Real-world and Innovative Multimodal Prediction and Prevention of Postoperative Mortality and Multi-morbidities
University of Pittsburgh
Summary
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.
Description
This study will cover the following two specific aims: Aim 1. A pragmatic, non-randomized study to assess the effectiveness of preoperative personalized prehabilitation with proactive cognitive and behavioral interventions versus standard of care on reducing postoperative cognitive outcomes (including postoperative delirium within 30 days, postoperative cognitive decline, and dementia), MACCE, and mortality in high-risk surgical elderly patients (≥65 years). Our Electronic Health Record (EHR)-based automated machine-learning risk prediction algorithm for postoperative mortality and MACCE has…