Search
Close
Search
Advanced Search
Search Menu
AI Discovery Assistant

Abstract

Background

Worldwide, atrial fibrillation (AF) is the most common sustained cardiac arrhythmia in adults and poses a significant burden to patients, physicians, and healthcare systems. We developed a quality of care score based on the Atrial Fibrillation Better Care pathway recommended by the European Society of Cardiology and the European Heart Rhythm Association guidelines. This is a 14-point score that we have termed the MAGIC score(Management of Atrial Fibrillation in Integrated Care and General Practice).

Objective

The objective of this pilot study was to develop and test a quality of care score for patients with permanent AF in general practice.

Methods

An observational cross-sectional pilot study was undertaken. Proportionate sampling was used across 11 practices from the Ireland East practice-based research network. The GPs completed a report form on each patient by undertaking a retrospective chart review. Eleven practices participated with a total of 1855 patients with AF. We received data on 153 patients.

Results

The main findings were that no patient met all 14 guideline based recommendations. The mean MAGIC score was 11.3. Points were most commonly deducted because the creatinine clearance and HAS-BLED score were not recorded, and the patient was not on the correct dose of oral anti-coagulation.

Conclusion

This study demonstrates the feasibility of using a quality of care score to measure the quality of AF management in general practice. This scoring system, which is based on internationally recognized quality of care markers, highlights key areas that can be targeted with quality improvement intervention.

Lay Summary

Atrial fibrillation (AF) is the most common arrhythmia in the world. An arrhythmia is when your heart beats in a disorganized way with no pattern. AF is a serious health problem because this rhythm can lead to other heart problems, stroke, and even death. Even though it is common, we know that people with AF do not always receive the correct treatment and monitoring. Treatment aims to control the heart rate, rhythm and minimize blood clot formation. Treating patients according to recommended guidelines will improve their medical care and outcomes. We created a 14-point quality-of-care score based on international clinical practice guidelines. This project was done to check if this score was practical to use and if it showed any patterns in the quality of care being delivered. Eleven GP practices from the Southeast of Ireland participated and gave us information on 153 patients with AF. We assigned each chart a quality-of-care score based on the tool we developed. No chart scored 14 points (full marks). The most common reasons for points being deducted were not recording kidney function, bleeding risk, and the patients being on the wrong dose of medication. With this information, we can now move forward and try to improve care for these patients by targeting the highlighted deficits.

atrial fibrillation, general practice, guideline adherence, Ireland, observational study, quality of health care
Key messages

  • Atrial fibrillation is the most common arrythmia worldwide

  • Using a quality of care score is a feasible way to measure quality

  • The HAS-BLED score and Creatine Clearance are often not recorded

  • These measures could be automated in the general practice software system

Introduction

Atrial fibrillation is the most common sustained cardiac arrhythmia in adults across the world and poses a significant burden to patients, physicians, and healthcare systems globally.1 The prevalence of AF is rising due to an ageing population, more vigorous searching for undiagnosed AF, and the increasing burden of co-morbidities.

Despite progress in the management of AF, it is still a leading cause of stroke, heart failure, and cardiovascular morbidity and mortality.2 AF is also associated with reduced quality of life (QoL), depression, cognitive decline, and hospitalization.2,3 Adherence to guideline-directed therapy can improve outcomes in AF, and yet there are wide variations in compliance to these guidelines.4–8

The latest ESC guidelines highlight that patients with AF have a broad range of adverse cardiovascular outcomes, and the approach to management should be comprehensive and holistic. This can be achieved using the Atrial Fibrillation Better Care (ABC) pathway.1 This ABC pathway streamlines integrated care of AF patients across all levels and specialities of healthcare and has shown significant benefits and improved clinical outcomes.1,9–11

Given the prevalence of AF, optimal management is paramount to improve quality of life and reduce the overall impact of AF on the health and social care system.12 Measuring quality and identifying opportunities for improvement is an essential part of optimal AF management.1 Guidelines are rarely absorbed organically into clinical practice sufficiently enough to create a meaningful change. Additional QI interventions are often needed, and usually a combination of education, decision support, ongoing audit and feedback, and organizational change are necessary for a sustained effect.13 Good quality data acquisition, audit, and feedback are essential to highlight areas in need of intervention and monitor progress. We know that audit and feedback can lead to important improvements in clinical care and drive behaviour change.14,15

The aim of this pilot study is to develop and test the feasibility of using a quality of care score called the MAGIC score (Management of Atrial Fibrillation in Integrated Care and General Practice) for patients with permanent atrial fibrillation in general practice.

Methods

Development of quality of care score

Quality indicators (QIs) serve both as a tool to assess the quality of care delivery and as an instrument to judge the effectiveness of quality improvement initiatives. Measuring QIs enables health care professionals (HCPs) to determine, compare, and improve adherence to guidelines.

The European Heart Rhythm Association (EHRA) developed a set of QIs that can be used to evaluate the quality of care and outcomes for adults with atrial fibrillation.2 In total, EHRA developed 17 main and 17 secondary outcomes in 6 domains of care for the diagnosis and management of AF.

Our MAGIC score is derived from these QIs. The score was developed by an expert panel of general practitioners using EHRA criteria that were (i) relevant to general practice, (ii) extractable from the patient record, and (3) amenable to a quality improvement intervention.

We have included all main quality indicators from Domain 1: Patient assessment, Domain 2: Anticoagulation, Domain 3: Rate control, and Domain 5: Risk factor management. We have not included any QIs from Domain 4: Rhythm Control as the rhythm control strategy for AF is managed in secondary care in Ireland. We have not included Domain 6: Outcomes, as this was a cross-sectional observation study, and we did not assess patient outcomes. Table 1 maps the MAGIC score to the EHRA QIs.

Table 1.
Open in new tab

MAGIC* Score for quality of care in general practice mapped to EHRA Quality indicators for the care of adults with atrial fibrillation.

