Development and validation of a nomogram for predicting bacterial infections in patients with acute exacerbation of chronic obstructive pulmonary disease.

Bacterial infection is a significant contributory factor in the pathogenesis of acute exacerbations of chronic obstructive pulmonary disease (AECOPD) and it has a pivotal role in exacerbating symptoms and precipitating mortality among patients with chronic obstructive pulmonary disease (COPD). The early identification of bacterial infection in individuals with COPD remains a challenge. Therefore, the present study aimed to create and validate a risk assessment tool using easily accessible serum biomarkers to predict bacterial infection in individuals with AECOPD. A retrospective cohort study was carried out at Pingxiang People's Hospital (Pingxiang, China) from January 2023 to December 2023, involving individuals diagnosed with AECOPD. A total of 544 patients with AECOPD were randomly allocated to the two following groups: The training set, which included 70% (n=384) of the patients, and the validation set, which included 30% (n=160) of the patients. Subsequently, a nomogram model was constructed using multivariate logistic regression analysis in the training set. Its discriminatory ability and calibration were internally validated, while decision curve analyses were employed to assess the clinical utility of the nomogram. The incidence of bacterial infection in hospitalized patients with AECOPD was 50% in the training set and 48.1% in the validation set. The nomogram model incorporated independent factors associated with bacterial infection, including C-reactive protein, neutrophil elastase, procalcitonin and eosinophils, identified by univariate and multivariate logistic regression analyses. The area under the curve of the nomogram model was 0.835 [95% confidence interval (CI): 0.795-0.875] in the training set and 0.785 (95% CI: 0.715-0.856) in the validation set. The model demonstrated excellent discrimination and calibration in the validation set [c-statistic: 0.79 (95% CI: 0.68-0.90)]. Furthermore, the discrimination and overfitting bias of the model were assessed through internal validation, revealing a C-index of 0.836 for the initial group and 0.788 for the subsequent validation set. The straightforward risk prediction model for early identification of bacterial infections is valuable for hospitalized patients with AECOPD.