# The predictive value of D-dimer combined with systemic immune-inflammation index for the presence of pulmonary thromboembolism in AECOPD patients

## TL;DR

This study shows that combining D-dimer levels and the systemic immune-inflammation index can help predict pulmonary thromboembolism in patients with acute COPD.

## Contribution

The study introduces a novel combination of D-dimer and SII as a predictive model for PTE in AECOPD patients.

## Key findings

- D-dimer and SII are independent risk factors for PTE in AECOPD patients.
- The combination of D-dimer and SII has a higher predictive accuracy (AUC 0.834) than either marker alone.
- The model combining D-dimer and SII shows robust performance and clinical utility in predicting PTE.

## Abstract

This study aims to evaluate the predictive potential of D-dimer levels and the systemic immune-inflammatory index (SII) for identifying concurrent pulmonary thromboembolism (PTE) in patients with acute exacerbations of chronic obstructive pulmonary disease (AECOPD).

We conducted a case–control study involving 75 patients with AECOPD and concurrent PTE, admitted to the Affiliated Hospital of Guangdong Medical University between June 2017 and December 2020. A control group comprising 76 AECOPD patients without PTE was included for comparison. Clinical characteristics and laboratory findings were compared between the two groups. Multivariate logistic regression was employed to identify independent risk factors for PTE in AECOPD patients. The predictive accuracy of these risk factors was assessed using receiver operating characteristic (ROC) curves, while Spearman correlation analysis evaluated associations between variables. Model robustness was validated internally using bootstrap techniques, and decision curve analysis (DCA) was applied to determine the clinical utility of the predictive model.

Multivariate logistic regression identified D-dimer [odds ratio (OR) 1.17, 95% confidence interval (CI) 1.03–1.38] and the SII (OR 1.003, 95% CI 1.000–1.006) as independent risk factors for PTE in patients with AECOPD. The area under the ROC curve (AUC) values for predicting PTE in AECOPD patients were 0.758 (95% CI 0.682–0.834) for D-dimer, 0.757 (95% CI 0.677–0.838) for SII, and 0.834 (95% CI 0.768–0.900) for their combination, respectively. The Bootstrap results demonstrated that the model combining D-dimer and SII had an AUC of 0.8409 (95% CI: 0.7649, 0.9156), indicating robust performance across resampled datasets. Both D-dimer and SII showed a positive correlation with hospital stay duration (r = 0.289, p = 0.047; r = 0.235, p = 0.043). DCA demonstrated that the combination of D-dimer and SII provides significant net benefit in clinical decision-making for predicting PTE in AECOPD patients.

D-dimer and SII are independent risk factors for predicting concurrent PTE in AECOPD patients. The combination of these two markers demonstrates robust predictive accuracy, and their levels may be positively correlated with disease severity. These findings suggest that D-dimer and SII could play a valuable role in clinical decision-making and risk stratification in AECOPD patients.