EHRA Quality Indicator2
Domain 01: Patient assessment
1CHA2DS2-VASc score recorded since diagnosis01MQI1: Proportion of patients with cardio-embolic risk assessment using CHA2DS2-VASc score.
To assess the risk of stroke, a risk factor-based approach is recommended using the CHA2DS2-VASc clinical stroke risk score in all patients with AF.1,2
2HAS-BLED score recorded since diagnosis01MQI2: Proportion of patients with bleeding risk assessment using a validated method, such as the HAS-BLED score.
A bleeding risk assessment is used to identify those at higher risk of bleeding who may need closer follow-up and to focus attention on modifiable bleeding risk factor.1 Using the HAS-BLED score can reduce bleeding risk and increase OAC use.16
3Renal function measured since diagnosis01MQI3 Proportion of patients with a measurement of their serum creatinine (or creatinine clearance)
Measuring renal function has implications for stroke risk, bleeding risk and dose of OAC.17 Calculation of creatinine clearance is required for appropriate NOAC dosing.17
4Creatinine Clearance calculated since diagnosis01MQI3 Proportion of patients with a measurement of their serum creatinine (or creatinine clearance)
Domain 02: Anticoagulation
5Appropriately prescribed anticoagulation according to CHA2DS2-VASc score02MQI1: Proportion of patients who are appropriately prescribed anticoagulation according to CHA2DS2-VASc score
02MQI2:Proportion of patients with a CHA2DS2-VASc score of 0 for men and 1 for women who are inappropriately prescribed long-term anticoagulation
The ESC 2020 guidelines recommend OAC for stroke prevention in males ≥ 1 and in females with a score of ≥ 2.1 This domain will assess the number of patients appropriately anti-coagulated and those at low risk who are inappropriately anti-coagulated.
6Prescribed correct NOAC dose02MQI3: Proportion of patients with ‘appropriate anticoagulation’ defined as: Appropriate dose for NOAC according to manufacturer recommendations.
Under and over dosing of NOAC therapy is common and associated with worse outcomes.18,19
Domain 03: Rate control
7Patients with permanent AF who are not inappropriately prescribed antiarrhythmic drugs03MQI1: Proportion of patients with permanent AF (i.e. where no attempt to restore sinus rhythm is planned), who are inappropriately prescribed antiarrhythmic drugs.
Antiarrhythmic drugs e.g. amiodarone are not recommended when no further attempts to restore sinus rhythm are planned.1,2
Domain 05: Risk factor management
Comprehensive risk factor management complements stroke prevention, reduces AF burden, symptom severity and recurrence.1 A large proportion of these risk factors are lifestyle related and therefore amenable to modification, e.g. hypertension,1 diabetes,20 weight.21
8TSH evaluation since diagnosis05MQI1: Proportion of patients who have their modifiable risk factors identified
9Fasting glucose/ HBa1C measurement since diagnosis05MQI1: Proportion of patients who have their modifiable risk factors identified
10Echocardiography performed since diagnosis05MQI1: Proportion of patients who have their modifiable risk factors identified
11BP measurement since diagnosis05MQI1: Proportion of patients who have their modifiable risk factors identified
12BP < 140/90 mmHg05MQI1: Proportion of patients who have their modifiable risk factors identified
13On anti-platelet only if indicated05MQI1: Proportion of patients who have their modifiable risk factors identified
14Weight recorded05MQI1: Proportion of patients who have their modifiable risk factors identified
EHRA Quality Indicator2
Domain 01: Patient assessment
1CHA2DS2-VASc score recorded since diagnosis01MQI1: Proportion of patients with cardio-embolic risk assessment using CHA2DS2-VASc score.
To assess the risk of stroke, a risk factor-based approach is recommended using the CHA2DS2-VASc clinical stroke risk score in all patients with AF.1,2
2HAS-BLED score recorded since diagnosis01MQI2: Proportion of patients with bleeding risk assessment using a validated method, such as the HAS-BLED score.
A bleeding risk assessment is used to identify those at higher risk of bleeding who may need closer follow-up and to focus attention on modifiable bleeding risk factor.1 Using the HAS-BLED score can reduce bleeding risk and increase OAC use.16
3Renal function measured since diagnosis01MQI3 Proportion of patients with a measurement of their serum creatinine (or creatinine clearance)
Measuring renal function has implications for stroke risk, bleeding risk and dose of OAC.17 Calculation of creatinine clearance is required for appropriate NOAC dosing.17
4Creatinine Clearance calculated since diagnosis01MQI3 Proportion of patients with a measurement of their serum creatinine (or creatinine clearance)
Domain 02: Anticoagulation
5Appropriately prescribed anticoagulation according to CHA2DS2-VASc score02MQI1: Proportion of patients who are appropriately prescribed anticoagulation according to CHA2DS2-VASc score
02MQI2:Proportion of patients with a CHA2DS2-VASc score of 0 for men and 1 for women who are inappropriately prescribed long-term anticoagulation
The ESC 2020 guidelines recommend OAC for stroke prevention in males ≥ 1 and in females with a score of ≥ 2.1 This domain will assess the number of patients appropriately anti-coagulated and those at low risk who are inappropriately anti-coagulated.
6Prescribed correct NOAC dose02MQI3: Proportion of patients with ‘appropriate anticoagulation’ defined as: Appropriate dose for NOAC according to manufacturer recommendations.
Under and over dosing of NOAC therapy is common and associated with worse outcomes.18,19
Domain 03: Rate control
7Patients with permanent AF who are not inappropriately prescribed antiarrhythmic drugs03MQI1: Proportion of patients with permanent AF (i.e. where no attempt to restore sinus rhythm is planned), who are inappropriately prescribed antiarrhythmic drugs.
Antiarrhythmic drugs e.g. amiodarone are not recommended when no further attempts to restore sinus rhythm are planned.1,2
Domain 05: Risk factor management
Comprehensive risk factor management complements stroke prevention, reduces AF burden, symptom severity and recurrence.1 A large proportion of these risk factors are lifestyle related and therefore amenable to modification, e.g. hypertension,1 diabetes,20 weight.21
8TSH evaluation since diagnosis05MQI1: Proportion of patients who have their modifiable risk factors identified
9Fasting glucose/ HBa1C measurement since diagnosis05MQI1: Proportion of patients who have their modifiable risk factors identified
10Echocardiography performed since diagnosis05MQI1: Proportion of patients who have their modifiable risk factors identified
11BP measurement since diagnosis05MQI1: Proportion of patients who have their modifiable risk factors identified
12BP < 140/90 mmHg05MQI1: Proportion of patients who have their modifiable risk factors identified
13On anti-platelet only if indicated05MQI1: Proportion of patients who have their modifiable risk factors identified
14Weight recorded05MQI1: Proportion of patients who have their modifiable risk factors identified

*MAGIC = Management of Atrial fibrillation in General practice and Integrated Care

Table 1.
Open in new tab

MAGIC* Score for quality of care in general practice mapped to EHRA Quality indicators for the care of adults with atrial fibrillation.

