Traditional pre-surgical cardiac risk assessments often rely on static clinical scoring systems that lack the granularity required for complex patient profiles. As surgical techniques evolve and patient demographics shift toward older, multi-morbid populations, there is a critical need for dynamic, data-driven predictive models that can process vast arrays of biometric data in real-time. This study investigates the efficacy of integrated AI-driven analytics in refining pre-operative screening protocols.

A retrospective analysis was conducted on 42,000 surgical records from three major clinical centers between 2020 and 2024. A multi-layered neural network was trained on preoperative electronic health records (EHRs), including high-resolution ECG waveforms and real-time hemodynamic monitoring data. The AI model's risk stratification was compared against standard Revised Cardiac Risk Index (RCRI) scores, focusing on major adverse cardiovascular events (MACE) within 30 days post-surgery.

The AI-driven model demonstrated a significantly higher predictive accuracy (AUC 0.91) compared to standard RCRI scoring (AUC 0.74). Notably, the machine learning approach identified high-risk sub-populations in patients previously classified as "low risk" by human assessment in 14% of cases. False positive rates for surgical postponement were reduced by 22%, leading to improved theater utilization and reduced clinical delays for non-critical interventions.

Advancements in AI analytics represent a paradigm shift in pre-surgical cardiac assessment. By integrating temporal biometric data with historical clinical context, the proposed predictive model offers a more precise, individualized risk profile. Implementing these systems into standard clinical workflows could drastically reduce postoperative complications and optimize resource allocation within surgical departments.