OHDSI MEETINGS THIS WEEK
Gold Standard Phenotype Library WG meeting - Tuesday at 10am EThttps://gatech.webex.com/webappng/sites/gatech/meeting/info/128704664173552569?MTID=mdd4af3e9b84212fc7df3eb0150703df5
The Book of OHDSI working group meeting - Tuesday at 11am EThttps://columbiauniversity.zoom.us/j/258043190
OHDSI Community Call - Tuesday at 12pm EThttps://meetings.webex.com/collabs/#/meetings/detail?uuid=M59X2V1U61WC9ASID2Z5N3UT95-D1JL&rnd=96139.930901412523321531221112212141232121131213113112112121536
CDM-Genomics WG - Wednesday at 0am KThttps://abmi.webex.com/webappng/sites/abmi/meeting/info/116787143000498361?MTID=m2747af107665e0586909629968cc2260&siteurl=abmi
ATLAS workgroup meeting - Wednesday at 10am EThttps://jjconferencing.webex.com/webappng/sites/jjconferencing/meeting/info/129429150951851372?MTID=mca074ae1ff6eb7d80245d36b59188c80
Natural Language Processing WG meeting Wednesday at 2pm EThttps://global.gotomeeting.com/join/707196421
Patient-Level Prediction WG - Wednesday at 2pm EThttps://global.gotomeeting.com/join/972917661
Population-Level Estimation WG (Western Hemisphere) - Thursday at 12pm ET*https://meetings.webex.com/collabs/#/meetings/detail?uuid=M3T9BZV9RSB6YNDM8WDDZMI19D-D1JL
You can find a full list of upcoming OHDSI meetings here: https://docs.google.com/document/d/1X0oa9R-V8cwpF1WQZDJOqcXZguPKRiCZ6XrQ2zXMiuQ/edit
ANNOUNCEMENTS
Looking for presenters for upcoming OHDSI community calls We are looking for collaborators to share their work on upcoming OHDSI calls. If you are interested in presenting on an upcoming OHDSI call please email me at beaton@ohdsi.org
2019 OHDSI Symposium – Titan Awards Don’t forget to submit your nominations for the 2019 Titan Awards. Submit your nominations here: https://docs.google.com/forms/d/e/1FAIpQLSc5d9G-KEW5G9wS2S5qxe8iGtsRVEHeMxDvwCCuj-Rg0qZYpw/viewform Deadline to submit nominations is 5pm ET on August 13th
2019 OHDSI Symposium - CREATIVE SUBMISSIONS - We want to give collaborators a chance to showcase their special talents! This could include, playing a musical instrument, singing, an interpretive dance, or an OHDSI-inspired painting. For more information about creative submissions, please check out our creative submissions page:https://www.ohdsi.org/creative-submissions/ The deadline for creative submission is today at 5pm ET
2019 OHDSI Symposium - TUTORIALS Registration is now open for tutorials at this year’s OHDSISymposium. Tutorials are set to take September 15th and 17th. More details about tutorials being offered is available here: https://www.ohdsi.org/tutorialworkshops2019/ https://www.ohdsi.org/tutorialregistration2019/
2019 OHDSI Symposium - Women in Real-World Analytics Leadership Forum As part of the 2019 OHDSI Symposium, the Women of OHDSI group will be hosting a leadership forum which is set to take place from 6-8pm on Sunday, September 15th at the Bethesda North Marriott in North Bethesda, MD. For more details and to RSVP, check out our event page: https://www.ohdsi.org/2019-women-in-real-world-analytics-leadership-forum/
Confidence is 10 percent hard work and 90 percent delusion
COMMUNITY PUBLICATIONS
DQueST: dynamic questionnaire for search of clinical trials.
