Keywords
recurrent VTE - cancer - prediction
Introduction
Venous thromboembolism (VTE), comprising deep vein thrombosis (DVT) and pulmonary
embolism (PE), is a frequent complication in patients with cancer.[1] Direct oral anticoagulants (DOACs) or low-molecular-weight heparin (LMWH) are recommended
for the treatment of acute VTE,[2]
[3]
[4]
[5]
[6] but the risk of recurrence nonetheless remains high.[7] In a meta-analysis of six randomized controlled trials (RCTs), the cumulative incidences
of recurrent VTE over a 6-month treatment period were 5.4 and 8.3% in patients receiving
DOAC or LMWH, respectively.[8]
Patients with cancer and acute VTE are usually treated for at least 3 to 6 months.
Anticoagulation is usually continued in case of active cancer or ongoing anticancer
treatment. Decisions about the optimal intensity and duration of anticoagulant treatment
should ideally be guided by the risk of recurrent VTE. For example, while in the RCTs
the dose of LMWH was typically reduced by 25% after the first month of treatment to
mitigate the risk of bleeding, but it is unknown if this dose reduction strategy should
be avoided in cancer patients at high risk of recurrent VTE. Currently, the only risk
stratification tool to determine the risk of recurrent VTE in cancer patients is the
Ottawa score, which stratifies the risk of recurrence based on tumor type, cancer
stage, and history of VTE.[9] However, several studies have shown poor discrimination of this score (c-statistics
ranging from 0.5 to 0.7), which has limited its use in clinical practice.[10]
[11] In addition, this score provides a risk classification rather than an individualized
risk estimate. Therefore, we sought to derivate and validate a novel clinical prediction
model for recurrent VTE in cancer patients with acute VTE.
Methods
Study Selection
This report adheres to the Transparent Reporting of a multivariable prediction model
for Individual Prognosis or Diagnosis (TRIPOD) guidance for individual patient data
(IPD) meta-analysis ([Supplementary Table S1], available in the online version).[12] We identified RCTs that evaluated anticoagulant treatment in patients with cancer
and acute VTE up to 2021 based on previously published systematic reviews.[7]
[13] Studies were eligible if they included adult patients with active cancer (other
than basal-cell or squamous-cell skin cancer) and acute symptomatic or incidental
DVT or PE, and had at least 6 months of follow-up. Of eight identified trials[2]
[3]
[4]
[5]
[6]
[14]
[15]
[16] ([Supplementary Table S2], available in the online version), IPD were obtained from four studies: Hokusai
VTE cancer trial,[2] SELECT-D,[3] CATCH,[14] and CLOT.[15] These trials enrolled patients between 1999 and 2016. In all studies, active cancer
was defined as a cancer diagnosis or cancer treatment in the 6 months prior to the
first VTE event, or the presence of recurrent, regionally advanced, or metastatic
solid cancer, or hematological cancer not in remission. The primary efficacy outcome
was symptomatic or incidentally detected recurrent VTE in Hokusai VTE cancer and SELECT-D,
while only symptomatic events were considered in the primary efficacy outcome of the
CLOT and CATCH studies. In CLOT and CATCH, a vitamin K antagonist was compared with
LMWH (dalteparin and tinzaparin, respectively), while Hokusai VTE cancer and SELECT-D
trials compared an oral factor Xa inhibitor (edoxaban or rivaroxaban, respectively)
with LMWH (dalteparin). Since vitamin K antagonists are no longer recommended as treatment
for cancer-associated thrombosis,[17]
[18]
[19] patients allocated to these agents were excluded from the present analysis. The
primary outcome was recurrent on-treatment VTE, which was defined a symptomatic or
incidentally detected DVT or PE that was diagnosed during use of study treatment.
In the original studies, all outcome events were adjudicated without knowledge of
treatment allocation. In the present analysis, only events that were adjudicated by
the original study as being on-treatment were included. The definition of the on-treatment
period was from randomization until 24 to 72 hours after last intake of study drug.
