BACKGROUND: Identifying the appropriate long-term anti-thrombotic therapy following acute ischaemic stroke is a challenging area in which computer-based decision support may provide assistance. AIM: To evaluate the influence on prescribing practice of a computer-based decision support system (CDSS) that provided patient-specific estimates of the expected ischaemic and haemorrhagic vascular event rates under each potential anti-thrombotic therapy. DESIGN: Cluster-randomized controlled trial. METHODS: We recruited patients who presented for a first investigation of ischaemic stroke or TIA symptoms, excluding those with a poor prognosis or major contraindication to anticoagulation. After observation of routine prescribing practice (6 months) in each hospital, centres were randomized for 6 months to either control (routine practice observed) or intervention (practice observed while the CDSS provided patient-specific information). We compared, between control and intervention centres, the risk reduction (estimated by the CDSS) in ischaemic and haemorrhagic vascular events achieved by long-term anti-thrombotic therapy, and the proportions of subjects prescribed the optimal therapy identified by the CDSS. RESULTS:Sixteen hospitals recruited 1952 subjects. When the CDSS provided information, the mean relative risk reduction attained by prescribing increased by 2.7 percentage units (95%CI -0.3 to 5.7) and the odds ratio for the optimal therapy being prescribed was 1.32 (0.83 to 1.80). Some 55% (5/9) of clinicians believed the CDSS had influenced their prescribing. CONCLUSIONS: Cluster-randomized trials provide excellent frameworks for evaluating novel clinical management methods. Our CDSS was feasible to implement and acceptable to clinicians, but did not substantially influence prescribing practice for anti-thrombotic drugs after acute ischaemic stroke.
RCT Entities:
BACKGROUND: Identifying the appropriate long-term anti-thrombotic therapy following acute ischaemic stroke is a challenging area in which computer-based decision support may provide assistance. AIM: To evaluate the influence on prescribing practice of a computer-based decision support system (CDSS) that provided patient-specific estimates of the expected ischaemic and haemorrhagic vascular event rates under each potential anti-thrombotic therapy. DESIGN: Cluster-randomized controlled trial. METHODS: We recruited patients who presented for a first investigation of ischaemic stroke or TIA symptoms, excluding those with a poor prognosis or major contraindication to anticoagulation. After observation of routine prescribing practice (6 months) in each hospital, centres were randomized for 6 months to either control (routine practice observed) or intervention (practice observed while the CDSS provided patient-specific information). We compared, between control and intervention centres, the risk reduction (estimated by the CDSS) in ischaemic and haemorrhagic vascular events achieved by long-term anti-thrombotic therapy, and the proportions of subjects prescribed the optimal therapy identified by the CDSS. RESULTS: Sixteen hospitals recruited 1952 subjects. When the CDSS provided information, the mean relative risk reduction attained by prescribing increased by 2.7 percentage units (95%CI -0.3 to 5.7) and the odds ratio for the optimal therapy being prescribed was 1.32 (0.83 to 1.80). Some 55% (5/9) of clinicians believed the CDSS had influenced their prescribing. CONCLUSIONS: Cluster-randomized trials provide excellent frameworks for evaluating novel clinical management methods. Our CDSS was feasible to implement and acceptable to clinicians, but did not substantially influence prescribing practice for anti-thrombotic drugs after acute ischaemic stroke.
Authors: K Ann McKibbon; Cynthia Lokker; Steven M Handler; Lisa R Dolovich; Anne M Holbrook; Daria O'Reilly; Robyn Tamblyn; Brian J Hemens; Runki Basu; Sue Troyan; Pavel S Roshanov Journal: J Am Med Inform Assoc Date: 2011-08-18 Impact factor: 4.497
Authors: Andrew Frazer; James Rowland; Alison Mudge; Michael Barras; Jennifer Martin; Peter Donovan Journal: Eur J Clin Pharmacol Date: 2019-09-11 Impact factor: 2.953
Authors: Brian J Hemens; Anne Holbrook; Marita Tonkin; Jean A Mackay; Lorraine Weise-Kelly; Tamara Navarro; Nancy L Wilczynski; R Brian Haynes Journal: Implement Sci Date: 2011-08-03 Impact factor: 7.327
Authors: K M Augestad; G Berntsen; K Lassen; J G Bellika; R Wootton; R O Lindsetmo Journal: J Am Med Inform Assoc Date: 2011-07-29 Impact factor: 4.497
Authors: Raghupathy Anchala; Maria P Pinto; Amir Shroufi; Rajiv Chowdhury; Jean Sanderson; Laura Johnson; Patricia Blanco; Dorairaj Prabhakaran; Oscar H Franco Journal: PLoS One Date: 2012-10-10 Impact factor: 3.240