C Liu, C Yuan, AM Butler, RD Carvajal, ZR Li, CN Ta and C Weng,
Journal of the American Medical Informatics Association : JAMIA , Aug 2019 07
Information overload remains a challenge for patients seeking clinical trials. We present a novel system (DQueST) that reduces information overload for trial seekers using dynamic questionnaires.DQueST first performs information extraction and criteria library curation. DQueST transforms criteria narratives in the ClinicalTrials.gov repository into a structured format, normalizes clinical entities using standard concepts, clusters related criteria, and stores the resulting curated library. DQueST then implements a real-time dynamic question generation algorithm. During user interaction, the initial search is similar to a standard search engine, and then DQueST performs real-time dynamic question generation to select criteria from the library 1 at a time by maximizing its relevance score that reflects its ability to rule out ineligible trials. DQueST dynamically updates the remaining trial set by removing ineligible trials based on user responses to corresponding questions. The process iterates until users decide to stop and begin manually reviewing the remaining trials.In simulation experiments initiated by 10 diseases, DQueST reduced information overload by filtering out 60%-80% of initial trials after 50 questions. Reviewing the generated questions against previous answers, on average, 79.7% of the questions were relevant to the queried conditions. By examining the eligibility of random samples of trials ruled out by DQueST, we estimate the accuracy of the filtering procedure is 63.7%. In a study using 5 mock patient profiles, DQueST on average retrieved trials with a 1.465 times higher density of eligible trials than an existing search engine. In a patient-centered usability evaluation, patients found DQueST useful, easy to use, and returning relevant results.DQueST contributes a novel framework for transforming free-text eligibility criteria to questions and filtering out clinical trials based on user answers to questions dynamically. It promises to augment keyword-based methods to improve clinical trial search.
FPheValuator: Development and Evaluation of a Phenotype Algorithm Evaluator.
JN Swerdel, G Hripcsak and PB Ryan,
Journal of biomedical informatics , Jul 29 2019
The primary approach for defining disease in observational healthcare databases is to construct phenotype algorithms (PAs), rule-based heuristics predicated on the presence, absence, and temporal logic of clinical observations. However, a complete evaluation of PAs, i.e., determining sensitivity, specificity, and positive predictive value (PPV), is rarely performed. In this study, we propose a tool (PheValuator) to efficiently estimate a complete PA evaluation.We used 4 administrative claims datasets: OptumInsight's de-identified Clinformatics™ Datamart (Eden Prairie,MN); IBM MarketScan Multi-State Medicaid); IBM MarketScan Medicare Supplemental Beneficiaries; and IBM MarketScan Commercial Claims and Encounters from 2000-2017. Using PheValuator involves 1) creating a diagnostic predictive model for the phenotype, 2) applying the model to a large set of randomly selected subjects, and 3) comparing each subject's predicted probability for the phenotype to inclusion/exclusion in PAs. We used the predictions as a 'probabilistic gold standard' measure to classify positive/negative cases. We examined 4 phenotypes: myocardial infarction, cerebral infarction, chronic kidney disease, and atrial fibrillation. We examined several PAs for each phenotype including 1-time (1X) occurrence of the diagnosis code in the subject's record and 1-time occurrence of the diagnosis in an inpatient setting with the diagnosis code as the primary reason for admission (1X-IP-1stPos).Across phenotypes, the 1X PA showed the highest sensitivity/lowest PPV among all PAs. 1X-IP-1stPos yielded the highest PPV/lowest sensitivity. Specificity was very high across algorithms. We found similar results between algorithms across datasets.PheValuator appears to show promise as a tool to estimate PA performance characteristics.
Challenges with quality of race and ethnicity data in observational databases.
FCG Polubriaginof, P Ryan, H Salmasian, AW Shapiro, A Perotte, MM Safford, G Hripcsak, S Smith, NP Tatonetti and DK Vawdrey,
Journal of the American Medical Informatics Association : JAMIA , Jul 31 2019
We sought to assess the quality of race and ethnicity information in observational health databases, including electronic health records (EHRs), and to propose patient self-recording as an improvement strategy.We assessed completeness of race and ethnicity information in large observational health databases in the United States (Healthcare Cost and Utilization Project and Optum Labs), and at a single healthcare system in New York City serving a racially and ethnically diverse population. We compared race and ethnicity data collected via administrative processes with data recorded directly by respondents via paper surveys (National Health and Nutrition Examination Survey and Hospital Consumer Assessment of Healthcare Providers and Systems). Respondent-recorded data were considered the gold standard for the collection of race and ethnicity information.Among the 160 million patients from the Healthcare Cost and Utilization Project and Optum Labs datasets, race or ethnicity was unknown for 25%. Among the 2.4 million patients in the single New York City healthcare system's EHR, race or ethnicity was unknown for 57%. However, when patients directly recorded their race and ethnicity, 86% provided clinically meaningful information, and 66% of patients reported information that was discrepant with the EHR.Race and ethnicity data are critical to support precision medicine initiatives and to determine healthcare disparities; however, the quality of this information in observational databases is concerning. Patient self-recording through the use of patient-facing tools can substantially increase the quality of the information while engaging patients in their health.Patient self-recording may improve the completeness of race and ethnicity information.
