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Risk assessment for VTE
Dr Roopen Arya
King’s College Hospital
V
Prevention of VTE in hospitalised patients:
Why the need for risk assessment for VTE?
Identifying at-risk patient
Counselling at-risk patient
Prescribe
thromboprophylaxis
Risk Assessment
• The highest ranking safety practice was the
appropriate use of prophylaxis to prevent VTE in
patients at risk.
AHRQ “Making Health Safer: A Critical Analysis of Patient Safety
Practices” 2001
• We recommend that every hospital develop a
formal strategy that addresses the prevention of
thromboembolic complications. This should
generally be in the form of a written
thromboprophylaxis policy especially for high
risk groups.
ACCP guidelines “ Prevention of VTE” 2004
Risk assessment models
• Group-specific (‘opt-out’)
• Individualized (‘opt-in’)
– Risk stratification
– Risk scores
• Linked to ACTION of thromboprophylaxis
VTE risk assessment in medical patients
VTE Risk Assessment for Adult Surgical Patients
Patient name:
Hospital no:
Please fill in this form, sign and file in notes
Prescribe appropriate prophylaxis on drug chart
DOB:
Risk
Category
Surgery
HIGH
MODERATE
LOW
Tick
Recommended Prophylaxis
Hip fracture, hip or knee
arthroplasty
Major trauma /spinal cord
injury
Major surgery with
additional risk factors (ARF)
Major surgery upto age 59
years with no ARF
Minor surgery with ARF
Enoxaparin 40 mg daily
+
TED stockings
+/Sequential compression device
Minor surgery with no ARF
Early mobilisation
Tick
Enoxaparin 40 mg daily
+
TED stockings
Additional Risk Factors (ARF)
Age >60 years
Personal or family history of VTE
Thrombophilia
Active cancer or treatment
Acute exacerbation of heart failure
Recent MI or ischaemic stroke
Acute on chronic respiratory disease
Sepsis
Contraindications
Enoxaparin
Creatinine >175 mol/l (CrCl< 30ml/min)
use unfractionated heparin 5000 u BD
Active bleeding
Thrombocytopenia (platelet count<50)
Known bleeding disorder
Previous HIT or allergy to enoxaparin
On therapeutic anticoagulation
Tick Additional Risk Factors (ARF)
Tick
Acute inflammatory disorder
Pregnancy and the post partum period
Hormone therapy e.g. HRT/COCP
Obesity (BMI >30kg/m2)
Immobility
Travel>3 hrs within 4 weeks of surgery
Nephrotic syndrome
Varicose veins
Tick Contraindications
Tick
Mechanical measures (TEDs / SCD)
SCD contraindicated if acute DVT present
Severe peripheral vascular disease
Severe dermatitis
Leg oedema
Leg deformity
Peripheral neuropathy
Recent skin graft
Doctor’s name
Doctor’s signature
Timing:
Duration:
Date
Thromboprophylaxis should start 6 hours post op and at 6pm daily thereafter.
Epidural/spinal analgesia - placement or removal of catheter should be delayed for 12 hrs after
administration of enoxaparin. Enoxaparin should not be given sooner than 4 hrs after catheter
removal.
At least 10 days prophylaxis is recommended for all high risk orthopaedic patients.
Extended prophylaxis (28 days) is recommended for elective hip replacement and hip fracture
patients.
Extended prophylaxis is recommended for selected high-risk general surgery patients e.g. major
cancer surgery.
2
High BMI (>30 mg/m ): use enoxaparin 40mg twice daily (or enoxaparin 60 mg bd if body weight >150kg)
Sequential compression device (SCD): Consider in high-risk patients & those unable to receive LMWH due to
high bleeding risk.
