Impact of an Alerting Clinical Decision Support System for Glucose Control on Protocol Compliance and Glycemic Control in the Intensive Cardiac Care Unit

Background: Glycemic control in patients with acute cardiac conditions is a clinical challenge but may substantially improve patient outcome. The aim of the current study was to evaluate the effect of implementing an automated version of an existing insulin protocol for glucose regulation in the Intensive Cardiac Care Unit (ICCU) on compliance with the protocol and achievement of glycemic targets. Methods: During an 11-month period, data of 667 patients with two or more glucose measurements were evaluated, 425 before and 242 after implementation of the clinical decision support system (CDSS) for glucose control at the Erasmus Medical Center ICCU (Rotterdam, The Netherlands). Results: After implementation, compliance with the advised measurement time increased from 40% to 52% (P < 0.001), and compliance regarding insulin dosage increased from 49% to 61% (P < 0.001). Also, more patients had a mean glucose level within the target range of 81-126 mg/dL (31% vs. 43% [P = 0.01]). Monthly evaluation identified reasons for protocol noncompliance (e.g., nutritional status and time of day) and will be used to improve the existing CDSS. Conclusions: The CDSS implementation of an insulin protocol in an ICCU improved compliance, identified targets for further improvement of the protocol, and resulted in improved glucose regulation after implementation

Development and Validation of a Cardiovascular Risk Assessment Model in Patients With Established Coronary Artery Disease

none13could contribute to the prevention of recurrent cardiovascular events. The purpose of the present study was to develop and validate risk prediction models for various cardiovascular end points in the EURopean trial On reduction of cardiac events with Perindopril in stable coronary Artery disease (EUROPA) database, consisting of 12,218 patients with established coronary artery disease, with a median follow-up of 4.1 years. Cox proportional hazards models were used for model development. The end points examined were cardiovascular mortality, noncardiovascular mortality, nonfatal myocardial infarction, coronary artery bypass grafting, percutaneous coronary intervention, resuscitated cardiac arrest, and combinations of these end points. The performance measures included Nagelkerke’s R2, time-dependent area under the receiver operating characteristic curves, and calibration plots. Backward selection resulted in a prediction model for cardiovascular mortality (464 events) containing age, current smoking, diabetes mellitus, total cholesterol, body mass index, previous myocardial infarction, history of congestive heart failure, peripheral vessel disease, previous revascularization, and previous stroke. The model performance was adequate for this end point, with a Nagelkerke R2 of 12%, and an area under the receiver operating characteristic curve of 0.73. However, the performance of models constructed for nonfatal and combined end points was considerably worse, with an area under the receiver operating characteristic curve of about 0.6. In conclusion, in patients with established coronary artery disease, the risk of cardiovascular mortality during longer term follow-up can be adequately predicted using the clinical characteristics available at baseline. However, the prediction of nonfatal outcomes, both separately and combined with fatal outcomes, poses major challenges for clinicians and model developers. 2013 Elsevier Inc. All rights reserved.noneLinda Battes; Rogier Barendse; Ewout W. Steyerberg; Maarten L. Simoons; Jaap W. Deckers; Daan Nieboer; Michel Bertrand; Roberto Ferrari; Willem J. Remme; Kim Fox; Johanna J.M. Takkenberg; Eric Boersma; and Isabella Kardys;Linda, Battes; Rogier, Barendse; Ewout W., Steyerberg; Maarten L., Simoons; Jaap W., Deckers; Daan, Nieboer; Michel, Bertrand; Ferrari, Roberto; Willem J., Remme; Kim, Fox; Johanna J. M., Takkenberg; Eric, Boersma; Isabella, Kardy