Goal:
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.
CLASSIF1 Classification:
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.
Conclusion:
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.