Residual cardiovascular risk reduction guided by lifetime benefit estimation in patients with symptomatic atherosclerotic disease: effectiveness and cost-effectiveness.

AIMS: To determine the (cost)-effectiveness of blood pressure lowering, lipid-lowering, and antithrombotic therapy guided by predicted lifetime benefit compared to risk factor levels in patients with symptomatic atherosclerotic disease. METHODS AND RESULTS: For all patients with symptomatic atherosclerotic disease in the UCC-SMART cohort (1996-2018; n = 7697) two treatment strategies were compared. The lifetime benefit-guided strategy was based on individual estimation of gain in cardiovascular disease (CVD)-free life with the SMART-REACH model. In the risk factor-based strategy, all patients were treated the following: low-density lipoprotein cholesterol (LDL-c) < 1.8 mmol/L, systolic blood pressure <140 mmHg, and antithrombotic medication. Outcomes were evaluated for the total cohort using a microsimulation model. Effectiveness was evaluated as total gain in CVD-free life and events avoided, cost-effectiveness as incremental cost-effectivity ratio (ICER). In comparison to baseline treatment, treatment according to lifetime benefit would lead to an increase of 24 243 CVD-free life years [95% confidence interval (CI) 19 980-29 909] and would avoid 940 (95% CI 742-1140) events in the next 10 years. For risk-factor based treatment, this would be an increase of 18 564 CVD-free life years (95% CI 14 225-20 456) and decrease of 857 (95% CI 661-1057) events. The ICER of lifetime benefit-based treatment with a treatment threshold of ≥1 year additional CVD-free life per therapy was €15 092/QALY gained and of risk factor-based treatment €9933/QALY gained. In a direct comparison, lifetime benefit-based treatment compared to risk factor-based treatment results in 1871 additional QALYs for the price of €36 538/QALY gained. CONCLUSION: Residual risk reduction guided by lifetime benefit estimation results in more CVD-free life years and more CVD events avoided compared to the conventional risk factor-based strategy. Lifetime benefit-based treatment is an effective and potentially cost-effective strategy for reducing residual CVD risk in patients with clinical manifest vascular disease.

Would treatment decisions about secondary prevention of CVD based on estimated lifetime benefit rather than 10-year risk reduction be cost-effective?

OBJECTIVE: To test the hypothesis that treatment decisions (treatment with a PCSK9-mAb versus no treatment) are both more effective and more cost-effective when based on estimated lifetime benefit than when based on estimated risk reduction over 10 years. METHODS: A microsimulation model was constructed for 10,000 patients with stable cardiovascular disease (CVD). Costs and quality-adjusted life years (QALYs) due to recurrent cardiovascular events and (non)vascular death were estimated for lifetime benefit-based compared to 10-year risk-based treatment, with PCSK9 inhibition as an illustration example. Lifetime benefit in months gained and 10-year absolute risk reduction were estimated using the SMART-REACH model, including an individualized treatment effect of PCSK9 inhibitors based on baseline low-density lipoprotein cholesterol. For the different numbers of patients treated (i.e. the 5%, 10%, and 20% of patients with the highest estimated benefit of both strategies), cost-effectiveness was assessed using the incremental cost-effectiveness ratio (ICER), indicating additional costs per QALY gain. RESULTS: Lifetime benefit-based treatment of 5%, 10%, and 20% of patients with the highest estimated benefit resulted in an ICER of €36,440/QALY, €39,650/QALY, or €41,426/QALY. Ten-year risk-based treatment decisions of 5%, 10%, and 20% of patients with the highest estimated risk reduction resulted in an ICER of €48,187/QALY, €53,368/QALY, or €52,390/QALY. CONCLUSION: Treatment decisions (treatment with a PCSK9-mAb versus no treatment) are both more effective and more cost-effective when based on estimated lifetime benefit than when based on estimated risk reduction over 10 years.

