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6th ESACP Congress, Heidelberg, April 7-11, 1999 |
A143
Background: Multiparametric flow and image cytometric measurements
contain high amounts of information which are frequently only
partially evaluated either by restriction to certain cell populations e.g.
lymphocytes, cancer cells etc. or to certain parameters e.g. percent cell
frequency or S-phase cells. These limitations are either due to intellectual concept
e.g. evaluation of only lymphocytes in lymphoma but frequently also to the lack of
suitable software which can treat hundreds or thousands of data columns during
learning processes and provide standardized i.e. portable as well as intelligible
classifications suitable e.g. for scientific hypothesis formation.
Aim: Several studies in different areas of clinical
medicine aimed to determine whether exhaustive extraction and evaluation
of multiparametric cytometric data in conjunction with their
automated triple matrix classification (http://www.biochem.mpg.de/valet/classif1.html)
was capable of either disease course predictions or risk assessments for individual patients.
Methods & Results: Data derived mostly from flow
cytometric list mode file analysis or cytophotometry with the analyzed cellular
systems being closely involved in the various disease processes. Clinical chemistry
parameters were also included in some instances. The results show that flow
cytometric cell function measurements or immunophenotyping is capable to predict
diseases course in between 90 to 100% correctly for intensive care patients (risk of
sepsis, shock, death, preoperative prediction of postoperative capillary leak syndrome)
for time periods between 1 and 10 days in advance. Furthermore risk assessment for
myocardial infarction risk patients from peripheral thrombocyte activation
analysis by immunophenotyping as well as for prediction on 10 year survival
of melanoma patients at surgery was possible with reasonable certainty (80-95%).
Lastly, the predictive capacity for survival/death in colo-rectal cancer patients
from cytophotometric G6PDH, Zn/Mn SOD and lipid peroxidation reaction
screening is clearly improved over predictions from Dukes classification or
patho-histological grading. Conclusion: Exhaustive data analysis on
cytometrically obtained biochemical cell and tissue parameters permits
to prognosticate disease course with high accuracy in individual patients
for a variety of clinically important disease states. This will allow to further
optimize the respective therapies and reduce unwanted therapeutic side effects.
PREDICTIVE MEDICINE BY CYTOMETRY
Valet G 1, Tarnok A 2, Kellermann W 3, van Driel BEM 4, van Noorden CJ 4,
Tschöpe D 5, Otto F 6, Kahle H 1
1) AG Zellbiochemie, Max-Planck-Institut für Biochemie, Martinsried,
2) Pediatric Cardiology, Heart Centre Leipzig GMBH, University Hospital,
Leipzig,
3) Anästhesiologische Abteilung, Krankenhaus Schwabing, TU München,
Germany
4) Lab.Cell Biology & Histology, Academic Medical Centre, Univ.Amsterdam,
Netherlands
5) Arb.Gruppe Zelluläre Hämostase & Klinische Angiologie, Diabetes Forschungsinstitut,
Heinrich-Heine-Universität, Düsseldorf
6) Fachklinik Hornheide, Münster, Germany