U.S. patent application number 14/126580 was filed with the patent office on 2014-05-08 for method of predicting a blood dilution risk value.
This patent application is currently assigned to KONIKLIJKE PHIPIPS N.V. The applicant listed for this patent is Bart Jacob Bakker, Rene Van Den Ham, HEndrik Jan Van Oojen. Invention is credited to Bart Jacob Bakker, Rene Van Den Ham, HEndrik Jan Van Oojen.
Application Number | 20140128707 14/126580 |
Document ID | / |
Family ID | 46508123 |
Filed Date | 2014-05-08 |
United States Patent
Application |
20140128707 |
Kind Code |
A1 |
Bakker; Bart Jacob ; et
al. |
May 8, 2014 |
METHOD OF PREDICTING A BLOOD DILUTION RISK VALUE
Abstract
The present invention provides for a clinical decision support
system which makes use of a numerical model which dynamically
describes a blood dilution of a blood circulation. Based on
measured coagulation data and maybe other patient information, loss
of hemostatic balance is predicted based on calculations of certain
protein concentrations. Additionally, a calculation arrangement
translates at least some of the calculated values of concentrations
of human blood proteins into a risk value which risk value
describes a risk of clotting and/or embolism and/or bleeding. A
time development of that risk value is displayed to the user.
Inventors: |
Bakker; Bart Jacob;
(Eindhoven, NL) ; Van Den Ham; Rene; (Utrecht,
NL) ; Van Oojen; HEndrik Jan; (Wijk En Aalburg,
NL) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Bakker; Bart Jacob
Van Den Ham; Rene
Van Oojen; HEndrik Jan |
Eindhoven
Utrecht
Wijk En Aalburg |
|
NL
NL
NL |
|
|
Assignee: |
KONIKLIJKE PHIPIPS N.V,
EINDHOVEN
NL
|
Family ID: |
46508123 |
Appl. No.: |
14/126580 |
Filed: |
June 14, 2012 |
PCT Filed: |
June 14, 2012 |
PCT NO: |
PCT/IB2012/052995 |
371 Date: |
December 16, 2013 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
|
61552119 |
Oct 27, 2011 |
|
|
|
Current U.S.
Class: |
600/369 |
Current CPC
Class: |
G16H 10/40 20180101;
G16B 5/30 20190201; G16H 20/10 20180101; G16H 50/20 20180101; A61B
5/7278 20130101; A61B 5/026 20130101; G16B 45/00 20190201; A61B
5/02028 20130101; A61B 5/7275 20130101; A61B 5/02042 20130101; G16H
50/50 20180101; A61B 5/742 20130101; A61B 5/7475 20130101; A61B
5/4848 20130101; G16H 50/30 20180101; G16H 40/60 20180101 |
Class at
Publication: |
600/369 |
International
Class: |
A61B 5/00 20060101
A61B005/00; A61B 5/02 20060101 A61B005/02 |
Foreign Application Data
Date |
Code |
Application Number |
Jun 16, 2011 |
EP |
11170089.4 |
Claims
1. Method of predicting a blood dilution risk value of a first
blood circulation, the method comprising the steps: providing
measured coagulation data describing a haemostatic situation of the
first blood circulation at a first point in time (S1), applying the
measured coagulation data as an input for a numerical model, the
numerical model being a mathematical and dynamical representation
of a blood dilution of the first blood circulation (S2), performing
a simulation of a time development of the haemostatic situation by
means of the numerical model and based on the measured coagulation
data used as an input for the numerical model (S3), calculating
values of concentrations of human blood proteins as an output of
the simulation (S4), and translating at least some of the
calculated values of the concentrations of the human blood proteins
into a risk value, which risk value describes a risk of clotting
and/or embolism and/or bleeding for the first blood circulation
(S5).
2. Method according to claim 1, wherein the calculation of the
values of the concentrations of the human blood proteins is
generating a predicted time development of said concentrations of
human blood proteins as the output of the simulation.
3. Method according to claim 1, wherein the calculated values of
the concentrations of the human blood proteins are m values of k
different proteins, the method further comprising the step:
choosing n values out of the m values (S6), wherein k, m and n are
integers and n<m, and wherein only the n values are taken into
account for the translation into the risk value.
4. Method according to claim 1, further comprising the step:
graphically displaying a calculated time development of the risk
value (801) on a graphical user interface (S7).
5. Method according to claim 1, wherein the at least some of the
values of concentrations of human blood proteins are translated
into the risk value by means of a numerical function of state
variables of the numerical model.
6. Method according to claim 5 wherein the state variables of the
numerical model are chosen from the group comprising concentrations
of the following proteins: Alpha-2-Macroglobulin (A2M), C4BP,
coagulation factor 10 (F10), F11, F13, prothrombin (F2), tissue
factor, F5, F7, F8, F9, fibrinogen, fibrin, protein C, protein S,
protein Z, protein Z related protein inhibitor (ZPI),
alpha-1-anti-trypsin (AAT), protein C inhibitor (PCI),
anti-thrombin (ATIII), PAI1, C1 inhibitor (C1inh), TAFI, TFPI,
Vitronectin, plasmin, plasminogen, A2AP, thrombomodulin, uPA, tPA,
the proteins' activated forms F10a, F11a, F13a, thrombin (F2a),
F5a, F7a, F8a, F9a, activated protein C, and TAFIa, or wherein the
state variables of the numerical model comprise a concentration of
complexes formed by at least two of the previously cited proteins
(e.g FVa-FXa), or wherein the state variables of the numerical
model comprise a mass-length ratio of fibrin fibers formed in
coagulation.
7. Method according to claim 5, further comprising the step: using
the numerical model to identify a set of most sensitive state
variables in a situation of blood dilution based on at least one
given sensitivity threshold (S8).
8. Method according to claim 1, further comprising the step:
providing data about human blood protein levels of a second blood
circulation at a second point in time as reference protein levels
(S9), wherein at the second point in time the second blood
circulation undergoes bleeding and/or clotting and/or embolism,
wherein the second point in time is before the first point in time,
and using said provided reference protein levels for the
translation into the risk value for the first blood circulation
(S10).
9. Method according to claim 1, wherein the at least some of the
values of the concentrations of the human blood proteins are
translated into the risk value by calculating a speed of sealing of
a hypothetical wound (S14a) as the output or by calculating an
extent of growth of a hypothetical thrombus as the output
(S14b).
10. Method according to claim 9, wherein the numerical model is a
model of a time development of a hypothetical sealing of a wound of
the first blood circulation, further comprising the steps:
performing the simulation of the time development of the
hypothetical sealing of the wound of the first blood circulation in
terms of at least one element of the group comprising: a wound
surface area, interaction of tissue factors in a wound surface area
with coagulation proteins in the first blood circulation, formation
of fibrin fibers, and aggregation of blood platelets and/or fibrin
fibers, which cover a wound surface and stop a clotting
process.
11. Method according to claim 10, further comprising the steps:
evaluating the simulated time development of the hypothetical
sealing of the wound by evaluating a time that passes between an
initialization of the clotting process, i.e. a formation of the
wound, and an cessation of the clotting process, i.e. a sealing of
the wound.
12. (canceled)
13. Clinical decision support system for predicting and displaying
a blood dilution risk value of a first blood circulation, the
system comprising: a first arrangement configured to receive
measured coagulation data describing a haemostatic situation of the
first blood circulation at a first point in time, a storing
arrangement on which a numerical model is stored, wherein the
numerical model is a mathematical and dynamical representation of a
blood dilution of the first blood circulation, a calculation
arrangement configured to perform a simulation of a time
development of the haemostatic situation by means of the numerical
model and based on the measured coagulation data used as an input
for the numerical model, wherein the calculation arrangement is
configured to calculate values of concentrations of human blood
proteins as an output of the simulation, and wherein the
calculation arrangement is configured to translate at least some of
the calculated values of concentrations of human blood proteins
into a risk value, which risk value describes a risk of clotting
and/or embolism and/or bleeding of the first blood circulation,
further comprising a display arrangement configured to display the
risk value.
14. Program element for predicting and displaying a blood dilution
risk value of a first blood circulation, which when being executed
by a processor is adapted to carry out: receiving measured
coagulation data describing a haemostatic situation of the first
blood circulation at a first point in time (S1a), applying the
measured coagulation data as an input for a numerical model, the
numerical model being a mathematical and dynamical representation
of a blood dilution of the first blood circulation (S2), performing
a simulation of a time development of the haemostatic situation by
means of the numerical model and based on the measured coagulation
data used as an input for the numerical model (S3), calculating
values of concentrations of human blood proteins as an output of
the simulation (S4), and translating at least some of the
calculated values of concentrations of human blood proteins into a
risk value, which risk value describes a risk of clotting and/or
embolism and/or bleeding for the first blood circulation (S5).
15. Computer readable medium in which a program element for
predicting and displaying a blood dilution risk value of a first
blood circulation is stored, which, when being executed by a
processor is adapted to carry out: receiving measured coagulation
data describing a haemostatic situation of the first blood
circulation at a first point in time (S1a), applying the measured
coagulation data as an input for a numerical model, the numerical
model being a mathematical and dynamical representation of a blood
dilution of the first blood circulation (S2), performing a
simulation of a time development of the haemostatic situation by
means of the numerical model and based on the measured coagulation
data used as an input for the numerical model (S3), calculating
values of concentrations of human blood proteins as an output of
the simulation (S4), and translating at least some of the
calculated values of concentrations of human blood proteins into a
risk value, which risk value describes a risk of clotting and/or
embolism and/or bleeding for the first blood circulation (S5).
Description
FIELD OF THE INVENTION
[0001] The present invention relates to clinical decision support
systems. In detail, the present invention relates to a method of
predicting a blood dilution risk value of a first blood
circulation, to a clinical decision support system for predicting
and displaying a blood dilution risk value, a program element for
predicting and displaying a blood dilution risk value and a
computer readable medium.
