Prediction and Prevention of Postoperative Mortality and Morbidity

This study will contribute to creating a prospective and automated preoperative risk assessment algorithm for predicting 30-day mortality, major adverse cardiac and cerebrovascular events (MACCE), and postoperative neurocognitive outcomes following elective cardiac and vascular surgery in older adults. It will evaluate associations between perioperative factors and longer-term neurocognitive outcomes, including postoperative neurocognitive disorder and dementia. In addition, this study will assess scalable, multimodal preoperative and intraoperative interventions to improve perioperative outcomes. This study will explore two main hypotheses: 1. Preoperative personalized prehabilitation with proactive cognitive and behavioral interventions will improve postoperative cognitive outcomes, morbidity, and mortality in high-risk elderly surgical patients. 2. Proactive bundled intraoperative interventions are superior to reactive standard of care in reducing postoperative cognitive outcomes, MACCE, and mortality. Expected Outcome: Improved EHR algorithm will have higher predictive accuracy for MACCE and mortality while predicting postoperative cognitive outcomes.

Part I Inclusion Criteria: * 65 years of age and older * Identified as higher risk (≥2.5%) for 30-day mortality and MACCE based on the UPMC's Perioperative Model (EHR risk prediction algorithm) * Scheduled for major cardiac surgeries including coronary artery bypass and valvular repair and/or vascular surgeries including carotid endarterectomy, aortic aneurysm repair, and major vascular surgeries * RAI score ≥ 30 * Informed consent * English speaking patients Part II Inclusion Criteria: * Enrolled in Aim 1 / Part I Preoperative Intervention Trial * Scheduled for high-risk, cardiac or vascular surgery requiring intraoperative neurophysiological monitoring (IONM) * Moderate and high risk for mortality based on Society of Thoracic Surgery score (score \>4) Part I Exclusion Criteria: * Children (\