From Principle to Practice: Bridging the Gap in Patient
Jonathan H. Foley1, Thomas Orfeo1, Anetta Undas2, Kelley C. McLean3, Ira M. Bernstein3,
Georges-Etienne Rivard4, Kenneth G. Mann1, Stephen J. Everse1, Kathleen E. Brummel-Ziedins1*
1Department of Biochemistry, University of Vermont, Burlington, Vermont, United States of America, 2Institute of Cardiology, Jagiellonian University School of Medicine,
Krakow, Poland, 3Department of Obstetrics, Gynecology, and Reproductive Sciences, University of Vermont, Burlington, Vermont, United States of America, 4Department
of Hematology-Oncology, Centre Hospitalier Universitaire Sainte-Justine, Montre ´al, Canada
The standard clinical coagulation assays, activated partial thromboplastin time (aPTT) and prothrombin time (PT) cannot
predict thrombotic or bleeding risk. Since thrombin generation is central to haemorrhage control and when unregulated, is
the likely cause of thrombosis, thrombin generation assays (TGA) have gained acceptance as ‘‘global assays’’ of haemostasis.
These assays generate an enormous amount of data including four key thrombin parameters (lag time, maximum rate, peak
and total thrombin) that may change to varying degrees over time in longitudinal studies. Currently, each thrombin
parameter is averaged and presented individually in a table, bar graph or box plot; no method exists to visualize
comprehensive thrombin generation data over time. To address this need, we have created a method that visualizes all four
thrombin parameters simultaneously and can be animated to evaluate how thrombin generation changes over time. This
method uses all thrombin parameters to intrinsically rank individuals based on their haemostatic status. The thrombin
generation parameters can be derived empirically using TGA or simulated using computational models (CM). To establish
the utility and diverse applicability of our method we demonstrate how warfarin therapy (CM), factor VIII prophylaxis for
haemophilia A (CM), and pregnancy (TGA) affects thrombin generation over time. The method is especially suited to
evaluate an individual’s thrombotic and bleeding risk during ‘‘normal’’ processes (e.g pregnancy or aging) or during
therapeutic challenges to the haemostatic system. Ultimately, our method is designed to visualize individualized patient
profiles which are becoming evermore important as personalized medicine strategies become routine clinical practice.
Citation: Foley JH, Orfeo T, Undas A, McLean KC, Bernstein IM, et al. (2013) From Principle to Practice: Bridging the Gap in Patient Profiling. PLoS ONE 8(1):
Editor: Hugo ten Cate, Maastricht University Medical Center, The Netherlands
Received August 14, 2012; Accepted December 14, 2012; Published January 25, 2013
Copyright: ? 2013 Foley et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: This work was supported by grants from the National Institutes of Health to KEB-Z (HL46703–Project 5) and IMB (HL 71944). The funders had no role in
study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing Interests: The authors have declared that no competing interests exist.
* E-mail: Kathleen.firstname.lastname@example.org
Thrombin generation is central to haemorrhage control and
when unregulated, is the most likely cause of thrombosis. The
dynamic roles of thrombin include procoagulant [1,2], anticoag-
ulant , fibrinolytic [4,5], mitogenic and motogenic [6,7]
processes. Since many functions of thrombin regulate or directly
cause clot formation, its generation is considered a good marker of
global haemostasis. For decades the most widely used coagulation
assays have been the activated partial thromboplastin time (aPTT)
and the prothrombin time (PT). These assays have been invaluable
in detecting gross abnormalities in the coagulation system such as
factor (f)VIII or fIX deficiency (haemophilias A or B, respectively)
or monitoring heparin (reviewed in ) or warfarin anticoagula-
tion therapies [9,10] but have been less useful in predicting
thrombotic risk  or clinical bleeding phenotype of the
haemophilias [11,12]. Pioneering work by Hemker and colleagues
, our group , and others [12,15] have demonstrated the
vast majority of thrombin is generated well after the plasma (or
blood) clot time which is the traditional end point for the aPTT
and PT assays. In recent years, thrombin generation assays,
thromboelastography and waveform analysis have gained in
popularity and become accepted as useful tools to measure
‘‘global haemostasis’’ . Unfortunately, standardization of
global assays remains a challenge and has hindered their
implementation into clinical practice.
Computational models based upon an individual’s coagulant
factor composition have also been utilized to further define an
individual’s thrombin phenotype , and therefore, global
haemostatic potential. An individual’s procoagulant and antico-
agulant factor levels act together to generate a unique coagulation
phenotype , which is represented by their thrombin generation
capacity, and like its empirical counterparts, has the potential to
identify underlying risk for disease progression. Previously, our
group demonstrated, using empirical plasma composition and
computational models that the theoretical normal range of
thrombin generation varies significantly among healthy individuals
with physiologically normal factor levels . This study
confirmed on a large scale what small observational studies have
shown in the past: that each phase of thrombin generation (i.e.
initiation, propagation and termination) is largely regulated by a
single or a few coagulation factors. Variation in the tissue factor
pathway inhibitor (TFPI) concentration, for instance, has a large
effect on the lag time (or clot time/time to 2 nM thrombin)
[19,20,21] whereas the formation rate and maximum level of
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thrombin is sensitive to the concentration of factors (fVIII, fIX)
which comprise the intrinsic tenase complex [19,22]. Clinically,
thrombin generation parameters such as peak thrombin and total
thrombin/endogenous thrombin potential have proved useful in
predicting venous thrombosis [23,24] in at risk individuals and
correlating global haemostasis to the bleeding phenotype among
patients with hemophilia .