EHRA Quality Indicator2
Domain 01: Patient assessment
1CHA2DS2-VASc score recorded since diagnosis01MQI1: Proportion of patients with cardio-embolic risk assessment using CHA2DS2-VASc score.
To assess the risk of stroke, a risk factor-based approach is recommended using the CHA2DS2-VASc clinical stroke risk score in all patients with AF.1,2
2HAS-BLED score recorded since diagnosis01MQI2: Proportion of patients with bleeding risk assessment using a validated method, such as the HAS-BLED score.
A bleeding risk assessment is used to identify those at higher risk of bleeding who may need closer follow-up and to focus attention on modifiable bleeding risk factor.1 Using the HAS-BLED score can reduce bleeding risk and increase OAC use.16
3Renal function measured since diagnosis01MQI3 Proportion of patients with a measurement of their serum creatinine (or creatinine clearance)
Measuring renal function has implications for stroke risk, bleeding risk and dose of OAC.17 Calculation of creatinine clearance is required for appropriate NOAC dosing.17
4Creatinine Clearance calculated since diagnosis01MQI3 Proportion of patients with a measurement of their serum creatinine (or creatinine clearance)
Domain 02: Anticoagulation
5Appropriately prescribed anticoagulation according to CHA2DS2-VASc score02MQI1: Proportion of patients who are appropriately prescribed anticoagulation according to CHA2DS2-VASc score
02MQI2:Proportion of patients with a CHA2DS2-VASc score of 0 for men and 1 for women who are inappropriately prescribed long-term anticoagulation
The ESC 2020 guidelines recommend OAC for stroke prevention in males ≥ 1 and in females with a score of ≥ 2.1 This domain will assess the number of patients appropriately anti-coagulated and those at low risk who are inappropriately anti-coagulated.
6Prescribed correct NOAC dose02MQI3: Proportion of patients with ‘appropriate anticoagulation’ defined as: Appropriate dose for NOAC according to manufacturer recommendations.
Under and over dosing of NOAC therapy is common and associated with worse outcomes.18,19
Domain 03: Rate control
7Patients with permanent AF who are not inappropriately prescribed antiarrhythmic drugs03MQI1: Proportion of patients with permanent AF (i.e. where no attempt to restore sinus rhythm is planned), who are inappropriately prescribed antiarrhythmic drugs.
Antiarrhythmic drugs e.g. amiodarone are not recommended when no further attempts to restore sinus rhythm are planned.1,2
Domain 05: Risk factor management
Comprehensive risk factor management complements stroke prevention, reduces AF burden, symptom severity and recurrence.1 A large proportion of these risk factors are lifestyle related and therefore amenable to modification, e.g. hypertension,1 diabetes,20 weight.21
8TSH evaluation since diagnosis05MQI1: Proportion of patients who have their modifiable risk factors identified
9Fasting glucose/ HBa1C measurement since diagnosis05MQI1: Proportion of patients who have their modifiable risk factors identified
10Echocardiography performed since diagnosis05MQI1: Proportion of patients who have their modifiable risk factors identified
11BP measurement since diagnosis05MQI1: Proportion of patients who have their modifiable risk factors identified
12BP < 140/90 mmHg05MQI1: Proportion of patients who have their modifiable risk factors identified
13On anti-platelet only if indicated05MQI1: Proportion of patients who have their modifiable risk factors identified
14Weight recorded05MQI1: Proportion of patients who have their modifiable risk factors identified
EHRA Quality Indicator2
Domain 01: Patient assessment
1CHA2DS2-VASc score recorded since diagnosis01MQI1: Proportion of patients with cardio-embolic risk assessment using CHA2DS2-VASc score.
To assess the risk of stroke, a risk factor-based approach is recommended using the CHA2DS2-VASc clinical stroke risk score in all patients with AF.1,2
2HAS-BLED score recorded since diagnosis01MQI2: Proportion of patients with bleeding risk assessment using a validated method, such as the HAS-BLED score.
A bleeding risk assessment is used to identify those at higher risk of bleeding who may need closer follow-up and to focus attention on modifiable bleeding risk factor.1 Using the HAS-BLED score can reduce bleeding risk and increase OAC use.16
3Renal function measured since diagnosis01MQI3 Proportion of patients with a measurement of their serum creatinine (or creatinine clearance)
Measuring renal function has implications for stroke risk, bleeding risk and dose of OAC.17 Calculation of creatinine clearance is required for appropriate NOAC dosing.17
4Creatinine Clearance calculated since diagnosis01MQI3 Proportion of patients with a measurement of their serum creatinine (or creatinine clearance)
Domain 02: Anticoagulation
5Appropriately prescribed anticoagulation according to CHA2DS2-VASc score02MQI1: Proportion of patients who are appropriately prescribed anticoagulation according to CHA2DS2-VASc score
02MQI2:Proportion of patients with a CHA2DS2-VASc score of 0 for men and 1 for women who are inappropriately prescribed long-term anticoagulation
The ESC 2020 guidelines recommend OAC for stroke prevention in males ≥ 1 and in females with a score of ≥ 2.1 This domain will assess the number of patients appropriately anti-coagulated and those at low risk who are inappropriately anti-coagulated.
6Prescribed correct NOAC dose02MQI3: Proportion of patients with ‘appropriate anticoagulation’ defined as: Appropriate dose for NOAC according to manufacturer recommendations.
Under and over dosing of NOAC therapy is common and associated with worse outcomes.18,19
Domain 03: Rate control
7Patients with permanent AF who are not inappropriately prescribed antiarrhythmic drugs03MQI1: Proportion of patients with permanent AF (i.e. where no attempt to restore sinus rhythm is planned), who are inappropriately prescribed antiarrhythmic drugs.
Antiarrhythmic drugs e.g. amiodarone are not recommended when no further attempts to restore sinus rhythm are planned.1,2
Domain 05: Risk factor management
Comprehensive risk factor management complements stroke prevention, reduces AF burden, symptom severity and recurrence.1 A large proportion of these risk factors are lifestyle related and therefore amenable to modification, e.g. hypertension,1 diabetes,20 weight.21
8TSH evaluation since diagnosis05MQI1: Proportion of patients who have their modifiable risk factors identified
9Fasting glucose/ HBa1C measurement since diagnosis05MQI1: Proportion of patients who have their modifiable risk factors identified
10Echocardiography performed since diagnosis05MQI1: Proportion of patients who have their modifiable risk factors identified
11BP measurement since diagnosis05MQI1: Proportion of patients who have their modifiable risk factors identified
12BP < 140/90 mmHg05MQI1: Proportion of patients who have their modifiable risk factors identified
13On anti-platelet only if indicated05MQI1: Proportion of patients who have their modifiable risk factors identified
14Weight recorded05MQI1: Proportion of patients who have their modifiable risk factors identified

*MAGIC = Management of Atrial fibrillation in General practice and Integrated Care

Study design

A descriptive, cross-sectional observational study was undertaken. This was a pilot study to determine the feasibility of using the MAGIC quality of care score.

Setting

The study involved general practices (n = 14) participating in a research network on the east coast of Ireland. GPs who agreed to participate (n = 11) returned a signed consent form and the total number of patients with permanent AF in their practice. Proportionate sampling was used to generate a sample size for individual practices.

A random number generator was used to identify patients from their individual lists in each practice to include in the study. A paper case report form was used to collect defined anonymised data on these patients. The report forms were returned to the research team in UCD. These data were then cleaned, pooled, and inputted onto a secure database.

