An analytical approach to characterize morbidity profile dissimilarity between distinct cohorts using electronic medical records

AbstractWe describe a two-stage analytical approach for characterizing morbidity profile dissimilarity among patient cohorts using electronic medical records. We capture morbidities using the International Statistical Classification of Diseases and Related Health Problems (ICD-9) codes. In the first stage of the approach separate logistic regression analyses for ICD-9 sections (e.g., “hypertensive disease” or “appendicitis”) are conducted, and the odds ratios that describe adjusted differences in prevalence between two cohorts are displayed graphically. In the second stage, the results from ICD-9 section analyses are combined into a general morbidity dissimilarity index (MDI). For illustration, we examine nine cohorts of patients representing six phenotypes (or controls) derived from five institutions, each a participant in the electronic MEdical REcords and GEnomics (eMERGE) network. The phenotypes studied include type II diabetes and type II diabetes controls, peripheral arterial disease and peripheral arterial disease controls, normal cardiac conduction as measured by electrocardiography, and senile cataracts

Clinical Data Reuse or Secondary Use: Current Status and Potential Future Progress

Objective: To perform a review of recent research in clinical data reuse or secondary use, and envision future advances in this field. Methods: The review is based on a large literature search in MEDLINE (through PubMed), conference proceedings, and the ACM Digital Library, focusing only on research published between 2005 and early 2016. Each selected publication was reviewed by the authors, and a structured analysis and summarization of its content was developed. Results: The initial search produced 359 publications, reduced after a manual examination of abstracts and full publications. The following aspects of clinical data reuse are discussed: motivations and challenges, privacy and ethical concerns, data integration and interoperability, data models and terminologies, unstructured data reuse, structured data mining, clinical practice and research integration, and examples of clinical data reuse (quality measurement and learning healthcare systems). Conclusion: Reuse of clinical data is a fast-growing field recognized as essential to realize the potentials for high quality healthcare, improved healthcare management, reduced healthcare costs, population health management, and effective clinical research

Exploring the utility of metabolic profiling in stratifying patient groups in Inflammatory Bowel Disease

The pathogenesis of IBD, involving dynamic interactions between the microbiome, innate and adaptive immune systems, genetics and environmental factors, is a major focus of academic interest, in order to reveal more about the heterogeneous clinical course of the disease and in pursuit of improved therapeutic targets. Metabonomics has been previously used with a variety of biofluids to successfully distinguish IBD from controls, but the complex metabolic data also have potential to unlock insights into pathogenesis and better understand how to better stratify patients for personalised clinical care. In the largest urinary metabonomics IBD study to date, changes in the white European cohort confirmed previous published findings, highlighting discriminatory metabolites of gut microbial and inflammatory pathway sources. Significant metabolic differences were seen when comparing IBD patients and controls from South Asia to white North Europeans, demonstrating the influence of ethnicity on the metabolic profile and showing metabolite changes related to host-nutrition-microbiome interactions. Results from longitudinal measurements of the IBD metabolome in the same individuals over several years indicate relative stability despite the relapsing-remitting course of the disease and different treatments. This early finding suggests clinical outcomes may only have subtly discernible changes on metabolic profiles, potentially limiting its application as a disease-monitoring tool. 16S rRNA profiling, employed to characterise the microbiome, showed reduced microbial diversity in IBD and 4 key bacterial genera - Veillonella, Acidaminococcus, Lactobacillus and Streptococcus - associated with disease. Significant urinary and faecal metabolites in the same patients were correlated with these bacteria to demonstrate the feasibility of multi-omic integration in IBD. Furthermore, the breath VOC profiles of IBD patients obtained by SIFT-MS were distinct from those of heathy controls, with the significant compounds originating from microbial sources, and inflammatory pathways, demonstrating the potential of this technology and another facet to metabolic profiling in IBD.Open Acces

Cardiac computed tomography radiomics for the non-invasive assessment of coronary inflammation

Radiomics, via the extraction of quantitative information from conventional radiologic images, can identify imperceptible imaging biomarkers that can advance the characterization of coronary plaques and the surrounding adipose tissue. Such an approach can unravel the underlying pathophysiology of atherosclerosis which has the potential to aid diagnostic, prognostic and, therapeutic decision making. Several studies have demonstrated that radiomic analysis can characterize coronary atherosclerotic plaques with a level of accuracy comparable, if not superior, to current conventional qualitative and quantitative image analysis. While there are many milestones still to be reached before radiomics can be integrated into current clinical practice, such techniques hold great promise for improving the imaging phenotyping of coronary artery disease.Kevin Cheng, Andrew Lin, Jeremy Yuvaraj, Stephen J. Nicholls and Dennis T.L. Won

Using dynamic microsimulation to understand professional trajectories of the active Swiss population

Within the social and economic sciences and of particular interest to demographers are life course events. Looking at life sequences we can better understand which states, or life events, precede or are precursors to vulnerability. A tool that has been used for policy evaluation and recently has been gaining ground in life course sequence simulation is dynamic microsimulation. Within this context dynamic microsimulation consists in generating entire life courses from the observation of portions of the trajectories of individuals of different ages. In this work, we aim to use dynamic microsimulation in order to analyse individual professional trajectories with a focus on vulnerability. The primary goal of this analysis is to deepen upon current literature by providing insight from a longitudinal perspective on the signs of work instability and the process of precarity. The secondary goal of this work which is to show how, by using microsimulation, data collected for one purpose can be analysed under a different scope and used in a meaningful way. The data to be used in this analysis are longitudinal and were collected by NCCR-LIVES IP207 under the supervision of Prof. Christian Maggiori and Dr. Gregoire Bollmann. Individuals aged 25 to 55 residing in the German-speaking and French-speaking regions of Switzerland were followed annually for four years. These individuals were questioned regarding, amongst their personal, professional and overall situations and well-being. At the end of the fourth wave, there were 1131 individuals who had participated in all waves. The sample remained representative of the Swiss population with women and the unemployed slightly over represented. Using the information collected from these surveys, we use simulation to construct various longitudinal data modules where each data module represents a specific life domain. We postulate the relationship between these modules and layout a framework of estimation. Within certain data modules a set of equations are created to model the process therein. For every dynamic (time-variant) data module, such as the labour-market module, the transition probabilities between states (ex. labour market status) are estimated using a Markov model and then the possible outcomes are simulated. The benefit of using dynamic microsimulation is that longitudinal sample observations instead of stylised profiles are used to model population dynamics. This is one of the main reasons large-scale dynamic microsimulation models are employed by many developed nations. There has been limited use, however, of such approaches with Swiss data. This work contributes to the analysis of professional trajectories of the active Swiss population by utilising dynamic microsimulation methods