Respiratory Variability in Disorders of Consciousness: Relationship and Clinical Applications

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. 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.