Using the data quality dashboard to improve the ehden network

Federated networks of observational health databases have the potential to be a rich resource to inform clinical practice and regulatory decision making. However, the lack of standard data quality processes makes it difficult to know if these data are research ready. The EHDEN COVID-19 Rapid Collaboration Call presented the opportunity to assess how the newly developed open-source tool Data Quality Dashboard (DQD) informs the quality of data in a federated network. Fifteen Data Partners (DPs) from 10 different countries worked with the EHDEN taskforce to map their data to the OMOP CDM. Throughout the process at least two DQD results were collected and compared for each DP. All DPs showed an improvement in their data quality between the first and last run of the DQD. The DQD excelled at helping DPs identify and fix conformance issues but showed less of an impact on completeness and plausibility checks. This is the first study to apply the DQD on multiple, disparate databases across a network. While study-specific checks should still be run, we recommend that all data holders converting their data to the OMOP CDM use the DQD as it ensures conformance to the model specifications and that a database meets a baseline level of completeness and plausibility for use in research.</p

RADAR-base/radar-android-faros: Release 0.1.1

Changes from 0.1.0 Updates to radar-commons-android 0.7.0 Improvements on artifact publishin

Stochastic simulation of prokaryotic two-component signalling indicates stochasticity-induced active-state locking and growth-rate dependent bistability

Signal transduction by prokaryotes almost exclusively relies on two-component systems for sensing and responding to (extracellular) signals. Here, we use stochastic models of two-component systems to better understand the impact of stochasticity on the fidelity and robustness of signal transmission, the outcome of autoregulatory gene expression and the influence of cell growth and division. We report that two-component systems are remarkably robust against copy number fluctuations of the signalling proteins they are composed of, which enhances signal transmission fidelity. Furthermore, we find that due to stochasticity these systems can get locked in an active state for extended time periods when (initially high) signal levels drop to zero. This behaviour can contribute to a bet-hedging adaptation strategy, aiding survival in fluctuating environments. Additionally, autoregulatory gene expression can cause two-component systems to become bistable at realistic parameter values. As a result, two sub-populations of cells can co-exist—active and inactive cells, which contributes to fitness in unpredictable environments. Bistability also proved robust with respect to cell growth and division, and is tunable by the growth rate. In conclusion, our results indicate how single cells can cope with the inevitable stochasticity occurring in the activity of their two-component systems. They are robust to disadvantageous fluctuations that scramble signal transduction and they exploit beneficial stochasticity that generates fitness-enhancing heterogeneity across an isogenic population of cells

Transforming and evaluating electronic health record disease phenotyping algorithms using the OMOP common data model: a case study in heart failure

OBJECTIVE: The aim of the study was to transform a resource of linked electronic health records (EHR) to the OMOP common data model (CDM) and evaluate the process in terms of syntactic and semantic consistency and quality when implementing disease and risk factor phenotyping algorithms. MATERIALS AND METHODS: Using heart failure (HF) as an exemplar, we represented three national EHR sources (Clinical Practice Research Datalink, Hospital Episode Statistics Admitted Patient Care, Office for National Statistics) into the OMOP CDM 5.2. We compared the original and CDM HF patient population by calculating and presenting descriptive statistics of demographics, related comorbidities, and relevant clinical biomarkers. RESULTS: We identified a cohort of 502 536 patients with the incident and prevalent HF and converted 1 099 195 384 rows of data from 216 581 914 encounters across three EHR sources to the OMOP CDM. The largest percentage (65%) of unmapped events was related to medication prescriptions in primary care. The average coverage of source vocabularies was >98% with the exception of laboratory tests recorded in primary care. The raw and transformed data were similar in terms of demographics and comorbidities with the largest difference observed being 3.78% in the prevalence of chronic obstructive pulmonary disease (COPD). CONCLUSION: Our study demonstrated that the OMOP CDM can successfully be applied to convert EHR linked across multiple healthcare settings and represent phenotyping algorithms spanning multiple sources. Similar to previous research, challenges mapping primary care prescriptions and laboratory measurements still persist and require further work. The use of OMOP CDM in national UK EHR is a valuable research tool that can enable large-scale reproducible observational research