Each global assay system, whether empirical or computational,
generates multiple outputs and consequently, wide spread use and
acceptance of these assays have practical limitations with respect to
data presentation. As new assays and technologies emerge and
ever increasing amounts of data are collected, it is imperative that
data analysis tools evolve to ensure that the data can be presented
in a clear, concise and informative manner.
In this paper, we present a novel method to visualize multiple
parameters over time. Here, we visualize four thrombin param-
eters simultaneously and show how each of these parameters
changes over time for a given individual in response to a
therapeutic intervention or during normal processes associated
with haemostatic challenge. Three populations, representing those
at risk of haemorrhage (haemophilia A, n=44), thrombosis (atrial
fibrillation, n=20) or both (pregnancy, n=20) are evaluated using
our method. Thrombin generation for each population was
measured differently, and therefore illustrates the utility of our
integrating methodology. These diverse populations show dynamic
changes in individual thrombin generation profiles over time and
thus are ideal for evaluating and validating our data presentation
Atrial fibrillation patients were recruited and enrolled by Dr. A
Undas and advised according to a protocol approved by the
Jagiellonian University Ethical Committee (Krako ´w, Poland).
Haemophilia patients were recruited and enrolled by Dr. G-E
Rivard and advised according to a protocol approved by the
Institutional Review Boards at the Centre Hospitalier Universi-
taire Sainte-Justine (Montre ´al, QC) and by the University of
Vermont Committees on Human Research (Burlington, VT).
Women who intended conception were enrolled and advised
according to a protocol approved by the University of Vermont
Committees on Human Research. Informed written consent was
obtained from all individuals.
Simulated thrombin generation
For each unique plasma sample, the time course of thrombin
generation was simulated using two empirically validated math-
ematical models termed the ‘‘Base model’’ [25,26] and the
‘‘Protein C model’’ . In principle, the models differ in their
ability to represent the anticoagulant properties of the vasculature.
In this regard, the ‘‘Base model’’ describes extravascular coagu-
lation whereas the ‘‘Protein C model’’ describes the coagulation
response in the context of the inhibitory potential derived from the
vascular endothelium. Both models are built around a series of
ordinary differential equations which make use of rate constants
derived from experimental measurements made under conditions
of saturating concentrations of phospholipids . The ‘‘Base
model’’ makes use of the following inputs: empirically determined
active concentrations of fII, fV, fVII/fVIIa, fVIII, fIX, fX and the
anticoagulants TFPI and antithrombin (AT). The ‘‘Protein C
model’’ uses all inputs from the ‘‘Base model’’ as well as the
empirically determined active protein C concentration and
nominal concentrations of thrombomodulin (TM) , an
essential anticoagulant cofactor found on the vascular endotheli-
um, which can be altered to represent the amount of TM
potentially found in various vessels [29,30]. Therefore, the
minimal information required to simulate thrombin generation is
the plasma concentrations of fII, fV, fVII/fVIIa, fVIII, fIX, fX,
TFPI and AT (and protein C for the ‘‘Protein C’’ model). For both
models, the starting concentration of fVIIa was set to 1% of the
starting fVII concentration. The models are initiated by exposing
the inputs to 0.5 pM tissue factor for haemophilia simulations
(Base model only) or 5 pM tissue factor for warfarin anticoagu-
lation simulations (Base and Protein C models). Using this
approach, the concentration versus time profiles for all reactants,
including thrombin are determined. Thrombin generation pa-
rameters such as the lag time (time to 2 nM thrombin), the
maximum rate of thrombin generation (max rate), peak thrombin
and total thrombin (area under the thrombin generation profile)
can be determined from the time course of thrombin generation
Empirical thrombin generation
Thrombin generation assays were performed as previously
described [32,33]. Briefly, a 20 mL solution containing 2.5 mM of
the thrombin substrate, Z-GGR-AMC and 0.1 M CaCl2 was
incubated with 80 mL of citrated plasma containing 0.1 mg/mL
corn trypsin inhibitor for 3 minutes at 37uC. After this incubation
period, thrombin generation was initiated by the addition of 20 mL
of relipidated TF (5 pM final) and PCPS (20 mM final) in HEPES
buffered saline. As thrombin cleaves Z-GGR-AMC there is an
increase in fluorescence which can be used with a series of
thrombin standards to calculate the amount of thrombin formed
over time in plasma. Using this experimental system, thrombin
generation was monitored continuously using a Synergy4 plate
reader (BioTek, Winooski, VT, USA). Thrombin generation
parameters such as the lag phase, the maximal rate, peak
thrombin and total thrombin can be determined from the
empirically generated thrombin generation plot.
Atrial fibrillation population
Patients with diagnosed atrial fibrillation (detailed patient
characteristics can be found in Table 1; n=20; 10 male and 10
female aged 5966.25 years) varied substantially with respect to
their individual risk factors for stroke. Blood was collected from the
enrolled patients on 6 occasions during the study period and used
to make citrated platelet poor plasma which was aliquoted and
stored at 280uC. The first sample was collected just prior to
starting warfarin therapy. Subsequent samples were collected on
days 3, 5, 7, 14 and 30 after initiating warfarin therapy. On each
day, each subjects’ plasma composition was determined (6
days620 subjects=120 unique plasma compositions) primarily
by using routine activity-based clinical clotting assays . The
concentrations of fII, fV, fVII/fVIIa, fVIII, fIX, fX and the
anticoagulants TFPI and AT were used to simulate thrombin
generation using the ‘‘Base model’’ and ‘‘Protein C model’’.
