OHDSI MEETINGS THIS WEEK
OHDSI Community Call - Tuesday at 12pm ET (5pm CET)https://meetings.webex.com/collabs/#/meetings/detail?uuid=M59X2V1U61WC9ASID2Z5N3UT95-D1JL&rnd=811649.98682213
Population-Level Estimation (Western hemisphere) workgroup meeting - Thursday at 12pm ET (5pm CET)https://meetings.webex.com/collabs/#/meetings/detail?uuid=M3T9BZV9RSB6YNDM8WDDZMI19D-D1JL&rnd=229240.54296
Hadoop WG meeting - Friday at 11am ET (4pm CET)https://meet.lync.com/quintiles-quintilesims/mui.vanzandt/R8V4N3S9
GIS working group meeting - Monday (March 19th) at 10am ET
Simple, modern video meetings for the global workforce. Join from anywhere, including your desktop, browser, mobile device, or video room device.
ANNOUNCEMENTS
First European Symposium a huge success! Check it out here: https://www.linkedin.com/pulse/ohdsi-europe-symposium-2018-big-success-peter-rijnbeek/
2018 OHDSI F2F: Selected F2F participants were notified on Friday, March, 23rd. If you signed up to attend but did not receive confirmation of your attendance place email beaton@ohdsi.org
2018 OHDSI Symposium - Collaborator showcase
OHDSI China Hack-a-thon - SAVE THE DATE Mark your calendars! The OHDSI China Hack-a-thon is officially set to take place on May 18th – 20th in Shanghai. More details to be posted soon.
COMMUNITY PUBLICATIONS
Generation of Web pages for Public Scientific Databases Using Schema.org https://2018.eswc-conferences.org/wp-content/uploads/2018/02/ESWC2018_paper_92.pdf
Empirical confidence interval calibration for population-level effect estimation studies in observational healthcare data
Observational healthcare data, such as electronic health records and administrative claims, offer potential to estimate effects of medical products at scale. Observational studies have often been found to be nonreproducible, however, generating...
Inferring pregnancy episodes and outcomes within a network of observational databases.
A Matcho, P Ryan, D Fife, D Gifkins, C Knoll and A Friedman,
PloS one , 2018
Administrative claims and electronic health records are valuable resources for evaluating pharmaceutical effects during pregnancy. However, direct measures of gestational age are generally not available. Establishing a reliable approach to infer the duration and outcome of a pregnancy could improve pharmacovigilance activities. We developed and applied an algorithm to define pregnancy episodes in four observational databases: three US-based claims databases: Truven MarketScan® Commercial Claims and Encounters (CCAE), Truven MarketScan® Multi-state Medicaid (MDCD), and the Optum ClinFormatics® (Optum) database and one non-US database, the United Kingdom (UK) based Clinical Practice Research Datalink (CPRD). Pregnancy outcomes were classified as live births, stillbirths, abortions and ectopic pregnancies. Start dates were estimated using a derived hierarchy of available pregnancy markers, including records such as last menstrual period and nuchal ultrasound dates. Validation included clinical adjudication of 700 electronic Optum and CPRD pregnancy episode profiles to assess the operating characteristics of the algorithm, and a comparison of the algorithm's Optum pregnancy start estimates to starts based on dates of assisted conception procedures. Distributions of pregnancy outcome types were similar across all four data sources and pregnancy episode lengths found were as expected for all outcomes, excepting term lengths in episodes that used amenorrhea and urine pregnancy tests for start estimation. Validation survey results found highest agreement between reviewer chosen and algorithm operating characteristics for questions assessing pregnancy status and accuracy of outcome category with 99-100% agreement for Optum and CPRD. Outcome date agreement within seven days in either direction ranged from 95-100%, while start date agreement within seven days in either direction ranged from 90-97%. In Optum validation sensitivity analysis, a total of 73% of algorithm estimated starts for live births were in agreement with fertility procedure estimated starts within two weeks in either direction; ectopic pregnancy 77%, stillbirth 47%, and abortion 36%. An algorithm to infer live birth and ectopic pregnancy episodes and outcomes can be applied to multiple observational databases with acceptable accuracy for further epidemiologic research. Less accuracy was found for start date estimations in stillbirth and abortion outcomes in our sensitivity analysis, which may be expected given the nature of the outcomes.
Sequence symmetry analysis in pharmacovigilance and pharmacoepidemiologic studies.
EC Lai, N Pratt, CY Hsieh, SJ Lin, A Pottegård, EE Roughead, YH Kao Yang and J Hallas,
European journal of epidemiology , 2017 07
Sequence symmetry analysis (SSA) is a method for detecting adverse drug events by utilizing computerized claims data. The method has been increasingly used to investigate safety concerns of medications and as a pharmacovigilance tool to identify unsuspected side effects. Validation studies have indicated that SSA has moderate sensitivity and high specificity and has robust performance. In this review we present the conceptual framework of SSA and discuss advantages and potential pitfalls of the method in practice. SSA is based on analyzing the sequences of medications; if one medication (drug B) is more often initiated after another medication (drug A) than before, it may be an indication of an adverse effect of drug A. The main advantage of the method is that it requires a minimal dataset and is computationally efficient. By design, SSA controls time-constant confounders. However, the validity of SSA may be affected by time-varying confounders, as well as by time trends in the occurrence of exposure or outcome events. Trend effects may be adjusted by modeling the expected sequence ratio in the absence of a true association. There is a potential for false positive or negative results and careful consideration should be given to potential sources of bias when interpreting the results of SSA studies.
Building a semantic web-based metadata repository for facilitating detailed clinical modeling in cancer genome studies.
DK Sharma, HR Solbrig, C Tao, C Weng, CG Chute and G Jiang,
Journal of biomedical semantics , Jun 2017 05
Detailed Clinical Models (DCMs) have been regarded as the basis for retaining computable meaning when data are exchanged between heterogeneous computer systems. To better support clinical cancer data capturing and reporting, there is an emerging need to develop informatics solutions for standards-based clinical models in cancer study domains. The objective of the study is to develop and evaluate a cancer genome study metadata management system that serves as a key infrastructure in supporting clinical information modeling in cancer genome study domains.We leveraged a Semantic Web-based metadata repository enhanced with both ISO11179 metadata standard and Clinical Information Modeling Initiative (CIMI) Reference Model. We used the common data elements (CDEs) defined in The Cancer Genome Atlas (TCGA) data dictionary, and extracted the metadata of the CDEs using the NCI Cancer Data Standards Repository (caDSR) CDE dataset rendered in the Resource Description Framework (RDF). The ITEM/ITEM_GROUP pattern defined in the latest CIMI Reference Model is used to represent reusable model elements (mini-Archetypes).We produced a metadata repository with 38 clinical cancer genome study domains, comprising a rich collection of mini-Archetype pattern instances. We performed a case study of the domain "clinical pharmaceutical" in the TCGA data dictionary and demonstrated enriched data elements in the metadata repository are very useful in support of building detailed clinical models.Our informatics approach leveraging Semantic Web technologies provides an effective way to build a CIMI-compliant metadata repository that would facilitate the detailed clinical modeling to support use cases beyond TCGA in clinical cancer study domains.
