Browsing by Author "Stefanović, Branislav (57210079550)"

The role of glucose-insulin-potassium (GIK) infusion in the management of acute myocardial infarction is not well established. This prospective, randomized study comprised 120 patients who had ST-elevation myocardial infarction that was treated within 12 hours from symptom onset with a high dose of GIK (25% glucose, 50 IU of soluble insulin per liter, and 80 mmol of potassium chloride per liter at 1 ml/kg/hour over 24 hours) as adjunct to thrombolytic therapy (1.5 MU of streptokinase/30 to 60 minutes; GIK group) or thrombolytic therapy alone (control group). The primary end point of the study was the rate of major adverse cardiac events (MACEs) at 1 month, defined as a composite of cardiac death, reinfarction, serious arrhythmias (ventricular fibrillation and/or tachycardia), and severe heart failure. The secondary end points were the rate of MACEs at 1 year and improvement in left ventricular systolic function. The incidence of MACEs at 1 month was significantly lower in the GIK group (10% vs 32.5%, relative risk 0.24, 95% confidence interval 0.09 to 0.63, p = 0.0043). Patients in the GIK group had significant decreases in ventricular tachycardia and/or fibrillation (1.3% vs 15.0%, p = 0.003) and severe heart failure (3% vs 12.5%, p = 0.031). The rate of MACEs at 1 year was also significantly lower in the GIK group (13% vs 40.0%, relative risk 0.22, 95% confidence interval 0.09 to 0.55, p = 0.0012). After 1 year, there was a significant improvement in left ventricular ejection fraction in the GIK group (from 48 ± 8% to 51 ± 10%, p <0.01), which was not observed in the control group. In conclusion, high-dose GIK, used as an adjunct to thrombolytic therapy, was safe and improved clinical outcome at 1 month. The beneficial effect of GIK infusion was maintained up to 1 year. © 2005 Elsevier Inc. All rights reserved.

Introduction/Objective The aim of this study was to analyze the usefulness and accuracy of artificial neural networks in the prognosis of infarcted patients with previous myocardial surgical revascularization. Methods The 13 predictor variables per patient were defined as a data set. All the patients were divided into two groups randomly: the training group and the test group, of 1090 patients each. The evaluation of the neural network performance was organized by using the original data, as well as the complementary test data, containing patient data not used for training the network. In generating the file of comparative results, the program compared the actual outcome for each patient with the predicted one. Results All the results were compared with 2 × 2 contingency table constructed from sensitivity, specificity, accuracy, and positive–negative prediction. The network was able to predict the outcome with the accuracy of 96.2%, sensitivity of 78.4%, specificity of 100%, positive predictivity of 100%, and negative predictivity of 96%. There was not efficient prognosis of infarcted patients previously operated on using linear discriminant analysis (accuracy 68.3%, sensitivity 66.4%, and positive predictivity 30.2%). Conclusion This study suggest that a neural network was better for almost all parameters in outcome prognosis of infarcted patients with previous myocardial surgical revascularization. © 2021, Serbia Medical Society. All rights reserved.

[No abstract available]