Development and validation of a predictive model for COPD: a multicenter study
Yaqin Wang1,2, Yanyan Lv1,2, Qiushuang Li3
1Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Chinese Medicine), Hangzhou, China.
Insights
This study developed a predictive model for chronic obstructive pulmonary disease (COPD), identifying age, smoke exposure, and cough as key risk factors. The model aids in early COPD diagnosis and risk stratification for better patient outcomes.
Area of Science:
- Pulmonary Medicine
- Medical Informatics
- Epidemiology
Background:
- Chronic obstructive pulmonary disease (COPD) is a significant global health concern, ranking as the third leading cause of mortality worldwide.
- COPD presents a substantial public health challenge, particularly in China, necessitating improved diagnostic and management strategies.
Purpose of the Study:
- To develop and validate a predictive model for diagnosing COPD.
- To identify key risk indicators for COPD not previously emphasized in diagnostic criteria.
- To enable risk stratification for patients with COPD.
Main Methods:
- Data from 1,056 inpatients and outpatients (COPD and non-COPD) were collected from January 2018 to December 2022 across three hospitals.
- Logistic regression was employed to construct a predictive model using a training set, with internal and external validation performed on separate datasets.
- The model's performance was assessed using accuracy, sensitivity, specificity, and Area Under the Curve (AUC).
Main Results:
- Six significant risk factors for COPD were identified: age, second-hand smoke exposure, cough, and three distinct patterns of wheezing.
- The developed predictive model demonstrated high performance with 94.1% accuracy, 98.5% sensitivity, and 89.2% specificity in internal validation (AUC=0.976).
- External validation yielded an AUC of 0.691, indicating the model's generalizability.
Conclusions:
- A robust predictive model for COPD diagnosis has been successfully developed.
- The model incorporates key clinical indicators, offering a valuable tool for early detection and risk assessment of COPD.
- The identified risk factors and the predictive equation provide a foundation for targeted interventions and improved patient management strategies.
Background:
Chronic obstructive pulmonary disease (COPD) is the third leading cause of death globally and a major public health issue in China. This study aims to develop a COPD predictive model and conduct risk stratification for key indicators not included.
Methods:
We collected data from inpatients and outpatients with COPD and non-COPD who were hospitalized between January 2018 and December 2022 at three different hospitals. The data were divided into a training set and an internal validation set, using logistic regression to build a COPD predictive model and perform internal validation. External validation of the model was performed using data from two additional units for the period November 2019 to June 2022.
Results:
A total of 1,056 cases were included: 740 in the training set, 316 in the internal validation set, and 408 in the external validation set. Six risk factors were identified: age (OR = 1.05, 95% CI: 1.02-1.08), second-hand smoke exposure (OR = 8.27, 95% CI: 2.70-25.34), cough (OR = 23.52, 95% CI: 12.64-43.77), "occasional episodes of wheezing that are mild and do not interfere with sleep or activity" (OR = 6.06, 95% CI: 2.59-14.19), "bouts of wheezing that worsen with movement" (OR = 21.40, 95%CI: 10.32-44.37), and "persistent episodes of wheezing, occurring at rest, unable to lie down" (OR = 10.97, 95% CI: 1.02-118.28). The predictive model equation was: y = -5.920 + 0.047 (age) + 2.113 (smoke exposure) + 3.158 (cough) + 1.801 (wheezing 1) + 3.063 (wheezing 2) + 2.396 (wheezing 3). The model achieved 94.1% accuracy, 98.5% sensitivity, and 89.2% specificity, with an AUC of 0.976 (internal) and 0.691 (external). The critical cut-off value was 0.258.
Conclusion:
We have successfully developed a model for the diagnosis of COPD. The predictive model equation was: y = -5.920 + 0.047 (age) + 2.113 (smoke exposure) + 3.158 (cough) + 1.801 (wheezing 1) + 3.063 (wheezing 2) + 2.396 (wheezing 3).
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