Participants

All practices in the Ireland East practice-based research network were invited to participate.

They identified patients in the practice with permanent AF.

Inclusion criteria

Age ≥ 18 years

Permanent AF detected on ECG, Holter recording, or event recorder

Active in practice software and has attended the practice in the last 24 months

Exclusion criteria

No electrocardiographic objectified AF

Study size

As this was a pilot study a formal sample size was not calculated.22 Based on previous work, it was estimated that 12 per group would be sufficient.23,24 We estimated that 80% of the practice would complete the study (11 practices). Therefore, we sought to recruit a minimum of 132 patients using proportional sampling.

MAGIC score

Each participant file was reviewed using the MAGIC score, and points were awarded if the relevant component was present. Points were not awarded if the information was not recorded or could not be determined. Each component of the score was equally weighted with a maximum score of 14.

Statistical methods

RedCap25 was used for data storage and analysis. The SPSS programme was used for statistical analysis. Missing data were either excluded from analysis or calculated based on information provided, e.g. creatinine clearance.

Results

Eleven practices consented to participate in this pilot study with a total number of 1,855 patients with AF. We received data on 153 patients proportionally sampled across these 11 practices.

The demographic details can be seen in Table 2.

Patient Demographics
GenderMaleFemaleMissing
104463
AgeMinMaxMeanMedianMissing
48 years96 years76 years78 years3
Medical Card StatusGMSDVCPrivateMissing
11611179
CDM ReviewYesNoMissing
115371
Smoking StatusCurrentEx-smokerNever smokerMissing
1752759
Alcohol Intake (units/ week)MinMaxMeanMissing
01404.824
BMIMinMaxMeanMedianMissing
174529.228.430
Co-MorbiditiesHypertensionCADDiabetesStroke/ TIAPVDOSAHyperthyroid
79574123763
Patient Demographics
GenderMaleFemaleMissing
104463
AgeMinMaxMeanMedianMissing
48 years96 years76 years78 years3
Medical Card StatusGMSDVCPrivateMissing
11611179
CDM ReviewYesNoMissing
115371
Smoking StatusCurrentEx-smokerNever smokerMissing
1752759
Alcohol Intake (units/ week)MinMaxMeanMissing
01404.824
BMIMinMaxMeanMedianMissing
174529.228.430
Co-MorbiditiesHypertensionCADDiabetesStroke/ TIAPVDOSAHyperthyroid
79574123763

CAD, coronary artery disease; DVC, doctor visit card; GMS, general medical services; OSA, obstructive sleep apnoea; PVD, peripheral vascular disease; TIA, transient ischaemic attack.

Patient Demographics
GenderMaleFemaleMissing
104463
AgeMinMaxMeanMedianMissing
48 years96 years76 years78 years3
Medical Card StatusGMSDVCPrivateMissing
11611179
CDM ReviewYesNoMissing
115371
Smoking StatusCurrentEx-smokerNever smokerMissing
1752759
Alcohol Intake (units/ week)MinMaxMeanMissing
01404.824
BMIMinMaxMeanMedianMissing
174529.228.430
Co-MorbiditiesHypertensionCADDiabetesStroke/ TIAPVDOSAHyperthyroid
79574123763
Patient Demographics
GenderMaleFemaleMissing
104463
AgeMinMaxMeanMedianMissing
48 years96 years76 years78 years3
Medical Card StatusGMSDVCPrivateMissing
11611179
CDM ReviewYesNoMissing
115371
Smoking StatusCurrentEx-smokerNever smokerMissing
1752759
Alcohol Intake (units/ week)MinMaxMeanMissing
01404.824
BMIMinMaxMeanMedianMissing
174529.228.430
Co-MorbiditiesHypertensionCADDiabetesStroke/ TIAPVDOSAHyperthyroid
79574123763

CAD, coronary artery disease; DVC, doctor visit card; GMS, general medical services; OSA, obstructive sleep apnoea; PVD, peripheral vascular disease; TIA, transient ischaemic attack.

Most patients—68% were male (N = 104), and the mean age was 76 years. The majority of patients (83%) had a “General Medical Service” (GMS) card or Doctor Visit Card (DVC), which entitles patients to free GP care in Ireland. These are awarded to all patients over 70 years, under 6 years of age, and those below a certain income level based on means testing. These patients are also eligible for a biannual structured chronic disease management (CDM) review which was introduced in March 2020. This CDM review is not available to patients without a GMS or DVC card. Most patients had a CHA2DS2-VASc score calculated (76%) and most patients (86%) were on oral anticoagulation (OAC). The most commonly used OAC was Apixaban (52.3%). Of the 21 patients not on OAC, this was only appropriate in 4 of them based on their CHA2DS2-VASc score.

The mean heart rate was 74 bpm (rang 42–124 bpm), and the mean blood pressure was 132/77 mmHg (systolic BP range 90–207 mmHg, diastolic BP range 51–103 mmHg). 75% of patients were on rate-controlling medication. 99.3% of patients had a HR < 110 bpm. Bisoprolol was the most common medication used (54%) (Fig. 1). 12% of patients were prescribed anti-arrhythmic drugs despite being in permanent atrial fibrillation.

Rate control. Rate control medication
Fig. 1.

Rate control. Rate control medication

Open in new tabDownload slide

The mean BMI was 29.2 kg/m2 (range 17–45 kg/m2) with 36.4% being above 30 kg/m2. 45% were current or ex-smokers, 49% never smoked, and the mean weekly alcohol intake was 4.8 units. Hypertension was the most common co-morbidity recorded followed by coronary artery disease.

Using the MAGIC score, no patient met all 14 guideline-based recommendation. Only 13.8% met 13 based recommendations. As illustrated on the graph, there is a sharp decline after a score of 10. The mean score was 11.3 points (Fig. 2). Points were most commonly deducted because the creatinine clearance and HAS-BLED score were not recorded, and the patient was not on the correct dose of OAC (Fig. 3).

Minimum no. of recommendations (MAGIC score).
Fig. 2.

Minimum no. of recommendations (MAGIC score).

Open in new tabDownload slide
MAGIC Score.
Fig. 3.

MAGIC Score.

Open in new tabDownload slide

Only 29 patients had their creatinine clearance calculated despite nearly all patients having their renal function checked (150 patients) since starting on OAC. Fifty-two patients were either taking Rivaroxaban, Edoxaban, or Dabigatran, which necessitates this calculation to ensure correct dosing.

Discussion

Key results

This study demonstrates the feasibility of using a quality of care score as an assessment tool for guideline adherence. In this cohort, the MAGIC score has shown that the majority of quality indicators described are being achieved.

The areas of the MAGIC score that had the poorest results were the recording of a HAS-BLED score and the recording of creatinine clearance, which are essential components of OAC management. These are amenable to electronic recording and calculation.