BACKGROUND OF THE INVENTION
[0002] Patients who undergo surgery experience blood loss and
lowered concentrations of blood clotting proteins due to reactions
of the hemostatic system to the surgical cuts. Apart from this, the
patient's clotting system may be inhibited by Heparin-like
compounds to prevent embolism. The blood loss and lowering of
coagulation protein concentrations is countered by transfusions of
blood plasma, IV fluids, protein substitution solutions etc. The
challenge of maintaining a safe hemostatic balance in a patient
undergoing surgery is a very difficult one, and multiple strategies
exist to meet this challenge.
[0003] The patient's coagulation state is monitored during surgery
through tests like hematocrit measurements, blood pressure
measurements and multiple coagulation tests (e.g.
thrombo-elastometry, INR, aPTT). The amounts of (blood) products
administered to the patient are of course monitored as well. In
current practice the monitored values indicate when the patient is
out of hemostatic balance (e.g. his clotting potential has become
dangerously low), upon which a countermeasure (e.g. administration
of protein substitutes) is put into effect.
[0004] A downside to the way of working as described above is that
countermeasures are only taken when the patient is already out of
balance, and stopped only when the hemostatic balance starts to tip
in the other direction.
SUMMARY OF THE INVENTION
[0005] It may be seen as an object of the present invention to
provide for an improved blood dilution analysis.
[0006] There may be a need to analyze blood loss and lowered
concentrations of blood clotting proteins and provide accurate
countermeasures before the analyzed blood circulation is out of
balance. Furthermore, there may be a need to provide a user with
the information when the countermeasures have to be stopped and to
provide the user with said information before the hemostatic
balance of the analyzed blood circulation starts to tip in the
other direction.
[0007] The present invention matches these needs.
[0008] The object of the present invention is solved by the
subject-matter of the independent claims. Further embodiments and
further advantages are incorporated in the dependent claims.
[0009] It should be noted that the embodiments of the invention
described in the following similarly pertain to the method, the
system, to the program element, as well as to the computer readable
medium. In other words, features that will be described with regard
to the embodiments relating to a method of predicting blood
dilution risk value of a first blood circulation shall be
understood to be comprised or implemented by the corresponding
system, the program element, and the computer readable medium of
the present invention, and vice versa. Especially, the clinical
decision support system according to the present invention can be
configured in such a way that all the below described method
embodiments of the present invention can be carried out by said
clinical decision support system.
[0010] According to an exemplary embodiment of the invention, a
method of predicting blood dilution risk value of a first blood
circulation is presented. The method comprises the step of
providing measured coagulation data describing a hemostatic
situation of the first blood circulation at a first point in time
and applying the measured coagulation data as an input for a
numerical model. The numerical model is defined to be a
mathematical and dynamical representation of a blood dilution of
the first blood circulation. In other words, the numerical model
describes a blood dilution situation or a blood dilution
development of the surveyed or analyzed blood circulation. The
method further comprises the step of performing a simulation of a
time development of the hemostatic situation by means of the
numerical model and based on the measured coagulation data used as
an input for the numerical model. Further, calculating values of
concentrations of human blood proteins as an output of the
simulation is performed. Furthermore, translating at least some of
the calculated values of concentrations of human blood proteins
into a risk value, which risk value describes a risk of clotting
and/or embolism and/or bleeding of the first blood circulation is
performed by the presented method.
[0011] In other words, the method is configured to provide for a
calculated risk value based on measured coagulation data, and
moreover the method is additionally configured to provide for a
predicted risk value based on a simulation by the numerical
model.
[0012] In other words, the numerical model used herein describes a
situation in which a blood circulation undergoes blood dilution due
to for example blood loss or lowered concentrations of blood
clotting proteins because of reactions of the hemostatic system due
to e.g. a cut.
[0013] Furthermore, the term "calculating protein concentrations"
may also be understood in the context of the present invention as
calculating a concentration of a complex which is formed by at
least two different proteins or as calculating a mass-length ratio
of a protein, like for example a fibrin fiber.
[0014] The step of providing measured coagulation data may for
example be embodied by entering a number of transfusion units, that
have been supplied to a blood circulation to a graphical user
interface, or may be embodied as providing a measured test value of
a test which was applied to the blood circulation like e.g. an INR,
aPTT, thrombo-elastometry (ampl) or thrombo-elastometry (lag time)
test value to a graphical user interface. Such a graphical user
interface may be connected with or comprised of a clinical decision
support system which performs the presented method. The user may
submit the previously and exemplarily mentioned data to a
calculation arrangement to allow to perform a corresponding
simulation as described above and in the following.
[0015] Furthermore the step of "translating" may be performed based
on given translation rules and may be performed with regard to a
desired specific embodiment of a risk value, like for example a
risk value which indicates the speed of sealing of a hypothetical
wound. This will be explained in more detail in the following, e.g.
with regard to FIG. 6.
[0016] Furthermore, the term "hemostatic situation" may be
understood to be used synonymously to a blood dilution state of the
blood circulation. In other words the herein presented numerical
model enables a user to calculate and predict the present and
future blood dilution states of the analyzed blood circulation and
translates the latter into a risk value, i.e. a blood dilution risk
value.
[0017] Furthermore the term "at least some of the calculated
values" shall be understood in the context of the present invention
that also only one value of concentration of a human blood protein
can be translated if desired. However, a plurality of
concentrations of human blood proteins is comprised by said
term.
[0018] Specifically the term "human blood protein" may be
understood in the present invention to also comprise coagulation
proteins.
[0019] In general, the risk value may be seen as a blood dilution
risk value. The risk value may be embodied as a value ranging
between 0 and 1 or may be embodied as a displayed color that may
have different nuances with respect to the present underlying
predicted risk. However, other different representations of the
risk value shall be comprised in the scope of the present
invention.
[0020] In this and every other exemplary embodiment the term
"calculating" may be understood as performing a simulation with a
model e.g. with the mathematical model described herein.
[0021] It may be of importance that the presented method for
performing the prediction does not require any contact with the
patient, as it is purely based on a mathematical model.
[0022] In other words the presented method makes use of a computer
model, based on a biochemical model, which will be explained with
certain embodiments hereinafter. The model may use biomedical
knowledge and experiments, may use the measured monitored test
values, i.e. the provided coagulation data like e.g. the above
presented number of transfusion units, and other patient
information to predict a state of blood dilution that involves a
risk of clotting and/or embolism and/or bleeding before it occurs.
In other words, when transfusion is given, blood will be diluted.
What is predicted by the present invention is whether a patient
will become at risk as a result of the dilution.
[0023] Such a predicted dangerous state of dilution may be
displayed to the user by the calculated and predicted blood
dilution risk value, which was generated by the model based on the
calculated and predicted blood protein concentrations. Furthermore,
the method may comprise to calculate the correspondingly expected
effect of each available countermeasure. The solution may be
offered to a user by a graphical user interface which interface may
be linked to a hospital information system. Therefore, the
presented method provides for a continuous estimation of the
patients stability, and if desired, an alarm when the patient
threatens to become unstable and may suggest for the optimal
countermeasure.
[0024] According to another exemplary embodiment of the present
invention the method comprises the steps of calculating a first
predicted risk value based on an assumed first countermeasure,
calculating a second predicted risk value based on an assumed
second countermeasure, comparing the first and second predicted
risk value, and recommending the countermeasure from the first and
the second countermeasure which provides for the lower risk
value.
[0025] According to another exemplary embodiment of the present
invention, the calculation of the values of the concentrations of
the human blood proteins is generating a predicted time development
of said concentrations of human blood proteins as the output of the
simulation.
[0026] In a further step a generated time development of said
concentrations may be translated into a time development of the
risk value, which may additionally be graphically displayed to a
user. In other words, the presented embodiment may use predicted
time series of coagulation proteins for the translation into a risk
value.
[0027] According to another exemplary embodiment of the invention,
the calculated values of the concentrations of human blood proteins
are m values of k different proteins, and the method further
comprises the step of choosing n values out of the m values.
Therein k, m and n are integers and n and m relate to each other as
follows: n<m. Furthermore only the n values are taken into
account for the translation into the risk value.
[0028] In other words the exemplary embodiment presented above may
automatically choose only a subset of the calculated and predicted
concentrations of the human blood proteins for the further
processing into the risk value. Thus, a fast and accurate way of
calculating a risk value is presented.
[0029] Certain criteria may be given in order to define thresholds,
defining whether a protein value is chosen or not. Such thresholds
may be stored in e.g. a database and the presented method may
compare the calculated concentrations of the human blood proteins
with the values retrieved from that database.
[0030] Furthermore, more than one concentration value of each
different protein may be calculated. In this case the following
would be true: m>k.
[0031] Additionally, a time development of each concentration of
each protein may be calculated and predicted. Depending on said
development n values out of the m values may then be chosen.
[0032] However, in each of the above identified cases for m, n and
k of the present exemplary embodiment, only the n values are taken
into a count for predicting and calculating the risk value.
[0033] According to another exemplary embodiment of the invention,
the method comprises the step graphically displaying a time
development of the risk value on a graphical user interface.
[0034] As can be gathered for example from FIG. 8, this embodiment
provides for the advantage for a user to be able to gather
information in an illustrative way about the future development of
the risk value. Hence, a fast and reliable decision can be made by
the user observing the graphical representation of the time
development of the risk value. Thus, according to another exemplary
embodiment of the invention, the method may comprise the step of
calculating a time development of the blood dilution a risk value
during or besides the translation. If desired, such a time
development of a risk value may be displayed in a x- and y-diagram,
wherein the x-axes depicts the time and the y-axes depicts the
blood dilution risk value of clotting and/or embolism and/or
bleeding of the blood circulation. In other words the risk value is
shown as a function of time.
