AimsTo determine whether changing patterns of anticoagulant use in atrial fibrillation (AF) have impacted on stroke rates in England.Methods and resultsEnglish national databases, 2006–2016, were interrogated to assess stroke admissions and oral anticoagulant use. The number of patients with known AF increased linearly from 692 054 to 983 254 (prevalence 1.29% vs. 1.71%). Hospital episodes of AF-related stroke/100 000 AF patients increased from 80/week in 2006 to 98/week in 2011 and declined to 86/week in 2016 (2006–2011 difference 18.0, 95% confidence interval (CI) 17.9–18.1, 2011–2016 difference −12.0, 95% CI −12.1 to −11.9). Anticoagulant use amongst patients with CHA2DS2-VASc ≥2 increased from 48.0% to 78.6% and anti-platelet use declined from 42.9% to 16.1%; the greatest rate of change occurred in the second 5 year period (for anticoagulants 2006–2011 difference 4.8%, 95% CI 4.5–5.1%, 2011–2016 difference 25.8%, 95% CI 25.5–26.1%). After adjustment for AF prevalence, a 1% increase in anticoagulant use was associated with a 0.8% decrease in the weekly rate of AF-related stroke (incidence rate ratio 0.992, 95% CI 0.989–0.994). Had the use of anticoagulants remained at 2009 levels, 4068 (95% CI 4046–4089) more strokes would have been predicted in 2015/2016.ConclusionBetween 2006 and 2016, AF prevalence and anticoagulant use in England increased. From 2011, hospitalized AF-related stroke rates declined and were significantly associated with increased anticoagulant uptake.

Introduction Atrial fibrillation (AF) is the most common cardiac arrhythmia and can lead to significant comorbidities and mortality. Persistence with oral anticoagulation (OAC) is crucial to prevent stroke but rates of discontinuation are high. This systematic review explored underlying reasons for OAC discontinuation. Methods A systematic review was undertaken to identify studies that reported factors influencing discontinuation of OAC in AF, in 11 databases, grey literature and backwards citations from eligible studies published between 2000 and 2019. Two reviewers independently screened titles, abstracts and papers against inclusion criteria and extracted data. Study quality was appraised using Gough’s weight of evidence framework. Data were synthesised narratively. Results Of 6,619 sources identified, 10 full studies and 2 abstracts met the inclusion criteria. Overall, these provided moderate appropriateness to answer the review question. Four reported clinical registry data, six were retrospective reviews of patients’ medical records and two studies reported interviews and surveys. Nine studies evaluated outcomes relating to dabigatran and/or warfarin and three included rivaroxaban (n = 3), apixaban (n = 3) and edoxaban (n = 1). Bleeding complications and gastrointestinal events were the most common factors associated with discontinuation, followed by frailty and risk of falling. Patients’ perspectives were seldom specifically assessed. Influence of family carers in decisions regarding OAC discontinuation was not examined. Conclusion The available evidence is derived from heterogeneous studies with few relevant data for the newer direct oral anticoagulants. Reasons underpinning decision-making to discontinue OAC from the perspective of patients, family carers and clinicians is poorly understood.

Objective The “Bow-tie” optimal pathway discovery analysis uses large clinical event datasets to map clinical pathways and to visualize risks (improvement opportunities) before, and outcomes after, a specific clinical event. This proof-of-concept study assesses the use of NHS Hospital Episode Statistics (HES) in England as a potential clinical event dataset for this pathway discovery analysis approach. Materials and Methods A metaheuristic optimization algorithm was used to perform the “bow-tie” analysis on HES event log data for sepsis (ICD-10 A40/A41) in 2016. Analysis of hospital episodes across inpatient and outpatient departments was performed for the period 730 days before and 365 days after the index sepsis hospitalization event. Results HES data captured a sepsis event for 76 523 individuals (>13 years), relating to 580 000 coded events (across 220 sepsis and non-sepsis event classes). The “bow-tie” analysis identified several diagnoses that most frequently preceded hospitalization for sepsis, in line with the expectation that sepsis most frequently occurs in vulnerable populations. A diagnosis of pneumonia (5 290 patients) and urinary tract infections (UTIs; 2 057 patients) most often preceded the sepsis event, with recurrent UTIs acting as a potential indicative risk factor for sepsis. Discussion This proof-of-concept study demonstrates that a “bow-tie” pathway discovery analysis of the HES database can be undertaken and provides clinical insights that, with further study, could help improve the identification and management of sepsis. The algorithm can now be more widely applied to HES data to undertake targeted clinical pathway analysis across multiple healthcare conditions.

