Presentation (CTI)

OBJECTIVES: Sarcopenia is a clinical condition common in patients with COPD. It can exacerbate respiratory difficulties and physical function decline in COPD. However, the relatively complicated screening process limits its use in clinical settings. This study aimed to establish a simplified tool for screening sarcopenia in patients with COPD.
METHODS: This study enrolled 268 patients from Tianjin, China, between June 2022 and December 2023. A screening nomogram was developed using logistic regression analysis to facilitate the diagnosis of sarcopenia. Ten investigative parameters, including clinical characteristics, body measurements, and medical coping, were used to develop the screening nomogram. We included 79 consecutive patients to validate the screening model.
RESULTS: The mean age of the enrolled patients (182 men and 86 women) was 71.49 ± 0.51. The overall rate of sarcopenia in patients with COPD was 31.81%. Multivariate logistic regression analysis revealed that low body mass index (BMI), high modified medical research council (mMRC) scores, history of alcohol consumption, and negative approach to medical coping were predictive factors for sarcopenia among patients with COPD. These factors were used to construct a nomogram model, which demonstrated good consistency and accuracy, with high C-indexes of 0.948(95% confidence interval [CI]:0.914-0.982) and 0.915 (95% CI: 0.837-0.992) in the training and verification sets, respectively. Additionally, it exhibited a well-fitted calibration curve and decision curve analysis, indicating good model discrimination. Additionally, P-values of the Hosmer-Lemeshow test for the training and validation cohorts were 0.882 and 0.931, respectively, indicating high calibration and good predictive performance of the model.
CONCLUSIONS: The predictive model indicates that patients with COPD with low BMI, high mMRC scores, a history of alcohol consumption, and a negative approach to coping with the disease are at a high risk of developing sarcopenia. This model offers valuable insights for clinical practitioners, facilitating early screening for sarcopenia in patients with COPD.