Twenty-two patients in this study had a HAS-BLED score recorded. Carrying out an individual stroke and bleeding risk assessment is a key component of AF management. Using the HAS-BLED score has been shown to improve appropriate OAC prescribing.26 In the mAFA-II trial, using the HAS-BLED score to dynamically assess bleeding risk was associated with increased prescribing of OAC, reduced bleeding events, and mitigation of modifiable risk factors for bleeding.16 Poor compliance with the assessment of bleeding risk means that those at higher risk of bleeding are not identified and, therefore, not managed correctly.

Most patients (86%) in this pilot study were on OAC. Based on the patient’s CHA2DS2-VASc score of 1 in a male or 2 in a female, OAC was indicated in 17 of the 21 patients not prescribed OAC. Of those taking NOACs, 80% were on the correct NOAC dose. Correct NOAC dosing requires an assessment of renal function. The dose reduction criteria for Apixaban include age, weight, and serum creatinine, whereas reduction of Rivaroxaban, Edoxaban, and Dabigatran is based on creatinine clearance. In this pilot study, 20% of patients (N = 21) taking a NOAC were on an incorrect dose. Inappropriate dosing can lead to significant adverse events such as bleeding, thromboembolic events, and increased all-cause mortality.18,27 Calculation of creatine clearance using the Cockcroft-Gault equation takes age, weight, sex, and creatinine into account and could be automated on the general practice software system.

In primary care, the focus is on rate control in those with permanent AF. Patients in this study had a mean heart rate of 74 bpm, and only one patient with a heart rate of >110 bpm was identified. Beta blockers with the addition of Digoxin were the most common rate controlling medications used (Fig. 1). AADs were potentially inappropriately prescribed in 18 patients. There is no role for long-term AAD in those with permanent AF (i.e. where no attempt to restore sinus rhythm is planned)2, and these patients may have been exposed to the side effect profile of these drugs unnecessarily.

Managing comorbidities is an important component of atrial fibrillation care. AF often results from a combination of underlying interacting factors leading to atrial remodelling/cardiomyopathy, including cardiovascular disease, cardiovascular risk-factor burden, co morbidities, and an unhealthy lifestyle.1 Hypertension is the most common aetiological factor for AF. The mean BP in in this cohort was 132/77 mmHG with 24.8% patients having a BP > 140/90 mmHg. The mean BMI was 29.2 kg/m2, which is classed as overweight and nearly obese. Obesity not only increases the risk of AF but also the risk of ischaemic stroke and death.1 Weight loss has been shown to be associated with better atrial fibrillation control.21

Our data suggest that the care in Irish general practice is of a similar standard to other published work in this area. For example, Wijtvliet et al. conducted a randomized control trial comparing nurse-led care to usual care in those with atrial fibrillation.28 As a secondary end point they measured adherence to 7 guideline-based recommendations; appropriate anticoagulation, thyroid function testing, echocardiography, appropriate application of rhythm control recommendations, renal function assessment, glucose level assessment, and blood pressure measurement.28 These 7 guideline-based recommendations are similar to those included in the MAGIC score. Wijtvliet et al. found that compliance to these recommendations was 61% under specialist nurse-led care and 26% in usual care.28 We found that 59.5% of patients met these same 7 guideline-based recommendation in the general practice cohort that we assessed.

Most patients in our study had a CDM review involving attendance with a general practice nurse and GP review at 6 monthly intervals. International guidelines for clinical AF management have recommended integrated care as the leading approach to manage AF resulting in improved clinical outcomes.1,9–11,29 Given that part of integrated care is the adherence to guideline-directed therapy,12 we hypothesize that the structured CDM programme in Ireland contributes to the high level of guideline adherence.

Previous studies have shown the importance of structured care in general practice.9,29,30 For example, a large RCT by van den Dries et al. demonstrated a significant reduction in all-cause mortality through an intervention involving regular review by nurses, monitoring of anticoagulation and easy access to specialist advice.29 This displays the value of integrated care, and a score such as MAGIC could be useful to help identify patients who need intervention. A further study of the effect of this approach is required.

A number of interventions have been tested, which have been shown to improve quality of care in chronic diseases. The American Heart Association’s (AHA) ‘Get With The Guidelines’ (GWTG) programme has been shown to improve the quality of care across a number of different cardiovascular diseases and has now been expanded to AF.13 Participating hospitals record patient details on a quality improvement registry and guideline adherence is facilitated using rapid – cycle quality improvement strategies and site-specific reporting.13 Sites are provided with performance data every 3 months which enables targeted interventions to be utilized mainly through education and support.13 The approaches used includes educational intervention, availability of treatment algorithms, and decision support tools.31 Using the GWTG approach, appropriate OAC prescription in patients with AF improved significantly demonstrating that a high level of guideline adherence is possible.31 The IMPACT-AF trial had similar findings. By using a multifaceted and multilevel educational intervention, the appropriate prescription for OAC significantly increased.32

These QI interventions have also been used successfully in primary care. It has been shown that a combination of education and specialist support resulted in an improvement in OAC prescribing and the number of patients undergoing both stroke and bleeding risk assessments.30

A group of general practitioners in the UK have developed an interactive dashboard to provide feedback on quality in AF care. They found that the dashboards were an acceptable and feasible way to communicate a summary of AF care.33 The prescribing section was rated as one of the most useful by the pilot group of GPs. Translating these approaches to the range of quality indicators in the MAGIC score could prove useful in improving the management of atrial fibrillation in general practice.

Strengths and limitations

The MAGIC score enables the identification of gaps in AF care and guideline adherence. This score is feasible and could be replicated across other general practice sites as the information we collected is readily available within the electronic health record. This data set was obtained using proportionate sampling across 11 practices providing a representative sample of care in Ireland. This type of score readily identifies areas that can be targeted with an intervention to improve the quality of care and is based on internationally recognized quality of care markers.

A number of limitations apply to this study also. We recognize that the threshold for OAC varies according to guideline and geography. The European Society of Cardiology recommends offering OAC to those with ≥1 non-sex stroke risk factors.1 The EHRA QIs are derived from these guidelines, and therefore appropriateness of OAC was defined as CHA2DS2-VASc score of ≥1 for men and ≥ 2 for women in the QIs and in this pilot study.1,2

In contrast, the 2014 American College of Cardiology/American Heart Association guidelines and the 2019 focussed update recommend OAC with a CHA2DS2-VASc score of ≥ 2 for men and ≥ 3 for women.34,35 Similarly, the NICE guidelines recommend OAC for those with a score of 2 or more on the CHA2DS2-VASc score.36

Even at higher CHA2DS2-VASc scores, OAC may not be appropriate due to patient-specific factors. In this study, for those not on anticoagulation or incorrect doses according to guidelines, we did not ascertain if there were reasons for the non-use or use of different NOAC doses. We also did not record the time in target range (TTR) for patients on warfarin.