VTE risk assessment
in surgical patients
Risk scoring for VTE: Kucher risk score
Clinical Feature
Score
Active cancer (treatment ongoing or within 6 months or palliative)
3
Personal history of VTE
3
Thrombophilia
3
Recent major surgery
2
Advanced age (≥ 75 years)
1
Obesity (BMI >29)
1
Bed rest (medical inpatient/immobilized >3d in last 4 wks/paralysis)
1
Hormonal therapy (OCP/HRT)
1
Kucher, N. et al. N Engl J Med 2005;352:969-977
Primary end point: Freedom from VTE
100
Freedom from
DVT or PE (%)
98
Intervention
96
94
P < 0.001
92
Control
90
Number at risk
Intervention
Control
0
30
60
90
1255
1251
977
976
900
893
853
839
Kucher, N. et al. N Engl J Med 2005;352:969-977
Time
(days)
Derivation and Validation
of a Prediction Tool for
Venous Thromboembolism (VTE):
a VERITY Registry Study
Study objective
• to develop a multiple regression model for
VTE risk, based on Kucher, and validate its
performance
• to employ the extensive VTE risk factor
data recorded in a UK VTE treatment
registry (VERITY)
– VERITY enrolls patients presenting to hospital with
suspected VTE
UK multi-centre observational VTE registry of
clinical management practices & patient outcomes
Features of VERITY
• National registry – outpatient VTE treatment
• Full spectrum of VTE – DVT and PE
• Records information on patients presenting
with suspected and confirmed VTE
• Expanded data on demographics,
presentation, management & outcomes
• Extensive risk factor data
Statistical plan – model development
• As a preliminary to a formal multiple regression
analysis, the effects of the 8 Kucher risk factors on
VTE risk were investigated individually by univariate
analysis
• Initial findings: univariate analysis (n=5928; 32.4% with
diagnosis of VTE) suggested VTE risk was not accounted
for by the 8 Kucher risk factors
• An additional 3 risk factors were added (leg paralysis,
smoking, IV drug use) and also patient sex, and the
model was created with these 12 factors
Statistical plan – model development
• The multiple logistic regression model was
developed using backward stepwise regression
• The open source statistical package ‘R’ was
employed to conduct the regression analysis
Statistical plan – model performance
• We tested the accuracy of the Kucher score and
the new logistic regression model to classify
patients by receiver operating characteristic (ROC)
curve analysis, plotted as 1-specificity versus
sensitivity for VTE diagnosis
– The c statistic (area under the curve), representing the
ability of the model to correctly classify patients, was
estimated using the nonparametric method of Hanley
and McNeil
• We validated the model using a risk factor
database of patients enrolled at an outpatient
DVT clinic at King’s College Hospital
Statistical plan – model performance
• We interpreted the predicted probabilities from
the logistic regression model as a risk score
– each tenth of predicted risk was scored as 1
• i.e. lower tenth of risk = risk score of 1; upper tenth of risk = risk
score of 10
• We assessed the degree of agreement between
the observed rate and the predicted rate of VTE
by plotting the risk score vs. observed VTE rate
– Differences in the rates of VTE vs. increasing risk score
were assessed using the χ2 test for trend
Results - study populations
VERITY
n=55996
DVT O/P
KCH
n=928
Assessment cohort (n=5938)
8 risk factors known
VTE status known
Univariate regression
analysis
Development cohort (n=5241)
12 risk factors known
VTE status known
Multiple regression
analysis
Validation Cohort (n=915)
12 risk factors known
VTE status known
Results – baseline characteristics
Assessment, development and validation cohorts
Assessment
Development
Validation