[0035] According to another exemplary embodiment, the at least some
of the values of the concentrations of the human blood proteins are
translated into a risk value by means of a numerical function of
state variables of the numerical model.
[0036] Furthermore, said state variables may be used to describe or
define a danger zone or a danger level regarding the blood
dilution. Such state variables may be embodied for example as a
concentration of proteins that play a role in the coagulation
process. Such a danger level or patient stability score may be
implemented as an aggregate variable which is the numerical
function. The numerical model may be used to identify the set of
most sensitive state variables in the situation of blood dilution,
i.e. those protein concentrations that at a small increase or
decrease of their value, determine whether a coagulation response
is too strong, i.e. a risk of embolism, or too weak, i.e. risk of
excessive bleeding. Different state variables may be related to the
two different risks, and some strong influences on risk may be due
to a combination of multiple parameters. Furthermore, model
analysis methods can be used for such a sensitivity analysis and
can be used to identify a panel of state variables. Furthermore, a
more exact definition the numerical function can be estimated
through a clinical study where the levels of the related protein
concentrations are measured in case of bleeding and/or embolism. In
other words the use of the numerical model in the presented
embodiment is two-fold. Firstly for the identification of the panel
of state variables, and secondly to predict the expected
development of the dynamic values of these state variables and thus
the risk value during a situation of blood dilution.
[0037] According to another exemplary embodiment, the state
variables of the numerical model are chosen from the group
comprising concentrations of the following proteins:
Alpha-2-Macroglobulin (A2M), C4BP, coagulation factor 10 (F10),
F11, F13, prothrombin (F2), tissue factor, F5, F7, F8, F9,
fibrinogen, fibrin, protein C, protein S, protein Z, protein Z
related protein inhibitor (ZPI), alpha-1-anti-trypsin (AAT),
protein C inhibitor (PCI), anti-thrombin (ATIII), PAI1, C1
inhibitor (C1inh), TAFI, TFPI, Vitronectin, plasmin, plasminogen,
A2AP, thrombomodulin, uPA, tPA, the proteins' activated forms F10a,
F11a, F13a, thrombin (F2a), F5a, F7a, F8a, F9a, activated protein
C, and TAFIa, or wherein the state variables of the numerical model
is a concentration of complexes formed by at least two of the
previously cited proteins (e.g. FVa-FXa), or wherein the state
variables of the numerical model is a mass-length ratio of fibrin
fibers formed in coagulation. Furthermore each protein comprises by
one of the following Tables 1 to 3 may be state variable according
to this embodiment.
[0038] According to another exemplary embodiment the method further
comprises the step of using the numerical model to identify a set
of most sensitive state variables in a situation of blood dilution
based on at least one given sensitivity threshold.
[0039] The presented embodiment may make use of a comparison
between calculated values of the state variables with the
sensitivity threshold.
[0040] Sensitive state variables can be identified through a
sensitivity analysis method, which involves the variation of one or
a set of model parameters or model inputs and the analysis of the
change in one or more model outputs as a result of the change in
the model parameters or model input. The case of sensitive state
variable selection in a hemostatic model involves the simulation of
a clotting response to a certain trigger, e.g. exposure of proteins
residing in blood to a wound in the blood vessel wall, and more
specifically to the tissue factor protein at the wound surface. The
varied model inputs are the initial values for the protein
concentrations state variable used in the model, whereas the
observed output can be any model feature that links to the strength
of the clotting response, e.g. the time between first exposure of
the blood to tissue factor and the moment that thrombin a key
protein that is produced in the clotting process exceeds a
threshold value of e.g. 10 nM.
[0041] In the simplest form of sensitivity analysis one model input
is varied within its theoretical limits while the other model
inputs are kept constant, i.e. local sensitivity analysis. The
resulting change in model output may be translated to a sensitivity
score, i.e. single value, for the one varied model input, where
this score will be low if the model output changes little with the
varying model input and high when the output changes strongly as a
result of the varying model input. Such a score may also depend on
the correlation between the change in the model input and the
change in the model output, i.e. a model output that rises
consistently with a rising model input may lead to a higher
sensitivity score than a model output that changes strongly, but
erratically with a rising model input. Local sensitivity analysis
calculates one sensitivity score for each model input relating to a
state variable; the highest sensitivity scores identify the most
sensitive state variables.
[0042] In the more complicated global sensitivity analysis all
model inputs e.g. initial values for the state variables are
changed simultaneously. This method is less sensitive to the choice
of fixed model inputs (in the local sensitivity analysis all model
inputs but one are fixed to a chosen value), but has the downside
that the response of the chosen model output to the change in a
chosen model input is influenced by the variation of all the other
model inputs. This makes the variation of the model output as a
function of the variation of one model input more chaotic by
definition. Sensitivity scores can still be calculated, e.g. as the
correlation coefficient between a model input and the selected
model output (see also Frey, H. C., Patil, S. R., Identification
and Review of Sensitivity Analysis Methods. Risk Anal., 2002.
22(3): p. 553-578.)
[0043] According to another exemplary embodiment of the invention,
the method further comprises the step of providing data about human
blood protein levels of a second blood circulation at a second
point in time as reference protein levels. Therein, at the second
point in time the second blood circulation undergoes bleeding
and/or clotting and/or embolism, wherein the second point in time
is before the first point in time. Furthermore, the embodiment
comprises using said provided reference protein levels for the
translation into the risk value for the first blood
circulation.
[0044] Thus, the first blood circulation may be different from the
second blood circulation, as the reference protein levels may
usually be used from different patients. In other words, this
embodiment describes the situation of the usage of previously
gathered information, how blood circulations and coagulations
systems may react in average during blood dilution. Said provided
data may be retrieved by a clinical decision support system from
internal or external data storage, where corresponding average
protein levels during blood dilution are stored. By means of
comparing values of the same protein, an estimation of the
numerical function is provided.
[0045] This embodiment may also be used to provide for a
patient-specific observation during surgery. In this case the first
blood circulation and the second blood circulation are the same.
Thus, if desired, as reference protein levels data may be used of
the first blood circulation, i.e. data from the same patient, from
a previous point in time, at which the patient was undergoing
bleeding and/or clotting and/or embolism or which may be related to
a bleeding and/or clotting and/or embolism event at a later point
in time. Consequently, the presented method makes use of knowledge,
how the protein level of the specific observed patient is
developing under said situations.
[0046] According to another exemplary embodiment of the invention,
the method further comprises the step of comparing said provided
reference protein levels with the calculated values of
concentration of human blood proteins, generating a comparison
value and using the comparison value for the translation into the
risk value for the first blood circulation.
[0047] According to another exemplary embodiment of the invention,
the at least some of the values of the concentration of the human
blood proteins are translated into the risk value by calculating a
speed of sealing of a hypothetical wound or by calculating a speed
of growth of a hypothetical thrombus as an output.
[0048] In other words, the presented embodiment involves a
prediction of certain clot elements, like for example fibrin or
fibrinogen concentration. Furthermore, the increase of fiber which
comprises or consists of fibrin or fibrinogen may be calculated.
Based on the previously cited steps, the presented method may
translate this into a wound closing time that is necessary to close
the hypothetical wound. The presented method may further translate
the wound closing time into a corresponding bleeding risk
value.
[0049] In a similar way based on the calculated concentration of
proteins, a size of a thrombus may be calculated. This may further
be related to an estimated wound closing. In other words, the
numerical model may describe how fast a thrombus may grow and thus
how fast the sealing of a wound takes place, all based on the
provided coagulation data. Both aspects, which are predicted and
calculated by the numerical model, i.e. the wound closing time and
the growth of a thrombus, are subsequently translated into a
corresponding risk value. Thus, the risk value may firstly be
embodied as a wound closing time risk value or may secondly be
embodied as a thrombus growing risk value. If desired, the
calculated and translated risk value may be based on both predicted
results, the wound closing time and the growth of a thrombus.
[0050] According to another exemplary embodiment, the numerical
model is a model of a time development of a hypothetical sealing of
a wound of the first blood circulation. Furthermore, this
embodiment further comprises the step of performing the simulation
of the time development of the hypothetical sealing of the wound of
the first blood circulation in terms of at least one element of the
group comprising a wound surface area, interaction of tissue factor
in a wound surface area with coagulation proteins in the first
blood circulation, formation of fibrin fibers, and aggregation of
blood platelets and/or fibrin fibers, which cover a wound surface
and stop a clotting process. In other words, the variables that are
simulated are of the group comprising the before cited
variables.
[0051] Thus the numerical model takes into account at least one of
the above cited elements in order to calculate as an output a wound
closing time. This wound closing time may then be translated, as
described above, into a risk value, which may then be graphically
displayed to a user.
[0052] According to another exemplary embodiment, the method
further comprises the step of evaluating the simulated time
development of the hypothetical sealing of the wound by evaluating
a time that passes between an initialization of the clotting
process, i.e. a formation of the wound, and a cessation of the
clotting process, i.e. a sealing of the wound. Then a risk value
may be calculated, namely a bleeding risk value.
[0053] According to another exemplary embodiment, the method
further comprises the step of evaluating a risk value based on size
and constitution of the thrombus. This may be risk value regarding
clotting or embolism.
[0054] According to another exemplary embodiment of the invention,
a clinical decision support system for predicting and displaying a
blood dilution risk value of a first blood circulation is presented
wherein the system comprises a first arrangement configured to
receive measured coagulation data describing a hemostatic situation
of the first blood circulation at a first point in time. The system
further comprises a storing arrangement on which a numerical model
is stored, wherein the numerical model is a mathematical and
dynamical representation of a blood dilution of the first blood
circulation. The system further comprises a calculation arrangement
configured to perform a simulation of a time development of the
hemostatic situation by means of the numerical model and based on
the measured coagulation data used as an input for the numerical
model. Therein, the calculation arrangement is configured to
calculate values of concentration of human blood proteins as an
output of the simulation. Furthermore, the calculation arrangement
is configured to translate at least some of the calculated values
of concentrations of the human blood proteins into a risk value,
which risk value describes of risk of clotting and/or embolism
and/or bleeding of the first blood circulation. Furthermore, the
system further comprises a display arrangement configured to
display the risk value.
