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CLINICAL TRIALS AND OBSERVATIONS
Development and validation of a predictive model for chemotherapy-associated
thrombosis
Alok A. Khorana,1 Nicole M. Kuderer,2 Eva Culakova,2 Gary H. Lyman,2 and Charles W. Francis1
1James
P. Wilmot Cancer Center and the Department of Medicine, University of Rochester, NY; and 2Duke University Medical Center and the Duke
Comprehensive Cancer Center, Durham, NC
Risk of venous thromboembolism (VTE)
is elevated in cancer, but individual risk
factors cannot identify a sufficiently highrisk group of outpatients for thromboprophylaxis. We developed a simple model
for predicting chemotherapy-associated
VTE using baseline clinical and laboratory variables. The association of VTE
with multiple variables was characterized
in a derivation cohort of 2701 cancer
outpatients from a prospective observational study. A risk model was derived
and validated in an independent cohort of
1365 patients from the same study. Five
predictive variables were identified in a
multivariate model: site of cancer (2 points
for very high-risk site, 1 point for highrisk site), platelet count of 350 ⴛ 109/L
or more, hemoglobin less than 100 g/L
(10 g/dL) and/or use of erythropoiesisstimulating agents, leukocyte count more
than 11 ⴛ 109/L, and body mass index of
35 kg/m2 or more (1 point each). Rates of
VTE in the derivation and validation cohorts, respectively, were 0.8% and 0.3%
in low-risk (score ⴝ 0), 1.8% and 2% in
intermediate-risk (score ⴝ 1-2), and 7.1%
and 6.7% in high-risk (score > 3) category over a median of 2.5 months (Cstatistic ⴝ 0.7 for both cohorts). This
model can identify patients with a nearly
7% short-term risk of symptomatic VTE
and may be used to select cancer outpatients for studies of thromboprophylaxis.
(Blood. 2008;111:4902-4907)
© 2008 by The American Society of Hematology
Introduction
Cancer and antineoplastic therapy are frequently complicated by
the development of venous thromboembolism (VTE). Several risk
factors for cancer-associated VTE have been described in recent
studies and include primary site of cancer, presence of metastatic
disease, and use of antineoplastic therapy including chemotherapy,
hormonal therapy, surgery, and erythropoiesis-stimulating agents.1-4
Cancer patients on active therapy are at greatest risk for development of VTE. In a population-based study, cancer was associated
with a 4.1-fold greater risk of thrombosis, whereas the use of
chemotherapy increased the risk 6.5-fold.5,6 In women with stage II
breast cancer, the risk of VTE decreases dramatically after chemotherapy is completed.7,8 The occurrence of VTE has important implications for the cancer patient including requirement for chronic anticoagulation, possible delays in delivering chemotherapy, a high risk of
recurrent VTE, risk of bleeding complications on anticoagulation,
decreased quality of life, and consumption of health care resources.9,10
Furthermore, cancer patients with VTE have a 2-fold or greater increase
in mortality compared with cancer patients without VTE, even after
adjusting for stage.11,12 Indeed, thromboembolism is a leading cause of
death in cancer patients.13
Primary VTE prophylaxis can reduce deep vein thrombosis
(DVT), pulmonary embolism (PE), and fatal PE in several highrisk populations such as hospitalized patients or in the postoperative setting.14-18 In the cancer population, identification of patients
most at risk for VTE followed by institution of effective prophylaxis
could improve morbidity, mortality, delivery of cancer therapy, cancerrelated outcomes, quality of life, and use of health care resources.
Clinical trials of thromboprophylaxis have been conducted in cancer
outpatients selected by individual risk factors, such as metastatic breast
and lung cancer or presence of intravenous catheters.19-22 However, with
a single exception,19 these studies have not shown a benefit for primary
thromboprophylaxis. Current consensus guidelines do not recommend
prophylaxis in cancer outpatients.23,24
To reduce the burden and consequences of VTE, it is therefore
important to identify a population of cancer patients at highest risk
for VTE that would benefit from thromboprophylaxis. A rate of
symptomatic VTE of approximately 5% to 7% would be similar or
greater than that reported in hospitalized or postoperative patients
for whom VTE prophylaxis has been shown to be highly effective.25-27 Formal risk assessment models for DVT and PE in other
populations have been developed and are used clinically.28-34
However, no such risk models exist for the cancer population.
