BackgroundSmoking is a UK public health threat but GPs can be effective in helping patients to quit; consequently, the Quality and Outcomes Framework (QOF) incentivises the recording of smoking status and delivery of cessation advice in patients’ medical records. This study investigates the association between smoking-related QOF targets and such recording, and the factors which influence these clinical activities.MethodsFor 2000 to 2008, using medical records in The Health Improvement Network (THIN) database, the annual proportions of i) patients who had a record of smoking status made in the previous 27 months and ii) current smokers recorded as receiving cessation advice in the previous 15 months were calculated. Then, for all patients at selected points before and after the QOF’s implementation, data on gender, age, Townsend score, and smoking-related morbidity were extracted. Multivariate logistic regression was used to investigate individual-level characteristics associated with the recording of smoking status and cessation advice.ResultsRapid increases in recording smoking status and advice occurred around the QOF’s introduction in April 2004. Subsequently, compliance to targets has been sustained, although rates of increase have slowed. By 2008 64.5% of patients aged 15+ had smoking status documented in the previous 27 months and 50.5% of current smokers had cessation advice recorded in the last 15 months. Adjusted odds ratios show that, both before and after the introduction of the QOF, those with chronic medical conditions, greater social deprivation and women were more likely to have a recent recording of smoking status or cessation advice. Since the QOF’s introduction, the strongest characteristic associated with recording activities was the presence of co-morbidity. An example of this was patients with COPD, who in 2008, were 15.38 (95% CI 13.70-17.27) times and 11.72 (95% CI 10.41-13.21) times more likely to have a record of smoking status and cessation advice, respectively.ConclusionsRates of recording smoking status and cessation advice plateaued after large increases during the QOF’s introduction; however, recording remains most strongly associated with the presence of chronic disease as specified by the QOF, and suggests that incentivised targets have a direct effect on clinical behaviour.

BackgroundPulse palpation has been recommended as the first step of screening to detect atrial fibrillation. We aimed to determine and compare the accuracy of different methods for detecting pulse irregularities caused by atrial fibrillation.MethodsWe systematically searched MEDLINE, EMBASE, CINAHL and LILACS until 16 March 2015. Two reviewers identified eligible studies, extracted data and appraised quality using the QUADAS-2 instrument. Meta-analysis, using the bivariate hierarchical random effects method, determined average operating points for sensitivities, specificities, positive and negative likelihood ratios (PLR, NLR); we constructed summary receiver operating characteristic plots.ResultsTwenty-one studies investigated 39 interventions (n = 15,129 pulse assessments) for detecting atrial fibrillation. Compared to 12-lead electrocardiography (ECG) diagnosed atrial fibrillation, blood pressure monitors (BPMs; seven interventions) and non-12-lead ECGs (20 interventions) had the greatest accuracy for detecting pulse irregularities attributable to atrial fibrillation (BPM: sensitivity 0.98 (95% confidence interval (CI) 0.92–1.00), specificity 0.92 (95% CI 0.88–0.95), PLR 12.1 (95% CI 8.2–17.8) and NLR 0.02 (95% CI 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% CI 12–33.7), NLR 0.09 (95% CI 0.06–0.14)). There were similar findings for smartphone applications (six interventions) although these studies were small in size. The sensitivity and specificity of pulse palpation (six interventions) were 0.92 (95% CI 0.85–0.96) and 0.82 (95% CI 0.76–0.88), respectively (PLR 5.2 (95% CI 3.8–7.2), NLR 0.1 (95% CI 0.05–0.18)).ConclusionsBPMs and non-12-lead ECG were most accurate for detecting pulse irregularities caused by atrial fibrillation; other technologies may therefore be pragmatic alternatives to pulse palpation for the first step of atrial fibrillation screening.

IntroductionIschaemic heart disease (IHD) is one of the most common causes of death in the UK and treatment of patients with IHD costs the National Health System (NHS) billions of pounds each year. Allopurinol is a xanthine oxidase inhibitor used to prevent gout that also has several positive effects on the cardiovascular system. The ALL-HEART study aims to determine whether allopurinol improves cardiovascular outcomes in patients with IHD.Methods and analysisThe ALL-HEART study is a multicentre, controlled, prospective, randomised, open-label blinded end point (PROBE) trial of allopurinol (up to 600 mg daily) versus no treatment in a 1:1 ratio, added to usual care, in 5215 patients aged 60 years and over with IHD. Patients are followed up by electronic record linkage and annual questionnaires for an average of 4 years. The primary outcome is the composite of non-fatal myocardial infarction, non-fatal stroke or cardiovascular death. Secondary outcomes include all-cause mortality, quality of life and cost-effectiveness of allopurinol. The study will end when 631 adjudicated primary outcomes have occurred. The study is powered at 80% to detect a 20% reduction in the primary end point for the intervention. Patient recruitment to the ALL-HEART study started in February 2014.Ethics and disseminationThe study received ethical approval from the East of Scotland Research Ethics Service (EoSRES) REC 2 (13/ES/0104). The study is event-driven and results are expected after 2019. Results will be reported in peer-reviewed journals and at scientific meetings. Results will also be disseminated to guideline committees, NHS organisations and patient groups.Trial registration number32017426, pre-results.