PatientExploreR: an extensible application for dynamic visualization of patient clinical history from electronic health records in the OMOP common data model.

MotivationElectronic health records (EHRs) are quickly becoming omnipresent in healthcare, but interoperability issues and technical demands limit their use for biomedical and clinical research. Interactive and flexible software that interfaces directly with EHR data structured around a common data model (CDM) could accelerate more EHR-based research by making the data more accessible to researchers who lack computational expertise and/or domain knowledge.ResultsWe present PatientExploreR, an extensible application built on the R/Shiny framework that interfaces with a relational database of EHR data in the Observational Medical Outcomes Partnership CDM format. PatientExploreR produces patient-level interactive and dynamic reports and facilitates visualization of clinical data without any programming required. It allows researchers to easily construct and export patient cohorts from the EHR for analysis with other software. This application could enable easier exploration of patient-level data for physicians and researchers. PatientExploreR can incorporate EHR data from any institution that employs the CDM for users with approved access. The software code is free and open source under the MIT license, enabling institutions to install and users to expand and modify the application for their own purposes.Availability and implementationPatientExploreR can be freely obtained from GitHub: https://github.com/BenGlicksberg/PatientExploreR. We provide instructions for how researchers with approved access to their institutional EHR can use this package. We also release an open sandbox server of synthesized patient data for users without EHR access to explore: http://patientexplorer.ucsf.edu.Supplementary informationSupplementary data are available at Bioinformatics online

In-roads to the spread of antibiotic resistance: regional patterns of microbial transmission in northern coastal Ecuador

The evolution of antibiotic resistance (AR) increases treatment cost and probability of failure, threatening human health worldwide. The relative importance of individual antibiotic use, environmental transmission and rates of introduction of resistant bacteria in explaining community AR patterns is poorly understood. Evaluating their relative importance requires studying a region where they vary. The construction of a new road in a previously roadless area of northern coastal Ecuador provides a valuable natural experiment to study how changes in the social and natural environment affect the epidemiology of resistant Escherichia coli. We conducted seven bi-annual 15 day surveys of AR between 2003 and 2008 in 21 villages. Resistance to both ampicillin and sulphamethoxazole was the most frequently observed profile, based on antibiogram tests of seven antibiotics from 2210 samples. The prevalence of enteric bacteria with this resistance pair in the less remote communities was 80 per cent higher than in more remote communities (OR = 1.8 [1.3, 2.3]). This pattern could not be explained with data on individual antibiotic use. We used a transmission model to help explain this observed discrepancy. The model analysis suggests that both transmission and the rate of introduction of resistant bacteria into communities may contribute to the observed regional scale AR patterns, and that village-level antibiotic use rate determines which of these two factors predominate. While usually conceived as a main effect on individual risk, antibiotic use rate is revealed in this analysis as an effect modifier with regard to community-level risk of resistance

Top 20 predictions of new drug-gene relationships for PharmGKB, and whether a PGx relationship has been documented in the literature.

<p>*** indicates that an association has been demonstrated experimentally between changes in the expression/activity of the gene/protein and the efficacy of the drug</p><p>** indicates that such an association is likely, but has not yet been studied</p><p>* indicates that the association has been studied experimentally, and the experiment refuted the association. Here we include only associations between pharmaceutical compounds and single genes; predicted associations involving endogenous compounds and/or groups of genes are included in the supplement, however.</p><p>Top 20 predictions of new drug-gene relationships for PharmGKB, and whether a PGx relationship has been documented in the literature.</p

Classifier performance at the task of recognizing (a) PGx associations (dense matrix), (b) drug-target associations (dense matrix), (c) PGx associations (sparse matrix) and (d) drug-target associations (sparse matrix).

<p>Classifier performance at the task of recognizing (a) PGx associations (dense matrix), (b) drug-target associations (dense matrix), (c) PGx associations (sparse matrix) and (d) drug-target associations (sparse matrix).</p

Explanation of the clusters shown in Fig 4.

<p>Clusters with 20 or fewer members are not described in the table in the interest of space.</p

Top 20 predictions of new drug-target relationships for DrugBank.

<p>*** indicates that the drug has been shown experimentally to have modified the activity of the gene/protein</p><p>** means that the interaction is known to DrugBank but is listed under an alternate drug or gene name</p><p>* means the interaction has been studied and is unlikely; P refers to a particular type of parser error in which the ligand of a receptor is mistaken for that receptor; L refers to a lexicon error (see <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004216#sec008" target="_blank">Discussion</a>).</p><p>Top 20 predictions of new drug-target relationships for DrugBank.</p

Selected dependency paths and representative sentences.

<p>The drug and gene names flanking each path are bolded. Some key abbreviations are listed here: <i>appos</i>: appositional modifier, <i>amod</i>: adjectival modifier, <i>prep</i>: prepositional modifier (if <i>prep_of</i>, the specific preposition used is “of”, if <i>prep_to</i>, it’s “to”, if <i>prep_for</i>, it’s “for”), <i>nsubjpass</i>: passive nominal subject, <i>agent</i>: complement of passive verb, <i>dobj</i>: direct object of active verb, <i>nsubj</i>: noun subject of active verb.</p><p>Selected dependency paths and representative sentences.</p