Selection of Candidate Predictors and Model Development
Candidate predictors were selected based on their known association with a first or
recurrent VTE in the literature and their availability in the databases.[20]
[21]
[22] Based on the (modified) Ottawa score, breast and lung cancers were evaluated as
binary predictors. In addition, we also evaluated cancer types associated with the
risk of a first VTE, including hepatobiliary cancer, gynecological cancer, hematological
cancer, and genitourinary cancer excluding prostate cancer. In an explorative analysis,
cancer type was categorized based on the risk of a first VTE using the classification
proposed by Li and colleagues that includes[23] very high-risk cancer (pancreatic, gastroesophageal, bile duct, and gall bladder
cancer), high-risk cancer (lung, ovarian, uterine, bladder, kidney, testicular, primary
brain cancer, aggressive non-Hodgkin lymphoma, multiple myeloma, and soft tissue sarcoma),
intermediate-risk cancer (colorectal cancer), and low-risk cancer (all other cancers).
Other candidate predictors included age (continuous), sex, body weight (continuous),
platelet count of >350 × 109/L, use of antiplatelet agents, type of anticoagulant treatment (LMWH vs. DOAC), and
index VTE type (PE with or without DVT vs. DVT only). The following candidate predictors
were identified but could not be used because they were not available in all databases:
hemoglobin level, leukocyte count, smoking, ethnicity, anticancer treatment, and plasma
creatinine. Partially missing data for candidate predictors up to 15% were imputed
within studies using multiple imputation with chained equations, using a model that
included most baseline variables as well as outcomes.[24] Systematically missing data were not imputed.
Candidate predictors were first evaluated in a univariable logistic regression model
within each study. Odds ratios (ORs) were pooled in a random-effects meta-analysis
using the Hartung–Knapp method. Between-study heterogeneity was assessed for each
predictor and displayed using forest plots. Variables were used for model development
if there was no evidence of substantial heterogeneity. These candidate predictors
were subsequently included in a multivariable logistic regression (“full model”).
Restricted cubic splines restricted to three knots were used to evaluate whether transformation
of continuous variables was appropriate. Variables in the final model were selected
using stepwise backward selection using Akaike's information criterion (p < 0.157).[25] Discrimination of the model was evaluated by calculating the c-statistic. The c-statistic
can be calculated by using all possible pairs of patients where one patient experienced
VTE and the other patient did not. The c-statistic is the proportion of such pairs
in which the patient with VTE had a higher predicted probability of experiencing VTE
than the subject who did not have VTE. Calibration was assessed by calculating the
ratio between the number of observed and expected events (O:E ratio) and a calibration
plot in each study. Ideally, the O:E ratio should be 1. If the OE ratio is <1, the
model overestimates the probability of having recurrent VTE. If the O:E ratio is >1,
the model underestimates the probability of having recurrent VTE. The model was validated
using internal–external cross-validation, in which a new model was iteratively derived
in n − 1 studies and subsequently evaluated in the remaining study. Performance measures
were pooled across the internal–external cross-validation iterations by a random-effects
meta-analysis with restricted maximum likelihood estimation and the Hartung–Knapp–Sidik–Jonkman
method to calculate 95% confidence intervals (CIs).[26] Prediction intervals were calculated as a measure of between-study heterogeneity,
which indicates expected model performance when the prediction model is applied within
a specific study. All analyses were performed using R, version 2.2.1 (www.R-project.org).
Results
Characteristics of Study Group
Data from Hokusai VTE Cancer (n = 1,046), SELECT-D (n = 406), CLOT (n = 676), and CATCH (n = 914) were used (see [Supplementary Table S2] (available in the online version) for study details). These trials enrolled patients
from North-America, Europe, and Oceania. After exclusion of patients treated with
vitamin K antagonists from CLOT and CATCH, the combined IPD set comprised 2,245 patients.
The mean age was 63 years (standard deviation: 12) and 51% were female ([Table 1]). The most frequent cancer types were colorectal (17%), lung (13%), and breast cancer
(12%; [Supplementary Table S3], available in the online version). At randomization, 1,300 patients (59%) had metastatic
cancer. Patients were randomly allocated to edoxaban (23%), rivaroxaban (9%), dalteparin
(47%), or tinzaparin (20%). During 6 months of follow-up, 150 (6.7%) patients developed
on-treatment recurrent VTE including PE with or without DVT (54%), DVT only (45%),
or other VTE (1%), and 30.4% died ([Table 1]).