We assessed if patients had ever had the relevant investigations since diagnosis of AF but did not record the time since diagnosis to the performance of the relevant investigation.

This MAGIC score has not been validated, and we do not know if a higher score translates to better clinical outcomes, although other work in this area has demonstrated that adherence to guidelines is beneficial for patients.4,5,29 Overall, this was a pilot feasibility study to develop and pilot the MAGIC score and these outcome measures were outside the scope of the project. In order to validate the MAGIC score, we intend to conduct a randomized control trial using a mixed methods approach to evaluate if patients with a higher MAGIC score have better outcomes.

Conclusion

The ESC highlights the importance of measuring quality with quality/performance indicators to identify opportunities for improvement.1 We propose that the MAGIC score can be used for this purpose. In this pilot study, the quality of AF care in Irish general practice is good and comparable with other published work. The MAGIC score has highlighted some key areas that can be targeted with intervention to improve the quality of care. Decision support tools, educational interventions, and visual summaries such as dashboards have all been shown to be effective QI strategies to improve guidelines adherence and will help inform future work.

Acknowledgments

This project received funding by internal University College Dublin School of Medicine/College of Health & Agricultural Sciences research fund and Ireland East Hospital Group. Dr. McErlean is funded by the Irish College of General Practitioners / /Health Service Executive Academic Clinical Fellowship Scheme.

Conflict of interest

The lead author Dr. McErlean receives funding from the Health Service Executive as an Academic Clinical Fellow. The project was funded by the Ireland East Hospital Group and the UCD School of Medicine/College of Health & Agricultural Sciences research fund. Professor Cullen is PI on programme of research on integrated models of care supported by Ireland East Hospital Group who funded this study. Dr. Mark Ledwidge has received funding from Novartis, Abbott Diagnostics, the European Commission Framework Programme 7, Genuity Science, Abbott, and the Health Research Board of the Irish Government outside of this submitted work. Dr. Gallagher is General Practice Lead in Cardiovascular Disease on Integrated Care Programme for Chronic Disease with the Health Service Executive and has received funding for educational activities from Novartis, Astra Zeneca & Daiichi Sankyo. The other authors declare no conflict of interest.

Ethical approval

Ethical approval was granted from the Irish College of General Practitioners ethics committee.

Data availability

The data underlying this article will be shared on reasonable request to the corresponding author.

References

1.

Hindricks
G
,
Potpara
T
,
Dagres
N
,
Arbelo
E
,
Bax
JJ
,
Blomström-Lundqvist
C
,
Boriani
G
,
Castella
M
,
Dan
G-A
,
Dilaveris
PE
, et al. ;
ESC Scientific Document Group
.
2020 ESC Guidelines for the diagnosis and management of atrial fibrillation developed in collaboration with the European Association for Cardio-Thoracic Surgery (EACTS)
.
Eur Heart J
.
2021
:
42
(
5
):
373
–
498
. doi:10.1093/eurheartj/ehaa612

Google Scholar

Crossref
Search ADS
PubMed

 
2.

Arbelo
E
,
Aktaa
S
,
Bollmann
A
,
D'Avila
A
,
Drossart
I
,
Dwight
J
,
Hills
MT
,
Hindricks
G
,
Kusumoto
FM
,
Lane
DA
, et al. ;
(Chair)
.
Quality indicators for the care and outcomes of adults with atrial fibrillation
.
Europace
.
2021
:
23
(
4
):
494
–
495
. doi:10.1093/europace/euaa253

Google Scholar

Crossref
Search ADS
PubMed

 
3.

Steinberg
BA
,
Kim
S
,
Fonarow
GC
,
Thomas
L
,
Ansell
J
,
Kowey
PR
,
Mahaffey
KW
,
Gersh
BJ
,
Hylek
E
,
Naccarelli
G
, et al. .
Drivers of hospitalization for patients with atrial fibrillation: results from the Outcomes Registry for Better Informed Treatment of Atrial Fibrillation (ORBIT-AF)
.
Am Heart J
.
2014
:
167
(
5
):
735
–
42.e2
. doi:10.1016/j.ahj.2014.02.003

Google Scholar

Crossref
Search ADS
PubMed

 
4.

Boriani
G
,
Proietti
M
,
Laroche
C
, et al. .
Contemporary stroke prevention strategies in 11 096 European patients with atrial fibrillation: a report from the EURObservational Research Programme on Atrial Fibrillation (EORP-AF) Long-Term General Registry
.
EP Europace
.
2018
:
20
(
5
):
747
–
757
. doi:10.1093/europace/eux301

Google Scholar

Crossref
Search ADS
PubMed

 
5.

Robson
J
,
Homer
K
,
Ahmed
Z
,
Antoniou
S.
Variation in anticoagulation for atrial fibrillation between English clinical commissioning groups: an observational study
.
Br J Gen Pract
.
2018
:
68
(
673
):
e551
–
e558
. doi:10.3399/bjgp18X697913

Google Scholar

Crossref
Search ADS
PubMed

 
6.

Lip
GYH
,
Laroche
C
,
Popescu
MI
,
Rasmussen
LH
,
Vitali-Serdoz
L
,
Dan
G-A
,
Kalarus
Z
,
Crijns
HJGM
,
Oliveira
MM
,
Tavazzi
L
, et al. .
Improved outcomes with European Society of Cardiology guideline-adherent antithrombotic treatment in high-risk patients with atrial fibrillation: a report from the EORP-AF General Pilot Registry
.
Europace
.
2015
:
17
(
12
):
1777
–
1786
. doi:10.1093/europace/euv269

Google Scholar

Crossref
Search ADS
PubMed

 
7.

Apenteng
PN
,
Virdone
S
,
Hobbs
FR
,
Camm
AJ
,
Fox
KA
,
Pieper
KS
,
Kayani
G
,
Fitzmaurice
D
;
GARFIELD UK investigators*
.
Two-year outcomes of UK patients newly diagnosed with atrial fibrillation: findings from the prospective observational cohort study GARFIELD-AF
.
Br J Gen Pract
.
2022
:
72
(
723
):
e693
–
e701
. doi:10.3399/BJGP.2021.0548

Google Scholar

Crossref
Search ADS
PubMed

 
8.

Krittayaphong
R
,
Winijkul
A
,
Kunjara-Na-Ayudhya
R
,
Apiyasawat
S
,
Siriwattana
K
,
Kanjanarutjawiwat
W
,
Dutsadeevettakul
S
,
Lip
GYH
;
COOL-AF Investigators
.
Adherence to anticoagulant guideline for atrial fibrillation improves outcomes in asian population: the COOL-AF registry
.
Stroke
.
2020
:
51
(
6
):
1772
–
1780
. doi:10.1161/STROKEAHA.120.029295

Google Scholar

Crossref
Search ADS
PubMed

 
9.

