Undertaking multi-centre randomised controlled trials in primary care: learnings and recommendations from the PULsE-AI trial researchers.

BACKGROUND: Conducting effective and translational research can be challenging and few trials undertake formal reflection exercises and disseminate learnings from them. Following completion of our multicentre randomised controlled trial, which was impacted by the COVID-19 pandemic, we sought to reflect on our experiences and share our thoughts on challenges, lessons learned, and recommendations for researchers undertaking or considering research in primary care. METHODS: Researchers involved in the Prediction of Undiagnosed atriaL fibrillation using a machinE learning AlgorIthm (PULsE-AI) trial, conducted in England from June 2019 to February 2021 were invited to participate in a qualitative reflection exercise. Members of the Trial Steering Committee (TSC) were invited to attend a semi-structured focus group session, Principal Investigators and their research teams at practices involved in the trial were invited to participate in a semi-structured interview. Following transcription, reflexive thematic analysis was undertaken based on pre-specified themes of recruitment, challenges, lessons learned, and recommendations that formed the structure of the focus group/interview sessions, whilst also allowing the exploration of new themes that emerged from the data. RESULTS: Eight of 14 members of the TSC, and one of six practices involved in the trial participated in the reflection exercise. Recruitment was highlighted as a major challenge encountered by trial researchers, even prior to disruption due to the COVID-19 pandemic. Researchers also commented on themes such as the need to consider incentivisation, and challenges associated with using technology in trials, especially in older age groups. CONCLUSIONS: Undertaking a formal reflection exercise following the completion of the PULsE-AI trial enabled us to review experiences encountered whilst undertaking a prospective randomised trial in primary care. In sharing our learnings, we hope to support other clinicians undertaking research in primary care to ensure that future trials are of optimal value for furthering knowledge, streamlining pathways, and benefitting patients.

Cardiovascular outcomes by time-varying New York Heart Association class among patients with obstructive hypertrophic cardiomyopathy: a retrospective cohort study.

AIMS: Assess the relationship between New York Heart Association (NYHA) functional class and cardiovascular (CV) outcomes in obstructive hypertrophic cardiomyopathy (HCM). MATERIALS AND METHODS: This retrospective cohort study used the Optum Market Clarity database with linked claims and electronic health records. Adults (aged ≥18 years) with obstructive HCM and ≥1 NYHA class assessment after first HCM diagnosis were eligible (selection period: 2007-2021). Thirteen outcomes were assessed following the index date (first documented NYHA class assessment after first HCM diagnosis in the study period): all-cause mortality; first occurrences of all-cause hospitalization; CV-related hospitalization; primary ischemic stroke or transient ischemic attack (TIA); myocardial infarction (MI); deep vein thrombosis (DVT) or pulmonary embolism (PE); and major adverse CV event (MACE); as well as first incident events of atrial fibrillation or flutter; primary ischemic stroke or TIA; heart failure; acute MI; DVT/PE; and a composite endpoint of pacemaker and cardiac resynchronization therapy. Their associations with the index NYHA class were described using the Kaplan-Meier method (mortality) or cumulative incidence functions (other outcomes). Hazard ratios between NYHA class over time and outcomes were evaluated using time-varying Cox models, adjusting for age at first observed HCM diagnosis, sex, and race. RESULTS: Among 4,631 eligible patients, the mean age was 59 years at the first observed HCM diagnosis (female, 47%; White, 77%). The risks of all outcomes increased with worse (higher) index NYHA class and worsening NYHA class over time. Deterioration in the NYHA class from the index date was associated with increased risks of outcomes. LIMITATIONS: The study population may not be representative of all patients with obstructive HCM in the real world. Documented NYHA classes may not fully reflect the longitudinal variation of NYHA class for each patient. CONCLUSIONS: Worsening NYHA class was associated with increased risks of all-cause mortality and CV outcomes in obstructive HCM.

