Detecting undiagnosed atrial fibrillation in UK primary care: Validation of a machine learning prediction algorithm in a retrospective cohort study

Aims To evaluate the ability of a machine learning algorithm to identify patients at high risk of atrial fibrillation in primary care. Methods A retrospective cohort study was undertaken using the DISCOVER registry to validate an algorithm developed using a Clinical Practice Research Datalink (CPRD) dataset. The validation dataset included primary care patients in London, England aged ≥30 years from 1 January 2006 to 31 December 2013, without a diagnosis of atrial fibrillation in the prior 5 years. Algorithm performance metrics were sensitivity, specificity, positive predictive value, negative predictive value (NPV) and number needed to screen (NNS). Subgroup analysis of patients aged ≥65 years was also performed. Results Of 2,542,732 patients in DISCOVER, the algorithm identified 604,135 patients suitable for risk assessment. Of these, 3.0% (17,880 patients) had a diagnosis of atrial fibrillation recorded before study end. The area under the curve of the receiver operating characteristic was 0.87, compared with 0.83 in algorithm development. The NNS was nine patients, matching the CPRD cohort. In patients aged ≥30 years, the algorithm correctly identified 99.1% of patients who did not have atrial fibrillation (NPV) and 75.0% of true atrial fibrillation cases (sensitivity). Among patients aged ≥65 years (n = 117,965), the NPV was 96.7% with 91.8% sensitivity. Conclusions This atrial fibrillation risk prediction algorithm, based on machine learning methods, identified patients at highest risk of atrial fibrillation. It performed comparably in a large, real-world population-based cohort and the developmental registry cohort. If implemented in primary care, the algorithm could be an effective tool for narrowing the population who would benefit from atrial fibrillation screening in the United Kingdom

Hospital admissions for stroke and bleeding in Hounslow following a quality improvement initiative

Objective Atrial fibrillation (AF) is the most common arrhythmia. Undiagnosed and poorly managed AF increases risk of stroke. The Hounslow AF quality improvement (QI) initiative was associated with improved quality of care for patients with AF through increased detection of AF and appropriate anticoagulation. This study aimed to evaluate whether there has been a change in stroke and bleeding rates in the Hounslow population following the QI initiative. Methods Using hospital admissions data from January 2011 to August 2018, interrupted time series analysis was performed to investigate the changes in standardised rates of admission with stroke and bleeding, following the start of the QI initiative in October 2014. Results There was a 17% decrease in the rate of admission with stroke as primary diagnosis (incidence rate ratio (IRR) 0.83; 95% CI 0.712 to 0.963; p<0.014). There was an even larger yet not statistically significant decrease in admission with stroke as primary diagnosis and AF as secondary diagnosis (IRR 0.75; 95% CI 0.550 to 1.025; p<0.071). No significant changes were observed in bleeding admissions. For each outcome, an additional regression model including both the level change and an interaction term for slope change was created. In all cases, the slope change was small and not statistically significant. Conclusion Reduction in stroke admissions may be associated with the AF QI initiative. However, the immediate level change and non-significant slope change suggests a lack of effect of the intervention over time and that the decrease observed may be attributable to other events

Europeanizing the Balkans: rethinking the post-communist and post-conflict transition

This paper argues that the post-communist and post-conflict transition of the Balkans requires a methodological shift in line with globalization, which shapes political and economic transformation from within through transnational networks. As a specially tailored mechanism leading to the accession of the Balkans into the European Union, the Stabilization and Association Process (SAp) sets the framework for political and economic transformation of the region. The paper posits that the weakness of the EU's approach derives from the fact that it is informed by the dominant transition paradigm, which marginalizes the impact of globalization, and specifically the role of transnational actors. The paper provides a critique of the transition literature and its explanatory potential to account for the post-conflict and post-communist transition in the Balkans. It goes on to examine the Balkan transnational space and the role of transnational actors in the process of transition as an important additional explanation, while taking into account a double legacy: the domestic legacy, inherited from communism, and the transnational and post-communist legacy acquired during the conflict. It advances an argument that a weak state offers us a conceptual nexus for the study of democratic transition in the Balkans in the global age. We demonstrate that transnational networks benefit from a weak state and perpetuate the very weakness that sustains them. At the same time, these networks exploit multi-ethnicity and stir ethnic tensions, lest stabilization should limit their scope for action. As a result, state- and nation building appear as mutually enfeebling rather than reinforcing, thus subverting the existing EU mechanisms