We therefore developed a simple risk model for predicting rates
of VTE using data from a multicenter prospective observational
study of cancer outpatients receiving chemotherapy, using baseline
clinical and laboratory variables. We also performed a validation of
this model in an independent cohort of patients derived from the
same observational study.
Submitted October 5, 2007; accepted January 19, 2008. Prepublished online
as Blood First Edition paper, January 23, 2008; DOI 10.1182/blood-2007-10116327.
The publication costs of this article were defrayed in part by page charge
payment. Therefore, and solely to indicate this fact, this article is hereby
marked ‘‘advertisement’’ in accordance with 18 USC section 1734.
An Inside Blood analysis of this article appears at the front of this issue.
© 2008 by The American Society of Hematology
4902
Methods
Study population
The study population comprised consecutively enrolled patients in the
Awareness of Neutropenia in Chemotherapy (ANC) Study Group Registry,
an observational study of cancer patients initiating a new chemotherapy
BLOOD, 15 MAY 2008 䡠 VOLUME 111, NUMBER 10
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BLOOD, 15 MAY 2008 䡠 VOLUME 111, NUMBER 10
regimen at 115 sites within the United States, balanced for practice volume
and geographic location. Detailed methodology as well as frequency and
risk factors for VTE in a preliminary analysis of this study have previously
been reported.2 Briefly, patients were followed prospectively for a maximum of 4 cycles of chemotherapy. Patients enrolled between March 2002
and October 2005 who had completed at least one cycle were included in
this analysis. The study was approved by a central institutional review
board (IRB) as well as the University of Rochester IRB.
Patients were required to have a histologically confirmed diagnosis of
cancer, with targeted enrollment of specific tumor types (breast, lung,
ovarian, sarcoma, colon, and lymphomas), although eligible patients with
other primary sites were allowed on study. Patients were also required to be
at the start of a new chemotherapy regimen, expected to complete 4 cycles
of chemotherapy, be age 18 years or older with no upper age limit, and to
provide informed consent in accordance with the Delcaration of Helsinki.
Patients were excluded if they were receiving concurrent cytotoxic,
biologic, or immunologic therapy for other conditions, or continuous
single-agent chemotherapy, if they had a diagnosis of acute leukemia, were
pregnant or lactating, had an active infection requiring treatment, were
currently participating in a double-blinded study, or had received stem cell
transplant. VTE was diagnosed by the treating clinician. Arterial thrombotic
events were not included. Only 3% of patients were lost to follow-up.
Derivation of model
Model derivation and validation were based on a split-sample method. Twothirds of the study participants were randomly assigned to a model
derivation cohort (n ⫽ 2701), and one-third (n ⫽ 1365) were reserved as an
independent validation cohort. Both cohorts were compared with respect to
clinical and laboratory variables.
The model was developed based on data from the derivation cohort
only. Clinical variables known to be associated with VTE as well as
potential risk factors including patient demographics, performance status,
ethnicity, site and stage of cancer, type of chemotherapy, body mass index
(BMI), comorbid conditions including history of myocardial infarction,
peripheral vascular disease, recurrent infections, liver disease, pulmonary
disease, diabetes mellitus and connective tissue disorders, recent surgery,
use of prophylactic myeloid growth factors, prior use of chemotherapy, use
of diuretics, use of corticosteroids, and available baseline laboratory
variables including complete blood count and liver function tests were
considered. Variables measured after the initiation of chemotherapy such as
the occurrence of infection or hospitalization were excluded since the goal
was to identify high-risk patients prior to starting therapy. All covariates
were assessed for missing values that were grouped with either the largest
category or the category with a similar relation to outcome. Among
variables considered during initial analysis, missing values were rare.
