BACKGROUND AND OBJECTIVES: We carried out a prospective, randomized trial to test whether a computer-based decision support system to initiate and maintain oral anticoagulant (OA) treatment can improve the laboratory quality of therapy. DESIGN AND METHODS: Two separate sets of patients on oral anticoagulants, in five Italian anticoagulant clinics, were studied: 335 patients in the first three months of treatment (stabilization phase), 916 patients (775 patient-years) beyond the third month of treatment (maintenance phase). Patients were randomized to a computerized system, which included algorithms able to suggest OA dosing and to schedule appointments (computer-aided dosing) or to an arm in which OA were prescribed by the same teams of expert physicians without such algorithms (control group). Primary outcomes were: A) the percentage of patients reaching a stable state of anticoagulation during each of the first three months of treatment; B) the percentage of time individuals spent within the aimed therapeutic range (maintenance phase). RESULTS: Patients in the computer-aided dosing group achieved a stable state significantly faster (p<0.01) and they spent more time within the therapeutic range during maintenance (p<0.001) than controls. The favorable effect of computer-aided dosing was mainly due to a reduction of the time spent below the therapeutic range and was associated with an increase of mean INR value, of anticoagulant drug dosage, and with a reduction of the number of appointments per patient (all changes significant: p<0.001). INTERPRETATION AND CONCLUSIONS: The computer decision-aided support improves the laboratory quality of anticoagulant treatment, both during long-term maintenance and in the early, highly unstable phase of treatment, and it also significantly reduces the number of scheduled laboratory controls.
RCT Entities:
BACKGROUND AND OBJECTIVES: We carried out a prospective, randomized trial to test whether a computer-based decision support system to initiate and maintain oral anticoagulant (OA) treatment can improve the laboratory quality of therapy. DESIGN AND METHODS: Two separate sets of patients on oral anticoagulants, in five Italian anticoagulant clinics, were studied: 335 patients in the first three months of treatment (stabilization phase), 916 patients (775 patient-years) beyond the third month of treatment (maintenance phase). Patients were randomized to a computerized system, which included algorithms able to suggest OA dosing and to schedule appointments (computer-aided dosing) or to an arm in which OA were prescribed by the same teams of expert physicians without such algorithms (control group). Primary outcomes were: A) the percentage of patients reaching a stable state of anticoagulation during each of the first three months of treatment; B) the percentage of time individuals spent within the aimed therapeutic range (maintenance phase). RESULTS:Patients in the computer-aided dosing group achieved a stable state significantly faster (p<0.01) and they spent more time within the therapeutic range during maintenance (p<0.001) than controls. The favorable effect of computer-aided dosing was mainly due to a reduction of the time spent below the therapeutic range and was associated with an increase of mean INR value, of anticoagulant drug dosage, and with a reduction of the number of appointments per patient (all changes significant: p<0.001). INTERPRETATION AND CONCLUSIONS: The computer decision-aided support improves the laboratory quality of anticoagulant treatment, both during long-term maintenance and in the early, highly unstable phase of treatment, and it also significantly reduces the number of scheduled laboratory controls.
Authors: Anne Holbrook; Sam Schulman; Daniel M Witt; Per Olav Vandvik; Jason Fish; Michael J Kovacs; Peter J Svensson; David L Veenstra; Mark Crowther; Gordon H Guyatt Journal: Chest Date: 2012-02 Impact factor: 9.410
Authors: Walter Ageno; Alexander S Gallus; Ann Wittkowsky; Mark Crowther; Elaine M Hylek; Gualtiero Palareti Journal: Chest Date: 2012-02 Impact factor: 9.410
Authors: Michela Basileo; Carlo Micheluzzi; Marina Minozzi; Luigi Lazzaroni; Alfonso Iorio Journal: Intern Emerg Med Date: 2011-04-06 Impact factor: 3.397
Authors: Natalie Oake; Alison Jennings; Alan J Forster; Dean Fergusson; Steve Doucette; Carl van Walraven Journal: CMAJ Date: 2008-07-29 Impact factor: 8.262