Kirchhof
P.
The future of atrial fibrillation management: integrated care and stratified therapy
.
Lancet
.
2017
:
390
(
10105
):
1873
–
1887
. doi:10.1016/s0140-6736(17)31072-3

Google Scholar

Crossref
Search ADS
PubMed

 
10.

Brieger
D
,
Amerena
J
,
Attia
J
,
Bajorek
B
,
Chan
KH
,
Connell
C
,
Freedman
B
,
Ferguson
C
,
Hall
T
,
Haqqani
H
, et al. ;
NHFA CSANZ Atrial Fibrillation Guideline Working Group
.
National Heart Foundation of Australia and the Cardiac Society of Australia and New Zealand: Australian Clinical Guidelines for the Diagnosis and Management of Atrial Fibrillation 2018
.
Heart Lung Circ
.
2018
:
27
(
10
):
1209
–
1266
. doi:10.1016/j.hlc.2018.06.1043

Google Scholar

Crossref
Search ADS
PubMed

 
11.

Benjamin
EJ
,
Al-khatib
SM
,
Desvigne-nickens
P
,
Alonso
A
,
Djoussé
L
,
Forman
DE
,
Gillis
AM
,
Hendriks
JML
,
Hills
MT
,
Kirchhof
P
, et al. .
Research priorities in the secondary prevention of atrial fibrillation: a National heart, lung, and blood institute virtual workshop report
.
J Am Heart Assoc
.
2021
:
10
(
16
):
e021566
. doi:10.1161/JAHA.121.021566

Google Scholar

Crossref
Search ADS
PubMed

 
12.

Ritchie
LA
,
Lip
GYH
,
Lane
DA.
Optimization of atrial fibrillation care: management strategies and quality measures
.
Eur Heart J
.
2021
:
7
(
2
):
121
–
133
. doi:10.1093/ehjqcco/qcaa063

Google Scholar

OpenURL Placeholder Text

 
13.

Lewis
WR
,
Piccini
JP
,
Turakhia
MP
,
Curtis
AB
,
Fang
M
,
Suter
RE
,
Page
RL
,
Fonarow
GC.
Get With The Guidelines AFIB: novel quality improvement registry for hospitalized patients with atrial fibrillation
.
Circ Cardiovasc qual outcomes
.
2014
:
7
(
5
):
770
–
777
. doi:10.1161/CIRCOUTCOMES.114.001263

Google Scholar

Crossref
Search ADS
PubMed

 
14.

Ivers
N
,
Jamtvedt
G
,
Flottorp
S
,
Young
JM
,
Odgaard-Jensen
J
,
French
SD
,
O'Brien
MA
,
Johansen
M
,
Grimshaw
J
,
Oxman
AD
.
Audit and feedback: effects on professional practice and healthcare outcomes Cochrane Effective Practice and Organisation of Care Group, ed
.
Cochrane Database Syst Rev
.
2012
(
6
):
1
–
221
. doi:10.1002/14651858.CD000259.pub3

Google Scholar

OpenURL Placeholder Text

 
15.

Tuti
T
,
Nzinga
J
,
Njoroge
M
,
Brown
B
,
Peek
N
,
English
M
,
Paton
C
,
van der Veer
SN.
A systematic review of electronic audit and feedback: intervention effectiveness and use of behaviour change theory
.
Implement Sci
.
2017
:
12
(
1
):
61
. doi:10.1186/s13012-017-0590-z

Google Scholar

Crossref
Search ADS
PubMed

 
16.

Guo
Y
,
Lane
DA
,
Chen
Y
,
Lip
GYH
;
mAF-App II Trial investigators
.
Regular bleeding risk assessment associated with reduction in bleeding outcomes: the mAFA-II randomized trial
.
Am J Med
.
2020
:
133
(
10
):
1195
–
1202.e2
. doi:10.1016/j.amjmed.2020.03.019

Google Scholar

Crossref
Search ADS
PubMed

 
17.

Steffel
J
,
Collins
R
,
Antz
M
, et al. .
European heart rhythm association practical guide on the use of non-vitamin K antagonist oral anticoagulants in patients with atrial fibrillation
.
Europace
.
2021
;
23
(
10
):
1612
–
1676
. doi: 10.1093/europace/euab065.

18.

Steinberg
BA
,
Shrader
P
,
Pieper
K
,
Thomas
L
,
Allen
LA
,
Ansell
J
,
Chan
PS
,
Ezekowitz
MD
,
Fonarow
GC
,
Freeman
JV
, et al. .
Frequency and outcomes of reduced dose non–vitamin k antagonist anticoagulants: results from ORBIT-AF II (The Outcomes Registry for Better Informed Treatment of Atrial Fibrillation II)
.
JAHA
.
2018
:
7
(
4
):
e007633
. doi:10.1161/JAHA.117.007633

Google Scholar

Crossref
Search ADS
PubMed

 
19.

Caso
V
,
de Groot
JR
,
Sanmartin Fernandez
M
,
Segura
T
,
Blomström-Lundqvist
C
,
Hargroves
D
,
Antoniou
S
,
Williams
H
,
Worsley
A
,
Harris
J
, et al. .
Outcomes and drivers of inappropriate dosing of non-vitamin K antagonist oral anticoagulants (NOACs) in patients with atrial fibrillation: a systematic review and meta-analysis
.
Heart
.
2023
:
109
(
3
):
178
–
185
. doi:10.1136/heartjnl-2022-321114

Google Scholar

Crossref
Search ADS
PubMed

 
20.

Donnellan
E
,
Aagaard
P
,
Kanj
M
,
Jaber
W
,
Elshazly
M
,
Hoosien
M
,
Baranowski
B
,
Hussein
A
,
Saliba
W
,
Wazni
O.
Association between pre-ablation glycemic control and outcomes among patients with diabetes undergoing atrial fibrillation ablation
.
JACC: Clinical Electrophysiology
.
2019
:
5
(
8
):
897
–
903
. doi:10.1016/j.jacep.2019.05.018

Google Scholar

PubMed
OpenURL Placeholder Text

 
21.

Middeldorp
ME
,
Pathak
RK
,
Meredith
M
,
Mehta
AB
,
Elliott
AD
,
Mahajan
R
,
Twomey
D
,
Gallagher
C
,
Hendriks
JML
,
Linz
D
, et al. .
PREVEntion and regReSsive Effect of weight-loss and risk factor modification on Atrial Fibrillation: the REVERSE-AF study
.
Europace
.
2018
:
20
(
12
):
1929
–
1935
. doi:10.1093/europace/euy117

Google Scholar

Crossref
Search ADS
PubMed

 
22.