[0055] In addition to the previously described configurations of
that clinical decision support system, this clinical decision
support system may also be configured to perform the described
methods as presented above and in the following. Such a clinical
decision support system is depicted in the following FIGS. 1 and
7.
[0056] The presented clinical decision support system makes use of
a numerical model which may be based on biochemical knowledge
and/or experiments, which uses the provided calculation data. If
desired other patient information may also be used to predict a
blood dilution risk value in future. Based on calculated future
protein concentrations, a risk value is calculated by the system
and graphically displayed to the user in order to provide him with
the necessary information before loss of hemostatic balance occurs.
Furthermore, the system may be configured to calculate an effect of
different countermeasures that may be performed by the user. The
clinical decision support system may be linked to a hospital
information system and provides for a continuous estimation of the
patient's stability. If desired, an alarm when the patient
threatens to become unstable may be provided to the user.
Additionally one or more suggestions of optimal countermeasures may
be applied to the user by the system. The suggestions may be ranked
based on the predicted chances of success.
[0057] According to another exemplary embodiment of the invention,
a program element for predicting and displaying a blood dilution
risk value of a first blood circulation, which when being executed
by a processor is adapted to carry out steps of receiving measured
coagulation data describing the hemostatic situation of the first
blood circulation at a first point in time, applying the measured
coagulation data as an input for a numerical model, the numerical
model being a mathematical and dynamical representation of a blood
dilution of the first blood circulation, and performing a
simulation of a time development of the hemostatic situation by
means of the numerical model, and based on the measured coagulation
data used as an input for the numerical model, and calculating
values of concentrations of human blood proteins as an output of
the simulation, and translating at least some of the calculated
values of concentrations of human blood proteins into a risk value,
which risk value describes a risk of clotting and/or embolism
and/or bleeding for the first blood circulation.
[0058] The computer program element may be part of a computer
program, but it can also be an entire program by itself. For
example, the computer program element may be used to update an
already existing computer program to get to the present
invention.
[0059] According to another exemplary embodiment, a computer
readable medium in which a program element for predicting and
displaying a blood dilution risk value of a first blood circulation
is stored, which, when being executed by a processor, is adapted to
carry out the steps of receiving measured coagulation data
describing the hemostatic situation of the first blood circulation
at a first point in time, and applying the measured coagulation
data as an input for a numerical model, the numerical model being a
mathematical and dynamical representation of a blood dilution of
the first blood circulation, and performing a simulation of a time
development of the hemostatic situation by means of the numerical
model and based on the measured coagulation data used as an input
for the numerical model, and calculating values of concentrations
of human blood proteins as an output of the simulation, and
translating at least some of the calculated values of
concentrations of human blood proteins into a risk value, which
risk value describes a risk of clotting and/or embolism and/or
bleeding for the first blood circulation.
[0060] The computer readable medium, as for example shown in FIG.
1, may be seen as a storage medium, such as for example, a USB
stick, a CD, a DVD, a data storage device, a hard disk, or any
other medium on which a program element as described above can be
stored.
[0061] It may be seen as a gist of the invention to use a numerical
model, which is a mathematical and dynamical representation of a
situation of blood dilution of the blood circulation, to predict
future protein concentrations of said blood circulation based on
the received coagulation data. Time developments of said protein
concentrations may thus be calculated and predicted by the model.
Based on said predicted protein concentrations an assessment of the
participating proteins is performed by means of which the most
relevant proteins are chosen. The group of chosen proteins is used
to calculate and predict a blood dilution risk value, and to show
said value or a time development of said value to a user.
Corresponding recommendations for countermeasures may be predicted
and displayed to the user, somehow rated with regard to the
respective chances of success. In other words, the invention is
able to provide for a present risk value based on measured
coagulation data, and the invention is additionally able to provide
for a predicted risk value based on simulation.
[0062] Furthermore, a person skilled in the art will gather from
the above and the following description that, unless otherwise
notified, in addition to any combination belonging to one type of
subject-matter, also any combination between features relating to
different subject-matters, in particular between features of the
apparatus type claims and features of the method type claims, is
considered to be disclosed with this application. Furthermore, all
features can be combined providing synergetic effects that are more
than the simple summation of the features.
[0063] The present invention will become apparent from and be
elucidated with reference to the embodiments described
hereinafter.
[0064] Exemplary embodiments of the invention will be described in
the following drawings.
BRIEF DESCRIPTION OF THE DRAWINGS
[0065] FIG. 1 schematically shows a clinical decision support
system according to an exemplary embodiment of the invention.
[0066] FIGS. 2 to 6 schematically show flow diagrams of a method
according to exemplary embodiments of the invention.
[0067] FIG. 7 schematically shows a clinical decision support
system according to an exemplary embodiment of the invention.
[0068] FIG. 8 schematically shows a graphical user interface to be
used in accordance with an exemplary embodiment of the
invention.
[0069] In principal, identical parts are provided with the same
reference symbols in the figures.
DETAILED DESCRIPTION OF EMBODIMENTS
[0070] FIG. 1 shows a clinical decision support system 100 for
calculating and displaying a prediction of a blood dilution risk
value a first blood circulation according to an exemplary
embodiment of the invention. The system 100 comprises a first
arrangement 101 configured to receive measured coagulation data 102
which data describe a hemostatic situation of the first blood
circulation at a first point in time. The system 100 further
comprises a storing arrangement 103 on which a numerical model 104
is stored. The numerical model is embodied as a mathematical and
dynamical representation of a blood dilution situation of the first
blood circulation. The calculation arrangement 105 is configured to
perform a simulation of a time development of the hemostatic
situation by means of using the numerical model and supplying the
measured coagulation data as an input to that model. Furthermore,
the calculation arrangement 105 is configured to calculate values
of concentrations of human blood proteins as an output of the
simulation. Furthermore, the calculation arrangement 105 is
configured to translate at least some of the calculated values of
concentrations of human blood proteins into a risk value which
describes a clotting and/or embolism and/or bleeding of the first
blood circulation. Furthermore, a display arrangement 106 is shown
in FIG. 1. By means of the display arrangement the calculated risk
value can be shown to the user. Additionally, a program element 107
is shown in FIG. 1 which is configured for calculating and
displaying a prediction of a hemostatic situation of a first blood
circulation, when being executed by the processor 108. As can be
gathered from FIG. 1, a computer readable medium 109 is shown on
which such a program element 107 is additionally stored. Moreover,
a data storage device 115 is shown which is linked to the present
clinical decision support system. For example, the data storage
device 115 may be embodied as a content information system or a
content delivery system (CDS). The display arrangement 106 provides
for a graphical user interface 118 which has an area 116 where the
predicted risk value, which has been calculated by means of the
previously described translation, is displayed. If desired, a time
development of that risk value is displayed there to the user. As
can be seen in FIG. 1, the graphical user interface additionally
provides for a first arrangement 101 which is configured to receive
measured coagulation data. FIG. 8 depicts an embodiment of a
graphical user interface that could be used as user interface 118
of FIG. 1. In combination with FIG. 8, where also a first
arrangement 101 configured to receive measured coagulation data is
shown, it becomes clear that information about e.g. already
supplied transfusion units or INR or aPTT test values may be
entered and submitted to the clinical decision support system by
the user. Additionally, the graphical user interface 118 comprises
an area 117 at which recommendations for countermeasures can be
displayed to the user, which countermeasures were calculated
previously by the clinical decision support system. These
recommended countermeasures are based on the model predictions as
described above and in the following.
[0071] In other words, said exemplary embodiment may be seen as a
clinical decision support system performing a computer-supported
decision method. By means of the calculated predictions a user may
decide which administration of drugs or other clinical action may
be useful. The result of the prediction may be accompanied by a
calculated suggestion of a change of administration of anti- or
pro-coagulation drugs. Said calculation of the prediction may be
performed e.g. on a computer or a processor of a computer.
[0072] A gist of this mathematical model can be seen in the
combination of a biochemical model calculating coagulation cascade
and fibrin polymerization. Thus, "enzymatic conversion" in
combination with "complex assembly" can be taken into account
during the prediction.
[0073] The mathematical model used in FIG. 1 will be explained in
more detail hereinafter. This model may be implemented in every
herein described embodiment of the invention. It is of utmost
importance that the below presented model is just one exemplary
embodiment of the mathematical model according to the present
invention.
[0074] It should be noted that the mathematical model can, if
desired, comprise partially or completely the reaction mechanisms
that are disclosed in Tables 1 to 3. Also the ordinary differential
equations disclosed in Tables 4 and 5 may be integrated into the
mathematical model according to the user's desire. In other words,
also a combination between different reaction mechanisms out of
Tables 1 to 3 with ordinary differential equations out of Tables 4
and 5 are possible. In other words the person skilled in the art
may take from the presented tables 1 to 5 the features he is
interested in regarding his special medical case. Thus, it is made
clear that the model presented herein is just a version of the
model, and that this model can be adapted, extended, reduced or
even completely replaced by another mathematical model which takes
into account biochemical and pharmacodynamical aspects.