Patients with clinically severe haemophilia A had fVIII:C ,1%
at the time of diagnosis (age range 16–33) . Each subjects’
plasma composition was determined primarily by using routine
activity-based clinical clotting assays. The concentrations of fII, fV,
fVII/fVIIa, fVIII, fIX, fX and the anticoagulants TFPI and AT
were used as measured to simulate thrombin generation using the
‘‘Base model’’. Since all subjects have clinically severe haemophilia
A (fVIII ,1%) and their fVIII levels varied significantly at the time
of blood collection, the fVIII concentration was electronically set
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at 100% at time zero (baseline). The thrombin generating capacity
was followed over 7 half-lives (42–168 hours; t1/2=6–24 hours) of
fVIII to demonstrate the theoretical fluctuations in thrombin
generating capacity during the course of fVIII prophylaxis.
Women who intended conception were enrolled in the initial
study . Study participants (aged 18–40 years) were healthy
non-smokers with no history of hypertension, diabetes mellitus,
autoimmune disease or haemostatic disorders. At the time of
enrollment, all women had regular menstrual cycles (n=20
pregnant; n=10 non-pregnant controls). Blood was collected
from enrolled patients up to 4 times during the study. Blood was
centrifuged immediately to produce citrated platelet poor plasma
which was subsequently aliquotted and stored at 280uC. Pre-
pregnancy samples were collected during the follicular phase of the
menstrual cycle. Early and late pregnancy samples were collected
at 11–15 menstrual weeks and 30–34 weeks, respectively. Post-
pregnancy samples were collected after breastfeeding ceased which
was between 6 and 24 months after delivery in all cases. Post-
pregnancy samples were also collected in the follicular phase of the
menstrual cycle. Enrolled women who did not become pregnant
remained in the study as control subjects (data not shown). These
women provided blood samples pre-pregnancy and approximately
2.5 years after the initial blood draw. The thrombin generation
capacities of these women were previously reported by McLean et
Dynamic visualization of thrombin generation
Thrombin generation parameters were determined either
computationally or empirically as described in the ‘‘Simulated/
Empirical thrombin generation’’ sections of the Online Methods.
Thrombin parameters depicting the kinetics of warfarin anticoag-
ulation or the net result of decreasing fVIII during prophylaxis in
haemophilia A were generated using the computational models.
Thrombin parameters depicting global haemostatic changes
during pregnancy were determined empirically. For each individ-
ual, the lag time (time to 2 nM thrombin), maximal rate of
thrombin generation, peak thrombin and total thrombin (area
under the curve) were plotted against time using the motion chart
gadget which is available in Google Docs (Mountain View, CA)
spreadsheets. Using this gadget, 5 dimensional plots were created.
In these plots, the lag time is depicted on the y-axis, maximal rate
of thrombin generation is depicted on the x-axis, peak thrombin is
Table 1. Atrial fibrillation patient characteristics.
PatientSexAgeCAD HT DB SMHCST ASAACEISTAT HFBMI
1M 680110111111 35
2F 590000000000 21
3M 660101100110 27
5M 590101100111 28
6F 540100000100 28
7F 520101000100 29
8F 600000000000 21
10M 680100000100 29
12F 510100100110 34
13M 480100100110 31
14M 690101000100 31
16M 590100100110 24
17F 530100100111 30
18M 610000000000 27
19M 640000100011 25
20F 541101111011 31
CAD: Coronary artery disease.
ST: Stroke/transient ischemic attack.
ACEI: ACE inhibitors.
HF: Heart failure.
BMI: Body mass index.
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represented by the colour, and total thrombin is represented by the
relative size of each data point . A large, red circle in the lower
right quadrant is representative of a high thrombin generating
capacity whereas a small, blue circle in the upper left quadrant
represents a low thrombin generating capacity. The time
component is shown by animating each point to move as
thrombin generation parameters change over time. Videos
depicting changes in thrombin generation over time were captured
using Screenflow software (Nevada City, CA). Labels were added
to videos using Final Cut Pro software (Apple, Inc., Cupertino,
CA). Each figure was created by taking screen captures of relevant
Simulated thrombin generation during warfarin therapy
in atrial fibrillation
Patients with atrial fibrillation were enrolled and provided us
with plasma samples just prior to commencing warfarin therapy
(day 0) and on days 3, 5, 7, 14 and 30 of warfarin therapy. The
coagulation factor composition for each unique plasma sample was
used to simulate the time course of thrombin generation using two
empirically validated mathematical models termed the ‘‘Base
model’’ and the ‘‘Protein C model’’. The mechanism of warfarin
anticoagulation is well-established (reviewed in ) and the
associated dynamic reduction in thrombin generating capacity
over time can be visualized in Movie S1. These data which depict
thrombin generation parameters derived from the ‘‘Base model’’
are consistent with previous reports (reviewed in ). Figure 1
was created by taking screenshots of Movie S1 over time. Figure 1
shows that all subjects, including the 3 highlighted (S1, S2 and S3),
have a reduced thrombin generating capacity in response to
warfarin therapy. After 3 days on warfarin, subjects S1, S2 and S3
have reduced peak and total thrombin and a reduced maximal
rate of thrombin generation compared to baseline. In addition,
each subject has a slightly prolonged lag time. This trend continues
through day 5 where S2 and S3 are approaching a stable thrombin
generating capacity suggesting stable anticoagulation. By day 30,
all 3 subjects are stably anticoagulated which is implied by their
consistent but drastically reduced thrombin generating capacity.
