Establishment and validation of a nomogram for predicting acute exacerbation of chronic obstructive pulmonary disease based on the C-reactive protein–triglyceride–glucose index

The C-reactive protein–triglyceride–glucose index (CTI) is becoming a new indicator for the comprehensive evaluation of inflammation and insulin resistance severity. This study aimed to analyze the correlation between CTI and the risk of acute exacerbation in chronic obstructive pulmonary disease (COPD), as well as its influencing factors, and construct and validate a risk prediction nomogram. We selected 447 COPD patients who visited the First People’s Hospital of Mengcheng County from January 2020 to May 2024, among whom 266 were acute exacerbation patients. They were randomly divided into a training set and a validation set in a 7:3 ratio. Clinical data were collected, and multiple logistic regression was used to explore the risk factors for acute exacerbation in COPD patients. Based on the results of the multiple logistic regression, a risk prediction nomogram was constructed. Internal validation of the nomogram was performed using receiver operating characteristic curve analysis to assess discrimination, Hosmer–Lemeshow test and calibration curve analysis to assess calibration, and decision curve analysis to evaluate the clinical usefulness of the nomogram. The results of multiple logistic regression analysis showed that smoking, hypertension, red blood cells, and CTI were risk factors for acute exacerbation of COPD ( P < .05). A risk prediction nomogram for acute exacerbation of COPD was constructed based on the results of multiple logistic regression analysis. The receiver operating characteristic curve analysis showed that the area under the curve of the nomogram for predicting acute exacerbation of COPD was 0.985 (95% CI: 0.976–0.994); the results of Hosmer–Lemeshow test and calibration curve analysis indicated that the nomogram had a good fit in the modeling group (χ 2 = 12.95, P = .1136). The decision curve analysis results showed that the net clinical benefit of the nomogram was > 0 when the threshold probability was > .05 in the modeling group. The nomogram model for predicting the risk of acute exacerbation in COPD patients based on CTI has good consistency, calibration, clinical applicability, and discriminability. The nomogram prediction model constructed by these factors can identify COPD patients with acute exacerbation early, which is helpful for early intervention and improvement of patient prognosis.