Ventilation Heterogeneity Across A-B-E Phenotypes in COPD: Insights from Spirometry and Electrical Impedance Tomography

Junsong Zhang¹Tian Liu²Zhongkai Chang²Meng Dai³Liqiang Song¹Lin Yang^4*Xinyu Ti^1*Shuoyao Qu^1*Zhanqi Zhao⁵

• ¹Department of Pulmonary and Critical Care Medicine, Xijing Hospital, Fourth Military Medical University, Xi’an, China
• ²Nanshan School, Guangzhou Medical University, Guangzhou, China
• ³Department of Biomedical Engineering, The Fourth Military Medical University, Xi’an, China
• ⁴Department of Aerospace Medicine, the Fourth Military Medical University, Xi’an, China
• ⁵School of Biomedical Engineering, Guangzhou Medical University, Guangzhou, China

Purpose This study aimed to evaluate the regional ventilation distributions in A-B-E phenotypes among patients with chronic obstructive pulmonary disease (COPD). The feasibility to better distinguish the phenotypes combining global spirometry and regional ventilation parameters derived from electrical impedance tomography (EIT) was explored. Methods A cohort undergoing pulmonary function testing was prospectively enrolled. Regional spatial and temporal ventilation parameters were calculated with EIT. Principal component analysis was used to visualize phenotypic clustering, while multinomial logistic regression evaluated discriminatory performance. Feature importance was interpreted using SHapley Additive exPlanations (SHAP). Results This study enrolled 88 COPD patients (Group A n=36, Group B n=21, Group E n=31). Spirometry and EIT parameters revealed significant intergroup differences in FEV₁%pred (P < 0.001), FEV₁/FVC (P < 0.001), GI-FEV₁ (regional distribution of FEV1%pred in functional EIT; P = 0.004), GI-FEV₁/FVC (regional distribution of FEV₁/FVC; P = 0.001) and expiratory time constant (P = 0.017). Group A demonstrated the best pulmonary function (FEV₁%pred: 77.67±20.40), while Group E showed the most pronounced flow limitation (longest time required to exhale 75% of FVC, T75). The multinomial model showed optimal discrimination for Group A (AUC: 0.827), while differentiation between Groups B and E was less satisfactory (AUC: 0.749). SHAP analysis identified FEV₁%pred as the most significant predictor (|SHAP|=0.477), with EIT-derived parameters GI-FEV₁/FVC (|SHAP|=0.203) and regional T75 (|SHAP|=0.189) providing substantial incremental value. Conclusions COPD phenotypes showed differences in global and regional flow limitations. The combination of global and regional information helped with distinguishing phenotypes.