Using a similar approach to that employed in the creation of
Movie S1, thrombin generation data displayed in Movie S2 was
generated based on a mathematical simulation that incorporated
the effect of the protein C pathway whereas the previous model
did not. Figure 2 was created by taking screenshots of Movie S2
over time. As with the Base model (presented in Movie S1 and
Figure 1), all subjects, including those highlighted (S1, S2 and S3;
same as highlighted in Figure 1) become stably anticoagulated as a
result of warfarin therapy. The key difference occurs after 3 days
on warfarin: most subjects including S1, S2 and S3 paradoxically
have an increased thrombin generating capacity compared to
baseline. Our simulations suggest that peak and total thrombin
and the maximal rate of thrombin generation increases during the
initial phase of warfarin therapy. After 3 days on warfarin, the lag
time remains constant for all three highlighted subjects as it does
for .75% of the other subjects. After 5 days on warfarin all 3
highlighted subjects have a reduced thrombin generating capacity
and subject S2 and S3 become stably anticoagulated. By day 30 all
subjects are stably anticoagulated.
Simulated thrombin generation during fVIII prophylaxis
in haemophilia A
Patients with severe haemophilia were enrolled and provided us
with plasma samples which were used to determine their factor
composition. The coagulation factor composition for each unique
plasma sample was used to simulate the time course of thrombin
generation using the empirically validated ‘‘Base model’’. Since all
subjects have clinically severe haemophilia A (fVIII ,1%) and
their fVIII levels varied significantly at the time of blood collection,
the fVIII concentration was set at 100% at time zero (baseline) to
reflect the ideal goal of administering fVIII (i.e. to restore fVIII
levels to normal). The thrombin generating capacity was followed
over 7 half-lives of fVIII (t1/2=12.2 hours) to demonstrate the
theoretical fluctuations in thrombin generating capacity during the
course of fVIII prophylaxis. At 100% (‘‘baseline’’) fVIII, there is
significant individual variation in thrombin generating capacity
among individuals with severe haemophilia A (Movie S3) which is
consistent with previous work [12,19]. The maximum rate of
thrombin generation ranges from 0.35 to 0.7 nM/s, peak
thrombin ranges from 100 to 200 nM and lag time ranges from
7 to 12 minutes. Figure 3 was created by taking screenshots of
Movie S3 over time. Using subject H1 as an example, it is evident
that maximal rate of thrombin generation and peak thrombin
levels decrease as time passes and fVIII decays. The lag time and
total thrombin levels are affected less in this tissue factor stimulated
model of coagulation and thrombin generation.
To show the effect of increased fVIII product half-life on
thrombin generating capacity, thrombin parameters were gener-
ated using our ‘‘Base model’’ and the coagulation factor levels of
subject H1 over 7 half-lives of fVIII. The effect of 4 hypothetical
fVIII products on thrombin generation is shown in Movie S4. The
products’ have half-lives range from 6 (6 hrs) to 24 hours (24 hrs).
As time passes, fVIII decays and the thrombin generating capacity
of subject H1 decreases. Movie S4 shows that fVIII products with
a longer half-life maintain a relatively higher thrombin generating
capacity for a longer period than the shorter half-life products. In
the video, once a given fVIII product level falls below 1%, the plot
disappears. The time to disappearance for each fVIII product
represents the approximate relative time between fVIII doses.
Figure 4 was created by taking a screenshot of Movie S4 32 hours
after electronic ‘‘infusion’’ of fVIII. Figure 4 depicts baseline
thrombin generating capacity just after fVIII infusion (i.e. 100%
fVIII) for subject H1 and the thrombin generating capacity
expected after 32 hours with the 4 hypothetical fVIII products.
The 32 hour time point represents the time required for the
6 hour fVIII product to fall to 1% which, based on modern
prophylactic regiments, is when an additional dose of (6 hour)
fVIII would be required . By definition the 12, 18 and 24 hour
fVIII products have not decayed as quickly and therefore do not
need to be supplemented with an additional dose of fVIII at this
Empirical thrombin generation during pregnancy
Patients planning pregnancy were enrolled and provided us with
plasma samples which were used to empirically measure thrombin
generation using a thrombin generation assay. Movie S5 shows
that most subjects (16 of 19) have a lag time of between 3 and
8 minutes at baseline (pre-pregnancy). All subjects have a
maximum rate of thrombin generation less than 100 nM/min
and peak thrombin less than 200 nM. Total thrombin ranges from
745 nM-min to 2675 nM-min in these individuals at baseline. In
early pregnancy (11 to 15 weeks), there is a trend toward a
procoagulant state with the lag time decreasing, maximum rate of
thrombin generation increasing and both peak and total thrombin
increasing. In late pregnancy (30 to 34 weeks), there is a further
reduction in the lag time. The maximum rate of thrombin
generation and peak and total thrombin levels increase further
compared to early pregnancy. After pregnancy and after breast
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feeding has ceased (6 to 24 months after delivery), the thrombin
generating capacity returns to the range observed pre-pregnancy.
Post-pregnancy, the lag time is between 3 and 8 minutes for most
individuals and the maximum rate of thrombin generation is less
than 100 nM/min for all but 2 individuals. Peak and total
thrombin are also similar to pre-pregnancy values in all but 2
Figure 1. The kinetics of warfarin anticoagulation in patients with atrial fibrillation. Thrombin generating capacity was simulated by
inputting each subjects’ factor composition into our mathematical model. Each point (circle) in the figure is representative of a single individual’s
thrombin generating capacity before and during warfarin therapy. A video showing the dynamic thrombin generating capacity over time can be
viewed from Movie S1. All subjects, including the 3 highlighted (S1: subject 1, S2: subject 2 and S3: subject 3), show a time dependent reduction in
thrombin generating capacity (marginally increased lag time, decreased maximal rate, decreased peak and total thrombin) in response to warfarin
therapy. Note that the peak thrombin scale ranges from 0–500 nM.
Figure 2. The effect of the protein C pathway on the kinetics of Warfarin anticoagulation in patients with atrial fibrillation. Thrombin
generating capacity was simulated by inputting each subjects’ factor composition into our mathematical model containing the protein C pathway.
