red blood cellsIntroduction
Anticoagulants: disconnect between clinical trials and clinical practice
Using technology to promote prescribing decisions







Atrial fibrillation (AF) is the most prevalent sustained cardiac dysrthymia,1 and is associated with significant mortality and morbidity, including cerebrovascular accidents (CVAs), heart failure, poor mental health and death.2

Patients who develop a stroke secondary to AF are more likely to have a severe stroke with associated disability,3 experience a fatal stroke4 and have an extended length of hospital stay and associated increase in healthcare costs5 when compared to other causes.

Current treatment for AF involves managing the rhythm disorder (cardioversion, ablation or medication rate and/or rhythm control) alongside reducing stroke risk that the condition predisposes patients to with oral anticoagulant therapy (OAC). Currently, physicians can utilise the CHA2DS2-VASc scoring system to calculate overall stroke risk, and HAS-BLED scoring to calculate bleeding risk to have an informed discussion with patients regarding the risk and benefits of treatment.


Anticoagulants: disconnect between clinical trials and clinical practice

The benefits of OACs for patients with AF in terms of stroke risk reduction, has been well documented.6 However, recent reports have stressed that OACs are used less in the UK than recommended in authoritative guidelines, with such underuse reflecting practical obstacles and, frequently unfounded, safety concerns.7

One commonly cited clinical study of patients over 65 years old with AF reported that, in the event of patients being assessed as clinically appropriate to receive warfarin, a person taking warfarin must fall about 295 times in one year for warfarin not to be considered the optimal therapy.8 However, these clinician safety concerns may relate to real world experience of these agents rather than from the gold standard clinical trial setting. These differences extend not only to side-effect profile, but also to clinical benefit, with one UK study reporting that the risk reduction observed in the study of 26% was substantially lower than in clinical trials.9 Despite the above, it should be stressed that the evidence for OAC prescriptions for the right patient is compelling6, 7 and cost-effective.10, 11


C Congestive heart failure (or left ventricular dysfunction) 1
H Hypertension 1
A2 Age > 75 years 2
D Diabetes mellitus 1
S2 Prior stroke or TIA or thromboembolism 2
V Vascular disease (previous MI, peripheral arterial disease or aortic plaque) 1
A Age 65–74 years 1
S Sex category (female) 1
Total Score 0


Using technology to promote prescribing decisions

There have been recent discussions about how to attempt to address the variation in prescribing of OAC in primary care and one area that has been developed has been the use of technology. One such example of using technology to assist in decision making for anticoagulation is the GRASP-AF tool (currently delivered in NHS England).12 This is a primary care tool that automates the identification of patients for whom OAC treatment should be started, and is freely available for GPs.

GRASP-AF is part of a broad NHS Improvement programme to raise awareness of AF and stroke risk, thereby reducing the number of AF-related strokes. The programme attempts to enhance the management of stroke risk in patients with AF by promoting appropriate risk assessment, and the prescribing of appropriate anticoagulation. GRASP- AF tool identifies patients with AF, calculates their stroke risk, and also details their current management/medications. This information is simply summarised, allowing GPs easily to audit their current management of AF against best practice guidelines and commence patient centered discussions and make decisions about OAC therapy for individual patients. GRASP-AF also provides a facility to upload the data to CHART, a web-based comparative analysis tool available to all GPs. This allows practices to compare their data to other practices across England. The GRASP-AF tool will be a very important clinical research tool in the coming months and years as novel anticoagulants become more commonly used.11

A further example of using technology to develop clinical care in this area is the newly developed Automated Risk Assessment for Stroke in Atrial Fibrillation [AURAS-AF], with the results of the UK trial reported in Stroke.13 Electronic health records in primary care allow for automated identification of patients whereby anticoagulant decisions may be relevant for, with the AURAS-AF programme aiming to identify such individuals during routine clinical care.

A total of 47 practices were randomised to routine clinical care (control practices) and using the AURAS-AF electronic programme and were followed up over 12 months. In the practice using the AURAS- AF programme, screen reminders appeared each time the electronic health records of an eligible patient was accessed until a decision had been taken over OAC treatment. Where OACs were not started, clinicians were prompted to indicate a reason. The primary outcome was the proportion of eligible individuals receiving OAC at six months. Secondary outcomes included rates of cardiovascular events and reports of adverse effects of the software on clinical decision-making.

