DynamicVPM (Vienna Prediction Model)

D-Dimer levels over time and the risk of

recurrent venous thromboembolism:

An update of the Vienna Prediction Model

Sabine Eichinger, Georg Heinze, Paul A. KyrleDept. of Medicine I & Center for Medical Statistics

Medical University of Vienna, Austria

Background I

• Patients with unprovoked venous thromboembolism (VTE)

are at increased risk of recurrence.

Kyrle, Lancet 2010

Recurrence risk after unprovoked VTE

Background II

• Patients with unprovoked venous thromboembolism (VTE)

are at increased risk of recurrence.

• By use of the „Vienna Prediction Model“ patients with

unprovoked VTE can be further stratified according to their

recurrence risk.

Points 0 10 20 30 40 50 60 70 80 90 100

Sexfemale

male

Locationdistal DVT pulmonary embolism

proximal DVT

DDimer (µg/l)100 150 200 250 400 500 750 1000 1500 2000

Total Points 0 50 100 150 200 250 300 350

12 months cumulative recurrence rate0.02 0.04 0.06 0.08 0.1 0.12 0.15

60 months cumulative recurrence rate0.1 0.2 0.3 0.4 0.5

Nomogram to predict recurrence60 9040

190

24%Eichinger, Circulation 2010

Study aim

• To expand the “Vienna Prediction Model” in

order to assess the recurrence risk also

from later time points on

Inclusion

• > 18 yrs

• First VTE

• OAC > 3 mo

• Objective Dx of VTE

Exclusion

• VTE provoked by

surgery, trauma,

pregnancy, female

hormone use

• AT-, PC-, PS-deficiency

• Lupus anticoagulant

• Cancer

• Antithrombotics

Patients

D-Dimer measurements and follow-up

OACstop

3 15months

9 24

D-Dimer

3 wbaseline

• Preselected variables: sex, location of VTE, D-Dimer

• Competing risk regression model to predict cumulative incidence

of recurrence using all variables

• Estimated a series of models to predict cumulative recurrence from

various time points after baseline

• Each model uses most recent D-Dimer values

“Dynamic Vienna Prediction Model”

Statistical analysis

Patient characteristics (n = 553)

Age (yrs); median (25th, 75th P) 53 (43, 62)

Women; n 219 (40%)

Location of first VTE; n

PE + proximal DVT distal DVT

464 (84%)

89 (16%)

BMI (kg/m2); median (25th, 75th P) 27.2 (24.4, 30.0)

F V Leiden; n 126 (23%)

F II G20210A; n 27 (5%)

Duration of anticoagulation (mo); median (25th, 75th P)

6.7 (6.2, 8.5)

Observation time (mo), median (25th, 75th P) 68 (46, 98)

Time Patients, n D-Dimer (µg/L)median (25th, 75th percentile)

Baseline 553 338 (226, 551)

3 months 534 339 (227, 551)

9 months 494 356 (239, 557)

15 months

457 363 (237, 572)

24 months

415 375 (245, 610)

D-dimer levels over time after anticoagulation

Subdistribution hazard ratios (SHR) for recurrent VTE

Variable Time point SHR (95% CI)

Male vs. female sex Baseline 2.4 (1.6, 3.8)

3 months 2.3 (1.5, 3.5)

9 months 2.0 (1.3, 3.0)

15 months 1.7 (1.1, 2.7)

Proximal DVT or PE

vs. distal DVT

Baseline 1.8 (1.0, 3.4)

3 months 1.7 (0.9, 3.1)

9 months 1.5 (0.8, 2.8)

15 months 1.4 (0.8, 2.7)

D-Dimer (per doubling) Baseline 1.3 (1.1, 1.6)

3 months 1.3 (1.1, 1.5)

9 months 1.2 (1.0, 1.4)

15 months 1.1 (0.9, 1.4)

Nomogram to predict recurrence from

3 months0 27 50

14%

Nomogram to predict recurrence from

9 months

Cross-validation

Summary

• In patients with a first unprovoked VTE D-Dimer levels do not

substantially change over time after anticoagulation.

• The effect of risk factors on the recurrence risk may change over

time (e.g., effect of male sex and location of first VTE weakened).

• By integrating patient’s sex, location of VTE and serial D-Dimer

measurements the recurrence risk after anticoagulation can be

assessed not only after 3 weeks but also from later time points on.

Conclusion

• The “ Dynamic Vienna Prediction Model” allows predicting the

recurrence risk from various later time points after VTE which

provides greater flexibility in counseling patients regarding their

individual recurrence risk and optimal anticoagulation.