Each point (circle) in the figure is representative of a single individual’s thrombin generating capacity before and during warfarin therapy. A video
showing the dynamic thrombin generating capacity over time can be viewed from the Movie S2. All subjects show a time dependent reduction in
thrombin generating capacity (increased lag time, increased maximal rate, decreased peak and total thrombin) in response to warfarin therapy. Most
subjects, including the subjects highlighted (S1: subject 1, S2: subject 2 and S3: subject 3), have an increased maximal rate, peak and total thrombin
and a marginally increased lag time 3 days after starting warfarin therapy. After day 3, every subjects’ thrombin generating capacity decreases in a
similar fashion to that shown using our ‘‘Base model’’ (figure 1). Note that the peak thrombin scale ranges from 0–200 nM.
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individuals. Figure 5 was created by taking screenshots of Movie
S5 over time.
Conventional approaches to data analysis combined with
standard statistical methods have been limited in their ability to
identify at risk individuals. Our method integrates multiple
selected measures characteristic of individual coagulation profiles
and provides a unique level of resolving power with respect to
differences between individuals including the potential for risk
assessment of hemorrhagic and thrombotic events and monitoring
of anticoagulation . Our method can be generalized further to
take multiple measures from any type of instrument or values from
standard clinical tests (i.e. PT, aPTT, etc), and repackage them
into an integrated form that allows individuals to be monitored
over time and directly compared to other individuals evaluated the
Our method has clear advantages over currently used data
presentation techniques which describe thrombin generation
parameters. Typically, these values are tabulated and reported
as a mean 6 standard deviation or graphically with each mean 6
standard deviation value presented in a bar graph or box plot. Our
method is unique in that it provides a visual representation of all
thrombin parameters in a single plot and captures how these
parameters change over time in response to clinical events or
therapies which alter an individual’s haemostatic potential.
Making use of three discrete populations with ‘‘abnormal’’
haemostasis we have demonstrated the utility of our method in
visualizing changes in thrombin generation during warfarin
therapy, fVIII prophylaxis for haemophilia A and pregnancy.
In the current study only one method of determining thrombin
generation was used for each population but based on the
extensive empirical validation of our mathematical model
[25,26,27] we expect that simulated and empirical thrombin
generation data would be similar. Our video plot (Movie S1) shows
that the atrial fibrillation group is stably anticoagulated within 5
days of commencing warfarin therapy. These data, generated
using computational methods, are consistent with the well-
established role of warfarin in decreasing the production of
vitamin K dependent proteins  which results in reduced
thrombin generation in vivo , in vitro  and in silico .
Figure 3. Dynamic reduction of thrombin generation parameters over time in a severe haemophilia A population. Thrombin
generating capacity was simulated by inputting each subject’s factor composition into our mathematical model. Each point (circle) in the figure is
representative of a single individual’s thrombin generating capacity. A video showing the effects of decaying fVIII on the dynamic thrombin
generating capacity can be viewed from the Movie S3. Since each subject has clinically severe haemophilia A (fVIII ,1%), the fVIII concentration was
set at 100% at time zero (baseline). The thrombin generating capacity was followed over 7 half-lives of fVIII (t1/2=12.2 hours) which represents the
approximate time between prophylactic fVIII doses. All individuals, including subject H1, showed a decrease in thrombin generating capacity
(decreased maximal rate and peak thrombin and marginally decreased total thrombin and marginally increased lag time) as fVIII decayed. Note that
the peak thrombin scale ranges from 0–200 nM.
Figure 4. The effect of factor VIII product half-life on the
dynamics of thrombin generation over time 32 hours post
administration of fVIII. Thrombin generating capacity was simulated
by inputting subject H1’s factor levels into our mathematical model. A
video demonstrating the relative extension between doses when the
half-life of fVIII is increased is provided in the Movie S4. The thrombin
generation capacity is also shown at 32 hours for 4 hypothetical fVIII
products with half-lives of 6, 12, 18 and 24 hours. The baseline (100%
fVIII) thrombin generating capacity at time zero is shown as a reference.
By 32 hours, the 6 hour product has decayed to ,1% which coincides
with the approximate timing between prophylactic doses of fVIII. Note
that the peak thrombin scale ranges from 0–200 nM.
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Adding the protein C pathway to our mathematical model and
plotting the simulated data using our video plot method, we have
identified a theoretical window in which patients on warfarin may
be at an increased risk of thrombosis. Our video plot based on the
‘‘Protein C model’’ (Movie S2) shows that all patients have an
increased thrombin generating capacity 3 days after starting
warfarin therapy. After day 3, the thrombin generating capacity
decreases substantially as each patient becomes stably anticoag-
ulated. This paradoxical and theoretical increase in thrombotic
risk can be explained by the relatively short half-life of protein C
compared to other vitamin K dependent proteins such as
prothrombin and fX . Since protein C levels decrease faster
during warfarin therapy than prothrombin and fX, there is a
window of time where the anticoagulant pathway afforded by
protein C is diminished to a greater extent than that of
procoagulant pathways comprising the other vitamin K dependent
proteins. Interestingly, an increased thrombin generating capacity
on day 3 is only marginally associated with an increased lag time.
The lag time is the thrombin parameter which most closely
resembles the clot time in the PT assay which is clinically used to
monitor warfarin therapy. The simulated lag times are consistent
with the insensitivity of the PT assay to protein C levels  but
nonetheless show a theoretical increase in thrombin generating
capacity during the early stages of warfarin therapy. Therefore,
modeling the kinetics of warfarin anticoagulation may be useful in
identifying individuals who are most at risk of thrombosis during
the early stages of warfarin therapy.
