OHDSI MEETINGS THIS WEEK
Vocabulary visualization WG meeting - Tuesday at 2pm EThttps://jjconferencing.webex.com/mw3000/mywebex/default.do?service=1&main_url=%2Fmc3000%2Fe.do%3Fsiteurl%3Djjconferencing%26AT%3DMI%26EventID%3D547792122%26MTID%3Dm2025b16c33f920fa1354f9a53ffd6023%26Host%3DQUhTSwAAAAPmis8F3632QJAcSLPqnudmJ5FxYSsNbErrnQJO-qJFlCcls4nr4ed1x4FOoHAFS434bRL_iSix48BEyNHjTaKg0%26FrameSet%3D2&siteurl=jjconferencing&nomenu=true
Patient-level prediction (eastern hemisphere) workgroup meeting - Wednesday at 3pm Hong Kong timehttps://global.gotomeeting.com/join/972917661
Population-level estimation (western hemisphere) workgroup meeting - Thursday at 12pm EThttps://meetings.webex.com/collabs/#/meetings/detail?uuid=M3T9BZV9RSB6YNDM8WDDZMI19D-D1JL
Hadoop WG meeting - Friday at 11am EThttp://cloudera.webex.com/meet/sdolley
ANNOUNCEMENTS
2017 OHDSI Symposium - The date and location for the 2017 OHDSI Symposium has been confirmed! This year’s symposium will take place on Wednesday, October 18th at the Bethesda North Marriott. Full-day tutorial sessions will take place on October 19-20th. If you’re interested in attending, please save the date.
OHDSI F2F - Materials from the OHDSI F2F to be posted soon.
OHDSI collaborator meetings - We’re looking for presenters for our weekly community meetings. If you’re interested in sharing your working with the community, please let us know by replying to this thread, or by emailing me at beaton@ohdsi.org .
COMMUNITY PUBLICATIONS
Sharing Clinical Big Data While Protecting Confidentiality and Security: Observational Health Data Sciences and Informatics.
Large-scale adverse effects related to treatment evidence standardization (LAERTES): an open scalable system for linking pharmacovigilance evidence sources with clinical data
Learning Effective Treatment Pathways for Type-2 Diabetes from a clinical data warehouse.
R Vashisht, K Jung and N Shah,
AMIA ... Annual Symposium proceedings. AMIA Symposium , 2016
Treatment guidelines for management of type-2 diabetes mellitus (T2DM) are controversial because existing evidence from randomized clinical trials do not address many important clinical questions. Data from Electronic Medical Records (EMRs) has been used to profile first line therapy choices, but this work did not elucidate the factors underlying deviations from current treatment guidelines and the relative efficacy of different treatment options. We have used data from the Stanford Hospital to attempt to address these issues. Clinical features associated with the initial choice of treatment were effectively re-discovered using a machine learning approach. In addition, the efficacies of first and second line treatments were evaluated using Cox proportional hazard models for control of Hemoglobin A1c. Factors such as acute kidney disorder and liver disorder were predictive of first line therapy choices. Sitagliptin was the most effective second-line therapy, and as effective as metformin as a first line therapy.
Comparing lagged linear correlation, lagged regression, Granger causality, and vector autoregression for uncovering associations in EHR data.
ME Levine, DJ Albers and G Hripcsak,
AMIA ... Annual Symposium proceedings. AMIA Symposium , 2016
Time series analysis methods have been shown to reveal clinical and biological associations in data collected in the electronic health record. We wish to develop reliable high-throughput methods for identifying adverse drug effects that are easy to implement and produce readily interpretable results. To move toward this goal, we used univariate and multivariate lagged regression models to investigate associations between twenty pairs of drug orders and laboratory measurements. Multivariate lagged regression models exhibited higher sensitivity and specificity than univariate lagged regression in the 20 examples, and incorporating autoregressive terms for labs and drugs produced more robust signals in cases of known associations among the 20 example pairings. Moreover, including inpatient admission terms in the model attenuated the signals for some cases of unlikely associations, demonstrating how multivariate lagged regression models' explicit handling of context-based variables can provide a simple way to probe for health-care processes that confound analyses of EHR data.
Adverse drug reactions associated with off-label use of ketorolac, with particular focus on elderly patients. An analysis of the Italian pharmacovigilance database and a population based study.
E Viola, G Trifirò, Y Ingrasciotta, L Sottosanti, M Tari, F Giorgianni, U Moretti and R Leone,
Expert opinion on drug safety , Dec 2016
This study aims to evaluate the frequency of off-label use of ketorolac in Italy and the related suspected adverse drug reactions (ADRs) reported.All the suspected cases associated with ketorolac recorded in the Italian Pharmacovigilance database were retrieved. Case evaluations were carried out in order to identify the off-label use of ketorolac. Moreover, an analysis of the inappropriate use of ketorolac was conducted using the 'Arianna' database of Caserta local health unit.Up to December 2014, 822 reports of suspected ADRs related to ketorolac were retrieved in the database. The use of ketorolac was classified as off-label for 553 reports and on-label for 269. Among the off-label cases, 58.6% were serious compared to 39.0% of on-label cases. Gastrointestinal events were more frequently reported with off-label use. The analysis of Arianna database showed that 37,729 out of 61,910 patients, were treated off-label.The off-label use of ketorolac is widespread in Italy. This use increases the risk of serious ADR, especially in in case of prolonged duration of treatment and in elderly patients. The Italian Medicine Agency has decided to accurately monitor the appropriate use of the drug in Italy and, if necessary, take measures in order to minimize the risks.
Comparative Adherence to Diabetes Drugs: An Analysis of Electronic Health Records and Claims Datahttp://onlinelibrary.wiley.com/doi/10.1111/dom.12931/full
How Good Are Provider Annotations?: A Machine Learning Approachhttps://scholarworks.iupui.edu/handle/1805/12064
Generating Multi-label Discrete Electronic Health Records using Generative Adversarial Networks
Suicide Following Deliberate Self-Harmhttp://ajp.psychiatryonline.org/doi/abs/10.1176/appi.ajp.2017.16111288
