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Development of a Bedside Decision Tree for Postexacerbation Risk Stratification: Translating Neuroimmune Biomarker
Cheng Chen1, Shanshan Liu2, Jian Dong1
1Department of Respiratory and Critical Care Medicine, Union Jiangbei Hospital, Huazhong University of Science and Technology, Wuhan Hubei, 430100, China, hust.edu.cn.
Canadian Respiratory Journal
|July 30, 2026
Summary
A novel decision tree using neuroimmune biomarkers effectively predicts outcomes for patients with acute exacerbation of chronic obstructive pulmonary disease (AECOPD). This tool aids in personalized risk stratification and discharge planning, outperforming traditional methods.
Area of Science:
- Pulmonary Medicine
- Neuroimmunology
- Biomarker Research
Background:
- Acute exacerbation of chronic obstructive pulmonary disease (AECOPD) involves systemic inflammation and neuroimmune changes.
- The impact of dynamic neuroimmune biomarker changes on AECOPD patient outcomes requires further investigation.
Purpose of the Study:
- To develop a bedside clinical decision tree using neuroimmune biomarker dynamics.
- To stratify post-AECOPD patient risk and guide personalized discharge planning.
Main Methods:
- Prospective observational study of 273 AECOPD patients.
- Serum levels of BDNF, PD-1, MMP-9, and cytokines measured at admission and discharge.
- Unsupervised clustering and logistic regression to analyze biomarker dynamics and 90-day outcomes.
Main Results:
- Three biomarker response patterns identified: 'Coordinated Improvement,' 'Inflammatory Rebound,' and 'Poor Neuro-repair.'
- The 'Poor Neuro-repair' phenotype strongly predicted 90-day exacerbations (aOR 3.42).
- A biomarker-based decision tree showed superior predictive accuracy (AUC 0.87) compared to clinical judgment (AUC 0.74).
Conclusions:
- Biomarker response patterns, especially 'Poor Neuro-repair' and dynamic BDNF changes, correlate with AECOPD outcomes.
- The developed decision tree offers a promising tool for AECOPD risk stratification and discharge planning.
- This biomarker-driven approach surpasses traditional clinical assessment methods in efficacy.