We have also illustrated the utility of our method in monitoring
thrombin generating capacity among patients with severe haemo-
philia. Movie S3, generated using simulated thrombin generation
data, shows that the maximal rate of thrombin generation and
peak thrombin decreases dramatically as fVIII decays while the lag
time and total thrombin are only marginally decreased. As
reviewed previously , the goal in prophylactic factor replace-
ment therapy is to keep the fVIII concentration above 1% to
significantly reduce the risk of bleeding. Our video (Movie S4)
shows the relative timing of reduced thrombin generating capacity
in haemophilia A during prophylactic fVIII replacement therapy
and illustrates very clearly the clinical benefit of theoretical fVIII
products with a prolonged half-life. Since the pharmacokinetics of
fVIII is not known in these patients, we fixed the fVIII
concentration at 100% and allowed fVIII to decay with a half-
life of 12.2 hours (Figure 3). An additional potential limitation is
that the effects of von Willebrand factor levels on the efficacy and
half-life of fVIII replacement products is not currently part of the
model. Thus, we acknowledge that this does not represent the
actual dynamic thrombin generating capacity of the patients
enrolled since the fVIII half-live is unlikely to be exactly
12.2 hours, but nonetheless the video demonstrates how thrombin
generating capacity changes over the course of fVIII prophylaxis
in patients with similar fVIII half-lives.
Finally, using our pregnant population we show that the utility
of this method of data presentation is not exclusive to simulated
thrombin generation parameters but can also be used to chart
thrombin generating capacity using empirical parameters from
thrombin generation assays. Consistent with previous reports
[44,45,46], our pregnant population has an increased procoagu-
lant tendency in early pregnancy which increases further in late
pregnancy. After delivery and cessation of breast feeding (post-
pregnancy) the video shows that thrombin generating capacity
returns to pre-pregnancy levels. The plot also very clearly identifies
subjects who contain an endogenous activator within their plasma
(lag time=0 minutes). Using a previously described assay , it
was determined that these subjects had endogenous fIXa or fXIa
The marriage between simulated thrombin generation and our
method allows for rapid identification of individuals with
abnormal thrombin generation kinetics. In recent years, consid-
erable effort and resources have been devoted to the development
of personalized medicine, but many hurdles remain . Any tool
Figure 5. Dynamic thrombin generation during the course of pregnancy. Thrombin generating capacity was determined empirically using a
thrombin generation assay. Each point (circle) in the figure is representative of a single individual’s thrombin generating capacity. A video showing
changes in the dynamic thrombin generating capacity during pregnancy can be viewed from the Movie S5. All subjects, including the 3 highlighted
(P1: pregnant subject 1, P2: pregnant subject 2 and P3: pregnant subject 3), have increased thrombin generating capacity (decreased lag time,
increased maximal rate, increased peak and total thrombin) in early pregnancy. The thrombin generation capacity increases further in late pregnancy
and post-pregnancy returns to near baseline levels for most individuals. Note that the peak thrombin scale ranges from 0–750 nM.
Dynamic Visualization of Global Haemostasis
PLOS ONE | www.plosone.org7 January 2013 | Volume 8 | Issue 1 | e54728
which simplifies the identification of at risk individuals will likely
streamline the implementation of personalized therapies, thus
improving patient care and outcomes. The ways that the general
population and scientific community consume and use data have
changed drastically over the past few years. As recently as 5 years
ago the utility of our method would have been limited to a desktop
computer. Today, however, the ubiquity of the internet combined
with advances in computing power make this method accessible
via desktop computers as well as tablets and smartphones.
with atrial fibrillation. Each unit of time (spanning 1900–1930) in
the video represents 1 day. Thrombin generating capacity was
simulated by inputting each subject’s factor composition into our
mathematical model. Each point (circle) in the figure is
representative of a single individual’s thrombin generating
capacity before and during warfarin therapy. Each patient
(n=20) is represented by a circle. The red arrow and
accompanying circle is representative of a theoretical individual
with 100% of all coagulation factors.
The kinetics of warfarin anticoagulation in patients
Warfarin anticoagulation in patients with atrial fibrillation. Each
unit of time (spanning 1900–1930) in the video represents 1 day.
Thrombin generating capacity was simulated by inputting each
subject’s factor composition into our mathematical model
containing the protein C pathway. Each point (circle) in the
figure is representative of a single individual’s thrombin generating
capacity before and during warfarin therapy. The red arrow and
accompanying circle is representative of a theoretical individual
with 100% of all coagulation factors.
The effect of the protein C pathway on the kinetics of
ters over time in a severe haemophilia A population. The fVIII
product used here has a half-life of 12.2 hours. Each unit of time
(spanning 1900–1912) in the video represents 8 hours. Thrombin
generating capacity was simulated by inputting each subject’s
factor composition into our mathematical model. Each point
Dynamic reduction of thrombin generation parame-
(circle) in the figure is representative of a single individual’s
thrombin generating capacity. The red arrow and accompanying
circle is representative of a theoretical individual with 100% of all
dynamics of thrombin generation over time post administration of
fVIII. Each unit of time (spanning 1900–1921) in the video
represents 8 hours. Thrombin generating capacity was simulated
by inputting a single subject’s (H1 from Figure 4) factor levels into
our mathematical model. The theoretical fVIII products used here
have half-lives of 6, 12.2, 18 and 24 hours. By definition the
6 hour product decays much faster than products with longer half-
lives and therefore the thrombin generating capacity of the 6 hour
product declines fastest followed by the 12.2, 18 and 24 hour
The effect of factor VIII product half-life on the
pregnancy. Each unit of time (spanning 1900–1904) in the video is
representative of a single phase of pregnancy (in order pre-
pregnancy, early pregnancy, late pregnancy and post pregnancy).