Serum albumin had the most missing values (n ⫽ 125, 3.1%). For numeric
variables, platelet count and hemoglobin level were categorized on the basis
of a prior analysis,2 while other laboratory variables were categorized based
on elevation beyond the upper limit of standard reference values.35
Variables were evaluated in univariate analysis using a ␹2 test. A multivariate logistic regression model was developed using a stepwise selection
process. Variables associated with an increased risk of VTE (P ⱕ .10) in
univariate analysis as well as variables selected a priori based on known
relevance were included in the pool of variables for the forward stepwise
regression model. Primary site of cancer and stage of disease were built into
the model at every step. Hemoglobin and use of erythropoiesis-stimulating
agents (ESAs) were combined into one variable based on prior analysis.2 To
avoid model instability, we required covariates to have an individual
category sample size of at least 75 patients. The covariates in the final
model were evaluated for collinearity, using the variance inflation factor
(VIF). First-order interaction terms were explored in the final model
between the primary site of cancer and all other variables in the model. No
significant interactions were found. A risk score was developed based on
regression coefficients from the final multivariate model.
PREDICTIVE MODEL FOR CHEMOTHERAPY-ASSOCIATED VTE
4903
Validation of model
Once the final model was developed, it was assessed in the separate validation
cohort of 1365 patients. The predictive accuracy of the model was assessed using
tests for discrimination and calibration. Model discrimination performance was
evaluated using the standard measures of sensitivity, specificity, and predictive
value. For overall assessment, discrimination was evaluated using the c-statistic
representing the area under the receiver operating characteristic curve with larger
values indicating better discrimination. The Hosmer-Lemeshow statistic was
used to assess model calibration or its fit to the data based on agreement between
predicted risk score probabilities using the model and the actual observed
probabilities. Statistical analysis was conducted using SAS statistical software
(SAS Institute, Cary, NC).
Results
Patient characteristics
The study population comprised 4066 patients, split into
2701 patients in the derivation cohort and 1365 patients in the
validation cohort. Overall, 88 patients (2.2%) developed VTE over
a median follow up of 73 days, (range, 5-364 days). There were
65 reported venous thrombotic events (1.6%) and 27 embolic
events (0.7%), including 4 patients with both. Seventy-five percent
of VTE events occurred in the first 2 cycles. General characteristics
of the derivation and validation cohorts are presented in Table 1.
Patient characteristics in the 2 cohorts were well balanced, with the
exception of recent history of surgery.
Development of risk model
In the derivation cohort, 60 patients (2.2%) developed VTE. In
univariate analysis, the following covariates were statistically
significantly associated with the development of symptomatic
VTE: primary site of cancer, prechemotherapy platelet count of
350 ⫻ 109/L or more, hemoglobin level less than 100 g/L (10 g/
dL), use of ESAs, leukocyte count more than 11 ⫻ 109/L, and body
mass index of 35 kg/m2 or more. VTE rates were higher in patients
with poor performance status (2.6% with performance status ⱖ 2,
n ⫽ 228) but this difference was not statistically significant.