The New York Heart Association (NYHA) class is a simple way for doctors to measure how bad a patient's heart failure is by how it affects a person's ability to do everyday activities. It is a 4-point scale from 1, indicating no limitations on activity and no shortness of breath, to 4, at which patients have symptoms even at rest and any activity leaves people struggling to catch their breath. NYHA class is also used to assess patients with obstructive hypertrophic cardiomyopathy (HCM), a disease that causes thickening of the heart muscle. While doctors know that as obstructive HCM becomes worse, patients are at greater risk of having to go to the hospital, getting other conditions (like atrial fibrillation or heart failure), having to have more treatments (like surgery), or even death, doctors and researchers do not know how much risk the patient has and how it changes as the disease changes over time. Although there have been some smaller studies that have estimated this risk, we studied a large, national database and found that patients with worse (higher) NYHA class over time had an increased risk of dying, having to go to the hospital for heart-related care, and developing other heart-related conditions. This finding suggests that it is important for doctors to follow up patients with obstructive HCM carefully and to adjust treatments in order to help patients to stay at lower NYHA classes to improve long-term outcomes.

Sex Differences in Oral Anticoagulation Therapy in Patients Hospitalized With Atrial Fibrillation: A Nationwide Cohort Study.

Background Important disparities in the treatment and outcomes of women and men with atrial fibrillation (AF) are well recognized. Whether introduction of direct oral anticoagulants has reduced disparities in treatment is uncertain. Methods and Results All patients who had an incident hospitalization from 2010 to 2019 with nonvalvular AF in Scotland were included in the present cohort study. Community drug dispensing data were used to determine prescribed oral anticoagulation therapy and comorbidity status. Logistic regression modeling was used to evaluate patient factors associated with treatment with vitamin K antagonists and direct oral anticoagulants. A total of 172 989 patients (48% women [82 833 of 172 989]) had an incident hospitalization with nonvalvular AF in Scotland between 2010 and 2019. By 2019, factor Xa inhibitors accounted for 83.6% of all oral anticoagulants prescribed, while treatment with vitamin K antagonists and direct thrombin inhibitors declined to 15.9% and 0.6%, respectively. Women were less likely to be prescribed any oral anticoagulation therapy compared with men (adjusted odds ratio [aOR], 0.68 [95% CI, 0.67-0.70]). This disparity was mainly attributed to vitamin K antagonists (aOR, 0.68 [95% CI, 0.66-0.70]), while there was less disparity in the use of factor Xa inhibitors between women and men (aOR, 0.92 [95% CI, 0.90-0.95]). Conclusions Women with nonvalvular AF were significantly less likely to be prescribed vitamin K antagonists compared with men. Most patients admitted to the hospital in Scotland with incident nonvalvular AF are now treated with factor Xa inhibitors and this is associated with fewer treatment disparities between women and men.

Trends in direct oral anticoagulant (DOAC) prescribing in English primary care (2014-2019).

BACKGROUND: In England, most prescribing of direct-acting oral anticoagulants for atrial fibrillation (AF) is in primary care. However, there remain gaps in our understanding of dosage and disparities in use. We aimed to describe trends in direct oral anticoagulant (DOAC) prescribing, including dose reduction in people with renal impairment and other criteria, and adherence. METHODS: Using English primary care sentinel network data from 2014 to 2019, we assessed appropriate DOAC dose adjustment with creatinine clearance (CrCl). Our primary care sentinel cohort was a subset of 722 general practices, with 6.46 million currently registered patients at the time of this study. RESULTS: Of 6 464 129 people in the cohort, 2.3% were aged ≥18 years with a diagnosis of AF, and 30.8% of these were prescribed vitamin K antagonist and 69.1% DOACs. Appropriate DOAC prescribing following CrCl measures improved between 2014 and 2019; dabigatran from 21.3% (95% CI 15.1% to 28.8%) to 48.7% (95% CI 45.0% to 52.4%); rivaroxaban from 22.1% (95% CI 16.7% to 28.4%) to 49.9% (95% CI 48.5% to 53.3%); edoxaban from 10.0% (95% CI 0.3% to 44.5%) in 2016 to 57.6% (95% CI 54.5% to 60.7%) in 2019; apixaban from 30.8% (95% CI 9.1% to 61.4%) in 2015 to 60.5% (95% CI 57.8% to 63.2%) in 2019.Adherence was highest for factor Xa inhibitors, increasing from 50.1% (95% CI 47.7% to 52.4%) in 2014 to 57.8% (95% CI 57.4% to 58.2%) in 2019. Asian and black/mixed ethnicity was associated with non-adherence (OR 1.81, 95% CI 1.56 to 2.09) as was male gender (OR 1.19, 95% CI 1.15 to 1.22), higher socioeconomic status (OR 1.60, 95% CI 1.52 to 1.68), being an ex-smoker (OR 1.12, 95% CI 1.06 to 1.19) and hypertension (OR 1.07, 95% CI 1.03 to 1.17). CONCLUSIONS: The volume and quality of DOAC prescribing has increased yearly. Future interventions to augment quality of anticoagulant management should target disparities in adherence.

