BACKGROUND:The objective of this study was to examine the use of preoperative cardiac stress testing (PCST) in the Southern California Vascular Outcomes Improvement Collaborative (So Cal VOICe). METHODS:A retrospective review was performed on data in all modules of the So Cal VOICe from September 2012 through May 2016. PCST was defined as stress echocardiogram or nuclear stress test. A new postoperative myocardial infarction (MI) was defined as troponin elevation and/or electrocardiogram/imaging changes with or without ischemic symptoms. Only elective cases in patients with asymptomatic cardiac status were included in the study. RESULTS:During the study period, 3,063 procedures meeting the inclusion criteria were performed in 7 registries: carotid endarterectomy (CEA), carotid artery stent, thoracic endovascular aneurysm repair, infrainguinal bypass (Infra), endovascular aneurysm repair (EVAR), suprainguinal bypass (Supra), and open abdominal aortic aneurysm repair (OAAA). PCST varied across registries from 17% in PVI to 62% in OAAA. PCST in CEA varied across 9 institutions from 10% to 79%. PCST in EVAR varied across 7 institutions from 14% to 83%. PCST in Infra varied across 4 institutions from 10% to 57%. Of the 12 patients across all registries who had a new MI, 6 had PCST, one of which was abnormal. CONCLUSIONS:The incidence of PCST varies widely across registries and institutions in the So Cal VOICe. Despite the wide variation, the incidence of new postoperative MI is exceptionally low. Further studies should evaluate the cost-effectiveness of the PCST practices and future quality improvement efforts should focus on standardization of indications for PCST.

Background The best medical therapy to control hypertension following abdominal aortic aneurysm repair is yet to be determined. We therefore examined whether treatment with renin-angiotensin-aldosterone system inhibitors (RAASIs) versus beta blockers influenced postoperative and 1-year clinical end points following abdominal aortic aneurysm repair in a Medicare-linked database. Methods and Results All patients with hypertension undergoing endovascular aneurysm repair and open aneurysm repair in the Vascular Quality Initiative Vascular Implant Surveillance and Interventional Outcomes Network database between 2003 and 2018 were included. Patients were divided into 2 groups based on their preoperative and discharge medications, either RAASIs or beta blockers. Our cohort included 8789 patients, of whom 3523 (40.1%) were on RAASIs, and 5266 (59.9%) were on beta blockers. After propensity score matching, there were 3053 matched pairs of patients in each group. After matching, RAASI use was associated with lower risk of postoperative mortality (odds ratio [OR], 0.3 [95% CI, 0.1-0.6]), myocardial infarction (OR, 0.1 [95% CI, 0.03-0.6]), and nonhome discharge (OR, 0.6 [95% CI, 0.5-0.7]). Before propensity score matching, RAASI use was associated with lower 1-year mortality (hazard ratio [HR], 0.4 [95% CI, 0.4-0.5]) and lower risk of aneurysmal rupture (HR, 0.7 [95% CI, 0.5-0.9]). These results persisted after propensity score matching for mortality (HR, 0.4 [95% CI, 0.4-0.5]) and aneurysmal rupture (HR, 0.7 [95% CI, 0.5-0.9]). Conclusions In this large contemporary retrospective cohort study, RAASI use was associated with favorable postoperative outcomes compared with beta blockers. It was also associated with lower mortality and aneurysmal rupture at 1 year of follow-up.

As buildings account for approximately 40% of global energy consumption and associated greenhouse gas emissions, their role in decarbonizing the power grid is crucial. The increased integration of variable energy sources, such as renewables, introduces uncertainties and unprecedented flexibilities, necessitating buildings to adapt their energy demand to enhance grid resiliency. Consequently, buildings must transition from passive energy consumers to active grid assets, providing demand flexibility and energy elasticity while maintaining occupant comfort and health. This fundamental shift demands advanced optimal control methods to manage escalating energy demand and avert power outages. Reinforcement learning (RL) emerges as a promising method to address these challenges. In this paper, we explore ten questions related to the application of RL in buildings, specifically targeting flexible energy management. We consider the growing availability of data, advancements in machine learning algorithms, open-source tools, and the practical deployment aspects associated with software and hardware requirements. Our objective is to deliver a comprehensive introduction to RL, present an overview of existing research and accomplishments, underscore the challenges and opportunities, and propose potential future research directions to expedite the adoption of RL for building energy management.