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Outcome Prediction in Sepsis Patients by Machine Learning, a Pilot Study |
1. Introduction:
Intensive care patients are in a life threatening condition when
affected by sepsis or non infectious shock, so the prediction of the
imminent danger for the development of these states is of high
clinical importance.
Clinical sepsis research focuses frequently on the determination of
biomarker levels in patient blood samples. Biomarkers like cytokines
are in many instances liberated from immune cells (lympho-/monocytes).
These mediators act in part upon effector cells like granulocytes.
Depending on immune and effector cell status, given mediator levels may
result in stronger or weaker cellular responses, that is mediator levels are
not directly correlated with effector cell activities.
Lymphocytes defend the organism by cellular and humoral (antibodies)
immunity, which typically requires weeks to be established while
sepsis often develops within hours.
Like in a medieval city, once the wall is broken (immune defense), the citys
fate depends critically on number and activity of intramural soldiers
such as granulocytes or monocytes, acting as important effectors
during the elimination of microorganisms or tissue breakdown products
by phagocytosis and degradation through oxidative or enzymatic action.
The overshooting release of granulocyte enzymes like elastase, of reactive
oxygen species like H2O2, O2-, OH. or of pharmacologically active mediators
like histamine may endanger the organism in case
these potent functionalities escape inhibitory control mechanisms
like in non infectious shock.
It seemed, therefore, promising to investigate granulocyte and monocyte
effector functions in blood samples of intensive care unit (ICU) patients
to determine early outcome predictors for individual ICU patients by
data pattern classification.
2. Concept and Goals:
Flow cytometric monitoring of bacteria phagocytosis by granulocytes
was promising but too complicated to perform in automatated
instruments
(CL1).
Cell function assays for the assessment of
oxidative burst and proteolytic activities in mono- and granulocytes
were therefore developed as an alternative
using:
1. humoral stimulators like e.g. cytokines and
2. newly developed sensitive oxidative
burst indicator dyes
dihydrorhodamine123 (DHR) (
8,
10,
11,
14)
and specific
rhodamine110 substrates
for the determination of
protease activity (
12,
13,
17-22,
24)
in vital cells. These developments have substantially simplified the
determination of blood cell functions in infection, sepsis or non infectious
shock.
3. Results:
The early flow cytometric work (
1,
2)
using
bacterial phagocytosis (
6,
7),
ADB
intracellular pH and esterase (
1,
2)
measurements as well as
acridine orange
as indicator of cellular and bacterial RNA and DNA(
7)
had shown for the first time that the prediction of imminent
danger of sepsis and non infectious shock in intensive care
(IC) patients was possible two to three days
prior to the appearence of life threatening clinical symptoms
(CL1).
Simplified assays using intracellular oxidative burst
and proteolytic capacities support these findings (details see below),
providing a significantly increased
therapeutic lead time for the clinician
(CL2
CL3).
4. CLASSIF1
Data Pattern Analysis
Flow cytometric data of such measurements are typically collected
as list mode files. They are evaluated in a
standardized and automated way by the CLASSIF1
(CL2
CL3)
multiparameter data classification program.
The analysis of the entire data set in this way
reveiled that the incubation of the blood samples:
- as collected (ex-vivo status)
- with physiological stimulators such as: suboptimal concentrations
of FMLP (formyl-methionyl-leucyl-phenylalanyl bacterial peptide),
TNF-alpha (tumor necrosis factor-alpha),
FMLP+TNF-alpha and
- with phorbol ester (PMA, phorbol-myristate-acetate) as
maximum stimulus
(CL2)
provides a sufficient amount of predictive information
(CL3, tab.4)
similarly as the determination of proteolytic enzyme activities
like cysteine or serine proteinases
- The optimization of the classification process
for the same group of septically admitted IC patients showed
that the most discriminatory predictive information was contained in
the FMLP and TNF-alpha stimulated oxidative burst (DHR123)
assays
(CL3, tab.8).
- As a practical consequence of the CLASSIF1
multiparameter data classification, only two out of the seven
performed assays were really required for survival prediction in this
group of ICU patients.
5. Conclusions:
A. Functional granulocyte and monocyte parameters provide
individualized predictive information for the two to three days in
advance recognition of life threatening sepsis occurence in ICU patients.
B. The proposed cell assays are suitable for automated preparation,
cytometric measurement and standardized data classification.
Literature References:
CL1.
G.Rothe, W.Kellermann, G.Valet: Flow cytometric parameters
of neutrophil function as early indicators of sepsis or trauma-related
pulmonary or cardiovascular organ failure.
J.Lab.Clin.Invest.115:52-61(1990).
CL2.
G.Rothe, W.Kellermann, J.Briegel, B.Schaerer, G.Valet:
Activation of neutrophils by tumor necrosis factor-alpha during sepsis, in:
Immune Consequences of Trauma, Shock and Sepsis Vol.II, Ed: E.Faist, J.Ninnemann,
D.Green, Springer Verlag, Berlin 1993, p.727-733.
CL3.
G.Valet, G.Roth, W.Kellermann: Risk assessment for intensive care
patients by automated classification of flow cytometric oxidative burst,
serine and cysteine proteinase measurements using CLASSIF1 triple matrix
analysis, in: Cytometric Cellular Analysis, Eds: J.P.Robinson, G.Babcock,
Wiley-Liss, New York 1998, p.289-306.
| © 2023 G.Valet |