Table 1
Baseline characteristics stratified by study
|
Demographics
|
Overall (n = 2,245)
|
CATCH[14] (n = 455)
|
CLOT[15] (n = 338)
|
Hokusai[2] (n = 1,046)
|
Select-D[3] (n = 406)
|
|
Mean age, years (SD)
|
63.4 (11.8)
|
60.2 (12.9)
|
62.4 (11.7)
|
64.0 (11.3)
|
66.2 (10.6)
|
|
Male sex, n (%)
|
1,102 (49.1)
|
189 (41.5)
|
159 (47.0)
|
540 (51.7)
|
214 (52.7)
|
|
Mean weight, kg (SD)
|
75.6 (18.0)
|
67.2 (17.2)
|
73.6 (15.5)
|
78.9 (18.0)
|
78.4 (17.4)
|
|
ECOG performance score, n (%)[a]
|
|
0
|
591 (26.5)
|
88 (19.4)
|
80 (23.7)
|
303 (29.2)
|
120 (30.0)
|
|
1
|
1,066 (47.8)
|
257 (56.6)
|
135 (39.9)
|
489 (47.1)
|
185 (46.2)
|
|
2
|
569 (25.5)
|
109 (24.0)
|
118 (34.9)
|
247 (23.8)
|
95 (23.8)
|
|
3
|
5 (0.2)
|
0 (0.0)
|
5 (1.5)
|
0 (0.0)
|
0 (0.0)
|
|
Li cancer type risk classification, n (%)[b]
|
|
Very high-risk
|
298 (13.3)
|
60 (13.2)
|
18 (5.3)
|
143 (13.7)
|
77 (19.1)
|
|
High-risk
|
691 (30.8)
|
142 (31.2)
|
79 (23.4)
|
362 (34.6)
|
108 (26.7)
|
|
Intermediate-risk
|
385 (17.2)
|
68 (14.9)
|
52 (15.4)
|
162 (15.5)
|
103 (25.5)
|
|
Low-risk
|
867 (38.6)
|
185 (40.7)
|
187 (55.3)
|
379 (36.2)
|
116 (28.7)
|
|
Hematological cancer, n (%)
|
226 (10.1)
|
44 (9.7)
|
38 (11.2)
|
111 (10.6)
|
33 (8.2)
|
|
Metastatic disease, n (%)
|
1,300 (58.8)
|
250 (54.9)
|
223 (66.0)
|
595 (58.2)
|
232 (58.6)
|
|
Use of antiplatelets, n (%)
|
177 (8.0)
|
46 (10.1)
|
54 (16.0)
|
44 (4.3)
|
33 (8.1)
|
|
Platelet count >350 × 109/L, n (%)
|
371 (16.6)
|
102 (22.6)
|
73 (22.0)
|
126 (12.1)
|
70 (17.2)
|
|
Index event, n (%)
|
|
PE ± DVT
|
1,209 (54%)
|
195 (42.9%)
|
103 (30.5%)
|
657 (62.8%)
|
295 (72.6%)
|
|
DVT only
|
1,036 (46%)
|
257 (56.0%)
|
235 (69.5%)
|
389 (37.2%)
|
111 (27.4%)
|
|
VTE treatment, n
|
|
Edoxaban
|
522 (23.3)
|
0
|
0
|
522
|
0
|
|
Rivaroxaban
|
203 (9.0)
|
0
|
0
|
0
|
203
|
|
Dalteparin
|
1,065 (47.4)
|
0
|
338
|
524
|
203
|
|
Tinzaparin
|
455 (20.3)
|
455
|
0
|
0
|
0
|
|
Recurrent VTE on treatment, n (%)
|
150 (6.7)
|
31 (6.8)
|
27 (8.0)
|
66 (6.3)
|
26 (6.4)
|
|
Recurrent VTE type, n (%)
|
|
PE ± DVT
|
81 (54.0)
|
20 (64.5)
|
13 (48.1)
|
35 (53.0)
|
13 (50.0)
|
|
DVT
|
67 (44.7)
|
11 (35.5)
|
14 (51.9)
|
31 (47.0)
|
11 (42.3)
|
|
Other
|
2 (<0.1)
|
0 (0.0)
|
0 (0.0)
|
0 (0.0)
|
2 (7.7)
|
|
All-cause mortality
|
925 (30.4%)
|
150 (33.4%)
|
130 (38.5%)
|
267 (25.5%)
|
104 (25.6%)
|
Abbreviations: DVT, deep vein thrombosis; ECOG, Eastern Cooperative Oncology Group;
PE, Pulmonary embolism; SD, standard deviation; VTE, venous thromboembolism.
a 14 patients had missing data on ECOG performance status score.
b Very high-risk cancer types: pancreatic, gastroesophageal, bile duct, and gall bladder
cancer; high-risk cancer types: lung, ovarian, uterine, bladder, kidney, testicular,
primary brain cancer, aggressive non-Hodgkin lymphoma, multiple myeloma, and soft
tissue sarcoma; intermediate-risk cancer type: colorectal cancer; low-risk cancers
are all other cancer types. For two patients in the CLOT and two patients in the SELECT-D
trial, data on cancer type was missing.
