Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2025 May 29:11:20552076251346674.
doi: 10.1177/20552076251346674. eCollection 2025 Jan-Dec.

Establishment of nomogram prediction model for patients with chronic obstructive pulmonary disease based on Lasso feature screening

Shuyi Zhang  1 , Jiaojiao Sha  1   2 , Tao Jiang  1 , Zhou Lu  3 , Yulin Shi  4 , Jiatuo Xu  1
Affiliations

Establishment of nomogram prediction model for patients with chronic obstructive pulmonary disease based on Lasso feature screening

Shuyi Zhang et al. Digit Health. .

Abstract

Background and objective: To establish a nomogram prediction model for patients with chronic obstructive pulmonary disease (COPD) based on Lasso feature screening using acoustic features and general clinical data, as well as a risk warning model for patients with acute exacerbation of COPD (AECOPD), and to investigate the performance and value of these two models.

Methods: A total of 240 male COPD patients, including 41 patients with acute exacerbation, and 82 healthy control male volunteers were enrolled as subjects from October 2022 to January 2024. Acoustic features and general clinical data were collected. Lasso regression was used to screen variables related to COPD and AECOPD diagnosis, and nomogram models were separately established and verified by the receiver operating characteristic (ROC) curve, calibration curve, and decision curve.

Results: Variables related to COPD diagnosis screened by Lasso regression included age, smoking history, a_Jitter, e_MFCC1, e_F2 frequency, i_H1-A3, i_F1 amplitude, o_F1 amplitude, and u_MFCC4， and the variables related to AECOPD included expectoration, mMRC grade, i_Jitter, i_F2 frequency, i_Alpha Ratio, and u_H1-H2. The ROC Curve showed that the Area Under the Curve (AUC) of the COPD nomogram model was 0.95, and the AUC of the AECOPD risk warning model was 0.83. The calibration curve indicated that nomogram models showed reasonable consistency, and the Mean Absolute Error (MAE) values were 0.026 and 0.028, respectively. The decision curve indicated that nomogram models showed good benefit, and the benefit thresholds were nearly full threshold, and 0.11-81 and 0.88-0.99, respectively.

Conclusion: The nomogram models for COPD prediction and risk warning of AECOPD can be used as a clinical auxiliary diagnostic and early screening method, providing new insights into the intelligent auscultation of COPD.

Keywords: Chronic obstructive pulmonary disease; Lasso; acoustic feature; auscultation; nomogram model.

PubMed Disclaimer

Conflict of interest statement

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.

Figures

Figure 1.
Figure 1.
Screening process of participants.
Figure 2.
Figure 2.
Research flow chart.
Figure 3.
Figure 3.
COPD nomogram prediction model.
Figure 4.
Figure 4.
ROC curve of the COPD nomogram prediction model.
Figure 5.
Figure 5.
Confusion matrix of the COPD nomogram prediction model.
Figure 6.
Figure 6.
Calibration curve of the nomogram prediction model.
Figure 7.
Figure 7.
Decision curve of the nomogram prediction model.
Figure 8.
Figure 8.
AECOPD risk warning model.
Figure 9.
Figure 9.
ROC curve of the AECOPD risk warning model.
Figure 10.
Figure 10.
Confusion matrix of the AECOPD risk warning model.
Figure 11.
Figure 11.
Calibration curve of the AECOPD risk warning model.
Figure 12.
Figure 12.
Decision curve of the AECOPD risk warning model.
See this image and copyright information in PMC

Similar articles

See all similar articles

References

    1. Chronic Obstructive Pulmonary Disease Group of Chinese Thoracic Society. Chronic Obstructive Pulmonary Disease Committee of Chinese Association of Chest Physician. Guidelines for the diagnosis and management of chronic obstructive pulmonary disease (revised version 2021). Chin J Tuberculosis Resp Dis 2021; 44: 170–205. - PubMed
    1. Wang C, Xu JY, Yang L, et al. Prevalence and risk factors of chronic obstructive pulmonary disease in China (the China Pulmonary Health [CPH] study): a national cross-sectional study. Lancet 2018; 391: 1706–1717. - PubMed
    1. Sha JJ, Lu Z, Jiang T, et al. Research progress in informatization of four-methods diagnosis for chronic obstructive pulmonary disease. Chin J Inf 2023; 30: 186–190.
    1. Expert group on management of acute exacerbation of chronic obstructive pulmonary disease. Expert consensus on the acute exacerbation of chronic obstructive pulmonary disease in China (revision in 2023). Int J Resp 2023; 43: 132–149.
    1. Deng TT, Chen Q, Guo ZQ, et al. Diagnostics in Chinese medicine. Shanghai: Shanghai Science and Technology Press, 1984, p. 41.

LinkOut - more resources