WANG Yongqing 1 , YUE Hongmei 1,2 , SHI Yaqiang 1 , SHI Ling 1 , QIAO Yuyang 1 , MA Ruiqin 1
  • 1. First Clinical Medical College of Lanzhou University, Lanzhou, Gansu 730000, P. R. China;
  • 2. Department of Respiratory and Critical Care Medicine, First Hospital of Lanzhou University, Lanzhou, Gansu 730000, P. R. China;
YUE Hongmei, Email: yuehongmei18@sina.com
Chronic obstructive pulmonary disease; acute exacerbation; systemic coagulation-inflammation index; adverse outcomes; prediction model
10.7507/1671-6205.202602054
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Objective  To investigate the predictive value of the systemic coagulation-inflammation index (SCI) for in-hospital adverse outcomes in patients hospitalized with acute exacerbation of chronic obstructive pulmonary disease (AECOPD). Methods  This single-center retrospective study included 325 AECOPD patients hospitalized in the Department of Respiratory and Critical Care Medicine at the First Hospital of Lanzhou University from June 1, 2024, to June 1, 2025. Based on the occurrence of composite adverse outcomes during hospitalization, the patients were divided into an adverse outcome group (n=48) and a non-adverse outcome group (n=277). Baseline data and laboratory indicators within 48 hours of admission were collected, and SCI was calculated. Baseline characteristics were compared between the two groups. The diagnostic performance of SCI and commonly used hematological indicators for in-hospital adverse outcomes in the AECOPD patients was evaluated using receiver operating characteristic (ROC) curve analysis. Variables were screened by LASSO regression to develop a baseline predictive model (Model 1) incorporating age, history of acute exacerbation, C-reactive protein, partial pressure of carbon dioxide, and lymphocyte count. An enhanced model (Model 2) was constructed by adding SCI. Model performance was assessed using the area under the ROC curve (AUC), Bootstrap internal validation, net reclassification improvement (NRI), integrated discrimination improvement (IDI), and decision curve analysis (DCA). Results  The median SCI in the adverse outcome group was 68.24, significantly lower than the 95.74 in the non-adverse outcome group (P<0.001), and low SCI was identified as an independent risk factor for in-hospital adverse outcomes in AECOPD patients. The AUC of Model 1 was 0.893 (95%CI 0.839-0.946). After adding SCI, Model 2 achieved an AUC of 0.909 (95%CI 0.859-0.960, P=0.044). Furthermore, after Bootstrap internal validation, the calibrated AUC of Model 2 was 0.899, confirming good model robustness.The IDI of Model 2 compared to Model 1 was 0.0426 (95%CI 0.0104-0.0790, P=0.012), indicating significant incremental predictive value. DCA demonstrated that using Model 2 for risk prediction provided greater net clinical benefit across a wide range of clinical decision thresholds. Conclusions  Low SCI is an independent risk factor for in-hospital adverse outcomes in AECOPD patients. A new predictive model combining traditional clinical indicators with SCI shows favorable diagnostic performance, supporting early identification of high-risk patients in clinical practice.

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