Billingham
SA
,
Whitehead
AL
,
Julious
SA.
An audit of sample sizes for pilot and feasibility trials being undertaken in the United Kingdom registered in the United Kingdom Clinical Research Network database
.
BMC Med Res Methodol
.
2013
:
13
(
1
):
104
. doi:10.1186/1471-2288-13-104

Google Scholar

Crossref
Search ADS
PubMed

 
23.

Julious
SA.
Sample size of 12 per group rule of thumb for a pilot study
.
Pharmaceut Statist
.
2005
:
4
(
4
):
287
–
291
. doi:10.1002/pst.185

Google Scholar

Crossref
Search ADS

 
24.

van Belle
G.
Statistical Rules of Thumb
. Second Edition.
Hoboken, NJ
:
John Wiley & Sons, Inc
.;
2008
. doi:10.1002/9780470377963

Google Scholar

OpenURL Placeholder Text

 
25.

Patridge
EF
,
Bardyn
TP.
Research Electronic Data Capture (REDCap)
.
J Med Libr Assoc
.
2018
:
106
(
1
):
142
–
144
. doi:10.5195/jmla.2018.319

Google Scholar

Crossref
Search ADS

 
26.

Berger
AS
,
Dunn
AS
,
Kelley
AS.
A standardized bleeding risk score aligns anticoagulation choices with current evidence
.
Crit Pathw Cardiol
.
2014
:
13
(
3
):
109
–
113
. doi:10.1097/HPC.0000000000000017

Google Scholar

Crossref
Search ADS
PubMed

 
27.

Barra
ME
,
Fanikos
J
,
Connors
JM
,
Sylvester
KW
,
Piazza
G
,
Goldhaber
SZ.
Evaluation of dose-reduced direct oral anticoagulant therapy
.
Am J Med
.
2016
:
129
(
11
):
1198
–
1204
. doi:10.1016/j.amjmed.2016.05.041

Google Scholar

Crossref
Search ADS
PubMed

 
28.

Wijtvliet
EPJP
,
Tieleman
RG
,
van Gelder
IC
, et al. .
Nurse-led vs usual-care for atrial fibrillation
.
Eur Heart J
.
2020
:
41
(
5
):
634
–
641
. doi:10.1093/eurheartj/ehz666

Google Scholar

Crossref
Search ADS
PubMed

 
29.

van den Dries
CJ
,
Van Doorn
S
,
Rutten
FH
,
Oudega
R
,
van de Leur
SJCM
,
Elvan
A
,
Oude Grave
L
,
Bilo
HJG
,
Moons
KGM
,
Hoes
AW
, et al. .
Integrated management of atrial fibrillation in primary care: results of the ALL-IN cluster randomized trial
.
Eur Heart J
.
2020
:
41
(
30
):
2836
–
2844
. doi:10.1093/eurheartj/ehaa055

Google Scholar

Crossref
Search ADS
PubMed

 
30.

Adeleke
Y
,
Matthew
D
,
Porter
B
,
Woodcock
T
,
Yap
J
,
Hashmy
S
,
Mathew
A
,
Grant
R
,
Kaba
A
,
Unger-Graeber
B
, et al. .
Improving the quality of care for patients with or at risk of atrial fibrillation: an improvement initiative in UK general practices
.
Open Heart
.
2019
:
6
(
2
):
e001086
. doi:10.1136/openhrt-2019-001086

Google Scholar

Crossref
Search ADS
PubMed

 
31.

Piccini
JP
,
Xu
H
,
Cox
M
,
Matsouaka
RA
,
Fonarow
GC
,
Butler
J
,
Curtis
AB
,
Desai
N
,
Fang
M
,
McCabe
PJ
, et al. ;
Get With The Guidelines-AFIB Clinical Working Group and Hospitals
.
Adherence to guideline-directed stroke prevention therapy for atrial fibrillation is achievable: first results from Get With The Guidelines–Atrial Fibrillation (GWTG-AFIB)
.
Circulation
.
2019
:
139
(
12
):
1497
–
1506
. doi:10.1161/CIRCULATIONAHA.118.035909

Google Scholar

Crossref
Search ADS
PubMed

 
32.

Vinereanu
D
,
Lopes
RD
,
Bahit
MC
,
Xavier
D
,
Jiang
J
,
Al-Khalidi
HR
,
He
W
,
Xian
Y
,
Ciobanu
AO
,
Kamath
DY
, et al. ;
IMPACT-AF investigators
.
A multifaceted intervention to improve treatment with oral anticoagulants in atrial fibrillation (IMPACT-AF): an international, cluster-randomised trial
.
Lancet
.
2017
:
390
(
10104
):
1737
–
1746
. doi:10.1016/S0140-6736(17)32165-7

Google Scholar

Crossref
Search ADS
PubMed

 
33.

de Lusignan
S
,
Hobbs
FDR
,
Liyanage
H
,
Ferreira
F
,
Tripathy
M
,
Munro
N
,
Feher
M
,
Joy
M.
Improving the management of atrial fibrillation in general practice: protocol for a mixed methods study
.
JMIR Res Protoc
.
2020
:
9
(
11
):
e21259
. doi:10.2196/21259

Google Scholar

Crossref
Search ADS
PubMed

 
34.

January
CT
,
Wann
LS
,
Calkins
H
,
Chen
LY
,
Cigarroa
JE
,
Cleveland
JC
,
Ellinor
PT
,
Ezekowitz
MD
,
Field
ME
,
Furie
KL
, et al. .
2019 AHA/ACC/HRS Focused Update of the 2014 AHA/ACC/HRS guideline for the management of patients with atrial fibrillation
.
J Am Coll Cardiol
.
2019
:
74
(
1
):
104
–
132
. doi:10.1016/j.jacc.2019.01.011

Google Scholar

Crossref
Search ADS
PubMed

 
35.

January
CT
,
Wann
LS
,
Alpert
JS
,
Calkins
H
,
Cigarroa
JE
,
Cleveland
JC
,
Conti
JB
,
Ellinor
PT
,
Ezekowitz
MD
,
Field
ME
, et al. ;
ACC/AHA Task Force Members
.
2014 AHA/ACC/HRS guideline for the management of patients with atrial fibrillation: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines and the Heart Rhythm Society
.
Circulation
.
2014
:
130
(
23
):
2071
–
2104
. doi:10.1161/CIR.0000000000000041

Google Scholar

Crossref
Search ADS
PubMed

 
36.

National Institute for Health and Care Excellence (NICE)
.
Atrial fibrillation: diagnosis and management (NICE guideline NG196)
. https://www.nice.org.uk/guidance/ng196.

OpenURL Placeholder Text

© The Author(s) 2024. Published by Oxford University Press.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
Issue Section:
Quality Improvement
Download all slides
  • Supplementary data

    • Supplementary data

    cmae001_suppl_Supplementary_Checklist - docx file