Table 3.

Results of mutivariable GEE model, odds ratios of warfarin usage within 30-day periods within 1 year pre- and post- injury (no. of patients=4622)

Comparison	Odds ratio of warfarin usage (95%CI)	P value
TBI
After injury vs. before injury	0.41(0.36 to 0.46)	<0.001
Hip fracture
After injury vs. before injury	0.93(0.87 to 1.00)	0.051
Major torso
After injury vs. before injury	0.69(0.60 to 0.80)	<0.001
Age at injury
(Five years increase)	0.84(0.80 to 0.87)	<0.001
Race
White vs. other	1.53(1.06 to 2.23)	0.025
Black vs. other	0.62(0.35 to 1.10)	0.101
Sex
Male vs. female	0.96(0.84 to 1.11)	0.592
Comorbid conditions (yes vs. no)
Valvular heart disease/valve replacement	1.03(0.91 to 1.17)	0.597
Atrial flutter	1.29(1.09 to 1.52)	0.003
Heart failure	1.17(1.07 to 1.28)	0.001
Stroke or TIA	0.98(0.87 to 1.09)	0.683
Chronic liver disease or alcohol abuse	0.82(0.70 to 0.96)	0.012
Chronic kidney disease	0.97(0.87 to 1.09)	0.609
Coagulation defect	1.19(1.00 to 1.42)	0.056
No. of other CCW chronic conditions (each additional condition)	1.01(0.98 to 1.04)	0.628

Note:

1. Abbreviations: TIA, transient ischemic attack; TBI, traumatic brain injury; CCW: Chronic Conditions Data Warehouse; GEE, generilzed estimating equations
2. We combined chronic liver disease with alchohol abuse, two similar disease condition categories with extremely small number of patients.
3. In multivariable regression model, we tested for the statistical significance of interaction terms between time (after injury vs. before injury) and injury type, age, sex, race and other confounders. The only statistically significant interaction was time*injury type.