Development and Assessment of Prediction Models for the Development of COPD in a Typical Rural Area in Northwest China
Received 24 December 2020
Accepted for publication 7 February 2021
Published 26 February 2021 Volume 2021:16 Pages 477—486
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 2
Editor who approved publication: Dr Richard Russell
Yide Wang,1 Zheng Li,1,2 Feng-sen Li1,2
1Department of Integrated Pulmonology, Fourth Affiliated Hospital of Xinjiang Medical University, Urumqi, People’s Republic of China; 2Xinjiang National Clinical Research Base of Traditional Chinese Medicine, Xinjiang Medical University, Ürümqi, People’s Republic of China
Correspondence: Zheng Li
Department of Integrated Pulmonology, Fourth Affiliated Hospital of Xinjiang Medical University, Urumqi, 830000, Xinjiang, People’s Republic of China
Email [email protected]
National Clinical Research Base of Traditional Chinese Medicine, Xinjiang Medical University, Urumqi, 830000, Xinjiang, People’s Republic of China
Email [email protected]
Objective: This study aimed to construct and evaluate a clinical predictive model for the development of COPD in northwest China’s rural areas.
Methods: A cross-sectional study of a natural population was performed in rural northwest China. After assessing demographic and disease characteristics, a clinical prediction model was developed. First, we used the least absolute shrinkage and selection operator regression model to screen possible factors influencing COPD. Then construct a logistic regression model and draw a nomogram. The discriminability of the model was further evaluated by the calibration diagram, C-index and ROC curve system. Clinical benefit was analyzed using the decision curve. Finally, the 1000 bootstrap resamples and Harrell’s C-index was used for internal verification of the nomogram.
Results: Among 3249 patients in the local rural natural population, 394 (12.13%) were diagnosed with COPD. The LASSO regression model was used to find the optimal combination of parameters, and the screened influencing factors included age, gender, barbeque, smoking, passive smoking, energy type, ventilation system and Post-Bronchodilator FEV1. These predictors are used to construct a nomogram. C index is 0.81 (95% confidence interval:0.79– 0.83). The combination of the calibration curve and ROC curve indicates that the model has high discriminability. The decision curve shows benefits in clinical practice when the threshold probability is > 6% and < 58%, respectively. The internal verification results using Harrell’s C-Index were 0.80 (95% confidence interval: 0.78– 0.83).
Conclusion: Combining information such as age, sex, barbeque, smoking, passive smoking, type of energy, ventilation systems, and Post-Bronchodilator FEV1 can be easily used to predict the risk of COPD in local rural areas.
Keywords: COPD, predictive models, nomograms, China
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