Thrombin generating capacity was determined empirically using a
thrombin generation assay. Each point (circle) in the figure is
representative of a single individual’s thrombin generating
capacity. The red arrow and accompanying circle is representative
of the mean thrombin generating capacity of the group at the pre-
pregnancy time point.
Dynamic thrombin generation during the course of
We would like to thank Matthew Gissel and Maria Cristina Bravo for their
Conceived and designed the experiments: JHF TO SJE KEBZ. Performed
the experiments: JHF. Analyzed the data: JHF KEBZ. Contributed
reagents/materials/analysis tools: AU KCM IMB GER KGM. Wrote the
paper: JHF KEBZ.
1. Nesheim ME, Taswell JB, Mann KG (1979) The contribution of bovine Factor
V and Factor Va to the activity of prothrombinase. J Biol Chem 254: 10952–
2. Butenas S, Branda RF, van’t Veer C, Cawthern KM, Mann KG (2001) Platelets
and phospholipids in tissue factor-initiated thrombin generation. Thromb
Haemost 86: 660–667.
3. Kisiel W, Canfield WM, Ericsson LH, Davie EW (1977) Anticoagulant
properties of bovine plasma protein C following activation by thrombin.
Biochemistry 16: 5824–5831.
4. Shatos MA, Orfeo T, Doherty JM, Penar PL, Collen D, et al. (1995) Alpha-
thrombin stimulates urokinase production and DNA synthesis in cultured
human cerebral microvascular endothelial cells. Arterioscler Thromb Vasc Biol
5. Levin EG, Marzec U, Anderson J, Harker LA (1984) Thrombin stimulates tissue
plasminogen activator release from cultured human endothelial cells. J Clin
Invest 74: 1988–1995.
6. Belloni PN, Carney DH, Nicolson GL (1992) Organ-derived microvessel
endothelial cells exhibit differential responsiveness to thrombin and other growth
factors. Microvasc Res 43: 20–45.
7. Herbert JM, Dupuy E, Laplace MC, Zini JM, Bar Shavit R, et al. (1994)
Thrombin induces endothelial cell growth via both a proteolytic and a non-
proteolytic pathway. Biochem J 303 (Pt 1): 227––231.
8. Eikelboom JW, Hirsh J (2006) Monitoring unfractionated heparin with the
aPTT: time for a fresh look. Thromb Haemost 96: 547–552.
9. Quick AJ, Stanley-Brown M, Bancroft FW (1935) A study of the coagulation
defect in hemophilia and in jaundice. Am J Med Sci 190: 501–511.
10. Adcock DM, Duff S (2000) Enhanced standardization of the International
Normalized Ratio through the use of plasma calibrants: a concise review. Blood
Coagul Fibrinolysis 11: 583–590.
11. Siegemund T, Petros S, Siegemund A, Scholz U, Engelmann L (2003)
Thrombin generation in severe haemophilia A and B: the endogenous thrombin
potential in platelet-rich plasma. Thromb Haemost 90: 781–786.
12. Dargaud Y, Beguin S, Lienhart A, Al Dieri R, Trzeciak C, et al. (2005)
Evaluation of thrombin generating capacity in plasma from patients with
haemophilia A and B. Thromb Haemost 93: 475–480.
13. Hemker HC, Al Dieri R, De Smedt E, Beguin S (2006) Thrombin generation, a
function test of the haemostatic-thrombotic system. Thromb Haemost 96: 553–
14. Mann KG, Brummel K, Butenas S (2003) What is all that thrombin for?
J Thromb Haemost 1: 1504–1514.
15. Hron G, Kollars M, Binder BR, Eichinger S, Kyrle PA (2006) Identification of
patients at low risk for recurrent venous thromboembolism by measuring
thrombin generation. JAMA 296: 397–402.
16. Nair SC, Dargaud Y, Chitlur M, Srivastava A (2010) Tests of global haemostasis
and their applications in bleeding disorders. Haemophilia 16 Suppl 5: 85–92.
17. Brummel-Ziedins KE, Pouliot RL, Mann KG (2004) Thrombin generation:
phenotypic quantitation. J Thromb Haemost 2: 281–288.
18. Brummel-Ziedins KE, Orfeo T, Rosendaal FR, Undas A, Rivard GE, et al.
(2009) Empirical and theoretical phenotypic discrimination. J Thromb Haemost
7 Suppl 1: 181–186.
19. Danforth CM, Orfeo T, Everse SJ, Mann KG, Brummel-Ziedins KE (2012)
Defining the boundaries of normal thrombin generation: investigations into
hemostasis. PLoS One 7: e30385.
Dynamic Visualization of Global Haemostasis
PLOS ONE | www.plosone.org8 January 2013 | Volume 8 | Issue 1 | e54728
20. Brodin E, Appelbom H, Osterud B, Hilden I, Petersen LC, et al. (2009)
Regulation of thrombin generation by TFPI in plasma without and with
heparin. Transl Res 153: 124–131.
21. van ’t Veer C, Mann KG (1997) Regulation of tissue factor initiated thrombin
generation by the stoichiometric inhibitors tissue factor pathway inhibitor,
antithrombin-III, and heparin cofactor-II. J Biol Chem 272: 4367–4377.
22. Allen GA, Wolberg AS, Oliver JA, Hoffman M, Roberts HR, et al. (2004)
Impact of procoagulant concentration on rate, peak and total thrombin
generation in a model system. J Thromb Haemost 2: 402–413.
