OHDSI MEETINGS THIS WEEK
OHDSI Community Call - Tuesday at 12pm ET
US TOLL: +1-415-655-0001
Meeting Number: 199 982 907
NLP working group meeting - Wednesday at 2pm ET
Dial +1 (571) 317-3122 (United States)
Enter conference ID: 707-196-421
Screen Sharing: https://global.gotomeeting.com/join/707196421
Population-Level Estimation (Eastern hemisphere) workgroup meeting - Wednesday at 3pm Hong Kong time
Patient-level prediction (Western hemisphere) WG Meeting - Wednesday at 12pm ET
Architecture WG meeting - Thursday at 1pm ET
Webex: https://jjconferencing.webex.com/mw3000/mywebex/default.do?service=1&main_url=%2Fmc3000%2Fe.do%3Fsiteurl%3Djjconferencing%26AT%3DMI%26EventID%3D283835502%26MTID%3Dmb7e839a762fbdaab0608f27500679223%26Host%3DQUhTSwAAAASTOYBxx2KWYTIL7ZSgHgHJoIHctjgxp8k5mgqAEPo2a1ESa12-8hjAVWbZznuDt96ugkT31G8sY5iSWLRwSwhI0%26FrameSet%3D2&siteurl=jjconferencing&nomenu=true ANNOUNCEMENTS
OHDSI in China - The China WG will be meeting on August 21st in Hangzhou, China (during MedInfo). More details: http://www.ohdsi.org/web/wiki/doku.php?id=projects:workgroups:china-wg
2017 OHDSI Symposium Registration - Registration for the 2017 OHDSI symposium is now open! Please register here: https://www.ohdsi.org/symposium-registration/
PLEASE NOTE: Registration is for the main symposium only (set to take place on October 18th). Registration for tutorials (set to take place on October 19-20th) is separate.
2017 OHDSI Tutorials Registration - Registration for OHDSI tutorials is now open! Information about each tutorial, including topics covered, faculty and prerequisites can be found here: https://www.ohdsi.org/about-event-tutorials/
Register here: https://www.ohdsi.org/tutorial-registration/
2017 OHDSI Symposium Accommodation - This year’s symposium will take place at the Bethesda North Marriott, just outside Washington DC. If you’d like to stay at the Marriott during the symposium we suggest booking your stay as soon as possible. October is a busy month in DC.
Possible discounted rate: For details about getting a lower room rate at the Marriott, check out @krfeeney’s forum post: 2017 OHDSI Symposium - Resources for hotel bookings
Call for Sponsorship - We’re almost at our goal of $200K to fund this year’s symposium! So far we’ve raised $150K of the funds needed to cover operating costs of venue rental, lunch, audio/visuals and recordings fees. We’re still looking for sponsors to support this important event. If you’re interested in sponsorship check out our forum post for more details: OHDSI Symposium 2017 - call for sponsorship
Updating - Over the next couple months we’ll be updating collaborator profiles and our data network list. If you have updates you’d like made to your OHDSI profile, or have updated information about your database, please email OHDSI.org email@example.com with the changes.
The best thing to hold onto in life is each other. COMMUNITY PUBLICATIONS
A Systems-Level Approach to Understand The Seasonal Factors Of Early Development With Clinical and Pharmacological Applications
Major developmental defects occur in 100,000 to 200,000 children born each year in the United States of America. 97% of these defects are from unidentified causes. Many fetal outcomes (e.g., developmental defects), result from interactions between...
An Interoperable Similarity-based Cohort Identification Method Using the OMOP Common Data Model version 5.0.
S Chakrabarti, A Sen, V Huser, GW Hruby, A Rusanov, DJ Albers and C Weng,
Journal of healthcare informatics research, Jun 2017
Cohort identification for clinical studies tends to be laborious, time-consuming, and expensive. Developing automated or semi-automated methods for cohort identification is one of the "holy grails" in the field of biomedical informatics. We propose a high-throughput similarity-based cohort identification algorithm by applying numerical abstractions on Electronic Health Records (EHR) data. We implement this algorithm using the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM), which enables sites using this standardized EHR data representation to avail this algorithm with minimum effort for local implementation. We validate its performance for a retrospective cohort identification task on six clinical trials conducted at the Columbia University Medical Center. Our algorithm achieves an average Area Under the Curve (AUC) of 0.966 and an average Precision at 5 of 0.983. This interoperable method promises to achieve efficient cohort identification in EHR databases. We discuss suitable applications of our method and its limitations and propose warranted future work.
Treatment of Medication-Related Osteonecrosis of the Jaw and its Impact on a Patient’s Quality of Life: A Single-Center, 10-Year Experience from Southern Italy.
