Identification of myocardial infarction risk patients from peripheral blood thrombocyte activation antigen expression (CD62, CD63, thrombospondin, IgG binding). The activation antigen expression is flow cytometrically determined on thrombocytes in platelet rich plasma.
The classification of the learning set of normal individuals, angiographically verified myocardial risk as well as of diabetic patients (type II) provides correct recognition of > 95% of normal and infarction risk patients, while diabetic patients are only recognized in around 50% of the cases.
The determination of CD62/63/thrombospondin/IgG provides good identification if myocardial infarction risk patients while diabetic patients are not well recognized by the pattern of these thrombocyte activation antigens.
Elaboration of CLASSIF1 Classifiers:
CLASSIF1 classifiers are typically established from data sets of clinically well characterized patients. Upon reception of a data set, patients #1,5,10,15 ... etc of each classification category remain a-priori inaccessible to the learning process and constitute the embedded unknown test set which serves to test the robustness of classification for unknown samples.
The remaining patient data are assigned to the learning set from which the classifier is learned according to the principles described previously.
Once the classifier is available, its classification capacities can be assessed by classification of the learning set but more importantly by the classification of the unknown test set patients.
A classifier which correctly classifies the learning set as well as the test set of patients is suitable for a test phase of prospective classification in the clinical environment. The correctness of prospective classification is checked for a certain time prior to classifier use in its operational phase in routine practice.