Echocardiographic parameters as predictors of in-hospital mortality in patients with acute coronary syndrome undergoing percutaneous coronary intervention.

Different ways have been used to stratify risk in acute coronary syndrome (ACS) patients. The aim of the study was to examine the usefulness of echocardiographic parameters as predictors of in-hospital outcome in patients with ACS after percutaneous coronary intervention (PCI). A data of 2030 patients with diagnosis of ACS hospitalized from December 2008 to December 2011 was used to develop a risk model based on echocardiographic parameters using the binary logistic regression. This model was independently evaluated in validation cohort prospectively (954 patients admitted during 2012). In-hospital mortality in derivation cohort was 7.73%, and 6.28% in validation cohort. Developed model has been designed with 4 independent echocardiographic predictors of in-hospital mortality: left ventricular ejection fraction (LVEF RR = 0.892; 95%CI = 0.854-0.932, P < 0.0005), aortic leaflet separation diameter (AOvs RR = 0.131; 95%CI = 0.027-0.627, P = 0.011), right ventricle diameter (RV RR = 2.675; 95%CI = 1.109-6.448, P = 0.028) and right ventricle systolic pressure (RVSP RR = 1.036; 95%CI = 1.000-1.074, P = 0.048). Model has good prognostic accuracy (AUROC = 0.84) and it retains good (AUROC = 0.78) when testing on the validation cohort. Risks for in-hospital mortality after PCI in ACS patients using echocardiographic measurements could be accurately predicted in contemporary practice. Incorporation of such developed model should facilitate research, clinical decisions, and optimizing treatment strategy in selected high risk ACS patients.

Personalized USB Biosensor Module for Effective ECG Monitoring.

The burden of chronic disease and associated disability present a major threat to financial sustainability of healthcare delivery systems. The need for cost-effective early diagnosis and disease prevention is evident driving the development of personalized home health solutions. The proposed solution presents an easy to use ECG monitoring system. The core hardware component is a biosensor dongle with sensing probes at one end, and micro USB interface at the other end, offering reliable and unobtrusive sensing, preprocessing and storage. An additional component is a smart phone, providing both the biosensor's power supply and an intuitive user application for the real-time data reading. The system usage is simplified, with innovative solutions offering plug and play functionality avoiding additional driver installation. Personalized needs could be met with different sensor combinations enabling adequate monitoring in chronic disease, during physical activity and in the rehabilitation process.

THE LEVEL OF GRAMMAR SCHOOL STUDENTS' KNOWLEDGE ON CARDIOVASCULAR DISEASE RISK FACTORS.

INTRODUCTION: Cardiovascular diseases are one of the leading causes of mortality and morbidity worldwide. The atherosclerotic process in the aorta starts in childhood, while atheroclerotic changes of coronary heart vessels start in adolescence. The aim of the study was to evaluate the knowledge of the students attending all four grades of grammar school about the risk factors for cardiovascular disease, with special attention to the risk factors that can be influenced by modification of life-style. MATERIAL AND METHODS: Data from the entrance and exit tests were collected from 197 students attending a grammar school in Novi Sad. Chi-square test and Student T-test or Mann-Whitney U test were used to examine the statistical difference between categorized variables and the continuous variables, respectively. RESULTS: The difference between the number of correct answers for all the students on the entrance test and exit test was statistically significant (p<0.0005) and the overall knowledge level after lectures was increased by 29.4%. The lowest level of knowledge on the entrance tests was noted among the students of the third grade of grammar school and after the lectures, the student's knowledge level was increased by 82.3% (p<0.0005). CONCLUSION: Children and adolescents from Vojvodina and Serbia should be well informed about the cardiovascular disease risk factors and their prevention with special attention paid to the risk factors that can be influenced by changing lifestyle habits.

DATA MINING APPROACH FOR IN-HOSPITAL TREATMENT OUTCOME IN PATIENTS WITH ACUTE CORONARY SYNDROME.

INTRODUCTION: Risk stratification is nowadays crucial when estimating the patient's prognosis in terms of treatment outcome and it also helps in clinical decision making. Several risk assessment models have been developed to predict short-term outcomes in patients with acute coronary syndrome. This study was aimed at developing an outcome prediction model for patients with acute coronary syndrome submitted to percutaneus coronary intervention using data mining approach. MATERIAL AND METHODS: A total of 2030 patients hospitalized for acute coronary syndrome and treated with percutaneous coronary intervention from December 2008 to December 2011 were assigned to a derivation cohort. Demographic and anamnestic data, clinical characteristics on admission, biochemical analysis of blood parameters on admission, and left ventricular ejection fraction formed the basis ofthe study. A number of machine learning algorithms available within Waikato Environment for Knowledge Discovery had been evaluated and the most successful was chosen. The predictive model was subsequently validated in a different population of 931 patients (validation cohort), hospitalized during 2012. RESULTS: The best prediction results were achieved using Alternating Decision Tree classifier, which was able to predict in-hospital mortality with 89% accuracy, and preserved good performance on validation cohort with 87% accuracy. Alternating Decision Tree classifier identified a subset of 6 attributes most relevant to mortality prediction: systolic and diastolic blood pressure, heart rate, left ventricular ejection fraction, age, and troponin value. CONCLUSION: Data mining approach enabled the authors to develop a model capable of predicting the in-hospital outcome following percutaneous coronary intervention. The model showed excellent sensitivity and specificity during internal validation.