Candidate Predictors
[Supplementary Fig. S1] (available in the online version) and [Supplementary Table S4] (available in the online version) show the association between the 15 candidate
predictors and recurrent VTE in each study. [Table 2] shows the results from the univariable logistic regression model. The candidate
predictors with the strongest association with recurrent VTE were DVT only at randomization
(OR: 1.80; 95% CI: 1.29–2.52, I
2 = 0%), breast cancer (OR: 0.41; 95% CI: 0.20–0.84, I
2 = 0%), and treatment with a DOAC (OR: 0.57; 95% CI: 0.38–0.85, I
2 = 0%; [Table 2]).
Table 2
Univariable and multivariable odds ratios for prediction of on-treatment recurrent
VTE
|
Model to predict on-treatment recurrent VTE
|
Univariable odds ratio (95% CI)
|
Multivariable odds ratio (95% CI)
|
p-Value multivariable odds ratios
|
|
Age 1 (restricted cubic spline)
|
0.98 (0.96–1.01)
|
0.99 (0.96–1.01)
|
0.22
|
|
Age 2 (restricted cubic spline)
|
0.98 (0.95–1.02)
|
0.98 (0.95–1.02)
|
0.31
|
|
Presence of metastasis
|
1.40 (0.85–2.30)
|
1.44 (1.01–2.05)
|
0.05
|
|
Breast cancer
|
0.41 (0.20–0.84)
|
0.42 (0.20–0.87)
|
0.02
|
|
Treatment with a DOAC
|
0.57 (0.38–0.85)
|
0.66 (0.44–0.98)
|
0.04
|
|
Index event is DVT only
|
1.80 (1.29–2.52)
|
1.72 (1.31–2.27)
|
<0.01
|
|
Other candidate predictors excluded during backward selection
|
|
ECOG performance score 1 or 2
|
1.23 (0.83–1.83)
|
n.a.
|
n.a.
|
|
Male sex
|
1.13 (0.81–1.58)
|
n.a.
|
n.a.
|
|
Use of antiplatelets
|
0.80 (0.37–1.47)
|
n.a.
|
n.a.
|
|
Platelet count > 350 × 109/L
|
0.98 (0.62–1.54)
|
n.a.
|
n.a.
|
|
Weight in kg
|
1.01 (0.97–1.01)
|
n.a.
|
n.a.
|
|
Lung cancer
|
0.99 (0.60–1.62)
|
n.a.
|
n.a.
|
|
Hepatobiliary cancer
|
1.53 (0.89–2.63)
|
n.a.
|
n.a.
|
|
Gynecological cancer
|
1.39 (0.89–2.17)
|
n.a.
|
n.a.
|
|
Urogenital cancer excluding prostate cancer
|
1.29 (0.68–2.45)
|
n.a.
|
n.a.
|
|
Hematological cancer
|
0.76 (0.41–1.40)
|
n.a.
|
n.a.
|
|
Li cancer risk classification (reference = low risk)
|
|
Very high risk
|
1.47 (0.90–2.40)
|
n.a.
|
n.a.
|
|
High risk
|
1.12 (0.75–1.68)
|
n.a.
|
n.a.
|
|
Intermediate risk
|
1.02 (0.62–1.68)
|
n.a.
|
n.a.
|
Abbreviations: CI, confidence interval; DOAC, direct oral anticoagulant; DVT, deep
vein thrombosis; ECOG, Eastern Cooperative Oncology Group; VTE, venous thromboembolism.
