A longitudinal analysis for the identification of the factors that affect the case mix index of hospitals in the U.S

Indiana University-Purdue University Indianapolis (IUPUI)The present thesis is an analysis of longitudinal data collected through the years 2011-2013, from a complex of four hospitals located in Indiana, USA. The aim of the analysis was the detection of changes (especially a decline) in the disease related group (DRG) weights (and thus, the case mix index (CMI)), and the determination of the predictors that significantly affect these changes. The document is divided in four major parts. In the first part it is described the statistical theory required for the the analysis, in the second part the reimbursement strategies for the hospitals in the USA, are briefly described and the concept of the DRG and CMI are explained. In the third part the actual analysis is presented while the last part contains a summary of the findings and some conclusions. The correlation between the observations was taken into account by modeling the data using linearmixed models (LMM). Three major factors were studied for their effect on the DRG weight of thehospitals: the changes in the type of cases (i.e. the product lines), the changes in the number of the Surgical cases, and also the changes of the length of stay (LOS). The analysis did not indicate any significant DRG change in any of the hospitals except from the H4. The H4 hospital has a significant decline over time regarding the Cardio-vascular (CV) DRG weights. For the hospitals H1, H2 and H3 the only decline observed in the product lines was that for the Medical-Surgical DRG. Finally, no significant change was observed for the LOS, or the number of Surgical cases. In addition to the three predictors studied, changes in the coding system, the documentation etc. may also affect the DRG and CMI. However, these changes are not possible to be detected through this analysis, since no available information was given in the present data

Biomarker-And Pathway-Informed Polygenic Risk Scores for Alzheimer's Disease and Related Disorders

Indiana University-Purdue University Indianapolis (IUPUI)Determining an individual’s genetic susceptibility in complex diseases like Alzheimer’s disease (AD) is challenging as multiple variants each contribute a small portion of the overall risk. Polygenic Risk Scores (PRS) are a mathematical construct or composite that aggregates the small effects of multiple variants into a single score. Potential applications of PRS include risk stratification, biomarker discovery and increased prognostic accuracy. A systematic review demonstrated that methodological refinement of PRS is an active research area, mostly focused on large case-control genome-wide association studies (GWAS). In AD, where there is considerable phenotypic and genetic heterogeneity, we hypothesized that PRS based on endophenotypes, and pathway-relevant genetic information would be particularly informative. In the first study, data from the NIA Alzheimer’s Disease Neuroimaging Initiative (ADNI) was used to develop endophenotype-based PRS based on amyloid (A), tau (T), neurodegeneration (N) and cerebrovascular (V) biomarkers, as well as an overall/combined endophenotype-PRS. Results indicated that combined phenotype-PRS predicted neurodegeneration biomarkers and overall AD risk. By contrast, amyloid and tau-PRSs were strongly linked to the corresponding biomarkers. Finally, extrinsic significance of the PRS approach was demonstrated by application of AD biological pathway-informed PRS to prediction of cognitive changes among older women with breast cancer (BC). Results from PRS analysis of the multicenter Thinking and Living with Cancer (TLC) study indicated that older BC patients with high AD genetic susceptibility within the immune-response and endocytosis pathways have worse cognition following chemotherapy±hormonal therapy rather than hormonal-only therapy. In conclusion, PRSs based on biomarker- or pathway- specific genetic information may provide mechanistic insights beyond disease susceptibility, supporting development of precision medicine with potential application to AD and other age-associated cognitive disorders

Progress in Polygenic Composite Scores in Alzheimer’s and other Complex Diseases

Advances in high-throughput genotyping and next-generation sequencing (NGS) coupled with larger sample sizes brings the realization of precision medicine closer than ever. Polygenic approaches incorporating the aggregate influence of multiple genetic variants can contribute to a better understanding of the genetic architecture of many complex diseases and facilitate patient stratification. This review addresses polygenic concepts, methodological developments, hypotheses, and key issues in study design. Polygenic risk scores (PRSs) have been applied to many complex diseases and here we focus on Alzheimer's disease (AD) as a primary exemplar. This review was designed to serve as a starting point for investigators wishing to use PRSs in their research and those interested in enhancing clinical study designs through enrichment strategies

Mining directional drug interaction effects on myopathy using the FAERS database

Mining high-order drug-drug interaction (DDI) induced adverse drug effects from electronic health record (EHR) databases is an emerging area, and very few studies have explored the relationships between high-order drug combinations. We investigate a novel pharmacovigilance problem for mining directional DDI effects on myopathy using the FDA Adverse Event Reporting System (FAERS) database. Our work provides information on the risk of myopathy associated with adding new drugs on the already prescribed medication, and visualizes the identified directional DDI patterns as user-friendly graphical representation. We utilize the Apriori algorithm to extract frequent drug combinations from the FAERS database. We use odds ratio (OR) to estimate the risk of myopathy associated with directional DDI. We create a tree-structured graph to visualize the findings for easy interpretation. Our method confirmed myopathy association with previously reported HMG-CoA reductase inhibitors like rosuvastatin, fluvastatin, simvastatin and atorvastatin. New, previously unidentified but mechanistically plausible associations with myopathy were also observed, such as the DDI between pamidronate and levofloxacin. Additional top findings are gadolinium-based imaging agents, which however are often used in myopathy diagnosis. Other DDIs with no obvious mechanism are also reported, such as that of sulfamethoxazole with trimethoprim and potassium chloride. This study shows the feasibility to estimate high-order directional DDIs in a fast and accurate manner. The results of the analysis could become a useful tool in the specialists' hands through an easy-to-understand graphic visualization

Transcriptome-Guided Imaging Genetic Analysis via a Novel Sparse CCA Algorithm

Imaging genetics is an emerging field that studies the influence of genetic variation on brain structure and function. The major task is to examine the association between genetic markers such as single nucleotide polymorphisms (SNPs) and quantitative traits (QTs) extracted from neuroimaging data. Sparse canonical correlation analysis (SCCA) is a bi-multivariate technique used in imaging genetics to identify complex multi-SNP-multi-QT associations. In imaging genetics, genes associated with a phenotype should at least expressed in the phenotypical region. We study the association between the genotype and amyloid imaging data and propose a transcriptome-guided SCCA framework that incorporates the gene expression information into the SCCA criterion. An alternating optimization method is used to solve the formulated problem. Although the problem is not biconcave, a closed-form solution has been found for each subproblem. The results on real data show that using the gene expression data to guide the feature selection facilities the detection of genetic markers that are not only associated with the identified QTs, but also highly expressed there