Evaluating the effect of inequalities in oral anti-coagulant prescribing on outcomes in people with atrial fibrillation.

Aims: Whilst anti-coagulation is typically recommended for thromboprophylaxis in atrial fibrillation (AF), it is often never prescribed or prematurely discontinued. The aim of this study was to evaluate the effect of inequalities in anti-coagulant prescribing by assessing stroke/systemic embolism (SSE) and bleeding risk in people with AF who continue anti-coagulation compared with those who stop transiently, permanently, or never start. Methods and results: This retrospective cohort study utilized linked Scottish healthcare data to identify adults diagnosed with AF between January 2010 and April 2016, with a CHA2DS2-VASC score of ≥2. They were sub-categorized based on anti-coagulant exposure: never started, continuous, discontinuous, and cessation. Inverse probability of treatment weighting-adjusted Cox regression and competing risk regression was utilized to compare SSE and bleeding risks between cohorts during 5-year follow-up. Of an overall cohort of 47 427 people, 26 277 (55.41%) were never anti-coagulated, 7934 (16.72%) received continuous anti-coagulation, 9107 (19.2%) temporarily discontinued, and 4109 (8.66%) permanently discontinued. Lower socio-economic status, elevated frailty score, and age ≥ 75 were associated with a reduced likelihood of initiation and continuation of anti-coagulation. Stroke/systemic embolism risk was significantly greater in those with discontinuous anti-coagulation, compared with continuous [subhazard ratio (SHR): 2.65; 2.39-2.94]. In the context of a major bleeding event, there was no significant difference in bleeding risk between the cessation and continuous cohorts (SHR 0.94; 0.42-2.14). Conclusion: Our data suggest significant inequalities in anti-coagulation prescribing, with substantial opportunity to improve initiation and continuation. Decision-making should be patient-centred and must recognize that discontinuation or cessation is associated with considerable thromboembolic risk not offset by mitigated bleeding risk.

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.

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.

A cost-utility study of the use of pregabalin added to usual care in refractory neuropathic pain patients in a Swedish setting.

OBJECTIVES: Patients refractory to older therapies for neuropathic pain (NeP) have few remaining therapeutic options. This study evaluates the cost-utility of pregabalin in the treatment of patients with refractory neuropathic pain in Sweden, from a healthcare and a societal perspective. STUDY LIMITATIONS: The use of non-randomized (observational) data to determine the effectiveness of treatments for NeP. The use of non-Swedish data for some input parameters in the model. METHODS: A previously constructed discrete event simulation model was adapted to compare pregabalin combined with usual care to usual care alone in a Swedish setting. Pain profiles were generated using clinical data from five non-randomized pregabalin studies in refractory NeP patients. Utility data were generated from a UK survey of patients with NeP. Cost data were generated from the Swedish Dental and Pharmaceutical Benefits Board (TLV's) product price database, a national NeP register, and a regional registry study. Indirect costs were estimated from published sources. One-way and probabilistic sensitivity analyses evaluated uncertainty in the model's output. RESULTS: The incremental cost-effectiveness ratio (ICER) for pregabalin plus usual care treatment compared to usual care was 51,616 SEK/€5364 and 123,993 SEK/€12,886 with and without indirect costs, respectively. One-way sensitivity analyses confirmed the clinical input data as the main driver of the model; even considerable changes to all other input parameters had only a modest effect on the ICER. The ICER remained well below a conservative threshold of 347,495 SEK/€36,113/£30,000 in all scenarios modelled. CONCLUSIONS: This study found pregabalin combined with usual care to be cost-effective compared to usual care in patients with refractory NeP from a Swedish Health Care perspective. Moreover, sensitivity analysis showed pregabalin's cost-effectiveness to be robust in all scenarios modelled.

A cost-utility study of the use of pregabalin in treatment-refractory neuropathic pain.

OBJECTIVES: A small but significant proportion of patients with peripheral neuropathic pain (NeP) are refractory to the typical treatments applied in clinical practice, including amitriptyline and gabapentin. Thus, they continue to suffer the debilitating effects of NeP. This study aimed to evaluate the cost-effectiveness of pregabalin in comparison to usual care, in patients with refractory NeP, from a third party payer's perspective (NHS). METHODS: A stochastic simulation model was constructed, using clinical data from four non-randomized studies, to generate pain pathways of patients receiving usual care and pregabalin. Treatment effect (pain reduction) was converted to quality-of-life (QoL) data, using a regression analysis based on new utility data, collected from a survey of refractory NeP patients presenting to pain clinics in Cardiff, Wales. All relevant direct costs were estimated using resource use from the survey data (where available) and unit costs from the British National Formulary (BNF). The analysis was run over a 5-year time horizon, with costs and benefits discounted at 3.5%. STUDY LIMITATIONS: The use of non-randomized (observational) data to characterize the effectiveness of treatments for NeP. Exclusion of productivity costs and consequences from the analysis. RESULTS: In the base case analysis, an incremental cost-effectiveness ratio (ICER) of £10,803 per quality adjusted life year (QALY) was attained. This result was found to be reasonably insensitive to variations in the key input parameters, with ICERs ranging from £8505 to £22,845 per QALY gained. CONCLUSIONS: The analysis shows that pregabalin is a cost-effective alternative to usual care in patients with refractory NeP, with an ICER well below the threshold typically adopted by UK health technology assessment groups, such as NICE.