A comprehensive model for measuring real-life cost-effectiveness in eyecare:automation in care and evaluation of system (aces-rwm™)

Abstract This paper describes a holistic, yet simple and comprehensible, ecosystem model to deal with multiple and complex challenges in eyecare. It aims at producing the best possible wellbeing and eyesight with the available resources. When targeting to improve the real-world cost-effectiveness, what gets done in everyday practice needs be measured routinely, efficiently and unselectively. Collection of all real-world data of all patients will enable evaluation and comparison of eyecare systems and departments between themselves nationally and internationally. The concept advocates a strategy to optimize real-life effectiveness, sustainability and outcomes of the service delivery in ophthalmology. The model consists of three components: (1) resource-governing principles (i.e., to deal with increasing demand and limited resources), (2) real-world monitoring (i.e., to collect structured real-world data utilizing automation and visualization of clinical parameters, health-related quality of life and costs), and (3) digital innovation strategy (i.e., to evaluate and benchmark real-world outcomes and cost-effectiveness). The core value and strength of the model lies in the consensus and collaboration of all Finnish university eye clinics to collect and evaluate the uniformly structured real-world outcomes data. In addition to ophthalmology, the approach is adaptable to any medical discipline to efficiently generate real-world insights and resilience in health systems

Genetic architecture of human plasma lipidome and its link to cardiovascular disease

Abstract Understanding genetic architecture of plasma lipidome could provide better insights into lipid metabolism and its link to cardiovascular diseases (CVDs). Here, we perform genome-wide association analyses of 141 lipid species (n = 2,181 individuals), followed by phenome-wide scans with 25 CVD related phenotypes (n = 511,700 individuals). We identify 35 lipid-species-associated loci (P <5 ×10−8), 10 of which associate with CVD risk including five new loci-COL5A1, GLTPD2, SPTLC3, MBOAT7 and GALNT16 (false discovery rate<0.05). We identify loci for lipid species that are shown to predict CVD e.g., SPTLC3 for CER(d18:1/24:1). We show that lipoprotein lipase (LPL) may more efficiently hydrolyze medium length triacylglycerides (TAGs) than others. Polyunsaturated lipids have highest heritability and genetic correlations, suggesting considerable genetic regulation at fatty acids levels. We find low genetic correlations between traditional lipids and lipid species. Our results show that lipidomic profiles capture information beyond traditional lipids and identify genetic variants modifying lipid levels and risk of CVD