Prediction Model
All candidate predictors were included in the full model. After stepwise backward
selection, the following five predictors were retained in the final multivariable
logistic regression model: age (continuous), breast cancer, metastatic disease, DOAC
or LMWH treatment, and DVT only as an index event ([Table 2]; formula provided in [Supplementary Table S5], available in the online version). The pooled apparent c-statistic of the model
was 0.66 (95% CI: 0.61–0.70), which decreased to 0.63 (95% CI: 0.54–0.72; 95%, summary
of confidence interval and prediction interval: 0.22–0.91) after internal–external
cross-validation ([Fig. 1]). Calibration-in-the-large was good with a ratio between observed and expected outcomes
of 1.01 (95% CI: 0.85–1.21; [Fig. 2]). Calibration across the studies varied though ([Supplementary Fig. S2], available in the online version), with poor calibration in the CLOT and CATCH trials
and better calibration in the Hokusai VTE Cancer and SELECT-D. Specifically, the model
underestimated recurrent VTE risk in SELECT-D trial and overestimated the risk in
the CATCH trial.
Fig. 1 C-statistics and prediction interval in internal–external cross-validation.
Fig. 2 Calibration plot. Calibration in one imputed dataset is shown.
Discussion
Using IPD from four RCTs including more than 2,000 patients with cancer and acute
VTE, five clinical predictors of recurrent on-treatment VTE were identified. The strongest
predictors were DVT only (OR: 1.80), breast cancer (OR: 0.41), and treatment with
a DOAC compared to LMWH (OR: 0.57). The clinical prediction model for the 6-month
risk of on-treatment recurrent VTE including these five predictors had modest discrimination
(c-statistic 0.63 after internal–external cross-validation) and calibration was inconsistent.
The Ottawa risk score is currently the only validated tool for assessment of the risk
of recurrence after cancer-associated VTE.[11] The score's items include sex, previous VTE, cancer stage, and cancer type (breast
or lung cancer). Two versions of the score have been developed: the original score
that classifies patients as low or high risk, while the modified Ottawa score also
includes an intermediate-risk group. Unfortunately, we were not able to formally evaluate
the performance of the Ottawa scores since data on TNM classification were not collected
in all RCTs. A systematic review and meta-analysis demonstrated that discrimination
of the original (c-statistic 0.7; 95% CI: 0.6−0.8) and modified Ottawa scores (c-statistic
0.5; 95% CI: 0.5–0.6) is comparable to that of the clinical prediction model presented
here.[11]
Another prediction model for cancer-associated recurrent VTE was recently developed
using Spanish electronic health record data from 16,407 cancer patients.[27] After feature selection and model training using machine learning, the items included
in the model were age, previous VTE, VTE type, metastasis, adenocarcinoma, hemoglobin
and serum creatinine levels, and platelet and leukocyte count. Discrimination of the
model was also modest, with c-statistics ranging between 0.66 and 0.69 depending on
the statistical technique used. Although this retrospective derivation study was well-powered,
it is unclear how many events occurred during anticoagulant treatment and what the
positive predictive value of the administrative codes used for recurrent VTE was.
The model has not been externally validated yet. Unfortunately, we were also unable
to validate this model due to missing information in our dataset, in particular several
laboratory data were not available.
Tumor type is by far the strongest predictor for a first episode of cancer-associated
VTE, but the prognostic value of tumor type for recurrent VTE is less clear.[28] A large Danish population-based cohort including 34,072 patients with cancer and
a first VTE diagnosis identified cancer type as a predictor for recurrent VTE, but
the associations were generally weak.[29] The strongest association was observed for genitourinary (subdistribution hazard
ratio [HR]: 1.35; 95% CI: 1.06–1.71) and lung cancer (subdistribution HR: 1.26; 95%
CI: 1.03–1.53). In the present study, only breast cancer was retained as a protective
risk factor in the final model for recurrent VTE. Discrimination was not improved
when the validated tumor risk classification for a first VTE proposed by Li and colleagues
was used.[23] Similarly, cancer type was not retained in the aforementioned model by Muñoz and
colleagues. These findings suggest that the association between cancer type and a
first VTE is stronger than that with a recurrent VTE, a similar phenomenon previously
observed for hereditary thrombophilia that has been attributed to collider bias.[30] Whether a specific cancer type risk classification for recurrent VTE improves prediction
needs further study.
The current study had several strengths. We were able to obtain high quality patient-level
data from the four open-label RCTs that were reasonably homogeneous in design and
outcome definitions. The proportion of missing data was low, few patients were lost
to follow-up, and all recurrent thromboembolic events were adjudicated. The number
of outcome events per variable included in the full model was about 27, which is generally
believed to be sufficient for model development. The internal–external cross-validation
procedure allowed us to validate the model using all available data unlike a split-sample
approach.