ObjectiveTo assess temporal clinical and budget impacts of changes in atrial fibrillation (AF)-related prescribing in England.MethodsData on AF prevalence, AF-related stroke incidence and prescribing for all National Health Service general practices, hospitals and registered patients with hospitalised AF-related stroke in England were obtained from national databases. Stroke care costs were based on published data. We compared changes in oral anticoagulation prescribing (warfarin or direct oral anticoagulants (DOACs)), incidence of hospitalised AF-related stroke, and associated overall and per-patient costs in the periods January 2011–June 2014 and July 2014–December 2017.ResultsBetween 2011–2014 and 2014–2017, recipients of oral anticoagulation for AF increased by 86.5% from 1 381 170 to 2 575 669. The number of patients prescribed warfarin grew by 16.1% from 1 313 544 to 1 525 674 and those taking DOACs by 1452.7% from 67 626 to 1 049 995. Prescribed items increased by 5.9% for warfarin (95% CI 2.9% to 8.9%) but by 2004.8% for DOACs (95% CI 1848.8% to 2160.7%). Oral anticoagulation prescription cost rose overall by 781.2%, from £87 313 310 to £769 444 028, (£733,466,204 with warfarin monitoring) and per patient by 50.7%, from £293 to £442, giving an incremental cost of £149. Nevertheless, as AF-related stroke incidence fell by 11.3% (95% CI −11.5% to −11.1%) from 86 467 in 2011–2014 to 76 730 in 2014–2017 with adjustment for AF prevalence, the overall per-patient cost reduced from £1129 to £840, giving an incremental per-patient saving of £289.ConclusionsDespite nearly one million additional DOAC prescriptions and substantial associated spending in the latter part of this study, the decline in AF-related stroke led to incremental savings at the national level.

ObjectivesAssess whether impactibility modelling is being used to refine risk stratification for preventive health interventions.DesignSystematic review.SettingPrimary and secondary healthcare populations.PapersArticles published from 2010 to 2020 on the use or implementation of impactibility modelling in population health management, reported with the terms ‘intervenability’, ‘amenability’, and ‘propensity to succeed’ (PTS) and associated with the themes ‘care sensitivity’, ‘characteristic responders’, ‘needs gap’, ‘case finding’, ‘patient selection’ and ‘risk stratification’.InterventionsQualitative synthesis to identify themes for approaches to impactibility modelling.ResultsOf 1244 records identified, 20 were eligible for inclusion. Identified themes were ‘health conditions amenable to care’ (n=6), ‘PTS modelling’ (n=8) and ‘comparison or combination with clinical judgement’ (n=6). For the theme ‘health conditions amenable to care’, changes in practice did not reduce admissions, particularly for ambulatory care sensitive conditions, and sometimes increased them, with implementation noted as a possible issue. For ‘PTS modelling’, high costs and needs did not necessarily equate to high impactibility and targeting a larger number of individuals with disorders associated with lower costs had more potential. PTS modelling seemed to improve accuracy in care planning, estimation of cost savings, engagement and/or care quality. The ‘comparison or combination with clinical judgement’ theme suggested that models can reach reasonable to good discriminatory power to detect impactable patients. For instance, a model used to identify patients appropriate for proactive multimorbid care management showed good concordance with physicians (c-statistic 0.75). Another model employing electronic health record scores reached 65% concordance with nurse and physician decisions when referring elderly hospitalised patients to a readmission prevention programme. However, healthcare professionals consider much wider information that might improve or impede the likelihood of treatment impact, suggesting that complementary use of models might be optimum.ConclusionsThe efficiency and equity of targeted preventive care guided by risk stratification could be augmented and personalised by impactibility modelling.