Identification of undiagnosed atrial fibrillation using a machine learning risk prediction algorithm and diagnostic testing (PULsE-AI) in primary care: cost-effectiveness of a screening strategy evaluated in a randomized controlled trial in England.

OBJECTIVE: The PULsE-AI trial sought to determine the effectiveness of a screening strategy that included a machine learning risk prediction algorithm in conjunction with diagnostic testing for identification of undiagnosed atrial fibrillation (AF) in primary care. This study aimed to evaluate the cost-effectiveness of implementing the screening strategy in a real-world setting. METHODS: Data from the PULsE-AI trial - a prospective, randomized, controlled trial conducted across six general practices in England from June 2019 to February 2021 - were used to inform a cost-effectiveness analysis that included a hybrid screening decision tree and Markov AF disease progression model. Model outcomes were reported at both individual- and population-level (estimated UK population ≥30 years of age at high-risk of undiagnosed AF) and included number of patients screened, number of AF cases identified, mean total and incremental costs (screening, events, treatment), quality-adjusted-life-years (QALYs), and incremental cost-effectiveness ratio (ICER). RESULTS: The screening strategy was estimated to result in 45,493 new diagnoses of AF across the high-risk population in the UK (3.3 million), and an estimated additional 14,004 lifetime diagnoses compared with routine care only. Per-patient costs for high-risk individuals who underwent the screening strategy were estimated at £1,985 (vs £1,888 for individuals receiving routine care only). At a population-level, the screening strategy was associated with a cost increase of approximately £322 million and an increase of 81,000 QALYs. The screening strategy demonstrated cost-effectiveness versus routine care only at an accepted ICER threshold of £20,000 per QALY-gained, with an ICER of £3,994/QALY. CONCLUSIONS: Compared with routine care only, it is cost-effective to target individuals at high risk of undiagnosed AF, through an AF risk prediction algorithm, who should then undergo diagnostic testing. This AF risk prediction algorithm can reduce the number of patients needed to be screened to identify undiagnosed AF, thus alleviating primary care burden.

Budget impact analysis of a machine learning algorithm to predict high risk of atrial fibrillation among primary care patients.

AIMS: We investigated whether the use of an atrial fibrillation (AF) risk prediction algorithm could improve AF detection compared with opportunistic screening in primary care and assessed the associated budget impact. METHODS AND RESULTS: Eligible patients were registered with a general practice in UK, aged 65 years or older in 2018/19, and had complete data for weight, height, body mass index, and systolic and diastolic blood pressure recorded within 1 year. Three screening scenarios were assessed: (i) opportunistic screening and diagnosis (standard care); (ii) standard care replaced by the use of the algorithm; and (iii) combined use of standard care and the algorithm. The analysis considered a 3-year time horizon, and the budget impact for the National Health Service (NHS) costs alone or with personal social services (PSS) costs. Scenario 1 would identify 79 410 new AF cases (detection gap reduced by 22%). Scenario 2 would identify 70 916 (gap reduced by 19%) and Scenario 3 would identify 99 267 new cases (gap reduction 27%). These rates translate into 2639 strokes being prevented in Scenario 1, 2357 in Scenario 2, and 3299 in Scenario 3. The 3-year NHS budget impact of Scenario 1 would be £45.3 million, £3.6 million (difference ‒92.0%) with Scenario 2, and £46.3 million (difference 2.2%) in Scenario 3, but for NHS plus PSS would be ‒£48.8 million, ‒£80.4 million (64.8%), and ‒£71.3 million (46.1%), respectively. CONCLUSION: Implementation of an AF risk prediction algorithm alongside standard opportunistic screening could close the AF detection gap and prevent strokes while substantially reducing NHS and PSS combined care costs.

Identification of undiagnosed atrial fibrillation using a machine learning risk-prediction algorithm and diagnostic testing (PULsE-AI) in primary care: a multi-centre randomized controlled trial in England.