[0075] Mathematical Definition of the Model
[0076] The mathematical model can be considered to consist of three
separate modules: coagulation cascade, fibrin polymerization and
pharmacokinetics and pharmacodynamics (PK/PD) of anticoagulant
drugs. If desired only the first two modules may be used. Whereas
the first two modules are based on the underlying (protein)
interactions of the coagulation response and may be used to
simulate in vitro tests like the thrombin generation assay,
prothrombin time (PT) and activated partial thromboplastin time
(aPTT), the latter is based on compartment modeling which is used
to simulate the (long-term) kinetics and effect of the
anticoagulants such as unfractionated heparin (UFH) and
low-molecular weight heparin (LMWH) in the human body.
[0077] Biochemical Model
[0078] The physiological system of biochemical reactions may be
represented as a closed volume element, representing a certain
volume of blood plasma in in vitro tests. Hence, there is no
transport in or out of this volume and clearance of proteins is
assumed to be not significant on this time-scale (minutes). This
means that there is conservation of mass in the volume element.
Besides that it is assumed that diffusion in the mixture does not
significantly influence the reaction velocities.
[0079] The mathematical model of the coagulation cascade and fibrin
polymerization consists of 216 state variables (concentrations of
proteins and protein complexes) and 100+ reaction rate constants
that are used to parameterize 91 reactions. An overview of the
reactions is given in Table 1, Table 2 and Table 3. All states,
initial concentrations and kinetic parameters were defined as
non-negative real numbers, IR+0. The initial concentrations of the
proteins are inferred from values reported in literature or set to
the actual measured concentrations. The model's kinetic parameters
were estimated from in-house generated experimental data by means
of solving the inverse problem. Nevertheless, the kinetic
parameters (but also the initial concentrations) are subjected to a
continuous update process to improve the accuracy of the found
values by means of additional experiments and analyses.
[0080] The functional description of the state equations can be
represented as follows (in state space formulation).
x t = f ( x ( t ) , u ( t ) , .theta. ) , with x 0 = x ( t 0 ) ( 1
) ##EQU00001##
[0081] Where x is the state vector, u the input vector of the test
conditions (e.g. certain tissue factor concentration to simulate
the PT), x0 is the vector of initial concentrations and f is a
vector field with non-linear functions parameterized with .theta..
The output, y, of the state model can be characterized by:
y(t,.theta.)=C(t,.theta.) (2)
[0082] Where matrix C selects a number of `interesting` states of
the model output.
[0083] The 91 reaction mechanisms derived from literature were
classified as either one of two types of elementary reaction
mechanisms. These reaction mechanisms were complex assembly and
enzymatic conversion.
[0084] Complex assembly is the process where substrate A and B
react to form complex A-B. It features in the formation of
coagulation complexes (e.g. FXa-FVa, FIXa-FVIIIa) and inhibition of
activated proteins by stochiometric inhibitors (e.g. FIIa-AT-III,
TF-FVIIa-FXa-TFPI). The related reaction equation reads:
##STR00001##
[0085] The association rate constant of complex formation, k1, is a
second order rate constant and the dissociation rate constant of A
and B from A-B, k-1, is a first-order rate constant. In some cases
the association reaction is irreversible, which means the complex
is stable and will not dissociate, e.g. inhibition of FIIa by
AT-III. Reaction scheme (3) was converted to the following set of
ordinary differential equations (ODEs) describing the change in
concentration, represented by [ . . . ], in time:
.differential. [ A ] .differential. t = .differential. [ B ]
.differential. t = - .differential. [ A - B ] .differential. t = -
k 1 [ A ] [ B ] + k - 1 [ A - B ] ( 4 ) ##EQU00002##
[0086] The enzymatic conversion of proteins by enzymes was the
second type of reaction mechanism exploited in the coagulation
model. All activation processes in the hemostasis model correspond
to this type of reaction. The reaction scheme of enzymatic
conversion can be represented schematically as:
##STR00002##
[0087] Where E is the enzyme and S the substrate concentration that
is converted into product P by E. Enzymatic conversion of proteins
was implemented in the mathematical model as follows:
.differential. [ E ] .differential. t = - k 1 [ E ] [ S ] + k - 1 [
E - S ] + k 2 [ E - S ] ( 6 ) .differential. [ S ] .differential. t
= - k 1 [ E ] [ S ] + k - 1 [ E - S ] ( 7 ) .differential. [ E - S
] .differential. t = k 1 [ E ] [ S ] - k - 1 [ E - S ] - k 2 [ E -
S ] ( 8 ) .differential. [ P ] .differential. t = k 2 [ E - S ] ( 9
) ##EQU00003##
[0088] Most of the proteins or protein-complexes participate in
multiple reactions in the biochemical model, hence all reactions
that the protein or protein-complex is participating in have to be
accounted for in the ODE of that specific protein's or
protein-complex' concentration. This results in one ODE per protein
or protein-complex, which consists of a summation of ODE
contributions from all reactions that the protein is participating
in. This is represented mathematically as follows (an alternative
representation of equation (1)).
.differential. x .differential. t = S _ R _ ( x ) = i = 1 m S i R i
( x ) ( 10 ) ##EQU00004##
[0089] Where x is the vector of concentrations of the different
substrates, S is the matrix with reaction rate constants and R is
the reaction matrix. Each column of the stochiometric matrix Si
corresponds to a particular reaction.
[0090] PK/PD Model
[0091] The mathematical model that simulates the long-term kinetics
and effects of the anticoagulants is based on a combination of
compartment models that are generally used in PK/PD modeling. Since
the PK/PD equations are not as standardized as the biochemical
equations (only complex assembly and enzymatic catalysis), the
complete ODEs of each state are shown in Table 4 and Table 5. The
ODEs belonging to the pharmacokinetic properties of unfractionated
heparin and low-molecular weight heparin are shown in Table 4. As a
result of these ODEs the blood kinetics of both types of heparin
can be calculated. The effects of both heparins are on the activity
of AT-III, and this is represented by equations v78-v91 in the
biochemical model, which uses the blood concentrations of UFH and
LMWH at the moment of blood withdrawal as input. The blood
concentrations of the coagulation proteins at the moment of blood
withdrawal are used as input for the biochemical model.
[0092] The Tables 1 to 4 are shown in the following. The
mathematical model described herein may thus take into account
several or all reaction mechanisms v1 to v91. The person skilled in
the art will combine them as needed or desired. Additionally the
ordinary differential equations described under PKPD1 to PKPD17 may
partially or completely be implemented in the mathematical
model.
[0093] The used model may also be described as follows: The
computer model may be seen as a representation of the coagulation
cascade and fibrin polymerization as a set of reaction mechanisms.
The time dynamics of each reaction mechanism may be described as an
ordinary differential equation or ODE that involves the
concentration(s) of the protein(s) and/or chemical molecule(s) that
are involved in the reaction and the reaction rate parameter(s). By
summation of all reaction mechanisms in which a particular protein
or other kind of chemical molecular is involved (a protein or
molecule can participate in more than one reaction), the time
dynamics of the concentration of that particular protein or other
kind of chemical entity may be calculated. Doing this for all
proteins or molecules, the whole system can calculate and keep
track of the evolution of all proteins and molecules over time,
however for this one may require, beside the reaction topology,
also the numerical values of the model parameters. These model
parameters include the initial conditions of the system, i.e. the
concentration of all proteins and molecules at t=0 (e.g. before
onset of bridging therapy), and the reaction rate parameters of the
reaction mechanisms. Part of the initial concentrations that are
the most important to the outcome of the system are measured from
the patient (in the laboratory or clinic), whereas others, less
determining proteins, are taken from literature (average patient
values, possibly corrected for gender and age, etc.). The reaction
rate parameters may be derived via solving an inverse problem, i.e.
model fitting to experimental data. The system of coupled ODEs may
be solved numerically, using the numerical values of the model
parameters, by employing standard ODE integration algorithms.
