A Two-Tier early warning system for postoperative cerebral edema: translating risk factors into action

Abstract Early identification of neurological deterioration remains a challenge in resource-limited settings, where reliance on single-parameter scores may delay timely intervention. Building on Abdelrahman et al.’s findings, we propose a Two-Tier Hybrid Early Warning System combining a simplified bedside physiological score with a lightweight, interpretable machine-learning model. This dual approach maintains the accessibility of traditional scoring while enhancing predictive precision and reducing false alarms through data-driven risk adjustment. Designed for environments with limited digital infrastructure, the hybrid model requires minimal training, integrates seamlessly into routine workflows, and offers clinicians a practical, scalable tool to support earlier recognition of at-risk patients. This framework provides a feasible pathway for improving neurologic monitoring and patient safety across diverse healthcare settings.