Aims: The aim of the PULsE-AI trial was to assess the effectiveness of a machine learning risk-prediction algorithm in conjunction with diagnostic testing for identifying undiagnosed atrial fibrillation (AF) in primary care in England. Methods and results: Eligible participants (aged ≥30 years without AF diagnosis; n = 23 745) from six general practices in England were randomized into intervention and control arms. Intervention arm participants, identified by the algorithm as high risk of undiagnosed AF (n = 944), were invited for diagnostic testing (n = 256 consented); those who did not accept the invitation, and all control arm participants, were managed routinely. The primary endpoint was the proportion of AF, atrial flutter, and fast atrial tachycardia diagnoses during the trial (June 2019-February 2021) in high-risk participants. Atrial fibrillation and related arrhythmias were diagnosed in 5.63% and 4.93% of high-risk participants in intervention and control arms, respectively {odds ratio (OR) [95% confidence interval (CI)]: 1.15 (0.77-1.73), P = 0.486}. Among intervention arm participants who underwent diagnostic testing (28.1%), 9.41% received AF and related arrhythmia diagnoses [vs. 4.93% (control); OR (95% CI): 2.24 (1.31-3.73), P = 0.003]. Conclusion: The AF risk-prediction algorithm accurately identified high-risk participants in both arms. While the proportions of AF and related arrhythmia diagnoses were not significantly different between high-risk arms, intervention arm participants who underwent diagnostic testing were twice as likely to receive arrhythmia diagnoses compared with routine care. The algorithm could be a valuable tool to select primary care groups at high risk of undiagnosed AF who may benefit from diagnostic testing.

Ectopy-triggering ganglionated plexuses ablation to prevent atrial fibrillation: GANGLIA-AF study.

BACKGROUND: The ganglionated plexuses (GPs) of the intrinsic cardiac autonomic system may play a role in atrial fibrillation (AF). OBJECTIVE: We hypothesized that ablating the ectopy-triggering GPs (ET-GPs) prevents AF. METHODS: GANGLIA-AF (ClinicalTrials.gov identifier NCT02487654) was a prospective, randomized, controlled, 3-center trial. ET-GPs were mapped using high frequency stimulation, delivered within the atrial refractory period and ablated until nonfunctional. If triggered AF became incessant, atrioventricular dissociating GPs were ablated. We compared GP ablation (GPA) without pulmonary vein isolation (PVI) against PVI in patients with paroxysmal AF. Follow-up was for 12 months including 3-monthly 48-hour Holter monitors. The primary end point was documented ≥30 seconds of atrial arrhythmia after a 3-month blanking period. RESULTS: A total of 102 randomized patients were analyzed on a per-protocol basis after GPA (n = 52; 51%) or PVI (n = 50; 49%). Patients who underwent GPA had 89 ± 26 high frequency stimulation sites tested, identifying a median of 18.5% (interquartile range 16%-21%) of GPs. The radiofrequency ablation time was 22.9 ± 9.8 minutes in GPA and 38 ± 14.4 minutes in PVI (P < .0001). The freedom from ≥30 seconds of atrial arrhythmia at 12-month follow-up was 50% (26 of 52) with GPA vs 64% (32 of 50) with PVI (log-rank, P = .09). ET-GPA without atrioventricular dissociating GPA achieved 58% (22 of 38) freedom from the primary end point. There was a significantly higher reduction in antiarrhythmic drug usage postablation after GPA than after PVI (55.5% vs 36%; P = .05). Patients were referred for redo ablation procedures in 31% (16 of 52) after GPA and 24% (12 of 50) after PVI (P = .53). CONCLUSION: GPA did not prevent atrial arrhythmias more than PVI. However, less radiofrequency ablation was delivered to achieve a higher reduction in antiarrhythmic drug usage with GPA than with PVI.

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.

Targeting the ectopy-triggering ganglionated plexuses without pulmonary vein isolation prevents atrial fibrillation.

