender in physiological circumstances.…”

Section: Discussionmentioning

confidence: 99%

A Novel Nomogram Based on Multi-Detector Computer Tomography Radiomics of Pectoral Muscle for Predicting Prone to Acute Exacerbation in COPD

Zhu,

He,

Yang

et al. 2023

Preprint

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Background Developing and validating a radiomics nomogram to predict prone to acute exacerbations in chronic obstructive pulmonary disease (COPD) patients. Methods 118 patients prone to acute exacerbation of COPD (PAECOPD) and 92 patients with relatively stable COPD (SCOPD) were split into a training cohort (n=146) and a validation cohort (n=64). Radiomics features of the pectoral muscle (PM) were extracted from the cross-sectional image above the level of the aortic arch on the chest unenhanced multi-detector computer tomography (MDCT) images. We constructed a radiomics signature and calculated a radiomics score (Rad- score). Combination of Rad-score and clinical factors (including quantitative indicators of PM on MDCT) associated with PAECOPD, a radiomics nomogram was constructed with a multivariate logistic regression model. We evaluate the performance of the radiomics nomogram concerning discrimination, calibration, and clinical usefulness. Results The radiomics signature model was built with twelve features. The radiomics nomogram displayed better discrimination capability (P< 0.05) both in the training cohort (area under the curve(AUC), 0.932; 95% confidence interval (CI), 0.891–0.973) and the validation cohort (AUC, 0.896; 95% CI, 0.816–0.975) compared with the clinical factor and radiomics signature, and displayed excellent calibration in the training cohort. According to the decision curve analysis (DCA), the radiomics nomogram demonstrated better clinical usefulness than the clinical factors and radiomics signature alone. Conclusion The radiomics nomogram based on MDCT combines radiomic signature and clinical factors for predicting acute COPD exacerbations non-invasively with favorable predictive efficacy.

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