Skip to main content
. 2018 Oct 17;2(20):2681–2690. doi: 10.1182/bloodadvances.2018021436

Table 3.

Competing risk models for thrombosis after PV diagnosis by treatments and patient characteristics (n = 820)

Characteristic* Model 1 Model 2
HR 95% CI P HR 95% CI P
Phlebotomy
 No 1.00
 Yes 0.52 0.42-0.66 <.01
Phlebotomy intensity, times per year 0.46 0.29-0.74 <.01
HU PDC, every 10% 0.92 0.89-0.96 <.01 0.92 0.88-0.95 <.01
Age, y 1.01 0.99-1.02 .55 1.01 0.99-1.03 .27
Sex
 Female 1.00 1.00
 Male 0.94 0.74-1.20 .61 0.98 0.77-1.24 .86
Race
 White 1.00 1.00
 Nonwhite 0.74 0.46-1.20 .23 0.85 0.53-1.38 .52
Modified Elixhauser score
 0 1.00 1.00
 1 1.28 0.94-1.75 .11 1.30 0.95-1.77 .10
 ≥2 1.37 1.01-1.86 .05 1.38 1.03-1.86 .03
Prior thrombosis
 No 1.00 1.00
 Yes 1.17 0.82-1.67 .40 1.01 0.71-1.43 .98
Disability
 No 1.00 1.00
 Yes 0.98 0.66-1.45 .92 .01 0.69-1.47 .98
Low-income subsidy
 No 1.00 1.00
 Yes 1.48 1.10-1.99 .01 1.38 1.03-1.85 .03
Influenza vaccination within 12 mo prior to PV diagnosis
 No 1.00 1.00
 Yes 0.99 0.78-1.26 .96 0.98 0.78-1.25 .88
*

All variables in the table were simultaneously included in the same model. The only difference between models 1 and 2 was that model 1 included phlebotomy as a binary variable and model 2 included the intensity of phlebotomy (number of phlebotomies each year).