Some limitations merit consideration. First, we were not able to assess other potential
predictors of recurrent VTE, such as cancer stage, kidney function, hemoglobin levels,
leukocyte count, history of VTE, and cancer treatment, as they were missing in one
or more studies. Platelet count had to be used dichotomously because continuous data
were not available in all studies. Second, we could not directly compare the performance
of the present model to other previously developed risk assessment tools such as the
Ottawa score, because of missing predictors in our database. Third, we did not have
access to data from more recent trials, such as CANVAS or Caravaggio.[5]
[6] Fourth, we only used data from RCTs which can limit generalizability. The strict
eligibility criteria used in the clinical trials likely resulted in patients with
a better prognosis than in the general population, with unclear potential effect on
the performance of the model. External validation of the model in other settings would
be needed. Fifth, participants in CATCH and CLOT were enrolled more than 10 to 20
years ago respectively, with resulting differences in cancer treatment, follow-up
(e.g., staging scans), and diagnostic procedures for VTE compared with the Hokusai
VTE cancer and SELECT-D trials. Also, there was some variation in the definition of
recurrent VTE across the trials. In CLOT, incidental VTE was not considered in the
primary outcome. Hokusai VTE cancer and CATCH adjudicated unexplained death as fatal
PE, since PE could not be ruled out. These differences may have led to the poor calibration
observed in the CATCH and CLOT trials. Furthermore, the discriminatory ability of
the final model was lower in the CATCH trial compared with the other three trials,
which might be explained by differences in case mix (e.g., differences in cancer type
with other recurrent VTE rates), differences in treatment (e.g., full-dose LMWH in
CATCH control group compared to maintenance-dose LMWH in the other trials), differences
in outcome definition (about half of recurrent VTE in CATCH were deaths for which
PE could not be ruled out), or just chance.
Discrimination of the present prediction model for recurrent VTE was not better than
that of the (modified) Ottawa score nor the model by Muñoz et al.[11]
[27] Discrimination of all these models is modest at best (c-statistics ≤ 0.70), but
comparable to performance of a prediction model for recurrent VTE in the general population.[31] Prediction of recurrent VTE is challenging because it is often provoked by factors
that occur during anticoagulant treatment, such as surgery, changes in systemic anticancer
therapy, hospitalization for an acute medical illness, or cancer progression. Other
contributing factors include interruptions of anticoagulation for surgery or bleeding
and adherence, which may be lower for LMWH than for DOACs. Such factors cannot be
incorporated in statistical prediction models that are applied only once at baseline.
Dynamic prediction models can overcome this limitation by allowing periodic reassessment,
but they are much harder to develop and validate. Extending the clinical model with
plasma biomarkers, such as soluble P-selectin, may improve prediction at start of
anticoagulation at the cost of adding complexity.[32]
Another important point is the timing of applying a prediction model to guide treatment
decisions. Patients classified as being at high risk of recurrent at the index VTE
should probably not have a LMWH dose reduction at 1 month, but it is less clear if
such patients should also continue full-dose anticoagulation beyond 3 to 6 months.
Ideally, a new assessment at 3 to 6 months is needed to guide this decision, which
is of particular interest given the upcoming studies that evaluate a low-dose DOAC
for secondary prevention in cancer patients, such as the API-CAT trial (NCT03692065)
and EVE trial, as well as trials evaluating factor XI inhibitors.[33] Accurate prediction of recurrent VTE at different time points during the course
of the disease remains an important unmet need.
In conclusion, we have developed a prediction model with five predictors using the
IPD of four RCTs. However, discrimination of the final clinical prediction model was
modest, indicating that prediction of cancer-associated recurrent VTE at diagnosis
of acute VTE remains challenging and that other contributing factors need to be identified.
What is known about this topic?
-
Recurrent on-treatment venous thromboembolism is a common complication of cancer-associated
thrombosis.
-
The Ottawa scores are validated scores for prediction of recurrent cancer-associated
thrombosis, but the use of the scores in clinical practice is limited due to modest
discriminatory ability.
What does this paper add?
-
This IPD meta-analysis of four large randomized controlled trials identified clinical
predictors for recurrent cancer-associated thrombosis before start of anticoagulant
treatment.
-
We derived a clinical prediction model based on age, breast cancer, metastatic disease,
treatment with a DOAC, and DVT only as index events.
-
The model only had modest discriminatory performance, highlighting the need for new
risk assessment tools for recurrent cancer-associated thrombosis during treatment.
