Introduction: The HerediGene Population Study is a collaborative between Intermountain Health and deCODE genetics that seeks to understand the genetics behind diseases to better predict and prevent disease. Here we describe the natural history of familial hypercholesterolemia (FH) diagnosis (dx), treatment, and outcomes in HerediGene patients (pts) with an LDLR gene variant. Methods: From the first 32,159 sequenced pts, 157 (0.05%) had a pathogenic/likely pathogenic variant in LDLR. These LDLR carriers were divided into three groups - pts with no prior FH dx (n=47; 30%), pts with an FH dx made after a major adverse cardiovascular event (including, myocardial infarction, heart failure hospitalization, stroke, peripheral artery disease, and carotid artery disease) (n=41; 26%), and pts with an FH dx made prior to any MACE (n=69, 44%) (this is the referent group). The natural history of FH was examined using pairwise comparisons between the referent group and each of the first two groups. Results: The clinical characteristics, LDL-C values, treatments, and outcomes are shown in the Table. The age at last visit or death was similar for all these groups (median=62). Compared to pts without an FH dx, pts with FH dx prior to a MACE had significantly more LDL-C measurements, increased statin and other lipid-lowering medications, and had a large change in LDL-C measurements. They were slightly, but not statistically, less likely to have subsequent MACE (25% vs 28%; p=0.72). The LDL-C measurements and statin use were similar for those pts with FH dx after MACE and pts with FH dx prior to MACE. However, pts with FH dx after a MACE had higher rates of death (36.6% vs 11.6%; p=0.002). Conclusions: Aggressive treatment for and subsequent decrease in LDL-C following FH dx in pts with LDLR mutation, highlight the need for early diagnosis. Furthermore, if this dx was made prior to MACE the death rate was lower. Larger sized studies are needed, but these findings provide support for early screening for FH.
Abstract Background Respiratory Syncytial Virus (RSV), a major cause of bronchiolitis, has a large impact on the census of pediatric hospitals during outbreak seasons. Reliable prediction of the week these outbreaks will start, based on readily available data, could help pediatric hospitals better prepare for large outbreaks. Methods Naïve Bayes (NB) classifier models were constructed using weather data from 1985-2008 considering only variables that are available in real time and that could be used to forecast the week in which an RSV outbreak will occur in Salt Lake County, Utah. Outbreak start dates were determined by a panel of experts using 32,509 records with ICD-9 coded RSV and bronchiolitis diagnoses from Intermountain Healthcare hospitals and clinics for the RSV seasons from 1985 to 2008. Results NB models predicted RSV outbreaks up to 3 weeks in advance with an estimated sensitivity of up to 67% and estimated specificities as high as 94% to 100%. Temperature and wind speed were the best overall predictors, but other weather variables also showed relevance depending on how far in advance the predictions were made. The weather conditions predictive of an RSV outbreak in our study were similar to those that lead to temperature inversions in the Salt Lake Valley. Conclusions We demonstrate that Naïve Bayes (NB) classifier models based on weather data available in real time have the potential to be used as effective predictive models. These models may be able to predict the week that an RSV outbreak will occur with clinical relevance. Their clinical usefulness will be field tested during the next five years.
Abstract The electronic Medical Records and Genomics (eMERGE) Network assessed the feasibility of deploying portable phenotype rule-based algorithms with natural language processing (NLP) components added to improve performance of existing algorithms using electronic health records (EHRs). Based on scientific merit and predicted difficulty, eMERGE selected six existing phenotypes to enhance with NLP. We assessed performance, portability, and ease of use. We summarized lessons learned by: (1) challenges; (2) best practices to address challenges based on existing evidence and/or eMERGE experience; and (3) opportunities for future research. Adding NLP resulted in improved, or the same, precision and/or recall for all but one algorithm. Portability, phenotyping workflow/process, and technology were major themes. With NLP, development and validation took longer. Besides portability of NLP technology and algorithm replicability, factors to ensure success include privacy protection, technical infrastructure setup, intellectual property agreement, and efficient communication. Workflow improvements can improve communication and reduce implementation time. NLP performance varied mainly due to clinical document heterogeneity; therefore, we suggest using semi-structured notes, comprehensive documentation, and customization options. NLP portability is possible with improved phenotype algorithm performance, but careful planning and architecture of the algorithms is essential to support local customizations.
Twenty years ago, chromosomal abnormalities were the only identifiable genetic causes of a small fraction of congenital heart defects (CHD). Today, a de novo or inherited genetic abnormality can be identified as pathogenic in one-third of cases. We refer to them here as monogenic causes, insofar as the genetic abnormality has a readily detectable, large effect. What explains the other two-thirds? This review considers a complex genetic basis. That is, a combination of genetic mutations or variants that individually may have little or no detectable effect contribute to the pathogenesis of a heart defect. Genes in the embryo that act directly in cardiac developmental pathways have received the most attention, but genes in the mother that establish the gestational milieu via pathways related to metabolism and aging also have an effect. A growing body of evidence highlights the pathogenic significance of genetic interactions in the embryo and maternal effects that have a genetic basis. The investigation of CHD as guided by a complex genetic model could help estimate risk more precisely and logically lead to a means of prevention.