TABLE-US-00001 TABLE 1 All reaction mechanisms incorporated in the
computer model of the coagulation cascade. It should be noted that
in this table the official gene symbole are used instead of the
popular scientific names Reaction Name Type Substrates Products
Cofactors/Catalyst Reaction site v1 F3-F7a complex Complex F3, F7a
F3-F7a Endothelial membrane assembly assembly v2 F3-F7 complex
Complex F3, F7 F3-F7 Endothelial membrane assembly assembly v3 F7
activation (1) Catalysis F7 F7a F3-F7a Endothelial membrane v4 F7
activation (2) Catalysis F7 F7a F10a Endothelial membrane v5 F7
activation (3) Catalysis F7 F7a F9a Endothelial membrane v6 F7
activation (4) Catalysis F7 F7a F2a v7 F9 activation (1) Catalysis
F9 F9a F11a, negative phospholipids v8 F9 activation (2) Catalysis
F9 F9a F3-F7a Endothelial membrane v9 F9a degradation Degradation
F9a Blood plasma v10 F8 activation (1) Catalysis F8 F8a F2a Blood
plasma?? v11 F8 degradation Degradation F8a PROCa-PROS1-F5ac
Platelet membrane v12 F9a-F8a complex Complex F9a, F8a F9a-F8a
Ca2+, neg phospholipid Platelet membrane assembly assembly v13 F2
activation (1) Catalysis F2 F2a F10a Blood plasma v14 F2 activation
(2) Catalysis F2 F2a F10a-F5a Platelet membrane v15 F2a degradation
Degradation F2a Blood plasma v16 F5 activation Catalysis F5 F5a F2a
Blood plasma v17 F5 anticoagulant Catalysis F5 F5ac PROCa Blood
plasma formation v18 F5a degradation Degradation F5a PROCa-PROS1
Blood plasma/ endothelial membrane v19 F10 activation (1) Catalysis
F10 F10a F3-F7a Endothelial membrane v20 F10 actication (2)
Catalysis F10 F10a F9a-F8a Platelet membrane v21 F10 activation (3)
Catalysis F10 F10a F9a Blood plasma?? v22 F10a degradation
Degradation F10a Blood plasma v23 F10a-F5a complex Complex assembly
F10a, F5a F10a-F5a Ca2+, neg phospholipid Platelet membrane
assembly v24 PROC activation (1) Catalysis PROC PROCa F2a Blood
plasma v25 PROS1-C4BP Complex assembly PROS1, C4BP PROS1-C4BP Blood
plasma complex assembly v26 PROCa-PROS1 Complex assembly PROCa,
PROS1 PROCa-PROS1 Ca2+, neg phospholipid Platelet membrane complex
assembly v27 PROCa-PROS1- Complex PROCa-PROS1, PROCa-PROS1- Ca2+,
neg phospholipid Platelet membrane F5ac assembly F5ac F5ac complex
assembly v28 F13 activation Catalysis F13 F13a F2a, Ca2+ (at least
Blood plasma 1 mM) v29 F12 activation (1) Catalysis F12 F12a F12a,
negative Negative surface phospholipds v30 F12 activation (2)
Catalysis F12 F12a KLKB1a Blood plasma v31 F12 activation (3)
Catalysis F12 F12a KNG1 Blood plasma v32 F12a degradation
Degradation F12a Blood plasma?? v33 KLKB1 activation Catalysis
KLKB1 KLKB1a F12a Blood plasma v34 F11 activation (1) Catalysis F11
F11a F12a Blood plasma v35 F11 activation (2) Catalysis F11 F11a
F2a, negative Negative surface phospholipids v36 F11 activation (3)
Catalysis F11 F11a F11a, negative Negative surface phospholipids
v37 F11a degradation Degradation F11a Blood plasma v38 CPB2
activation (1) Catalysis CPB2 CPB2a F2a Blood plasma v39 CPB2a
degradation Degradation CPB2a Blood plasma v40 F10a-TFPI complex
Complex assembly TFPI, F10a F10a-TFPI Blood plasma?? assembly v41
F10a-F3-F7a-TFPI Complex assembly F10a-TFPI, F3-F7a
F10a-F3-F7a-TFPI Ca2+ Endothelial membrane complex assembly v42
F3-F7a-TFPI Complex assembly F3-F7a, TFPI F3-F7a-TFPI Endothelial
membrane complex assembly v43 F11a-SERPINC1 Complex assembly F11a,
SERPINC1 F11a-SERPINC1 SERPIND1 Blood plasma complex assembly v44
F12a-SERPINC1 Complex assembly F12a, SERPINC1 F12a SERPINC1
SERPIND1 Blood plasma complex assembly v45 F9a-SERPINC1 Complex
assembly F9a, SERPINC1 F9a-SERPINC1 SERPIND1 Blood plasma complex
assembly v46 F2a-SERPINC1 Complex assembly F2a, SERPINC1
F2a-SERPINC1 SERPIND1 Blood plasma complex assembly v47
F10a-SERPINC1 Complex assembly F10a, SERPINC1 F10a-SERPINC1
SERPIND1 Blood plasma complex assembly v48 F3-F7a-SERPINC1 Complex
assembly F3-F7a, SERPINC1 F3-F7a-SERPINC1 SERPIND1 Blood plasma
complex assembly v49 PROCa-SERPINA1 Complex assembly PROCa,
SERPINA1 PROCa-SERPINA1 Blood plasma complex assembly v50
PROCa-SERPINA5 Complex assembly PROCa, SERPINA5 PROCa-SERPINA5
Heparin dependent Blood plasma complex assembly v51 F2a-SERPINA5
Complex assembly F2a, SERPINA5 F2a-SERPINA5 Heparin dependent Blood
plasma complex assembly v52 F10a-SERPINA5 Complex assembly F10a,
SERPINA5 F10a-SERPINA5 Heparin dependent Blood plasma complex
assembly v53 KLK1a-SERPINA5 Complex assembly KLKB1a,
KLKB1a-SERPINA5 Blood plasma?? complex assembly SERPINA5 v54
PROZ-SERPINA10 Complex assembly PROZ, SERPINA10 PROZ-SERPINA10
Blood plasma complex assembly v55 F9a-SERPINA10 Complex assembly
F9a, SERPINA10 F9a-SERPINA10 Blood plasma complex assembly v56
F10a-PROZ- Complex assembly PROZ-SERPINA10, F10a-PROZ- Ca2+,
Phospholipids Membrane SERPINA10 F10a SERPINA10 complex assembly
v57 F11a-SERPINA10 Complex assembly F11a, SERPINA10 F11a-SERPINA10
Blood plasma complex assembly v58 PROCa-SERPINE1 Complex assembly
PROCa, SERPINE1 PROCa-SERPINE1 Blood plasma?? complex assembly v59
F2a-SERPINE1 Complex assembly F2a, SERPINE1 F2a-SERPINE1 Blood
plasma complex assembly v60 VTN-SERPINE1 Complex assembly VTN,
SERPINE1 VTN-SERPINE1 Membrane surface complex assembly v61
F2a-VTN- Complex assembly F2a, VTN- F2a-VTN-SERPINE1 Membrane
surface SERPINE1 SERPINE1 complex assembly v62 CPB2a-SERPINE1
Complex assembly CPB2a, SERPINE1 CPB2a-SERPINE1 Blood plasma
complex assembly v63 SERPINE1 Degradation SERPINE1 Blood plasma??
degradation v64 F11a-SERPINE1 Complex assembly F11a, SERPING1
F11a-SERPING1 Blood plasma?? complex assembly v65 F12a-SERPING1
Complex assembly F12a, SERPING1 F12a-SERPING1 Blood plasma??
complex assembly v66 KLKB1-SERPING1 Complex assembly KLKB1a,
KLKB1a-SERPING1 Blood plasma?? complex assembly SERPING1 v67
F2a-.alpha.2-M complex Complex assembly F2a, .alpha.2-M
F2a-.alpha.2-M assembly v68 Substrate catalysis Catalysis subs
subsa F2a v69 Substrate catalysis Catalysis subs subsa
F2a-.alpha.2-M
TABLE-US-00002 TABLE 2 All reaction mechanisms incorporated in the
computer model of the fibrin polymerization. Reaction Name Type
Substrates Products Cofactors/Catalyst Reaction site v70 FpA
cleavage from Fg Catalysis Fg desAA-Fg, 2 FpA F2a Blood plasma v71
FpB cleavage from Fg Catalysis Fg desBB-Fg, 2 FpB F2a Blood plasma
v72 FpA cleavage from Catalysis desAA-Fg Fn, 2 FpA F2a Blood plasma
desAA-Fg v73 FpB cleavage from Catalysis desBB-Fg Fn, 2 FpB F2a
Blood plasma desBB-Fg v74 FpA cleavage from Catalysis Fg-F2a
desAA-Fg-F2a F2a Blood plasma Fg-F2a v75 FpB cleavage from
Catalysis Fg-F2a desBB-Fg-F2a F2a Blood plasma Fg-F2a v76
Protofibril Complex assembly* P.sub.n, P.sub.m P.sub.n+m Blood
plasma formation/growth v77 Fiber Complex assembly** F.sub.o, F
F.sub.n+m F.sub.n Blood plasma formation/growth *P.sub.n + P
.fwdarw.P V.sub.n+m .ltoreq. 29, n > 0, m > 0 **P.sub.n +
P.sub.p.fwdarw.P V.sub.n+p .ltoreq. 9.0 > 0, p > 0 indicates
data missing or illegible when filed
TABLE-US-00003 TABLE 3 in the computer model regarding the effect
of and low-molecul weight h (LMWH) on the function of should be
noted that symbol of is used instead of the popular scientific
names. Reaction Name Type Substrates Products Cofactors/Catalyst
Reaction site v78 SERPINC1-UFH Complex assembly SERPINC1, UFH
SERPINC1-UFH Blood plasma complex assembly v79 F11a-SERPINC1-UFH
Complex assembly F11a, SERPINC1-UFH F11a-SERPINC1-UFH Blood plasma
complex assembly v80 F9a-SERPINC1-UFH Complex assembly F9a,
SERPINC1-UFH F9a-SERPINC1-UFH Blood plasma complex assembly v81
F2a-SERPINC1-UFH Complex assembly F2a, SERPINC1-UFH
F2a-SERPINC1-UFH Blood plasma complex assembly v82
F10a-SERPINC1-UFH Complex assembly F10a, SERPINC1-UFH
F10a-SERPINC1-UFH Blood plasma complex assembly v83
F3-F7a-SERPINC1- Complex assembly F3-F7a, SERPINC1-
F3-F7a-SERPINC1- Blood plasma UFH complex UFH UFH assembly v84
F10a-F5a-SERPINC1- Complex assembly F10a-F5a, SERPINC1-
F10a-F5a-SERPINC1- Blood plasma UFH complex UFH UFH assembly v85
SERPINC1-LMWH Complex assembly SERPINC1, LMWH SERPINC1-LMWH Blood
plasma complex assembly v86 F11a-SERPINC1- Complex assembly F11a,
SERPINC1- F11a-SERPINC1- Blood plasma LMWH complex LMWH LMWH
assembly v87 F9a-SERPINC1-LMWH Complex assembly F9a, SERPINC1-
F9a-SERPINC1-LMWH Blood plasma complex assembly LMWH v88
F2a-SERPINC1-LMWH Complex assembly F2a, SERPINC1- F2a-SERPINC1-LMWH
Blood plasma complex assembly LMWH v89 F10a-SERPINC1- Complex
assembly F10a, SERPINC1- F10a-SERPINC1- Blood plasma LMWH complex
LMWH LMWH assembly v90 F3-F7a-SERPINC1- Complex assembly F3-F7a,
SERPINC1- F3-F7a-SERPINC1- Blood plasma LMWH complex LMWH LMWH
assembly v91 F10a-F5a-SERPINC1- Complex assembly F10a-F5a,
SERPINC1- F10a-F5a-SERPINC1- Blood plasma LMWH complex LMWH LMWH
assembly indicates data missing or illegible when filed
TABLE-US-00004 TABLE 4 The ordinary differential equations Reaction
Name Equation Description PKPD1 UFH in blood compartment d [ UFH ]
dt = IV Vd UPH - k ? [ UFH ] ##EQU00005## [UFH]: concentration of
UFH in blood compartment PKPD2 LMWH in absorption compartment dA
LMWH dt = - k ? A LMWH ##EQU00006## A.sub.LMWH: amount of LMWH in
absorption compartment PKPD3 LMWH in blood compartment d [ LMWH ]
dt = k ? A LMWH Vc LMWH - k ? [ LMWH ] + k ? A LMWH , p Vc LMWH - k
? [ LMWH ] ##EQU00007## [LMWH]: concentration of LMWH in blood
compartment PKPD4 LMWH in peripheral compartment dA LMWH , p dt = k
? [ LMWH ] Vc LMWH - k ? A LMWH , p ##EQU00008## A.sub.LMWH: amount
of LMWH in peripheral compartment indicates data missing or
illegible when filed
[0094] As mentioned, the pharmacodynamical modeling and/or
pharmacokinetic modeling may not be used in some embodiments of the
invention, if the user desires to do so. Compared to embodiments
which use models that involve pharmacodynamical modeling and/or
pharmacokinetic modeling, the embodiments avoiding such usage do
not need to calculate certain aspects, as no drug distribution and
interaction with the body is necessary. Therefore, said embodiments
are faster in providing the user with the necessary information as
said embodiments including PK/PD modeling.