Developing a Regional Distributed Data Network for Surveillance of Chronic Health Conditions: The Colorado Health Observation Regional Data Service.
E Bacon, G Budney, J Bondy, MG Kahn, EV McCormick, JF Steiner, D Tabano, JA Waxmonsky, R Zucker and AJ Davidson,
Journal of public health management and practice : JPHMP , Sep/Oct 2019
Electronic health records (EHRs) provide an alternative to traditional public health surveillance surveys and administrative data for measuring the prevalence and impact of chronic health conditions in populations. As the infrastructure for secondary use of EHR data improves, many stakeholders are poised to benefit from data partnerships for regional access to information. Electronic health records can be transformed into a common data model that facilitates data sharing across multiple organizations and allows data to be used for surveillance. The Colorado Health Observation Regional Data Service, a regional distributed data network, has assembled diverse data partnerships, flexible infrastructure, and transparent governance practices to better understand the health of communities through EHR-based, public health surveillance. This article describes attributes of regional distributed data networks using EHR data and the history and design of Colorado Health Observation Regional Data Service as an emerging public health surveillance tool for chronic health conditions. Colorado Health Observation Regional Data Service and our experience may serve as a model for other regions interested in similar surveillance efforts. While benefits from EHR-based surveillance are described, a number of technology, partnership, and value proposition challenges remain.
Association of APOL1 Risk Alleles with Cardiovascular Disease in African Americans in the Million Veteran Program.
AG Bick, E Akwo, C Robinson-Cohen, K Lee, J Lynch, TL Assimes, S DuVall, T Edwards, H Fang, SM Freiberg, A Giri, JE Huffman, J Huang, L Hull, RL Kember, D Klarin, JS Lee, M Levin, DR Miller, P Natarajan, D Saleheen, Q Shao, YV Sun, H Tang, O Wilson, KM Chang, K Cho, J Concato, JM Gaziano, S Kathiresan, CJ O'Donnell, DJ Rader, PS Tsao, PW Wilson, AM Hung and SM Damrauer,
Circulation , Jul 24 2019
Approximately 13% of African-American individuals carry two copies of the APOL1 risk alleles G1 or G2, which are associated with 1.5-2.5 fold increased risk of chronic kidney disease (CKD). There have been conflicting reports as to whether an association exists between APOL1 risk alleles and cardiovascular disease, independent of the effects of APOL1 on kidney disease. We sought to test the association of APOL1 G1/G2 alleles with coronary artery disease (CAD), peripheral artery disease (PAD), and stroke among African American individuals in the Million Veteran Program (MVP).We performed a time-to-event analysis of retrospective electronic health record (EHR) data using Cox proportional hazard and competing risks Fine and Gray sub-distribution hazard models. The primary exposure was APOL1 risk allele status. The primary outcome was incident CAD amongst individuals without CKD during the 12.5 year follow up period. Separately we analyzed the cross-sectional association of APOL1 risk allele status with lipid traits and 115 cardiovascular diseases using phenome-wide association.Among 30,903 African American MVP participants, 3,941 (13%) carried the two APOL1 risk allele high-risk genotype. Individuals with normal kidney function at baseline with two risk alleles had slightly higher risk of developing CAD compared to those with no risk alleles (Hazard Ratio (HR): 1.11, 95% Confidence Interval (CI): 1.01-1.21, p=0.039). Similarly, modest associations were identified with incident stroke (HR: 1.20, 95% CI: 1.05-1.36, p=0.007) and PAD (HR: 1.15, 95% CI:1.01-1.29, p=0.031). When modeling both cardiovascular and renal outcomes, APOL1 was strongly associated with incident renal disease, while no significant association with the cardiovascular disease endpoints could be detected. Cardiovascular phenome-wide association analyses did not identify additional significant associations with cardiovascular disease subsets.APOL1 risk variants display a modest association with cardiovascular disease and this association is likely mediated by the known APOL1 association with CKD.