The mean proportion–prescribed OAC at six months was 66.3% (SD=9.3) in the intervention arm and 63.9% (9.5) in the control arm (adjusted difference 1.21% [95% confidence interval −0.72 to 3.13]). Incidence of recorded transient ischaemic attack was higher in the intervention practices (median 10.0 versus 2.3 per 1,000 patients with AF; P=0.027), but at 12 months, the authors found a lower incidence of both all cause stroke (P=0.06) and haemorrhage (P=0.054). No adverse effects of the software were reported, and the software was reported as being helpful in promoting decisions about anticoagulation.

The authors found in the study group that there was no significant change in OAC prescribing, a greater rate of diagnosis of transient ischaemic attack (possibly because of improved detection or overdiagnosis) and a reduction (of borderline significance) in stroke and haemorrhage over 12 months. Although the study was unsuccessful at achieving its primary endpoint, it provides evidence that such programmes may play an important role at promoting clinical decision-making and can be implemented at minimal cost and without major disruption to clinical care providers. Longer-term follow-up of the trial is planned.


Hypertension 1
Abnormal liver function 1
Abnormal renal function 1
Stroke 1
Bleeding 1
Labile INRs 1
Elderly (Age >65) 1
Drugs 1
Alcohol 1
Your score 0


Technology to date has been considered to be a driving force in developing many aspects of healthcare provision and point of access care. Over the past decade there have been large scale attempts to deliver computerised systems to allow clinicians to provide more efficient clinical care, and find information more easily.

In primary care, the use of electronic patient records provides an opportunity to inter- link electronic systems (EMIS / VISION) with computerised programmes that may aid clinical teams in providing clinical care.

In areas such as AF and anticoagulation, the decisions being taken have major implications for long-term health and potential risk of harm. It is hoped that programmes such as the GRASP-AF and AURAS-AF are a way of helping promote clinicians to make decisions and provide easily accessible information to provide the basis for patient-centred discussions. It was particularly reassuring to see that the AURAS-AF actively sought feedback from clinical teams to ensure that the software was helpful and not creating extra work. However, a co-ordinated approach should be utilised to avoid duplication of such systems and ensure that clinical duplication of such systems and ensure that clinical providers do not become over-burdened with clinical software within clinical programmes. It will be very interesting to see how technology can be utilised in the coming years to try to address pertinent clinical questions.


Lloyd D Hughes, GP Speciality Trainee, NHS Fife

Conflict of interest: none declared



1. Kannel WB, Wolf PA, Benjamin EJ, et al. Prevalence, incidence, prognosis, and predisposing conditions for atrial fibrillation: population-based estimates. Am J Cardiol 1998; 82: 2N–9N

2. Stewart S, Hart CL, Hole DJ, et al. A population-based study of the long- term risks associated with atrial fibrillation: 20-year follow-up of the Renfrew/Paisley study. Am J Med 2002; 113: 359–64

3. Jorgensen HS, Nakayama H, Reith J, et al. Acute stroke with atrial fibrillation. The Copenhagen Stroke Study. Stroke 1996; 27: 1765–9

4. Steger C, Pratter A, Martinek-Bregel M, et al. Stroke patients with atrial fibrillation have a worse prognosis than patients without: data from the Austrian Stroke registry. Eur Heart J 2004; 25: 1734–40

5. Winter Y, Wolfram C, Schaeg M, et al. Evaluation of costs and outcome in cardioembolic stroke or TIA. J Neurol 2009; 256: 954–63

6. Prevention of stroke in patients with atrial fibrillation. A Guide for Primary Care. Health Improvement Scotland. Scottish Intercollegiate Guidelines Network. Edinburgh, 2014.

7. The AF Report. Atrial Fibrillation: Preventing A Stroke Crisis. Available from: (accessed 27/07/17)

8. Man-Son Hing M, Nichol G, Lau A, Laupacis A. Choosing antithrombotic therapy for elderly patients with atrial fibrillation who are at risk of falls. Arch Intern Med 1999; 159: 677–85

9. Currie CJ, Jones M, Goodfellow J, et al. Evaluation of survival and ischaemic and thromboembolic event rates in patients with non-valvar atrial fibrillation in the general population when treated and untreated with warfarin. Heart 2006; 92: 196–200

10. Szucs TD, Bramkamp M. Pharmaco- economics of anticoagulation therapy for stroke prevention in atrial fibrillation: a review. J Thromb Haemost 2006; 4: 1180–85

11. Appleby J, Devlin N, Parkin D. NICE’s cost effectiveness threshold. BMJ 2007; 335: 358–9

12. GRASP-AF Program. University of Nottingham. Available from: Last (Accessed: 27/072017)

13. Holt TA, Dalton A, Marshall T, et al. Automated Software System to Promote Anticoagulation and Reduce Stroke Risk. Cluster-Randomized Controlled Trial. Stroke 2017; 48: 787–90