BACKGROUND: Ganglionated plexuses (GPs) are implicated in atrial fibrillation (AF). Endocardial high-frequency stimulation (HFS) delivered within the local atrial refractory period can trigger ectopy and AF from specific GP sites (ET-GP). The aim of this study was to understand the role of ET-GP ablation in the treatment of AF. METHODS: Patients with paroxysmal AF indicated for ablation were recruited. HFS mapping was performed globally around the left atrium to identify ET-GP. ET-GP was defined as atrial ectopy or atrial arrhythmia triggered by HFS. All ET-GP were ablated, and PVs were left electrically connected. Outcomes were compared with a control group receiving pulmonary vein isolation (PVI). Patients were followed-up for 12 months with multiple 48-h Holter ECGs. Primary endpoint was ≥30 s AF/atrial tachycardia in ECGs. RESULTS: In total, 67 patients were recruited and randomized to ET-GP ablation (n = 39) or PVI (n = 28). In the ET-GP ablation group, 103 ± 28 HFS sites were tested per patient, identifying 21 ± 10 (20%) GPs. ET-GP ablation used 23.3 ± 4.1 kWs total radiofrequency (RF) energy per patient, compared with 55.7 ± 22.7 kWs in PVI (p = <.0001). Duration of procedure was 3.7 ± 1.0 and 3.3 ± 0.7 h in ET-GP ablation group and PVI, respectively (p = .07). Follow-up at 12 months showed that 61% and 49% were free from ≥30 s of AF/AT with PVI and ET-GP ablation respectively (log-rank p = .27). CONCLUSIONS: It is feasible to perform detailed global functional mapping with HFS and ablate ET-GP to prevent AF. This provides direct evidence that ET-GPs are part of the AF mechanism. The lower RF requirement implies that ET-GP targets the AF pathway more specifically.

Identification of undiagnosed atrial fibrillation patients using a machine learning risk prediction algorithm and diagnostic testing (PULsE-AI): Study protocol for a randomised controlled trial.

Atrial fibrillation (AF) is associated with an increased risk of stroke, enhanced stroke severity, and other comorbidities. However, AF is often asymptomatic, and frequently remains undiagnosed until complications occur. Current screening approaches for AF lack either cost-effectiveness or diagnostic sensitivity; thus, there is interest in tools that could be used for population screening. An AF risk prediction algorithm, developed using machine learning from a UK dataset of 2,994,837 patients, was found to be more effective than existing models at identifying patients at risk of AF. Therefore, the aim of the trial is to assess the effectiveness of this risk prediction algorithm combined with diagnostic testing for the identification of AF in a real-world primary care setting. Eligible participants (aged ≥30 years and without an existing AF diagnosis) registered at participating UK general practices will be randomised into intervention and control arms. Intervention arm participants identified at highest risk of developing AF (algorithm risk score ≥ 7.4%) will be invited for a 12­lead electrocardiogram (ECG) followed by two-weeks of home-based ECG monitoring with a KardiaMobile device. Control arm participants will be used for comparison and will be managed routinely. The primary outcome is the number of AF diagnoses in the intervention arm compared with the control arm during the research window. If the trial is successful, there is potential for the risk prediction algorithm to be implemented throughout primary care for narrowing the population considered at highest risk for AF who could benefit from more intensive screening for AF. Trial Registration: NCT04045639.

The ectopy-triggering ganglionated plexuses in atrial fibrillation.

BACKGROUND: Epicardial ganglionated plexuses (GP) have an important role in the pathogenesis of atrial fibrillation (AF). The relationship between anatomical, histological and functional effects of GP is not well known. We previously described atrioventricular (AV) dissociating GP (AVD-GP) locations. In this study, we hypothesised that ectopy triggering GP (ET-GP) are upstream triggers of atrial ectopy/AF and have different anatomical distribution to AVD-GP. OBJECTIVES: We mapped and characterised ET-GP to understand their neural mechanism in AF and anatomical distribution in the left atrium (LA). METHODS: 26 patients with paroxysmal AF were recruited. All were paced in the LA with an ablation catheter. High frequency stimulation (HFS) was synchronised to each paced stimulus for delivery within the local atrial refractory period. HFS responses were tagged onto CARTO™ 3D LA geometry. All geometries were transformed onto one reference LA shell. A probability distribution atlas of ET-GP was created. This identified high/low ET-GP probability regions. RESULTS: 2302 sites were tested with HFS, identifying 579 (25%) ET-GP. 464 ET-GP were characterised, where 74 (16%) triggered ≥30s AF/AT. Median 97 (IQR 55) sites were tested, identifying 19 (20%) ET-GP per patient. >30% of ET-GP were in the roof, mid-anterior wall, around all PV ostia except in the right inferior PV (RIPV) in the posterior wall. CONCLUSION: ET-GP can be identified by endocardial stimulation and their anatomical distribution, in contrast to AVD-GP, would be more likely to be affected by wide antral circumferential ablation. This may contribute to AF ablation outcomes.

A Systematic Review of Network Meta-Analyses and Real-World Evidence Comparing Apixaban and Rivaroxaban in Nonvalvular Atrial Fibrillation.