Prophylactic myeloid growth factors were associated with leukocytosis (prophylactic use in 21% of patients with white blood
cell [WBC] count ⱕ11 ⫻ 109/L and in 26% with WBC
count ⱖ11 ⫻ 109/L, P ⫽ .01) but not with VTE. For further
analysis, sites of cancer were categorized into “very high risk” with
VTE rates 3-fold or higher than the average risk for the population
(gastric, pancreatic), “high risk” with rates higher than average
(lung, lymphoma, gynecologic, genitourinary excluding prostate),
and “others” with rates at or below average (breast, colorectal,
prostate, and others). There was a strong association between
hemoglobin level less than 100 g/L (10 g/dL) and use of ESAs,
with 75% of patients with a baseline hemoglobin level less
than 100 g/L (10 g/dL) receiving ESAs by the beginning of cycle
2. These were analyzed as one variable. Age was not significantly
associated with VTE either when the population was dichotomized
at 65 years or categorized as younger than 40 years, 40 to younger
than 65 years, and 65 years or older. In the final multivariate
analysis, the following variables were independently associated
with risk of VTE: primary site of cancer (very high risk or high
risk), prechemotherapy platelet count of 350 ⫻ 109/L or more,
hemoglobin level less than 100 g/L (10 g/dL) and/or use of red cell
growth factors, leukocyte count more than 11 ⫻ 109/L, and body
mass index of 35 kg/m2 or more (Table 2). We assigned points for
the risk model based on the regression coefficients obtained from
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4904
BLOOD, 15 MAY 2008 䡠 VOLUME 111, NUMBER 10
KHORANA et al
Table 1. Characteristics of patients in the derivation and validation
cohorts
Category
All patients
All VTE
Derivation
Validation
cohort, no. (%) cohort, no. (%)
2701 (100)
1365 (100)
60 (2.2)
28 (2.1)
Age
Younger than 65 y
1618 (59.9)
850 (62.3)
65 y or older
1083 (40.1)
515 (37.7)
Sex
P
Female
882 (32.7)
455 (33.3)
1819 (67.3)
910 (66.7)
Performance status, ECOG
2473 (91.6)
1242 (91.0)
2 to 4
228 (8.4)
123 (9.0)
Primary site of cancer
935 (34.6)
472 (34.6)
Colorectal
297 (11.0)
163 (11.9)
Lung
554 (20.5)
236 (17.3)
Gynecologic
259 (9.6)
142 (10.40)
Gastric and pancreatic
54 (2.0)
19 (1.4)
Lymphoma
328 (12.1)
184 (13.5)
Other sites
274 (10.1)
149 (10.9)
1653 (61.2)
873 (64.0)
997 (36.9)
477 (34.9)
Stage
1 to 3
4
Unknown
Very high risk (stomach, pancreas)
1.46
4.3 (1.2-15.6)
High risk (lung, lymphoma, gynecologic,
0.43
1.5 (0.9-2.7)
0.0
1.0 (reference)
genitourinary excluding prostate)
Low risk (breast, colorectal, head and neck)
Prior chemotherapy
15 (1.1)
645 (23.9)
306 (22.4)
1.8 (1.1-3.2)
Hemoglobin level less than 100 g/L or use of red cell
0.89
2.4 (1.4-4.2)
0.77
2.2 (1.2-4)
0.90
2.5 (1.3-4.7)
Prechemotherapy leukocyte count more than
BMI 35 kg/m2 or more
.3
Cerebrovascular disease
54 (2.0)
23 (1.7)
.49
Moderate or severe renal disease
27 (1.0)
18 (1.3)
.36
Chronic pulmonary disease
217 (8.0)
110 (8.1)
.98
Surgery within past month
829 (30.7)
473 (34.7)
.01
Diabetes mellitus
312 (11.6)
169 (12.4)
.44
BMI 35 kg/m2 or more
332 (12.3)
166 (12.2)
.90
Platelet count 350 ⫻ 109/L or more
604 (22.4)
295 (21.6)
.59
Hemoglobin level less than 100 g/L
178 (6.6)
73 (5.3)
.12
.75
Baseline laboratory values
WBC count more than 11 ⫻ 109/L
344 (12.7)
169 (12.4)
Bilirubin level more than 17.1 ␮mol/L
103 (3.8)
60 (4.4)
.37
Albumin level less than 35 g/L
544 (20.1)
267 (19.6)
.66
89 (3.3)
45 (3.3)
.999
764 (28.3)
358 (26.2)
.17
Prophylactic myeloid growth factors
566 (21.0)
299 (21.9)
.48
Antibiotics
131 (4.9)
62 (4.5)
.66
Corticosteroids
712 (26.4)
357 (26.2)
.89
Drugs
Erythropoiesis-stimulating agents
0.60
11 ⫻ 109/L
Comorbidities
Creatinine level more than 114.4 ␮mol/L
Prechemotherapy platelet count 350 ⫻ 109/L or more
growth factors
.06
51 (1.9)
Odds ratio*
(95% CI)
.14
.13
Breast
␤
Site of cancer
.54
0 to 1
Patient characteristic
.72
.66
Male
Table 2. Predictors of venous thromboembolism in the derivation
cohort by multivariate logistic regression analysis
reported up to cycle 2
the final model and divided the population into 3 risk categories
based on the score from the risk model: low (score 0), intermediate
(score 1-2), and high (score ⱖ 3; Table 3).
