Extrapolated longer-term effects of the DAPA-CKD trial: a modelling analysis.

BACKGROUND: The Dapagliflozin and Prevention of Adverse Outcomes in Chronic Kidney Disease (DAPA-CKD) trial assessed dapagliflozin versus placebo, in addition to standard therapy, in patients with chronic kidney disease (CKD) and albuminuria, and was terminated prematurely due to overwhelming efficacy. The study objective was to model the long-term clinical outcomes of DAPA-CKD beyond the trial follow-up. METHODS: A Markov model extrapolated event incidence per 1000 patients and CKD progression rates for patients receiving dapagliflozin or placebo over a 10-year time horizon. We derived treatment-specific CKD stage transition matrices using DAPA-CKD trial data. We extrapolated relevant efficacy endpoints using parametric survival equations for all-cause mortality and generalized estimating equations for recurrent events. RESULTS: When extrapolated over a 10-year period, patients randomized to dapagliflozin spent more time in CKD stages 1-3 and less in stages 4-5 than placebo [0.65 (95% CrI 0.41, 0.90) and -0.23 (95% CrI -0.45, 0.00) years per patient, respectively]. Dapagliflozin prevented an estimated 83 deaths and 51 patients initiating kidney replacement therapy per 1000 patients over 10 years. Predicted rates of hospitalized heart failure and abrupt declines in kidney function were reduced (19 and 39 estimated events per 1000 patients, respectively). CONCLUSIONS: Adding dapagliflozin to standard therapeutic management of CKD is expected to have long-term cardiorenal benefit beyond what has been demonstrated in the DAPA-CKD trial, with patients predicted to live longer with fewer complications.

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.

Cardiorenal disease in the United States: Future health care burden and potential impact of novel therapies.

BACKGROUND: Currently, concerted efforts to identify, prevent, and treat type 2 diabetes mellitus (T2DM), heart failure (HF), and chronic kidney disease (CKD) comorbidities are lacking at the institutional level, with emphasis placed on individual specialties. An integrated approach to tackle T2DM, HF, and CKD within the context of cardiorenal disease has the potential to improve outcomes and reduce costs at the system level. OBJECTIVE: To synthesize published evidence describing the burden of those diagnosed with T2DM, HF, and CKD in the United States as individual discrete chronic conditions, in order to evaluate the potential economic impact of novel therapies in this population. METHODS: We developed a compartmental Markov model with an annual time cycle to model an evolving prevalent US patient population with T2DM, HF, or CKD over the period 2021-2030 (either in isolation or combined). The model was used to explore the potential impact of novel therapies such as sodium-glucose cotransporter 2 inhibitors on future disease burden, by extrapolating the results of relevant clinical trials to representative patient populations. RESULTS: The model estimates that total prevalence across all disease states will have increased by 28% in 2030. Cumulatively, the direct health care cost of cardiorenal disease between 2021 and 2030 is estimated at $4.8 trillion. However, treatment with dapagliflozin has the potential to reduce disease prevalence by 8.0% and estimated cumulative service delivery costs by 3.6% by 2030. CONCLUSIONS: Considering a holistic approach when managing patients with cardiorenal disease offers an opportunity to reduce the disease burden over the next 10 years in the US population. DISCLOSURES: This work was funded by AstraZeneca, which provided support for data analysis. McEwan, Morgan, and Boyce are employees of Health Economics and Outcomes Research Ltd., Cardiff, UK, which received fees from AstraZeneca in relation to this study. Song and Huang are employees of AstraZeneca. Bergenheim is an employee of AstraZeneca and holds AstraZeneca stocks/stock options. Green has no conflicts of interest to declare.

The cost-effectiveness of dapagliflozin in treating high-risk patients with type 2 diabetes mellitus: An economic evaluation using data from the DECLARE-TIMI 58 trial.

AIM: To undertake a cost-effectiveness analysis of dapagliflozin in treating high-risk patients with type 2 diabetes mellitus (T2DM), using both directly observed events in the DECLARE-TIMI 58 trial and surrogate risk factors to predict endpoints not captured within the trial. METHODS: An established T2DM model was adapted to integrate survival curves derived from the DECLARE-TIMI 58 trial, and extrapolated over a lifetime for all-cause mortality, hospitalization for heart failure, stroke, myocardial infarction, hospitalization for unstable angina, and end-stage kidney disease. The economic analysis considered the overall DECLARE trial population, as well as reported patient subgroups. Total and incremental costs, life-years and quality-adjusted life-years associated with dapagliflozin versus placebo were estimated from the perspective of the UK healthcare payer. RESULTS: In the UK setting, treatment with dapagliflozin compared to placebo was estimated to be dominant, with an expected increase in quality-adjusted life-years from 10.43 to 10.48 (+0.06) and a reduction in lifetime total costs from £39 451 to £36 899 (-£2552). Across all patient subgroups, dapagliflozin was estimated to be dominant, with the greatest absolute benefit in the prior heart failure subgroup (incremental lifetime costs -£4150 and quality-adjusted life-years +0.11). CONCLUSIONS: The results of this study demonstrate that dapagliflozin compared to placebo appears to be cost-effective, when considering evidence reported from the DECLARE-TIMI 58 trial, at established UK willingness-to-pay thresholds. The findings highlight the potential of dapagliflozin to have a meaningful impact in reducing the economic burden of T2DM and its associated complications across a broad T2DM population.

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.