Construction and validation of a nomogram for predicting chronic obstructive pulmonary disease with bronchiectasis

Area of Science:

• Pulmonology and Respiratory Medicine
• Clinical Prediction Modeling
• Epidemiology of Respiratory Diseases

Background:

• Chronic obstructive pulmonary disease (COPD) coexisting with bronchiectasis (BE) significantly worsens patient prognosis, increasing symptom severity and mortality.
• Accurate identification of COPD-BE is crucial for implementing targeted management strategies and improving clinical outcomes.
• Existing diagnostic approaches may not sufficiently identify BE in COPD patients early.

Purpose of the Study:

• To identify independent risk factors (RFs) associated with the presence of bronchiectasis (BE) in patients with chronic obstructive pulmonary disease (COPD).
• To develop and validate a nomogram-based clinical prediction model for early identification of COPD-BE.
• To provide clinicians with a tool for timely diagnosis and tailored management of COPD-BE patients.

Main Methods:

• Retrospective analysis of 382 COPD patients, randomly divided into training (268) and validation (114) cohorts.
• Logistic regression (univariate, LASSO, and multivariable) employed to identify independent RFs for COPD-BE.
• A predictive nomogram was constructed using identified RFs and validated using ROC analysis, calibration, and decision curve analysis (DCA).

Main Results:

• Independent risk factors for COPD-BE included female sex, hemoptysis, history of pulmonary tuberculosis, Pseudomonas aeruginosa infection, globulin levels, and mechanical ventilation duration.
• The developed nomogram showed good discriminative ability with an AUC of 0.840 in the training set and 0.829 in the validation set.
• Calibration analyses confirmed good agreement between predicted and actual outcomes, and DCA suggested potential clinical utility.

Conclusions:

• Female sex, hemoptysis, pulmonary tuberculosis history, Pseudomonas aeruginosa infection, globulin level, and mechanical ventilation duration are significant independent RFs for COPD-BE.
• The nomogram demonstrates robust predictive performance for identifying COPD-BE, validated through internal testing.
• This nomogram shows promise as a valuable clinical tool for early COPD-BE detection, pending external validation.