23. Lutsey PL, Folsom AR, Heckbert SR, Cushman M (2009) Peak thrombin
generation and subsequent venous thromboembolism: the Longitudinal
Investigation of Thromboembolism Etiology (LITE) study. J Thromb Haemost
24. Besser M, Baglin C, Luddington R, van Hylckama Vlieg A, Baglin T (2008)
High rate of unprovoked recurrent venous thrombosis is associated with high
thrombin-generating potential in a prospective cohort study. J Thromb Haemost
25. Hockin MF, Jones KC, Everse SJ, Mann KG (2002) A model for the
stoichiometric regulation of blood coagulation. J Biol Chem 277: 18322–18333.
26. Butenas S, Orfeo T, Gissel MT, Brummel KE, Mann KG (2004) The
significance of circulating factor IXa in blood. J Biol Chem 279: 22875–22882.
27. Bravo MC, Orfeo T, Mann KG, Everse SJ (2012) Modeling of human factor Va
inactivation by activated protein C. BMC Syst Biol 6: 45.
28. Brummel-Ziedins KE, Orfeo T, Callas PW, Gissel M, Mann KG, et al. (2012)
The prothrombotic phenotypes in familial protein C deficiency are differentiated
by computational modeling of thrombin generation. PLoS One 7: e44378.
29. Busch C, Cancilla PA, DeBault LE, Goldsmith JC, Owen WG (1982) Use of
endothelium cultured on microcarriers as a model for the microcirculation. Lab
Invest 47: 498–504.
30. Mann KG (2011) Thrombin generation in hemorrhage control and vascular
occlusion. Circulation 124: 225–235.
31. Brummel-Ziedins K, Vossen CY, Rosendaal FR, Umezaki K, Mann KG (2005)
The plasma hemostatic proteome: thrombin generation in healthy individuals.
J Thromb Haemost 3: 1472–1481.
32. Hemker HC, Giesen P, AlDieri R, Regnault V, de Smed E, et al. (2002) The
calibrated automated thrombogram (CAT): a universal routine test for hyper-
and hypocoagulability. Pathophysiol Haemost Thromb 32: 249–253.
33. McLean KC, Bernstein IM, Brummel-Ziedins KE (2012) Tissue factor-
dependent thrombin generation across pregnancy. Am J Obstet Gynecol 207:
34. Brummel-Ziedins K, Undas A, Orfeo T, Gissel M, Butenas S, et al. (2008)
Thrombin generation in acute coronary syndrome and stable coronary artery
disease: dependence on plasma factor composition. J Thromb Haemost 6: 104–
35. Gissel M, Whelihan MF, Ferris LA, Mann KG, Rivard GE, et al. (2012) The
influence of prophylactic factor VIII in severe haemophilia A. Haemophilia 18:
36. Hale SA, Schonberg A, Badger GJ, Bernstein IM (2009) Relationship between
prepregnancy and early pregnancy uterine blood flow and resistance index.
Reprod Sci 16: 1091–1096.
37. Hirsh J, Dalen J, Anderson DR, Poller L, Bussey H, et al. (2001) Oral
anticoagulants: mechanism of action, clinical effectiveness, and optimal
therapeutic range. Chest 119: 8S–21S.
38. Manco-Johnson M (2007) Comparing prophylaxis with episodic treatment in
haemophilia A: implications for clinical practice. Haemophilia 13 Suppl 2: 4–9.
39. Conway EM, Bauer KA, Barzegar S, Rosenberg RD (1987) Suppression of
hemostatic system activation by oral anticoagulants in the blood of patients with
thrombotic diatheses. J Clin Invest 80: 1535–1544.
40. Dargaud Y, Desmurs-Clavel H, Marin S, Bordet JC, Poplavsky JL, et al. (2008)
Comparison of the capacities of two prothrombin complex concentrates to
restore thrombin generation in plasma from orally anticoagulated patients: an in
vitro study. J Thromb Haemost 6: 962–968.
41. Orfeo T, Gissel M, Butenas S, Undas A, Brummel-Ziedins KE, et al. (2011)
Anticoagulants and the propagation phase of thrombin generation. PLoS One 6:
42. Brummel-Ziedins K, Orfeo T, Jenny NS, Everse SJ, Mann KG (2009) Blood
Coagulation and Fibrinolysis. In: Greer JP, Foerster J, Rodgers GM, Paraskevas
F, Glader B et al., editors. Wintrobe’s Clinical Hematology. Philadelphia:
Wolters Kluwer, Lippincott Williams & Wilkins. pp. 528–619.
43. Khor B, Van Cott EM (2010) Laboratory tests for protein C deficiency.
Am J Hematol 85: 440–442.
44. Eichinger S, Weltermann A, Philipp K, Hafner E, Kaider A, et al. (1999)
Prospective evaluation of hemostatic system activation and thrombin potential in
healthy pregnant women with and without factor V Leiden. Thromb Haemost
45. Dargaud Y, Hierso S, Rugeri L, Battie C, Gaucherand P, et al. (2010)
Endogenous thrombin potential, prothrombin fragment 1+2 and D-dimers
during pregnancy. Thromb Haemost 103: 469–471.
46. Rosenkranz A, Hiden M, Leschnik B, Weiss EC, Schlembach D, et al. (2008)
Calibrated automated thrombin generation in normal uncomplicated pregnan-
cy. Thromb Haemost 99: 331–337.
47. Butenas S, Undas A, Gissel MT, Szuldrzynski K, Zmudka K, et al. (2008) Factor
XIa and tissue factor activity in patients with coronary artery disease. Thromb
Haemost 99: 142–149.
48. Wulfkuhle KC, Butenas S, Bernstein I, Brummel-Ziedins K (2011) Tissue factor
dependent and independent thrombin generation across pregnancy. J Thromb
Haemost 9 Suppl 2: 431.
49. (2012) What happened to personalized medicine? Nat Biotechnol 30: 1.
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