Accuracy and validation of risk model
The risk model was tested in the validation cohort of 1365 patients
of whom 28 (2.1%) developed VTE. At the cutoff point for high
risk (score ⱖ 3), the model had a negative predictive value
(probability of no VTE in those designated low risk) of 98.5%, a
positive predictive value (probability of VTE in those designated
high risk) of 7.1%, a sensitivity (probability of high risk in those
experiencing VTE) of 40.0%, and a specificity (probability of low
risk in those not experiencing VTE) of 88% in the derivation
cohort. Similarly, in the validation cohort, the model had a negative
predictive value of 98.5%, a positive predictive value of 6.7%, a
sensitivity of 35.7%, and a specificity of 89.6%. In both the
*Odds ratios are adjusted for stage.
derivation and validation cohorts, the C statistic was 0.7. The
model also demonstrated good calibration. The Hosmer-Lemeshow
goodness-of-fit test results were nonsignificant (P ⫽ .75 for derivation cohort and P ⫽ .15 for validation cohort), indicating that the
observed and predicted numbers of patients with and without VTE
were not significantly different. The observed rates of VTE
according to the prespecified risk category for both cohorts are
shown in Figure 1. The accuracy of the score and the proportion of
patients classified into each risk category were similar in the
derivation and validation cohorts.
Discussion
In this analysis of data from an observational study, we identified
5 clinical and laboratory parameters that were independently
predictive of symptomatic VTE in cancer patients initiating a new
chemotherapy regimen. These parameters were combined into a
simple risk assessment model that allows providers to classify
patients into 3 discrete categories corresponding to the risk of
chemotherapy-associated VTE. The model was then validated in an
independent cohort of patients from the same observational study.
In the validation cohort, the rates of symptomatic VTE over a
median period of follow up of 2.5 months in the low-, intermediate-, and high-risk categories were 0.3%, 2%, and 6.7%, respectively (Figure 1).
Many of the risk factors identified in this analysis agree with
earlier studies. Our findings of a strong association of VTE with
gastrointestinal, gynecologic and lung cancer, and lymphoma are
consistent with prior reports.1-3,12,36,37 Our study did not include
sufficient numbers of patients with brain or renal cancers and
myeloma, all of which have also been strongly associated with
VTE. The association of an elevated prechemotherapy platelet
count with VTE was previously reported in an initial analysis of
Table 3. Predictive model for chemotherapy-associated VTE
Patient characteristic
Risk
score
Site of cancer
Very high risk (stomach, pancreas)
2
High risk (lung, lymphoma, gynecologic, bladder, testicular)
1
Prechemotherapy platelet count 350 ⫻ 109/L or more
1
Hemoglobin level less than 100 g/L or use of red cell growth factors
1
Prechemotherapy leukocyte count more than 11 ⫻ 109/L
1
BMI 35 kg/m2 or more
1
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BLOOD, 15 MAY 2008 䡠 VOLUME 111, NUMBER 10
Figure 1. Rates of VTE according to scores from the risk model in the derivation
and validation cohorts.
this study,2 and has also recently been suggested in lung cancer.38
A retrospective study of medical inpatients including cancer
patients also identified an admission platelet count of more than
350 ⫻ 109/L as predictive for VTE during hospitalization.39 Obesity is a known risk factor for VTE in the general population
(reviewed in Ageno et al40), although it has not been studied
specifically in cancer patients. In a recent meta-analysis, use of
ESAs was associated with a 1.67-fold increase in thromboembolic
events.41 The association of leukocytosis with VTE reported in this
study is novel. Recent large studies have described an association
of leukocyte count with vascular events in the general population,42
and particularly with venous thrombotic events in patients with
myeloproliferative disorders (reviewed in Tefferi et al43) possibly
driven by cross-talk between granulocytes and platelets and/or
endothelial cells. In cancer patients, leukocytosis may also be a
surrogate for advanced disease burden not captured by stage
classification.44 There did not appear to be a correlation of VTE
with use of prophylactic myeloid growth factors in our study.