[0095] Furthermore, the numerical model used in said embodiments
which avoid said usage of PK/PD modeling may work on a shorter time
scale, i.e. minutes rather than days.
[0096] When including the countermeasures, specifically relating to
the administration of pro- and anti-coagulants it might be an
advantage to provide for PK/PD modeling. Administrations may be
directly into the bloodstream, so the part of PK/PD modeling that
represents uptake by the body, digestion by the liver etc may not
be necessary as one can simply model it as a direct increase of the
concentration of the drug in the blood and time scales are indeed
much shorter. Interaction of a drug with the other blood proteins
is however required if the user wants to predict the effect of a
countermeasure involving a drug. The present invention matches
theses needs.
[0097] FIG. 2 is a flow diagram of a method of predicting a blood
dilution risk value of a first blood circulation, wherein the
presented method comprises the steps S1 to S5. Providing measured
coagulation data describing the hemostatic situation of a first
blood circulation at a first point in time is depicted as step S1.
Furthermore, applying the measured coagulation data as an input for
a numerical model is presented as step S2, wherein the numerical
model is mathematical and dynamical representation of a blood
dilution situation of the first blood circulation. Moreover, in
step S3, a simulation of a time development of the hemostatic
situation by means of the numerical model and based on the measured
coagulation data used as an input for the numerical model is
performed. In step S4, a calculation of values of concentrations of
human blood proteins as an output of the simulation is performed.
The step S5 describes the translating of at least some of the
calculated values of the concentrations of the human blood proteins
into a risk value, which risk value describes a risk of clotting
and/or embolism and/or bleeding for the first blood
circulation.
[0098] To perform the presented method a CDS system may be used
that incorporates all of the monitored and additional patient data
and uses this to provide an early warning when the patient's
hemostatic level starts to move into the danger zone, and provides
advice on the optimal countermeasure to regain hemostatic balance.
The solution uses a computer model that takes into account the
patient's data and the estimated dilution of the blood (through
blood loss and transfusions) and predicts when the patient's
hemostatic balance shifts to a dangerous level. The same model can
simulate the expected effect of a number of procedures that are
designed to regain hemostatic balance, and recommend the procedure
with the highest chance of success. The model may make its
calculation near instantaneously, before e.g. a next test is
performed. This may provide the anaesthesiologist with the
opportunity to keep the patient within the stable hemostatic range,
instead of returning the patient to safety after (s)he has left the
stable range. If the patient should stray outside the stable range
after all, the model can estimate the expected results for each of
a set of available countermeasures. This will allow the
anaesthesiologist to choose the optimal countermeasure, performed
in the optimal way, and thus to stabilize the patient as fast as
possible.
[0099] The aforementioned model used in the embodiment of FIG. 2
may be implemented as a differential equation model that describes
inter alia the interactions of coagulation proteins and formation
and break-down of the thrombus. Such model is a dynamic model, as
it describes the evolution of system states like protein
concentrations or thrombus size over time. The time dynamics of
each interaction mechanism is described as an ordinary differential
equation or ODE that involves the concentration(s) of the
protein(s) and/or chemical molecule(s) that are involved in the
reaction and the reaction rate parameter(s). By summation of all
reaction mechanisms in which a particular protein or other kind of
chemical molecular is involved (a protein or molecule can
participate in more than one reaction), the time dynamics of the
concentration of that particular protein or other kind of chemical
entity is calculated. The whole system can be calculated by keeping
track of the evolution of all proteins and molecules.
[0100] As model parameters, the initial conditions of the system,
i.e. the concentration of all proteins and molecules at t=0 and the
reaction rate parameters of the reaction mechanisms may be entered
into the model by a user. Part of the initial concentrations may
also be measured in a laboratory or a clinic, whereas others may be
taken from literature, i.e. average patient values, possibly
corrected for gender and age. The reaction rate parameters may be
derived via solving an inverse problem, i.e. model fitting to
experimental data. The system of ODEs may be solved numerically,
using the numerical values of the model parameters, by employing
ODE integration algorithms.
[0101] The method of FIG. 2 may comprise the step of automatically
suggesting an application for administration of a coagulant and/or
an anti-coagulant.
[0102] FIG. 3 shows another exemplary embodiment of a method of
predicting a hemostatic situation of a first blood circulation by
means of a flow diagram. With regard to steps S1 to S4 and S5, it
is referred to the presented disclosure and description of FIG. 2.
However, compared to FIG. 2, the embodiment of FIG. 3 provides a
step S6. The method of FIG. 3 specifies that the calculated values
of the concentrations of the human blood proteins are m values of k
different proteins. The step S6 defines to choose n values out of
the m values. Therein, k, m and n are integers and n<m.
Furthermore, only the n values are taken into account for the
translation into the risk value which is performed in step S5.
Moreover, the method of FIG. 3 defines to graphically display a
time development of the risk value on a graphical user interface
within step S7. If desired, additional information may be stored on
for example the storing arrangement 103 of FIG. 1, which
information is used by the presented method in order to decide
which n values out of the m values are chosen. For example, only
the most sensitive types of human blood proteins are chosen out of
the proteins for which concentrations have been calculated.
However, also other criteria may be applied.
[0103] FIG. 4 describes another exemplary embodiment of a method of
predicting a hemostatic situation of a first blood circulation
according to an embodiment of the present invention. Regarding
steps S1 to S5, it is kindly referred to the description and
disclosure of FIG. 2. Additionally, step S8 is comprised by the
method of FIG. 4. The step of using the numerical model to identify
a set of most sensitive state variables in a situation of blood
dilution based on at least one given sensitivity threshold is
depicted by step S8. Details additional about such an
identification have been described already above and said details
may be integrated in the embodiment of FIG. 4.
[0104] FIG. 5 shows another exemplary embodiment of a method
according to the present invention. Providing measured coagulation
data which describe the hemostatic situation of the first blood
circulation at a first point in time is depicted with step S1.
Additionally, data about human blood protein levels of a second
blood circulation at a second point in time as reference protein
levels are provided, which provision is depicted by step S9. The
first and the second blood circulation may be the same, which would
lead to a specific observation of one patient. Furthermore, the
second point in time is before the first point in time and the
blood circulation undergoes bleeding and/or clotting and/or
embolism at the second point in time. Therefore, it can be assured
that the used reference protein levels indicate realistic levels of
the corresponding proteins that occur during a bleeding and/or
clotting and/or embolism. However, if desired, the first and the
second blood circulation may be different, which means that
reference values are retrieved from another patient. Regarding
steps S2 to S4, it is kindly referred to the previously presented
description of FIG. 2. Furthermore, comparing said provided
reference protein levels with the calculated values of
concentrations of human blood proteins is depicted in FIG. 5 by
step S11. Generating a comparison value, 512, and using the
comparison value for the translation into the blood dilution risk
value for the first blood circulation, S13, complete the presented
method. There is to be noted that step S5 is performed in this
embodiment in such a way that the risk value is calculated based on
the comparison value was previously generated.
[0105] FIG. 6 shows another flow diagram of a method according to
an exemplary embodiment of the invention. Regarding steps S1 to S4,
it is kindly referred to the disclosure of previously described
FIG. 2. As can be seen in FIG. 6, three different possibilities of
how to proceed during or after step S4 are presented by FIG. 6. In
detail, FIG. 6 discloses three different embodiments of the present
invention. The flow diagram of FIG. 6 describes that at least some
of the predicted values of the concentrations of the human blood
proteins are translated into the risk value by either calculating a
speed of sealing of a hypothetical wound, step S14a, or by
calculating an extent of growth of a hypothetical thrombus (i.e.
the resulting thrombus size), step S14b, as an output. FIG. 6
depicts, that firstly only the aspect of the speed of sealing of
the hypothetical wound can be calculated. In such a case, the user
would choose to use the left branch of FIG. 6. In case he is
interested in calculating a size of the hypothetical thrombus, step
S14b, he would choose the centered branch of FIG. 6. However, if
the user desires to be provided with results of both calculations,
he may choose the right branch of FIG. 6. In any of the three
presented branches, based on the previously calculations and based
on the generated corresponding outputs, a translation into the risk
value is performed, which risk value describes a risk of clotting
and/or embolism and/or bleeding for the first blood circulation.
