Development and validation of a clinical nomogram for frailty prediction in older COPD patients: a machine learning approach

Frailty is associated with adverse clinical outcomes in older patients with chronic obstructive pulmonary disease (COPD). Early prediction for frailty could be beneficial for more accurate prevention and management. This study aimed to construct a practical and convenient risk prediction model for frailty in older COPD patients. Older COPD patients from China Elderly Comorbidity Medical Database(CECMed) were assessed using the FRAIL scale. Data included demographics, socioeconomic status, medical history, medication use, vital signs, and comprehensive geriatric assessment. Predictive variables were selected using LASSO regression. Five machine learning algorithms were employed to construct prediction models. The optimal model was determined by AUC values and evaluated through calibration curve analysis and DCA. The model was shown by a nomogram. A total of 860 older COPD patients were analyzed, and 357 (41.5%) were pre-frailty and 195 (22.7%) were frailty. LASSO regression identified six predictors: age, CCI, bronchodilator utilization, DBP, gait speed, and grip strength. The optimal model was the logistic regression model (AUC = 0.858, 95%CI 0.815–0.897, 61.9% of sensitivity and 90.2% of specificity). Validation through bootstrapping revealed excellent calibration, and DCA indicated that the model has good performance in the threshold range of 0.25 to 1.0. The nomogram was constructed based the six predictors for predicting frailty and pre-frailty. This study revealed the prevalence of frailty in older COPD patients and identified low DBP as a novel risk factor for frailty. We developed a predictive model based on readily available clinical parameters, providing a practical and convenient tool for predicting frailty in older COPD patients.