Further investigations into the role of leukocytes in cancerassociated thrombosis would be of interest.
Older age and stage of disease were not associated with VTE
in our analysis, in contrast to previous reports.1,12,36 This may be
a reflection of the excellent performance status of most patients
enrolled in this study. Rates of VTE were indeed higher in the
small number of patients with poor performance status in our
study. Recent data demonstrate that the risk of VTE remains
high for a prolonged period after surgery in cancer patients.45
Although history of recent surgery was not a significant
predictor of VTE in our analysis, postsurgical prophylaxis and
patient selection for chemotherapy may be potential confounding factors. VTE in cancer patients often occurs in the initial
period after diagnosis.1 In our analysis, VTE was more frequent
in chemotherapy-naive patients than in those who had undergone chemotherapy (2.3% vs 1.6%) but this difference was not
statistically significant; however, 75% of events occurred in the
first 2 cycles of chemotherapy suggesting the initial period of
chemotherapy remains high risk for VTE.
VTE prophylaxis is effective in settings where the incidence of
VTE is high, such as in hospitalized patients, or in the postoperative setting.14-18,46 However, trials of prophylaxis conducted in
cancer patients identified as high risk by individual factors, such as
primary site of cancer or use of venous access devices, have been
largely unsuccessful. The goal of our analysis was to develop a risk
assessment model that would incorporate multiple variables and
PREDICTIVE MODEL FOR CHEMOTHERAPY-ASSOCIATED VTE
4905
allow providers to identify patients at high risk. Formal risk
assessment models for DVT in other high-risk populations have
been developed and are used clinically.28-33 We focused on
variables that are simple to evaluate and routinely collected before
initiating chemotherapy. According to the model, approximately
27% of patients had a risk of VTE as low as 0.3%; an additional
60% of patients had a risk of VTE of approximately 2%. It is
unlikely that patients classified in these groups would benefit from
thromboprophylaxis. In contrast, patients in the high-risk group
had a nearly 7% risk of developing VTE over a period of only
2.5 months (2.7%/month). This risk is comparable with that
observed in other high-risk settings such as hospitalized medically
ill patients for whom prophylaxis has been shown to be effective
and safe.46 This rate of 7% in the high-risk group is higher than the
rate of 4.4% symptomatic VTE seen in the placebo arm of the
Levine et al study, which showed a benefit of prophylaxis in breast
cancer patients,19 and also higher than the 3.4% rate of symptomatic VTE in the TOPIC-II lung study, which showed a trend toward
benefit for prophylaxis.22 However, cancer patients are also paradoxically at increased risk of bleeding complications as well,9 and
the safety of prophylaxis in the high-risk group identified by this
model needs to be evaluated in clinical trials. Future trials of
prophylaxis should focus on identifying and testing patients in the
high-risk group identified by this model.
The statistical issues surrounding the derivation and validation
of risk models merit discussion. A commonly accepted rule for
modeling specifies that the number of events should be at least
10 times the number of variables included in the model.47,48
In the derivation cohort, 60 patients developed VTE and our final
model comprised only 5 variables. We followed accepted recommendations for development of the model, including identifying a
list of potential predictors, using logistic regression to identify the
most powerful predictors, using regression coefficients to develop
the score, and validating the model.49,50 Since patients accrued
on this study represented a heterogeneous population across
115 community-based practices nationwide, we believe that the
results of our analysis can be generalized. External validation
represents the ideal approach to validating models, but we were
unable to identify a dataset of cancer patients that had reliably
collected data regarding VTE as well as the variables identified in
this model. We therefore chose to conduct a split-sample validation. It would certainly be important to further validate this model
in other large observational studies that are currently in accrual and
are collecting the required data. The value of the C statistic
suggests that incorporating additional variables may increase the
robustness of this model. Since the goal of this analysis was to
identify pretreatment predictors of VTE, we did not study
variables developing during treatment, although these could certainly have influenced the development of VTE. Promising new
data suggest that tissue factor expression by tumor cells may be a
predictive biomarker for VTE.51 We plan to test this model further
and evaluate tissue factor and other biomarkers in a prospective
outcomes study.
