AN EVIDENCE-BASED APPROACH TO PREDICT LONG-TERM PATIENT OUTCOMES IN PATIENTS WITH FAMILIAL CHYLOMICRONEMIA SYNDROME
Author(s)
Baid H1, Hurst S2, Kumar A1, Priedane E1
1Huron Consulting Group, London, UK, 2Akcea Therapeutics, Cambridge, MA, USA
OBJECTIVES : Familial chylomicronemia syndrome (FCS) is a rare genetic disorder affecting the body’s ability to break down fats (lipids), characterized by severe hypertriglyceridemia and an increased risk of pancreatitis. In rare cases, pancreatitis can lead to acute complications which can lead to death. Pancreatitis can also have longer term complications including chronic pancreatitis or diabetes. Despite these potential serious complications, the mortality impact of pancreatitis is not well-understood. This study aimed to develop an analytical framework to extrapolate 2-year pivotal trial data to understand long-term risks of morbidity and mortality in patients treated with volanesorsen and standard-of-care (SoC) vs. SoC alone. METHODS : Clinical trial data on patient outcomes for volanesorsen with SoC was assessed and compared to patients receiving only SoC treatment. Using evidence from literature, the key risks due to short-term outcomes were used to estimate long-term effects. Pre-determined criteria were used to filter the evidence based on quality, plausibility, and relevance. A patient flow analysis was constructed, and probabilistic modelling was applied to determine the likelihood of each complication/outcome to occur for each cohort. Model outcomes were then validated with clinicians/KOLs. RESULTS : The model provided a probabilistic comparison of different outcomes for patients with FCS. Results of the model showed that patients on SoC alone had a 76.8% increase in the risk of mortality by age 75 years compared to a 4.4% increase in the risk of mortality for patients receiving volanesorsen with SoC. CONCLUSIONS : This model provides a framework to assess the potential impact of a novel therapy in a rare disease but must not be taken out of context as a clinical claim. As a next step, monitoring will focus on how closely real-world outcomes compare with model outputs for further refinements.
Conference/Value in Health Info
2019-11, ISPOR Europe 2019, Copenhagen, Denmark
Code
PRO128
Topic
Clinical Outcomes, Health Service Delivery & Process of Care, Methodological & Statistical Research
Topic Subcategory
Artificial Intelligence, Machine Learning, Predictive Analytics, Comparative Effectiveness or Efficacy, Disease Management, Relating Intermediate to Long-term Outcomes
Disease
Rare and Orphan Diseases