VTE negative
VTE positive
VTE negative
VTE positive
VTE negative
VTE positive
(N=4019)
(N=1919)
(N=3563)
(N=1678)
(N=712)
(N=203)
N
N
N
%
N
%
N
%
N
%
%
%
Female sex
2530
62.95
906
47.21
2254
63.26
777
46.31
497
69.80
110
54.19
Age75
1009
25.11
442
23.03
896
25.15
386
23.00
108
15.17
35
17.24
BMI30
1610
40.06
602
31.37
1446
40.58
527
31.41
70
9.83
8
3.94
Medical Inpatient
176
4.38
219
11.41
151
4.24
172
10.25
33
4.63
34
16.75
Major surgery
281
6.99
254
13.24
244
6.85
224
13.35
37
5.2
27
13.3
Hormonal factor
442
11.00
187
9.74
389
10.92
166
9.89
59
8.29
21
10.34
Personal History
524
13.04
503
26.21
458
12.85
454
27.06
139
19.52
43
21.18
Thrombophilia
25
0.62
42
2.19
21
0.59
40
2.38
9
1.26
3
1.48
Leg paralysis
154
3.83
145
7.56
124
3.48
121
7.21
4
0.56
2
0.99
Cancer
158
3.93
234
12.19
145
4.07
207
12.34
23
3.23
20
9.85
Smoking
NK
NK
NK
NK
704
19.76
430
25.63
83
11.66
33
16.26
IV drug use
NK
NK
NK
NK
21
0.59
101
6.02
10
1.40
9
4.43
Results – risk factor findings in multiple
logistic regression model
β coefficient
SE
P value
Sex Male
1.044
0.086
<0.001
2.840 (2.40, 3.36)
Age 75
0.477
0.107
<0.001
1.611 (1.31, 1.99)
Obesity
-0.334
0.071
<0.001
0.716 (0.62, 0.82)
Inpatient
1.100
0.155
<0.001
3.005 (2.22, 4.07)
Surgery
1.116
0.169
<0.001
3.054 (2.19, 4.25)
Hormonal risk factor
0.309
0.166
0.063
1.361 (0.98, 1.89)
Previous history
1.075
0.084
<0.001
2.931 (2.49, 3.45)
Leg paralysis
1.337
0.220
<0.001
3.809 (2.47, 5.86)
Cancer
1.670
0.178
<0.001
5.314 (3.75, 7.53)
Intravenous drug abuse
2.660
0.324
<0.001
14.302 (7.58, 26.98)
Smoking
0.174
0.066
0.009
1.190 (1.05, 1.35)
Known thrombophilia
1.024
0.299
<0.001
2.784 (1.55, 5.00)
Model Terms
OR (95% CI)
Pair-wise interactions for VTE risk in multiple
logistic regression model
β coefficient
SE
P value
Sex Male x Age 75
-0.937
0.156
<0.001
0.392 (0.29, 0.53)
Sex Male x Surgery
-0.498
0.217
0.022
0.608 (0.40, 0.93)
Sex Male x Leg paralysis
-0.976
0.299
0.001
0.377 (0.21, 0.68)
Sex Male x Cancer
-0.699
0.242
0.004
0.497 (0.31, 0.80)
Age 75 x Surgery
-0.428
0.265
0.106
0.651 (0.39, 1.10)
Age 75 x Leg paralysis
-1.085
0.368
0.003
0.338 (0.16, 0.70)
Obesity x Hormone
0.685
0.255
0.007
1.983 (1.20, 3.27)
Inpatient x Surgery
-1.184
0.279
<0.001
0.306 (0.18, 0.53)
Hormone x Previous history
-1.167
0.382
0.002
0.311 (0.15, 0.66)
Hormone x Cancer
-1.009
0.534
0.059
0.364 (0.13, 1.04)
Previous history x Drug
-1.009
0.559
0.071
0.365 (0.12, 1.09)
Cancer x Drug
-3.580
0.918
<0.001
0.028 (0.00, 0.17)
Model Terms
OR (95% CI)
Receiver operating characteristic (ROC)
curves for risk score prediction of VTE
Kucher (––)
c statistic 0.617
95% CI
0.599–0.634
VERITY (- - -)
c statistic 0.720
95% CI
0.705–0.735
VERITY significantly
better than Kucher
(p<0.001)
Proportion of patients with VTE
vs. risk score
Strong positive correlation
between an increasing risk score
and the percentage of VTE-positive
cases in the development cohort
VERITY
score
(P<0.001
by χ2 testrisk
for trend).
Kucher risk score
Validation cohort: ROC curves for risk
score prediction of VTE
Kucher (––)
c statistic 0.587
95% CI
0.542–0.632
VERITY (- - -)
c statistic 0.678
95% CI
0.635–0.721
VERITY c statistic no
different from
development cohort
(p=NS)
Conclusions
• The c statistic for this VERITY risk model (0.72)
indicates a good test for likelihood of VTE diagnosis
• This VERITY risk model was superior to Kucher for
predicting the likelihood of a diagnosis of VTE in a
cohort in whom the diagnosis was suspected
• This risk model was validated in an independent
VTE database
• A prospective study is required to determine clinical
value as a risk prediction tool for VTE at the time of
hospital admission to assist in assessing prophylaxis
needs