An alternative approach identified optimal risk thresholds for treatment indication: an illustration in coronary heart disease.

OBJECTIVES: Treatment thresholds based on risk predictions can be optimized by considering various health (economic) outcomes and performing marginal analyses, but this is rarely performed. We demonstrate a general approach to identify treatment thresholds optimizing individual health (economic) outcomes, illustrated for statin treatment based on 10-year coronary heart disease (CHD) risk predicted by the Framingham risk score. STUDY DESIGN AND SETTING: Creating a health economic model for a risk-based prevention strategy, risk thresholds can be evaluated on several outcomes of interest. Selecting an appropriate threshold range and decrement size for the thresholds and adapting the health economic model accordingly, outcomes can be calculated for each risk threshold. A stepwise, or marginal, comparison of clinical as well as health economic outcomes, that is, comparing outcomes using a specific threshold to outcomes of the former threshold while gradually lowering the threshold, then takes into account the balance between additional numbers of individuals treated and their outcomes (additional health effects and costs). In our illustration, using a Markov model for CHD, we evaluated risk thresholds by gradually lowering thresholds from 20% to 0%. RESULTS: This approach can be applied to identify optimal risk thresholds on any outcome, such as to limit complications, maximize health outcomes, or optimize cost-effectiveness. In our illustration, keeping the population-level fraction of statin-induced complications <10% resulted in thresholds of T = 6% (men) and T = 2% (women). Lowering the threshold and comparing quality-adjusted life-years (QALYs) after each 1% decrease, QALYs were gained down to T = 1% (men) and T = 0% (women). Also accounting for costs, net health benefits were favorable down to T = 3% (men) and T = 6% (women). CONCLUSION: Using a stepwise risk-based approach to threshold optimization allows for preventive strategies that optimize outcomes. Presenting this comprehensive overview of outcomes will better inform decision makers when defining a treatment threshold.

Cost-Effectiveness of Intensifying Lipid-Lowering Therapy With Statins Based on Individual Absolute Benefit in Coronary Artery Disease Patients.

BACKGROUND: A validated prediction model estimates the absolute benefit of intensive versus standard lipid-lowering therapy (LLT) with statins on next major cardiovascular events for individual patients with coronary artery disease. We aimed to assess whether targeting intensive LLT therapy to coronary artery disease patients with the highest predicted absolute benefit is cost-effective compared to treating all with standard or all with intensive LLT. METHODS AND RESULTS: A lifetime Markov model was constructed for coronary artery disease patients (n=10 000) with mean age 61 years. Number of major cardiovascular events, (non) vascular death, costs, and quality-adjusted life years (QALYs) were estimated for the following strategies: (1) standard LLT for all (reference strategy); (2) intensive LLT for those with 5-year absolute major cardiovascular events risk reduction (ARR) ≥3%, ≥2.3%, or ≥1.5% (corresponding to ≥20%, ≥15%, or ≥10% 5-year major cardiovascular events risk); and (3) intensive LLT for all. With intensive LLT for those with ≥3% 5-year ARR (13% of patients), 380 QALYs were gained for €2423/QALY. Using a threshold of ≥2.3% ARR (26% of patients), 630 QALYs were gained for €5653/QALY. Using a threshold of ≥1.5% ARR (56% of patients), 1020 QALYs were gained for €10 960/QALY. By treating all intensively, 1410 QALYs were gained (0.14 QALY per patient) for €17 223/QALY. With benefit-based treatment, 0.16 to 0.17 QALY was gained per treated patient. CONCLUSIONS: Intensive LLT with statins for all coronary artery disease patients results in the highest overall QALY gain against acceptable costs. However, the number of QALYs gained with intensive LLT by statins in individual patients can be increased with selective benefit-based treatment. CLINICAL TRIAL REGISTRATION: URL: http://www.clinicaltrials.gov. Unique identifiers: NCT00327691 and NCT00159835.