Development of delivery indicators and delivery enablers for cardiovascular disease in the UK: a modified Delphi study.

INTRODUCTION: Standards to define and measure quality in healthcare for cardiovascular disease risk reduction and secondary prevention are available, but there is a paucity of indicators that could serve as facilitators of structural change at a system level. This research study aimed to develop a range of delivery indicators to help cardiac clinical networks assess delivery of and progress towards cardiovascular disease objectives. METHODS: This study used an adapted version of the European Society of Cardiology's four-step process for the development of quality indicators. The four steps in this study were as follows: identify critical factors of enablement, construct a list of candidate indicators, select a final set of indicators and assess availability of national data for each indicator. In this iterative process, a core project group of six members was supported by a wider review group of 21 people from the National Health Service (NHS) clinical and management personnel database. RESULTS: The core project group identified six relevant cardiovascular disease priorities in the NHS Long Term Plan and used an iterative process to identify 21 critical factors that impact on their implementation. A total of 57 potential indicators that could be measures of implementation were developed. The core project group agreed on a set of 38 candidate indicators that were circulated to the review group for rating. Based on these scores, the core project group excluded 5 indicators to arrive at a final set of 33 delivery indicators. National datasets were available for 22 of the final indicators, which were designated as delivery indicators. The remaining 11, for which national datasets were not available but locally available datasets could be used, were designated as delivery enablers. CONCLUSION: The suite of delivery indicators and delivery enablers for cardiovascular disease could allow a more focused evaluation of factors that impact on delivery of healthcare for cardiovascular disease.

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.

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.

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.

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 non-anticoagulation costs of atrial fibrillation management: findings from an observational study in NHS Primary Care.

BACKGROUND: Atrial fibrillation (AF) management represents a significant burden on the UK NHS. Understanding this burden will be important in informing future health care planning and policy development. AIM: To describe the non-anticoagulation costs associated with AF management in routine UK clinical practice. MATERIALS PATIENTS AND METHODS: A retrospective observational study of 825 patients with AF undertaken in eight UK primary care practices. Data collected from routine clinical and prescribing records of all eligible, consenting patients, for a period of up to 3 years. The first 12 weeks following diagnosis was defined as the 'initiation phase'; the period after week 12 was defined as the 'maintenance phase'. RESULTS: Mean (SD) total cost of AF management was £941 (£1094) per patient in the initiation phase and £426 (£597) per patient-year in the maintenance phase. AF-related inpatient admissions contributed most to total costs; the mean (SD) total cost per patient in the initiation phase was £2285 (£900) for admitted and £278 (£252) for non-admitted patients. Mean maintenance phase costs per year were £1323 (£755) and £168 (£234), respectively, for admitted and non-admitted patients. Significant patient variables contributing to high cost in the initiation phase were hypertension and younger patient age, although only accounting for 6% of cost variability. Significant variables in the maintenance phase (18% of cost variability) were the presence of congestive heart failure, structural heart disease or diabetes and the frequency of day case admissions, ECGs and hospitalisations in the initiation phase. CONCLUSIONS: Inpatient admissions contributed most to total AF management costs. Given the burden of hospital care, future work should focus on evaluating the appropriateness and reasons for hospitalisation in patients with AF and the factors affecting length of stay, with the aim of identifying opportunities to safely reduce inpatient costs. A number of significant patient characteristics and initiation phase variables were identified, which accounted for 18% of the variability in total maintenance phase costs. However, none of these could adequately predict high maintenance phase costs.

Current management of atrial fibrillation: an observational study in NHS primary care.

OBJECTIVE: To describe National Health Service (NHS) resource use and pharmacological management of atrial fibrillation (AF) in routine UK primary care. DESIGN: Multicentre retrospective study. SETTING: Seven primary care practices in England, one in Wales. PATIENTS: Patients with AF were identified and approached for consent. Data were collected on the first 12 weeks post-diagnosis ('initiation') and, for established patients, up to the most recent 3 years of management ('maintenance'). RESULTS: Data collected on 825 patients with AF, 56% men. Mean age (at diagnosis) 70.5 years. Mean 2.4 (SD 2.2) visits to primary care per patient during the initiation phase; 1.5 (SD 1.8) per patient-year during the maintenance phase. Mean 0.4 (SD 0.6) inpatient admissions for AF per patient during the initiation phase and 0.1 (SD 0.3) per patient-year during the maintenance phase. The mean length of hospital stay per admitted patient was 5.6 days during initiation and 6.4 days per patient-year during maintenance. During the initiation phase, 46.1% (143/310) patients received a ß-blocker and 97 (31.3%) received no rate/rhythm control. Only 234 (75.5%) patients received thromboprophylaxis in the 12 weeks postdiagnosis and 674 (87.7%) in the maintenance phase. 440 (57.2%) patients were deemed to be at high risk of stroke at the end of the maintenance phase; 55% (242/440) of these were receiving appropriate anticoagulation therapy. CONCLUSIONS: The results suggest that there are opportunities for optimisation of treatment and there is significant NHS resource associated with AF management, the details of which are invaluable for future healthcare planning and policy development in this area.