Phenprocoumon is the second most commonly used oral anticoagulant worldwide
and the most common agent in many European countries including Switzerland.
Given its long half-life of about one week, an initial loading-dose is generally applied.
A high loading-dose is helpful to rapidly reach a therapeutic concentration but may be
associated with an increased risk of bleeding if the effect overshoots.
Phenprocoumon has a narrow therapeutic range, and individual dose requirements
are highly variable. In clinical practice the initial dose-finding process for
phenprocoumon is largely empiric and often delegated to inexperienced staff
members. Thus, both a prolonged loading phase and overshooting of anticoagulation
is commonly observed.
Question under study
The general aim of the thesis was to define one or more algorithms for the loading
phase of phenprocoumon-treatment. These algorithms should be easily applicable in
a clinical setting and help to improve the drug safety of phenprocoumon especially
during the initial dose-finding process.
Retrospective study
In a retrospective study, predictors of individual dosing needs for a target-INR of 2.0
to 3.0 in medical and orthopaedic inpatients were determined. Several significant
predictors of the loading dose could be identified. Using these predictors two simple
clinical algorithms for the initial dosing of phenprocoumon in medical and orthopedic
inpatients were developed. One algorithm contains clinical data and, additionally,
serum albumin; the second algorithm contains clinical data only.
Prospective study
The aim of the prospective, randomized interventional study was to validate the efficacy and
safety of the two previously proposed dosing algorithms for the initiation of oral
anticoagulation with phenprocoumon. Additionally, the predictive value of
pharmacogenetic markers was to be studied.
Both algorithms could be validated and were slightly optimized. They proved to be
very safe and effective in hospitalized patients with a high rate of comorbidity. The
algorithm using clinical data can be especially recommended due to its simplicity of
use