There is no direct evidence comparing the 2 most commonly prescribed direct oral anticoagulants, apixaban and rivaroxaban, used for stroke prevention in nonvalvular atrial fibrillation (NVAF). A number of network meta-analyses (NMAs) of randomized control trials and real-world evidence (RWE) studies comparing the efficacy, effectiveness, and safety of apixaban and rivaroxaban have been published; however, a comprehensive evidence review across the available body of evidence is lacking. In this study, we aimed to systematically review and evaluate the clinical outcomes of apixaban and rivaroxaban using a combination of data gleaned from both NMAs and RWE studies. The review identified 21 NMAs and 5 RWE studies. The data demonstrated that apixaban was associated with fewer major bleeding events compared to rivaroxaban. There was no difference in the efficacy/effectiveness profiles between these treatments. Bleeding is a serious complication of anticoagulation therapy for the management of NVAF, and is associated with increased rates of hospitalization, morbidity, mortality, and health-care expenditure. The majority of studies in this comprehensive evidence review suggests that apixaban has a lower risk of major bleeding events compared to rivaroxaban in patients with NVAF.

Single Ectopy-Triggering Ganglionated Plexus Ablation Without Pulmonary Vein Isolation Prevents Atrial Fibrillation.

A 58-year-old woman with drug-refractory symptoms of paroxysmal atrial fibrillation (AF) was referred for AF ablation. A single site of ganglionated plexus triggering pulmonary vein ectopy and AF was ablated, without pulmonary vein isolation. This procedure led to long-term freedom from AF. (Level of Difficulty: Advanced.).

Application of a machine learning algorithm for detection of atrial fibrillation in secondary care.

Atrial fibrillation (AF) is the most common sustained heart arrhythmia and significantly increases risk of stroke. Opportunistic AF testing in high-risk patients typically requires frequent electrocardiogram tests to capture the arrhythmia. Risk-prediction algorithms may help to more accurately identify people with undiagnosed AF and machine learning (ML) may aid in the diagnosis of AF. Here, we applied an AF-risk prediction algorithm to secondary care data linked to primary care data in the DISCOVER database in order to evaluate changes in model performance, and identify patients not previously detected in primary care. We identified an additional 5,444 patients who had an AF diagnosis only in secondary care during the data extraction period. 2,696 (49.5%) were accepted by the algorithm and the algorithm correctly assigned 2,637 (97.8%) patients to the AF cohort. Using a risk threshold of 7.4% in patients aged ≥ 30 years, algorithm sensitivity and specificity was 38% and 95%, respectively. Approximately 15% of AF patients assigned to the AF cohort by the algorithm had a secondary care diagnosis with no record of AF in primary care. These additional patients did not substantially alter algorithm performance. The additional detection of previously undiagnosed AF patients in secondary care highlights unexpected potential utility of this ML algorithm.

Cost-effectiveness of targeted screening for the identification of patients with atrial fibrillation: evaluation of a machine learning risk prediction algorithm.

Aims: As many cases of atrial fibrillation (AF) are asymptomatic, patients often remain undiagnosed until complications (e.g. stroke) manifest. Risk-prediction algorithms may help to efficiently identify people with undiagnosed AF. However, the cost-effectiveness of targeted screening remains uncertain. This study aimed to assess the cost-effectiveness of targeted screening, informed by a machine learning (ML) risk prediction algorithm, to identify patients with AF.Methods: Cost-effectiveness analyses were undertaken utilizing a hybrid screening decision tree and Markov disease progression model. Costs and outcomes associated with the detection of AF compared traditional systematic and opportunistic AF screening strategies to targeted screening informed by a ML risk prediction algorithm. Model analyses were based on adults ≥50 years and adopted the UK NHS perspective.Results: Targeted screening using the ML risk prediction algorithm required fewer patients to be screened (61 per 1,000 patients, compared to 534 and 687 patients in the systematic and opportunistic strategies) and detected more AF cases (11 per 1,000 patients, compared to 6 and 8 AF cases in the systematic and opportunistic screening strategies). The targeted approach demonstrated cost-effectiveness under base case settings (cost per QALY gained of £4,847 and £5,544 against systematic and opportunistic screening respectively). The targeted screening strategy was predicted to provide an additional 3.40 and 2.05 QALYs per 1,000 patients screened versus systematic and opportunistic strategies. The targeted screening strategy remained cost-effective in all scenarios evaluated.Limitations: The analysis relied on assumptions that include the extended period of patient life span and the lack of consideration for treatment discontinuations/switching, as well as the assumption that the ML risk-prediction algorithm will identify asymptomatic AF.Conclusions: Targeted screening using a ML risk prediction algorithm has the potential to enhance the clinical and cost-effectiveness of AF screening, improving health outcomes through efficient use of limited healthcare resources.

