Objective: To investigate the current status of acquired weakness in elderly patients with chronic obstructive pulmonary disease(COPD), identify its risk factors, and develop a predictive model. Methods: A retrospective analysis was conducted on the clinical data of 215 elderly COPD patients hospitalized in our hospital from January 2021 to April 2024. Patients were divided into a acquired frailty group(n=102) and a non-acquired frailty group(n=113) based on the presence of acquired frailty. The general characteristics and clinical-related indicators were compared between the two groups, and factors which had statistic significance were incorporated into multivariate Logistic regression analysis to construct a predictive model. The Hosmer-Lemeshow test was used to assess the goodness-of-fit of the model, and the receiver operating characteristic(ROC) curve was plotted to evaluate the predictive value of the model. Results: Among the 215 elderly COPD patients, the prevalence of acquired frailty was 47.44%(102/215). The non-acquired frailty group showed significantly better performance in closed-eyed standing test(CST), 6-minute walking distance(6MWD), geriatric locomotive function scale(GLFS), forced expiratory volume in 1 second(FEV1, and forced vital capacity(FVC) compared to the acquired frailty group(P<0.05). Multivariate Logistic regression analysis identified age, physical activity rank scale (PARS-3), comorbidities, sleep disorder, COPD duration, nutritional risk, procalcitonin(PCT), C-reactive protein(CRP), and neutrophil-to-lymphocyte ratio(NLR) as independent risk factors for acquired frailty in elderly COPD patients(P<0.05). A predictive model was constructed based on these influencing factors. The Hosmer-Lemeshow test indicated good calibration (χ2=9.047, P=0.483). The AUC was 0.817(95%CI 0.729-0.905), with a sensitivity of 0.784, and a specificity of 0.818. Conclusion: Acquired frailty can significantly reduce physical performance and adversely affect respiratory function in elderly COPD patients. The risk prediction model based on clinical and laboratory indicators facilitates early identification of high-risk patients, enabling timely interventions by clinical care providers to improve patients' functional status and quality of life. |