G Oteri, G Trifirò, M Peditto, L Lo Presti, I Marcianò, F Giorgianni, J Sultana and A Marcianò,
Drug safety, 2018 01
No official guidelines are available for the management of medication-related osteonecrosis of the jaw (MR-ONJ). The additional benefit of surgery after pharmacological treatment is debated by both clinicians and patients.The aim of this study was to evaluate the changes in patients' MR-ONJ-related quality of life (QoL) after pharmacological treatment with or without surgery in a large cohort affected by MR-ONJ.Anonymized data on patients diagnosed with MR-ONJ were extracted from the database of the Osteonecrosis of the Jaw Treatment Center (University of Messina, Italy) in the years 2005-2015. QoL was evaluated at the moment of MR-ONJ diagnoses (T0), after pharmacological treatment with or without surgery (T1 and T2, respectively), based on scores from the European Organisation for Research and Treatment of Cancer (EORTC) QOL Module for Head and Neck Cancer (global oral health status [GOHS]) and a visual analog scale (VAS), stratified by indication for use.Among 100 patients, 36% were affected by osteoporosis (OSTEO group) and 64% were affected by cancer (ONC group). Considering T0, QoL scores were higher in the OSTEO group then in the ONC group. At T1, GOHS and VAS increased in both groups (OSTEO group: +9.9% and +39.9%; ONC group: +35.4 and +97.2%, respectively). Pharmacological treatment was effective in reducing pain (OSTEO group: -22.0%; ONC group: -44.8%), and social contact troubles (OSTEO group: -40.3%; ONC group: -26.7%). At T2, GOHS and VAS further increased. Scores related to 'pain' and the troubles related to the 'social dimension' also decreased (OSTEO group: -91.3% and -72.0%; ONC group: 50.8% and -16.4%, respectively).MR-ONJ-related QoL increased after pharmacological treatment and, more notably, after surgery, which may offer benefits to selected patients. QoL data may help clinicians in promoting tailored management of MR-ONJ.
Enabling Open Science for Health Research: Collaborative Informatics Environment for Learning on Health Outcomes (CIELO).
P Payne, O Lele, B Johnson and E Holve,
Journal of medical Internet research, Jul 31 2017
There is an emergent and intensive dialogue in the United States with regard to the accessibility, reproducibility, and rigor of health research. This discussion is also closely aligned with the need to identify sustainable ways to expand the national research enterprise and to generate actionable results that can be applied to improve the nation's health. The principles and practices of Open Science offer a promising path to address both goals by facilitating (1) increased transparency of data and methods, which promotes research reproducibility and rigor; and (2) cumulative efficiencies wherein research tools and the output of research are combined to accelerate the delivery of new knowledge in proximal domains, thereby resulting in greater productivity and a reduction in redundant research investments.AcademyHealth's Electronic Data Methods (EDM) Forum implemented a proof-of-concept open science platform for health research called the Collaborative Informatics Environment for Learning on Health Outcomes (CIELO).The EDM Forum conducted a user-centered design process to elucidate important and high-level requirements for creating and sustaining an open science paradigm.By implementing CIELO and engaging a variety of potential users in its public beta testing, the EDM Forum has been able to elucidate a broad range of stakeholder needs and requirements related to the use of an open science platform focused on health research in a variety of "real world" settings.Our initial design and development experience over the course of the CIELO project has provided the basis for a vigorous dialogue between stakeholder community members regarding the capabilities that will add the greatest value to an open science platform for the health research community. A number of important questions around user incentives, sustainability, and scalability will require further community dialogue and agreement.
Correlating eligibility criteria generalizability and adverse events using Big Data for patients and clinical trials.
A Sen, PB Ryan, A Goldstein, S Chakrabarti, S Wang, E Koski and C Weng,
Annals of the New York Academy of Sciences, 2017 01
Randomized controlled trials can benefit from proactive assessment of how well their participant selection strategies during the design of eligibility criteria can influence the study generalizability. In this paper, we present a quantitative metric called generalizability index for study traits 2.0 (GIST 2.0) to assess the a priori generalizability (based on population representativeness) of a clinical trial by accounting for the dependencies among multiple eligibility criteria. The metric was evaluated on 16 sepsis trials identified from ClinicalTrials.gov, with their adverse event reports extracted from the trial results sections. The correlation between GIST scores and adverse events was analyzed. We found that the GIST 2.0 score was significantly correlated with total adverse events and serious adverse events (weighted correlation coefficients of 0.825 and 0.709, respectively, with P < 0.01). This study exemplifies the promising use of Big Data in electronic health records and ClinicalTrials.gov for optimizing eligibility criteria design for clinical studies.