Predicting Intraoperative Immune Dysregulation: Machine Learning-Driven Mechanical Ventilation Profiles in Patients with Pre-existing Chronic Respiratory Disease

Original Research | 2026 | Volume 1 | Issue 1 | Page 15-19

1. Dr. Shahan Layek, Independent Researcher, West Bengal, India

2. Dr. Manoj Kumar, Tutor, Department of Physiology, JHMC, WB

Predicting Intraoperative Immune Dysregulation: Machine Learning-Driven Mechanical Ventila.

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Corresponding Author:-

Dr. Manoj Kumar

Tutor, Department of Physiology

JHMC, WB

ABSTRACT

BACKGROUND: Intraoperative immune dysregulation poses a significant risk for postoperative complications in patients with pre-existing chronic respiratory disease, yet predicting these events remains clinically challenging. Mechanical ventilation parameters, while essential for patient stability, may serve as unrecognized biophysical triggers for immune activation during surgery. This study aims to develop a machine learning (ML) framework that utilizes intraoperative mechanical ventilation profiles to predict individual risk of immune dysregulation.

METHODS: A retrospective analysis was conducted on a cohort of patients with pre-existing chronic respiratory disease undergoing elective thoracic surgery. High-frequency intraoperative data, including tidal volume, airway pressure, and respiratory frequency, were integrated with longitudinal immunological markers (cytokine profiles and leukocyte activation status) collected pre- and post-operatively. An ensemble ML model was trained to identify non-linear patterns between ventilation waveforms and systemic immune shifts. Model performance was validated using area under the receiver operating characteristic curve (AUROC) metrics and feature importance analysis.

RESULTS: The machine learning model successfully predicted intraoperative immune dysregulation with high diagnostic accuracy (AUROC = 0.89). Feature importance analysis revealed that specific combinations of dynamic airway pressure and peak inspiratory flow, rather than absolute ventilation volume alone, were the most robust predictors of systemic inflammatory spikes. The model identified distinct "ventilation-immune signatures" that characterized patients susceptible to rapid post-induction immune suppression or subsequent compensatory cytokine storms.

CONCLUSION: This study demonstrates that machine learning-driven analysis of mechanical ventilation profiles can effectively predict immune dysregulation in high-risk patients. By identifying vulnerable mechanical-immune signatures in real-time, this predictive tool offers a foundation for precision intraoperative care, allowing clinicians to optimize ventilation strategies dynamically to mitigate adverse immune responses in patients with chronic respiratory conditions.

KEYWORDS: Machine Learning, Intraoperative Ventilation, Immune Dysregulation, Chronic Respiratory Disease, Precision Medicine, Anesthesiology.