Decreasing Incidence of Major Adverse Cardiac Events in Non-Cardiac Surgery Patients

The leading cause of morbidity and mortality following anesthesia is major adverse cardiac events, or MACE (Smilowitz et al., 2017; Sabate et al., 2011). These events include cardiac arrest, myocardial infarction, new onset congestive heart failure and dysrhythmias, and angina (Sabate et al., 2011). MACE has been reduced over the last decade likely due to the advent of beta-blocker and statin therapy, yet continues to occur at approximately 2.6% of adult non-cardiac surgeries (Smilowitz et al., 2017). To add, the 2009 Healthcare Information Technology for Economic and Clinical Health Act and the 2010 Affordable Care Act provide incentives for certified electronic health records, yet only 27% of perioperative services were using this tool as of 2011 (Simpao, Ahumada, & Rehman, 2015; Wanderer & Ehrenfeld, 2013). There are two potential pathways for improvement of this issue during pre-operative assessment and prevention: updating cardiac risk assessment guidelines, and improving guideline adherence and risk assessment accuracy with clinical decision support tools. The PICOT question is “Do clinical decision support tools in pre-anesthesia evaluation and intraoperative management reduce major adverse cardiac events (MACE) in adult non-cardiac surgery patients immediately to 30 days post-anesthesia?” Current literature demonstrates poor predictive abilities of current guidelines and lack of consensus at data end-points for decision making (Cohn & Fleisher, 2017; Arora et al., 2016; Bihorac, 2015; Devereaux & Sessler, 2015; Hobson et al., 2015; Carabini et al., 2014; Fleisher et al., 2014a; Fleisher et al., 2014b; Tashiro et al., 2014; American College of Surgeons, 2013; Aitken et al., 2013; Kidney Disease Improving Global Outcomes, 2012; Nallegowda et al., 2012; The VISION Pilot Study Investigators, 2011). Clinical decision support tools have demonstrated their use with an enhanced learning experience for anesthesia students (Ehrenfeld, McEvoy, Furman, Snyder, & Sandberg, 2014), improved clinical outcomes such as decreased post-operative nausea and vomiting (Simpao et al, 2017, Kappen et al., 2015, Koojie et al., 2012), and decreased unnecessary testing for patients (Hand et al, 2014; Flamm et al., 2013). There are additional applications of clinical decision support tools such as provider alerts along the continuum of anesthesia care to prevent further insult to patients at risk of a major adverse cardiac event. Clinical decision support tools offer a valuable strategy in real time use of clinical data and show strong evidence in assisting providers to thoroughly apply guidelines for practice (Hand et al., 2014; Wanderer & Ehrenfeld, 2013), which are particularly complex in cardiac risk assessment.