Ablation versus Anti-Arrhythmic Therapy for Reducing All Hospital Episodes from Recurrent Atrial Fibrillation (AVATAR-AF): Design and rationale.

Atrial Fibrillation (AF) ablation using the cryoballoon is effective at reducing symptomatic AF episodes. The prevalence of AF is increasing with the aging population and access to such treatment would be enhanced by reducing the resource requirements. Relinquishing electrical mapping of the pulmonary veins (PV) removes the need for PV catheters, electrical recording equipment and staff trained in using this equipment. Moreover, the majority of complications are peri-procedural so overnight hospitalization maybe unnecessary. We tested this streamlined approach to AF ablation against medical therapy using the endpoint of time to all hospital episodes. METHODS: The AVATAR-AF study is a prospective, multicenter, randomized controlled trial testing the primary hypothesis that AF ablation done without PV mapping or overnight hospitalization is more effective than anti-arrhythmic drugs at reducing all hospital episodes related to recurrent atrial arrhythmias. We included a third arm to test a secondary hypothesis that confirming PV entrance block as per consensus guidelines can improve outcomes. Three hundred twenty-one patients with documented paroxysmal AF will be randomized in a 1:1:1 manner to one of three investigation arms: (1) AVATAR protocol cryoballoon ablation without assessment of acute PV isolation or overnight hospitalization; (2) medical therapy with anti-arrhythmic drugs; or (3) conventional cryoballoon ablation with assessment of acute PV isolation. The primary endpoint is defined as the time to all hospital episodes (including outpatient consultation) related to treatment for atrial arrhythmia. CONCLUSION: The AVATAR-AF study will determine whether the resource utilization for AF ablation can be reduced whilst maintaining superiority over medical therapy.

A novel approach to mapping the atrial ganglionated plexus network by generating a distribution probability atlas.

INTRODUCTION: The ganglionated plexuses (GPs) of the intrinsic cardiac autonomic system are implicated in arrhythmogenesis. GP localization by stimulation of the epicardial fat pads to produce atrioventricular dissociating (AVD) effects is well described. We determined the anatomical distribution of the left atrial GPs that influence atrioventricular (AV) dissociation. METHODS AND RESULTS: High frequency stimulation was delivered through a Smart-Touch catheter in the left atrium of patients undergoing atrial fibrillation (AF) ablation. Three dimensional locations of points tested throughout the entire chamber were recorded on the CARTO™ system. Impact on the AV conduction was categorized as ventricular asystole, bradycardia, or no effect. CARTO maps were exported, registered, and transformed onto a reference left atrial geometry using a custom software, enabling data from multiple patients to be overlaid. In 28 patients, 2108 locations were tested and 283 sites (13%) demonstrated (AVD-GP) effects. There were 10 AVD-GPs (interquartile range, 11.5) per patient. Eighty percent (226) produced asystole and 20% (57) showed bradycardia. The distribution of the two groups was very similar. Highest probability of AVD-GPs (>20%) was identified in: inferoseptal portion (41%) and right inferior pulmonary vein base (30%) of the posterior wall, right superior pulmonary vein antrum (31%). CONCLUSION: It is feasible to map the entire left atrium for AVD-GPs before AF ablation. Aggregated data from multiple patients, producing a distribution probability atlas of AVD-GPs, identified three regions with a higher likelihood for finding AVD-GPs and these matched the histological descriptions. This approach could be used to better characterize the autonomic network.

Drivers of Atrial Fibrillation: Theoretical Considerations and Practical Concerns.

Understanding the mechanisms responsible for driving AF is key to improving the procedural success for AF ablation. In this review, we look at some of the proposed drivers of AF, the disagreement between experts and the challenges confronted in attempting to map AF. Defining a 'driver' is also controversial, but for the purposes of this review we will consider an AF driver to be either a focal or localised source demonstrating fast, repetitive activity that propagates outward from this source, breaking down in to disorganisation further away from its origin.