RADAR-base/radar-android-phone: Release 0.5.0

Changes from 0.1.5 Poll battery on set interval dependencies to latest version

RADAR-base/RADAR-Schemas: radar-schemas 0.3.5

&lt;p&gt;Changes since version 0.3.4:&lt;/p&gt; &lt;ul&gt; &lt;li&gt;Added question ID for questionnaires&lt;/li&gt; &lt;li&gt;Upgraded Gradle&lt;/li&gt; &lt;li&gt;Upgraded dependencies&lt;/li&gt; &lt;li&gt;Wait for zookeeper and Kafka before registering topics&lt;/li&gt; &lt;li&gt;Register all topics simultaneously&lt;/li&gt; &lt;li&gt;Enable connector sources in the Catalog server&lt;/li&gt; &lt;li&gt;Add vender, model and version in connectors&lt;/li&gt; &lt;li&gt;Fixed Bittium Faros temperature topic name&lt;/li&gt; &lt;/ul&gt

RADAR-base/RADAR-Schemas: RADAR-Schemas release version 0.5.2

Changes since version 0.5.1 &lt;ul&gt; &lt;li&gt;Adds Active app interaction events schema and specifications&lt;/li&gt; &lt;li&gt;More explicit bootstrap server logging&lt;/li&gt; &lt;li&gt;Update dependencies&lt;/li&gt; &lt;/ul&gt

RADAR-base/RADAR-Schemas: radar-schemas version 0.4.3

&lt;p&gt;Changes since version 0.4.2:&lt;/p&gt; &lt;ul&gt; &lt;li&gt;Deprecated &lt;code&gt;active/questionniare/notification&lt;/code&gt; schema&lt;/li&gt; &lt;li&gt;Added &lt;code&gt;ApplicationDeviceInfo&lt;/code&gt; to &lt;code&gt;radar-android-application-status&lt;/code&gt;&lt;/li&gt; &lt;li&gt;Added &lt;code&gt;timeNotification&lt;/code&gt; field to active app schemas&lt;/li&gt; &lt;li&gt;Updated tool dependencies&lt;/li&gt; &lt;/ul&gt

Transforming and evaluating the UK Biobank to the OMOP Common Data Model for COVID-19 research and beyond

Objective: The COVID-19 pandemic has demonstrated the value of real-world data for public health research. International federated analyses are crucial for informing policy makers. Common data models (CDM) are critical for enabling these studies to be performed efficiently. Our objective was to convert the UK Biobank, a study of 500,000 participants with rich genetic and phenotypic data to the Observational Medical Outcomes Partnership (OMOP) CDM. Materials and methods: We converted UK Biobank data to OMOP CDM v. 5.3. We transformedparticipant research data on diseases collected at recruitment and electronic health records (EHR) from primary care, hospitalizations, cancer registrations, and mortality from providers in England, Scotland, and Wales. We performed syntactic and semantic validations and compared comorbidities and risk factors between source and transformed data. Results: We identified 502,505 participants (3,086 with COVID-19) and transformed 690 fields (1,373,239,555 rows) to the OMOP CDM using eight different controlled clinical terminologies and bespoke mappings. Specifically, we transformed self-reported non-cancer illnesses 946,053 (83.91% of all source entries), cancers 37,802 (70.81%), medications 1,218,935 (88.25%), and prescriptions 864,788 (86.96%). In EHR, we transformed 1,3028,182 (99.95%) hospital diagnoses, 6,465,399 (89.2%) procedures, 337,896,333 primary care diagnoses (CTV3, SNOMED-CT), 139,966,587 (98.74%) prescriptions (dm+d) and 77,127 (99.95%) deaths (ICD-10). We observed good concordance across demographic, risk factor, and comorbidity factors between source and transformed data. Discussion and conclusion: Our study demonstrated that the OMOP CDM can be successfully leveraged to harmonize complex large-scale biobanked studies combining rich multimodal phenotypic data. Our study uncovered several challenges when transforming data from questionnaires to the OMOP CDM which require further research. The transformed UK Biobank resource is a valuable tool that can enable federated research, like COVID-19 studies