Background
Screening for atrial fibrillation (AF) has been recommended but is yet to be implemented in clinical practice. However, the most effective approaches for screening are not known and it is unclear if screening could feasibly be implemented in primary care.
Aims and methods
The overall aims were to determine how AF screening might feasibly and effectively be introduced into primary care in the United Kingdom (UK). Objectives were: 1) to determine the range and accuracies of methods for detecting pulse irregularities attributable to AF, 2) to determine the range and accuracies of methods for diagnosing AF using 12-lead electrocardiograms (ECGs) and 3) to investigate the feasibility and opinions of healthcare professionals (HCPs) in primary care about implementing AF screening.
Three studies were undertaken: 1) a systematic review and meta-analysis of the diagnostic accuracy of methods for detecting pulse irregularities caused by AF, 2) a systematic review and meta-analysis of the diagnostic accuracy of methods for diagnosing AF using 12-lead ECG and 3) a survey of HCPs in primary care about screening implementation.
Results
Study 1: Blood pressure monitors (BPMs) and non-12-lead ECGs had the greatest accuracy for detecting pulse irregularities attributable to AF [BPM: sensitivity 0.98 (95% CI 0.92-1.00), specificity 0.92 (95% CI 0.88-0.95), positive likelihood ratio (PLR) 12.1 (95% C.I 8.2-17.8) and negative likelihood ratio (NLR) 0.02 (95% C.I 0.00-0.09); non-12-lead ECG: sensitivity 0.91 (95% CI 0.86-0.94), specificity 0.95 (95% CI 0.92-0.97), PLR 20.1 (95% C.I 12-33.7), NLR 0.09 (95% C.I 0.06 to 0.14); there were similar findings for smart-phone applications although these studies were small in size. The sensitivity and specificity of pulse palpation were 0.92 (95% CI 0.85-0.96) and 0.82 (95% CI 0.76-0.88), respectively (PLR 5.2 (95% C.I 3.8-7.2), NLR 0.1 (0.05-0.18)].
Study 2: The sensitivity and specificity of automated software were 0.89 (95% CI 0.82-0.93) and 0.99 (95% CI 0.99-0.99), respectively; PLR 96.6 (95% C.I 64.2-145.6); NLR 0.11 (95% C.I 0.07-0.18). ECG interpretation by any HCPs had a similar sensitivity for diagnosing AF as automated software but a lower specificity [sensitivity 0.92 (95% CI 0.81-0.97), specificity 0.93 (95% CI 0.76-0.98), PLR 13.9 (95% C.I 3.5-55.3), NLR 0.09 (95% C.I 0.03-0.22). Sub-group analyses of primary care professionals found greater specificity for General Practitioners (GPs) than nurses [GPs: sensitivity 0.91 (95% C.I 0.68-1.00); specificity 0.96 (95% C.I 0.89-1.00). Nurses: sensitivity 0.88 (95% C.I 0.63-1.00); specificity 0.85 (95% C.I 0.83-0.87)].
Study 3: 39/48 (81%) practices had an ECG machine and diagnosed AF in-house. Fewer non-GP HCPs reported having excellent knowledge about ECG interpretation, diagnosing and treating AF than GPs [Proportion (95% CI): ECG interpretation = GPs: 5.9 (2.8-12.0); healthcare assistants (HCAs): 0; nurses: 2.0 (0.3-13.9); Nurse practitioners (NPs): 11.8 (3.0-36.4). Diagnosing AF = GPs: 26.3 (17.8-37.0); HCAs: 0; nurses: 2.0 (0.3-12.9); NPs: 11.8 (2.7-38.8). Treating AF = GPs: 16.9 (9.9-27.4); HCAs: 0; nurses: 0; NPs: 5.9 (0.8-34.0)]. A greater proportion of non-GP HCPs reported they would benefit from ECG training specifically for AF diagnosis than GPs [proportion (95% CI) GPs: 11.9% (6.8-20.0); HCAs: 37.0% (21.7-55.5); nurses: 44.0% (30.0-59.0); NPs 41.2% (21.9-63.7)]. Barriers included time, workload and capacity to undertake screening activities, although training to diagnose and manage AF was a required facilitator.
Conclusions
BPMs and non-12-lead ECG were most accurate for detecting pulse irregularities caused by AF. Automated ECG-interpreting software most accurately excluded AF, although its ability to diagnose this was similar to all other HCP groups. Within primary care, the specificity of AF diagnosis was greater for GPs than nurses. Inner-city general practices were found to have adequate access to resources for AF screening. Non-GP HCPs would like to up-skill in the diagnosis and management of AF and they may have a role in future AF screening. However, organisational barriers, such as lack of time, staff and capacity, should be overcome for AF screening to be feasibly implemented within primary car
