CNS neuroscience & therapeutics · 2026
Respiratory Variability in Disorders of Consciousness: Relationship and Clinical Applications.
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Wu Yongli, Pan Huaping, Feng Hui, Fu Juanjuan, Han Yuqing
Аннотация
BACKGROUND: Respiratory variability (RV) reflects the dynamic modulation of breathing patterns by the central nervous system and may serve as a physiological marker of consciousness. However, its predictive value in disorders of consciousness (DOC) remains unclear.
OBJECTIVE: To investigate the relationship between RV and the level of consciousness, and to evaluate the potential of RV-based analysis for predicting clinical outcomes in DOC patients.
METHODS: Patients with disorders of consciousness and healthy controls were assessed using inertial measurement unit (IMU) sensors to record triaxial acceleration signals. RV indicators were extracted from respiratory waveforms. A generalized additive model (GAM) was applied to adjust for confounding variables. Group differences were evaluated using Bootstrap resampling and the Mann-Whitney U test. Machine learning models-including random forest, elastic net, support vector machine, and partial least squares regression-were employed to predict Coma Recovery Scale-Revised (CRS-R) scores and clinical outcomes.
RESULTS: Significant differences in multiple RV indicators were observed between patient and control groups (p < 0.05), indicating an association between RV and the pathophysiology of consciousness disorders. Among the tested models, the random forest algorithm achieved the best predictive performance for CRS-R scores (mean squared error = 3.76, R2 = 0.76) and for clinical outcomes (AUC = 0.74, sensitivity = 0.86), outperforming other models.
CONCLUSIONS: Respiratory variability, particularly when analyzed via random forest modeling, shows strong potential for prognostic assessment and clinical decision support in disorders of consciousness. RV may represent a non-invasive biomarker reflecting the neural control of respiration and consciousness state.
OBJECTIVE: To investigate the relationship between RV and the level of consciousness, and to evaluate the potential of RV-based analysis for predicting clinical outcomes in DOC patients.
METHODS: Patients with disorders of consciousness and healthy controls were assessed using inertial measurement unit (IMU) sensors to record triaxial acceleration signals. RV indicators were extracted from respiratory waveforms. A generalized additive model (GAM) was applied to adjust for confounding variables. Group differences were evaluated using Bootstrap resampling and the Mann-Whitney U test. Machine learning models-including random forest, elastic net, support vector machine, and partial least squares regression-were employed to predict Coma Recovery Scale-Revised (CRS-R) scores and clinical outcomes.
RESULTS: Significant differences in multiple RV indicators were observed between patient and control groups (p < 0.05), indicating an association between RV and the pathophysiology of consciousness disorders. Among the tested models, the random forest algorithm achieved the best predictive performance for CRS-R scores (mean squared error = 3.76, R2 = 0.76) and for clinical outcomes (AUC = 0.74, sensitivity = 0.86), outperforming other models.
CONCLUSIONS: Respiratory variability, particularly when analyzed via random forest modeling, shows strong potential for prognostic assessment and clinical decision support in disorders of consciousness. RV may represent a non-invasive biomarker reflecting the neural control of respiration and consciousness state.