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Background: Myocardial infarction and stroke represent a major public health problem in most developing countries. This study explores genetic predisposition of acute myocardial infarction in the Czech population. Methods and Results: Genome-wide expression study used matched case-control design. Peripheral blood samples of the controls were matched to those of cases based on gender, age, status of diabetes mellitus and smoking status. Six months cardiovascular survival status of the cases was used to identify two distinct subgroups among the cases. Linear models for microarray data were employed to identify differential gene expression. Shrunken centroids technique helped in identifying the subsets of differentially expressed genes with predictive properties in independent samples. Predictive properties were evaluated using bootstrap sampling. Sixty transcripts were found to be both clinically and statistically differentially expressed among the cases not surviving the six months follow-up period relative to controls, while no such transcripts were observed among other surviving cases.The two subgroups of cases exhibited fourteen differentially expressed transcripts. Predictive modeling indicated sixteen out of sixty transcripts to best discriminate between the controls and cases that died during the follow-up period from cardiovascular causes, while for the surviving cases the already non-significant set of transcripts could not be further reduced. Eleven out of fourteen transcripts were found to best discriminate between the two groups of cases using shrunken centroids. Conclusions: The study identified genes associated with excess genetic risk of acute myocardial infarction, including those associated with the six months fatality of the cases.

Objectives: The aim of our work was to implement a prototype of a decision support system which has the form of a web-based classification service. Because the data analysis component of decision support systems often happens to be unsuitable for high-dimensional data, special attention must be paid to the sophisticated selection of the most relevant variables before learning the classification rule. Methods: We implemented a prototype of a diagnostic decision support system called SIR. The system has the ability to select the most relevant variables based on a set of high-dimensional measurements by means of a forward procedure optimizing a decision-making criterion. This allows to learn a reliable classification rule. Results:The implemented prototype was tested on a sample of patients involved in a cardiology study. We used SIR to perform an information extraction from a cardiological clinical study containing both clinical and gene expression data. The classification performance was evaluated by means of a cross validation study. Conclusions: The proposed classification system can be useful for clinicians in primary care to support their decision-making tasks with relevant information extracted from any available clinical study. It is especially suitable for analyzing high-dimensional data, e.g. gene expression measurements.

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Republic). Reperfusion therapy in ST-segment elevation myocardial infarction-data from a pilot registry of myocardial infarction.