Despite advantages of a large sample size and the ability to
conduct a split-sample validation, our study had some limitations.
The observational study from which these data were derived was
designed to assess febrile neutropenia and related complications as
its primary end point, and not the occurrence of VTE. However,
data regarding VTE were prospectively collected, and the sample
size is adequate for an analysis of this nature. Certain cancers
known to be strongly associated with VTE, such as brain tumors
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BLOOD, 15 MAY 2008 䡠 VOLUME 111, NUMBER 10
KHORANA et al
(n ⫽ 4), and poor performance status patients were underrepresented in the study population. For clinical use, the score for
high-risk sites may be generalized to these cancers based on
multiple studies documenting high rates of VTE. There may be an
association between specific antineoplastic agents and VTE. Initial
exploratory analyses in this dataset suggested such relationships
(data not shown), but there was significant overlap between type of
chemotherapy drug and primary site of cancer. In many instances,
the number of events per drug category was too small to be
characterized definitively. Further, there were few patients in this
study receiving known thrombogenic agents such as thalidomide,
lenalidomide, and bevacizumab since only a few myeloma patients
were enrolled and the study ended in October 2005, only a short
period after bevacizumab was made available. Therefore, larger
studies including these agents are needed. Data regarding central
venous catheters, a potential risk factor for VTE, were not
collected. However, results from recent randomized studies show
that the risk of catheter-associated thrombosis is too low to warrant
routine prophylaxis, and this is supported by a recent consensus
guideline.23,52,53 Data regarding prior history of VTE, use of
anticoagulation, or correlation with response to chemotherapy were
not available. Finally, VTE was diagnosed by treating physicians
based on symptoms and VTE events were not independently
adjudicated. However, this suggests that clinically important VTE
events were observed, although the true rate may be overestimated
or underestimated. In this regard, it is important to note that in
clinical trials of medically ill patients, rates of asymptomatic
thrombosis are several-fold increased over symptomatic VTE.25,26
Studies in other settings that included cancer patients have
furthermore shown a strong association between asymptomatic
DVT and subsequent development of symptomatic VTE54-57 as
well as a possible association with mortality.58
In summary, this study demonstrates that the risk of VTE in
cancer patients initiating chemotherapy can be reliably predicted
using a simple risk assessment model based on 5 clinical and
laboratory parameters. Cancer-associated thrombosis is an important cause of morbidity and mortality in cancer patients, and it may
be preventable with effective prophylaxis strategies. This risk
model may be used by clinicians for assessing risk for VTE in
patients seen in clinical practice, as well as in the design of future
trials involving cancer outpatients who would benefit from
thromboprophylaxis.
Acknowledgments
This work was supported by a Career Development Award to
A.A.K. from the National Cancer Institute K23 CA120587. The
Awareness of Neutropenia in Chemotherapy (ANC) Study Group
received research grant support from Amgen for the development
of the patient registry. N.M.K. is supported by a National Institutes
of Health grant T32HL07152-30.
Authorship
Contribution: A.A.K. contributed to study design, analysis and
interpretation of data, and writing of paper; N.M.K. contributed
to analysis and interpretation of data and writing of paper; E.C.
contributed to statistical analysis and writing of paper; G.H.L.
and C.W.F. contributed to study design, data analysis, and
writing of paper.
Conflict-of-interest disclosure: The authors declare no competing financial interests.
Correspondence: Alok A. Khorana, 601 Elmwood Ave, Box 704,
Rochester, NY 14642; e-mail: [email protected].
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2. Khorana AA, Francis CW, Culakova E, Lyman
GH. Risk factors for chemotherapy-associated
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From www.bloodjournal.org by guest on July 31, 2017. For personal use only.
2008 111: 4902-4907
doi:10.1182/blood-2007-10-116327 originally published
online January 23, 2008
Development and validation of a predictive model for
chemotherapy-associated thrombosis
Alok A. Khorana, Nicole M. Kuderer, Eva Culakova, Gary H. Lyman and Charles W. Francis
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