This is described by the corresponding step S5.
[0106] In other words, the embodiment of FIG. 6 comprises a model
which is used to simulate the growth of the thrombus or sealing of
a hypothetical wound itself. If the model predicts uncontrolled
growth of the thrombus, to the point where it occludes a vessel or
may break into circulating pieces, and if no countermeasure is
taken, the patient stability score or risk value will exceed one
threshold of the save zone. In case the model of FIG. 6 predicts a
sealing of the wound which is so slow that blood loss grows to a
dangerous level, the save zone has been left on the other side.
However, the present invention provides for improvements of such
situations, as an alert may be applied to the user. Alternatively
counter measures may be suggested or at least a prediction of how
the risk value will evolve is supplied to the user.
[0107] If desired, any of the three embodiments of FIG. 6 may
comprise the additional step of evaluating the simulated time
development of the hypothetical sealing of the wound by evaluating
a time that passes between an initialization of the clotting
process, i.e. a formation of the wound, and a cessation of the
clotting process, i.e. a sealing of the wound.
[0108] If desired, any of the embodiments described within FIG. 6,
may additionally comprise a step of evaluating the risk of
occlusion of a blood vessel by evaluating a size of the thrombus.
This may be expressed e.g. as the total mass of fibrin present in
the thrombus, the volume of the thrombus or the thickness of the
thrombus, i.e. the minimum or maximum distance between the wound
and/or blood vessel wall and the outer edge of the thrombus.
[0109] Additionally if desired, any of the three embodiments of
FIG. 6 may comprise the additional step of evaluating the risk of
embolism by evaluating a constitution of the thrombus. This may for
example be embodied through the calculation of the thrombus' mass
density or the mass-length ratio of the comprising fibrin fibers
for the purpose of risk of embolism, caused by pieces breaking off
of the main thrombus.
[0110] FIG. 7 shows a clinical decision support system according to
an exemplary embodiment of the invention. In the following,
clinical decision support system will be described by the shown
basic structure of FIG. 7; however, it will become apparent that
such a clinical decision support system may comprise many
additional optional features. In general the clinical decision
support system of FIG. 7 provides a solution to the above
identified problems of the prior art and may comprise of a content
delivery system that may incorporate all of the measured and
monitored and, if desired, additional patient data and may use this
to provide an early warning when the patient's hemostatic level
starts to move into the danger zone. Furthermore the clinical
decision support system of FIG. 7 may provide for an advice on the
optimal countermeasure to a user such hemostatic balance is
regained. The clinical decision support system makes use of or
comprises a computer model that takes into account the patient's
data and the estimated dilution of the blood through e.g. blood
loss and e.g. transfusions. Furthermore the computer model may be
configured to predict when the patient's hemostatic balance shifts
to a dangerous level. The same model may be used to simulate the
expected effect of a number of procedures i.e. countermeasures that
are designed to regain hemostatic balance, and may further
recommend the procedure i.e. countermeasure with the highest chance
of success to the user.
[0111] One advantage of the use of the computer model according to
the exemplary embodiment of FIG. 7 may be seen in that it can
calculate e.g. the effect of the administration of the next unit of
transfusion fluid and subsequent dilution of the blood in terms of
existing diagnostic values like INR or thrombo-elastometry
measurement outputs. The model can make this calculation near
instantaneously, before the unit is actually administered, and
before the next test is performed. This provides the
anesthesiologist with the opportunity to keep the patient within
the stable hemostatic range, instead of returning the patient to
safety after the patient has left the stable range. If the patient
should stray outside the stable range after all, the model can
estimate the expected results for each of a set of available
countermeasures. This will allow the anaesthesiologist to choose
the optimal countermeasure, performed in the optimal way, and thus
to stabilize the patient as fast as possible.
[0112] Regarding the computer model of this exemplary embodiment of
FIG. 7 the following should be noted. The aforementioned model may
be implemented as a differential equation model that describes e.g.
the interactions of coagulation proteins, formation and break-down
of the thrombus, the effect of anti-coagulants like Heparin, etc.
Said model may be seen as a dynamic model which describes the
evolution of states variables of the model like e.g. protein
concentrations or e.g. thrombus size over time. The time dynamics
of each interaction mechanism is described as an ordinary
differential equation or ODE that may involve the concentration(s)
of the protein(s) and/or chemical molecule(s) that are involved in
the reaction and the reaction rate parameter(s). By summation of
some or all reaction mechanisms in which a particular protein or
other kind of chemical molecular is involved, a protein or molecule
can participate in more than one reaction, the time dynamics of the
concentration of that particular protein or other kind of chemical
entity is calculated. The whole system can be calculated by keeping
track of the evolution of some or all proteins and molecules. Thus
the presented dynamic model can predict future evolution of the
patient's hemostatic system based on e.g. measurements of the
present.
[0113] In certain cases this may require however that besides the
reaction topology, the numerical values of the model parameters are
known as well. These model parameters include the initial
conditions of the system, i.e. the concentration of all proteins
and molecules at t=0, e.g. before any blood loss, and the reaction
rate parameters of the reaction mechanisms. Part of the initial
concentrations may be measured in the laboratory or clinic, whereas
others may be taken from literature, i.e. average patient values,
that are possibly corrected for gender and age. The reaction rate
parameters may be derived via solving an inverse problem, i.e.
model fitting to experimental data. The system of ODEs may be
solved numerically, using the numerical values of the model
parameters, by employing ODE integration algorithms. The treating
physician is the operator of the clinical decision support system,
hence is able to give user input to the system. The other type of
input can be clinical measurements, e.g. INR, aPTT, vitamin
K-proteins' activity. The user interface is connected via software
to the computer model; the information flow of the user interface
is diverted to the computer model to serve as input. The computer
model uses the given input and calculates the expected future
evolution of the blood dilution of the blood circulation, which are
forwarded to the user interface via the connecting software. The
clinical decision support system of FIG. 7 further comprises a user
interface. This exemplary embodiment of the invention may be
integrated in e.g. a hospital IT system, and can be accessed via a
graphical user interface on one of the computer terminals. The
clinical decision support system can be provided with coagulation
data about the patient that is available before instability in the
haemostatic situation due to blood dilution is feared. This may be
done either manually or through a retrieval of data from a data
storage like e.g. a server or e.g. through a direct interface with
an information system like a hospital information system or an
electronic patient records. This may be seen in FIG. 8, top left
panel. The clinical decision support system may interface with the
monitoring and measurement devices that are used during a surgery,
and/or have a simple way to enter new patient data into the system
manually. This may be seen in FIG. 8, bottom left panel. One screen
of the clinical decision support system should show the current
risk value of the patient, e.g. visualized by a graph that plots a
`safety value` or a set of values for the patient over time and
indicates when this value threatens to leave the safe zone. This
may be seen in FIG. 8, top right panel. An indication of threat may
be delivered to the user both in the graph and through an alarm.
Such risk values can be as simple as INR or aPTT values, or a more
elaborate diagnostic score value.
[0114] In more detail, FIG. 7 describes a clinical decision support
system 700, which comprises a first arrangement 701, which is
embodied as a user interface. Furthermore, it is shown that
coagulation data 702 are supplied by means of an input 707 of a
user into the user interface 701. Software 709 is shown in FIG. 7
as well as a storing arrangement 703, which simultaneously acts as
a calculation arrangement 705. The numerical model 704 is stored on
the storing arrangement 703. Additionally, clinical measurements or
clinical data 706 may be supplied to the user interface. In
combination with the following description of FIG. 8, the
advantages and gist of the system will become even more
apparent.
[0115] FIG. 8 shows a user interface as may be comprised by a
clinical decision support system according to an exemplary
embodiment of the invention. In FIG. 8 the top right panel shows
both the observed risk values, shown in the left zone 802 and the
predicted risk values in the right zone 803 for the monitored risk
value. The observed risk values can be calculated by the numerical
model based on the provided coagulation data which are provided via
the bottom-left panel. Moreover, the numerical model can predict a
risk value based on calculated predictions of the concentrations of
the participating proteins. Upon prediction of leaving the safe
zone 805 the clinical decision support system may display one or a
number of countermeasures 807 to 810 to the screen, sorted by their
probability of success and paired to a preference score indicating
the system's preference for each measure, see bottom right panel of
FIG. 8. It should be noted that this example and illustration is
meant to explain the type of graphical user interface that may be
used in accordance with the present invention, and is in no way
exhaustive in e.g. the list of possible test values to enter or
recommended actions to output. Additionally, the time development
of risk value 801 is depicted in the graphical user interface 800.
The separation between the area 802 describing the observed risk
values and the area 803 of the predicted risk values can clearly be
gathered from the top right panel of FIG. 8. In other words, the
clinical decision support system is configured to provide for a
calculated risk value based on measured coagulation data, and the
clinical decision support system is additionally configured to
provide for a predicted risk value based on simulation.
[0116] As can be seen in FIG. 8 the graphical representation of
risk value 801 is displayed in an x and y diagram which provides on
the y-axes a stable zone 805 detailed as an area which the risk
value may develop without causing an alert. Furthermore, a danger
zone 804, i.e. a danger area is shown in FIG. 8. An alert sign 806
is comprised by the graphical user interface. Furthermore, personal
data about the patient can be received from external or internal
databases and shown on the top left panel. Personal information 814
is displayed to the user. By means of access button 813, a
connection to a database may be established. Furthermore, measured
coagulation data 102 can be received by the graphical user
interface via the bottom left panel. By means of button 812, this
received and measured coagulation data may be submitted to the
underlying numerical model in order to perform the desired
predictions. Measured coagulation data may be provided to the
receiving arrangement 101. Exemplarily values of a ROTEM tests are
shown, which is a brand name. In general this may be called a value
of a thrombo-elastometry test.
* * * * *