No abstract

Background and objectives: Acute kidney injury (AKI) occurs commonly after cardiac surgery. Most patients who undergo cardiac surgery receive long-term treatment with angiotensin-converting enzyme inhibitors (ACEI) or angiotensin II receptor blockers (ARB). The aim of this study was to determine whether long-term use of ACEI/ARB is associated with an increased incidence of AKI after cardiac surgery.Design, setting, participants, & measurements: This was a retrospective cohort study of 1358 adult patients who underwent cardiac surgery between January 1, 2001, and December 31, 2005, in two tertiary care hospitals in Buffalo, NY. The incidence of AKI was determined after cardiac surgery. Clinical data were collected using a standardized form that included comorbid condition, use of ACEI/ARB, and intraoperative and postoperative complications.Results: Overall, 40.2% of patients developed AKI. Preoperative variables that were significantly associated with development of AKI included increasing age; nonwhite race; combined valve surgery and coronary artery bypass grafting compared with coronary artery bypass grafting alone; American Society of Anesthesiologists (ASA) Risk Score category 4/5 compared with 2 to 3; presence of diabetes, congestive heart failure, or neurologic disease at baseline; use of ACEI/ARB; and emergency surgery. Intra-and postoperative factors that were associated with postoperative AKI were hypotension during surgery, use of vasopressors, and postoperative hypotension. Multiple regression logistic model confirmed an independent and significant association of AKI and preoperative use of ACEI/ARB. This was confirmed using a bivariate-probit and propensity score model that adjusts for confounding by indication of use and selection bias.Conclusions: Preoperative use of ACEI/ARB is associated with a 27.6% higher risk for AKI postoperatively. Stopping ACEI or ARB before cardiac surgery may reduce the incidence of AKI.

Cloud computing is the latest computing paradigm that delivers IT resources as services in which users are free from the burden of worrying about the low-level implementation or system administration details. However, there are significant problems that exist with regard to efficient provisioning and delivery of applications using Cloud-based IT resources. These barriers concern various levels such as workload modeling, virtualization, performance modeling, deployment, and monitoring of applications on virtualized IT resources. If these problems can be solved, then applications can operate more efficiently, with reduced financial and environmental costs, reduced underutilization of resources, and better performance at times of peak load. In this paper, we present a provisioning technique that automatically adapts to workload changes related to applications for facilitating the adaptive management of system and offering endusers guaranteed Quality of Services (QoS) in large, autonomous, and highly dynamic environments. We model the behavior and performance of applications and Cloud-based IT resources to adaptively serve end-user requests. To improve the efficiency of the system, we use analytical performance (queueing network system model) and workload information to supply intelligent input about system requirements to an application provisioner with limited information about the physical infrastructure. Our simulation-based experimental results using production workload models indicate that the proposed provisioning technique detects changes in workload intensity (arrival pattern, resource demands) that occur over time and allocates multiple virtualized IT resources accordingly to achieve application QoS targets.