Wang Y, et al. - Researchers undertook a cross-sectional study to develop as well as assess a clinical predictive model for COPD development in northwest China’s rural regions. The optimal combination of parameters was identified using the LASSO regression model, and age, gender, barbeque, smoking, passive smoking, energy type, ventilation system and Post-Bronchodilator FEV1 were the screened influencing factors. A nomogram was developed using these predictors. C index was 0.81. A high discriminability of the model was suggested by the combination of the calibration curve and ROC curve. In clinical practice, benefits were conferred when the threshold probability was > 6% and < 58%, respectively, as demonstrated by the decision curve. Based on these findings, experts inferred that for predicting COPD risk in local rural areas, integrating information such as age, gender, barbeque, smoking, passive smoking, type of energy, ventilation systems, and Post-Bronchodilator FEV1 can be easily employed.