OHDSI MEETINGS THIS WEEK
OHDSI Community Call - Tuesday at 12pm EThttps://meetings.webex.com/collabs/#/meetings/detail?uuid=M59X2V1U61WC9ASID2Z5N3UT95-D1JL&rnd=811649.9868221112
Population-Level Estimation (Eastern hemisphere) workgroup meeting - Wednesday at 3pm Hong Kong timehttps://meetings.webex.com/collabs/meetings/join?uuid=M6WE9AOKFETH2VEFPVCZWWBIT0-D1JL
Architecture Working Group - Thursday at 10am EThttps://jjconferencing.webex.com/mw3100/mywebex/default.do?service=1&siteurl=jjconferencing&nomenu=true&main_url=%2Fmc3100%2Fe.do%3Fsiteurl%3Djjconferencing%26AT%3DMI%26EventID%3D610982452%26UID%3D501476547%26Host%3DQUhTSwAAAAQu4P1o9qm71JJ1Zj4-uvZbjQttsCinu71JCRxBAHAXnzjjRAiTspTzU9ojLmjMF4CcTBWw4zn1dqYPTWu5vJ9_0%26FrameSet%3D2%26MTID%3Dm3e1ceeca56f1e94c9fcf1ae98c10e02e
GIS working group meeting - Next Monday (May 21st) 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
OHDSI Europe - Videos from the 2018 OHDSI European Symposium are officially online:
European Symposium: https://www.youtube.com/watch?v=qULDL9CgWwA&feature=youtu.be
OMOP-CDM and Standardized Vocabularies Tutorialhttps://www.youtube.com/watch?v=wLTpWVmuuxg&feature=youtu.be
OHDSI Tool Ecosystem -https://www.youtube.com/watch?v=q4q1yAFlQrs&feature=youtu.be
F2F Materials - All Materials from the 2018 OHDSI F2F, including slides, meeting notes, the study protocol and photos have been uploaded here: https://www.ohdsi.org/past-events/
2018 OHDSI Symposium - DATES CHANGED! https://www.ohdsi.org/events/2018-ohdsi-symposium/
2018 OHDSI Symposium - CALL FOR PARTICIPATION https://www.ohdsi.org/collaborator-showcase/
You can’t climb the ladder of success with your hands in your pockets.
COMMUNITY PUBLICATIONS
Modeling recovery curves with application to prostatectomy.
F Wang, C Rudin, TH Mccormick and JL Gore,
Biostatistics (Oxford, England) , May 2018 05
In many clinical settings, a patient outcome takes the form of a scalar time series with a recovery curve shape, which is characterized by a sharp drop due to a disruptive event (e.g., surgery) and subsequent monotonic smooth rise towards an asymptotic level not exceeding the pre-event value. We propose a Bayesian model that predicts recovery curves based on information available before the disruptive event. A recovery curve of interest is the quantified sexual function of prostate cancer patients after prostatectomy surgery. We illustrate the utility of our model as a pre-treatment medical decision aid, producing personalized predictions that are both interpretable and accurate. We uncover covariate relationships that agree with and supplement that in existing medical literature.
Bleeding events attributable to concurrent use of warfarin and other medications in high-risk elderly: meta-analysis and Italian population-based investigation.
RI Comoretto, F Rea, E Lucenteforte, A Mugelli, G Trifirò, S Cascini, G Roberto, A Chinellato, A Filippelli and G Corrao,
European journal of clinical pharmacology , May 2018 07
The aim of this study was to estimate the proportion of bleedings that occurred among warfarin users attributable to the concomitant use of other medications. A general approach for measuring the impact of the prescriptive inappropriateness on drug adverse outcomes at the population level is described.A meta-analysis was conducted to obtain summary relative risks of bleeding associated with concurrent use of warfarin and other medications compared to warfarin use alone. A population-based investigation was performed, in an Italian cohort of cardiopathic patients aged 65 years or older, to estimate the prevalence of concurrent users of warfarin and other medicaments. The population attributable fraction was computed by combining data on summary relative risks and prevalence of concurrent users.Concomitant use of warfarin and cotrimoxazole, amiodarone, quinolones, macrolides, platelet aggregation inhibitors, SSRIs, NSAIDs, and lipid-lowering agents was associated with an increased risk of bleeding. The corresponding attributable fractions were 3% (95% CI 2 to 4%), 21% (1 to 41%), 21% (17 to 25%), 9% (8 to 10%), 14% (12 to 16%), 6% (5 to 8%), 10% (1 to 20%), and 8% (0 to 18%), respectively.More than half of bleeding events occurring among frail elderly using warfarin are attributable to a concomitant use of warfarin with certain drugs. Because some of these drugs appear to be essential for the treatment/prevention of cardiovascular conditions, and their concomitant use with warfarin could be acceptable in some cases, proper INR-monitoring and warfarin dose adjustments are requested.
Phenotype Instance Verification and Evaluation Tool (PIVET): A Scaled Phenotype Evidence Generation Framework Using Web-Based Medical Literature.
J Henderson, J Ke, JC Ho, J Ghosh and BC Wallace,
Journal of medical Internet research , May 2018 04
Researchers are developing methods to automatically extract clinically relevant and useful patient characteristics from raw healthcare datasets. These characteristics, often capturing essential properties of patients with common medical conditions, are called computational phenotypes. Being generated by automated or semiautomated, data-driven methods, such potential phenotypes need to be validated as clinically meaningful (or not) before they are acceptable for use in decision making.The objective of this study was to present Phenotype Instance Verification and Evaluation Tool (PIVET), a framework that uses co-occurrence analysis on an online corpus of publically available medical journal articles to build clinical relevance evidence sets for user-supplied phenotypes. PIVET adopts a conceptual framework similar to the pioneering prototype tool PheKnow-Cloud that was developed for the phenotype validation task. PIVET completely refactors each part of the PheKnow-Cloud pipeline to deliver vast improvements in speed without sacrificing the quality of the insights PheKnow-Cloud achieved.PIVET leverages indexing in NoSQL databases to efficiently generate evidence sets. Specifically, PIVET uses a succinct representation of the phenotypes that corresponds to the index on the corpus database and an optimized co-occurrence algorithm inspired by the Aho-Corasick algorithm. We compare PIVET's phenotype representation with PheKnow-Cloud's by using PheKnow-Cloud's experimental setup. In PIVET's framework, we also introduce a statistical model trained on domain expert-verified phenotypes to automatically classify phenotypes as clinically relevant or not. Additionally, we show how the classification model can be used to examine user-supplied phenotypes in an online, rather than batch, manner.PIVET maintains the discriminative power of PheKnow-Cloud in terms of identifying clinically relevant phenotypes for the same corpus with which PheKnow-Cloud was originally developed, but PIVET's analysis is an order of magnitude faster than that of PheKnow-Cloud. Not only is PIVET much faster, it can be scaled to a larger corpus and still retain speed. We evaluated multiple classification models on top of the PIVET framework and found ridge regression to perform best, realizing an average F1 score of 0.91 when predicting clinically relevant phenotypes.Our study shows that PIVET improves on the most notable existing computational tool for phenotype validation in terms of speed and automation and is comparable in terms of accuracy.
