4L-6 RETHINKING METHODS TO IDENTIFY OPTIMAL TREATMENT AND DIAGNOSTIC THRESHOLDS: EVALUATING RISK-BASED OUTCOMES IN CORONARY HEART DISEASE

Tuesday, June 14, 2016: 15:00
Stephenson Room, 5th Floor (30 Euston Square)

Anoukh van Giessen, MSc1, Ardine de Wit, PhD1, Carl Moons, PhD1 and Hendrik Koffijberg, PhD2, (1)Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, Netherlands, (2)University of Twente, Enschede, Netherlands

Purpose: Current approaches to defining treatment or diagnostic thresholds are commonly based on average effects, which may lead to incorrect decisions on individual level. We demonstrate a general approach to identify treatment or diagnostic thresholds optimizing individual health outcomes, illustrated for statin treatment based on 10-year coronary heart disease (CHD) risk predicted by the Framingham risk score (FRS).

Method(s): A health economic model was created to evaluate risk-based preventive statin treatment. Based on the Atherosclerosis Risk in Communities study cohorts of men and women aged 50–59 years at low-intermediate or high CHD-risk were simulated and followed for 30 years. Strategies gradually including more individuals by lowering the treatment threshold T (20%-0%;1% decrements) were compared. Differences in health outcomes, quality-adjusted life-years (QALYs) and cost-effectiveness, were assessed at each step to identify optimal treatment thresholds. Cost-effectiveness was evaluated by calculating the net health benefit (NHB) for a willingness-to-pay of $50,000/QALY. At every threshold T both incremental (compared to T=20%) and marginal (compared to T=T+1%) outcomes were evaluated.

Result(s): QALYs ranged from 12.621 in men and 13.696 in women at T=20% to a maximum of 12.689 in men at T=1% and 13.734 in women at T=0%. Keeping the population-level fraction of statin-induced complications <10% resulted in thresholds of T=6% for men and T=2% for women. Lowering the threshold and comparing outcomes after each 1% decrease, QALYs were gained down to T=1% for men and T=0% for women. The incremental NHB was favorable for every threshold down to T=0% among men and down to T=2% among women (Figure 1A). The incremental NHB achieved a maximum at T=3% for men and at T=6% for women, with a NHB of 3,919 and 834 QALYs among 100,000 men and women, respectively. Correspondingly, the marginal NHB was favorable down to T=3% for men and T=6% for women (Figure 1B).

Conclusion(s): Many approaches can be taken to arrive at a treatment or diagnostic threshold. However, current intuition-based approaches leave ample room for health gain and cost savings. Using a stepwise risk-based approach to threshold optimization allows for treatment and diagnostic strategies that optimize outcomes in all individuals instead of on average. This approach can be applied to any outcome, such as to limit complications or missed diagnoses, to maximize health outcomes, or to optimize cost-effectiveness.