OHDSI MEETINGS THIS WEEK
Gold Standard Phenotype Library WG meeting - Tuesday at 9am ET
The Book of OHDSI working group meeting - Tuesday at 11am ET
Zoom URL: https://columbiauniversity.zoom.us/j/258043190
OHDSI Community Call - NO MEETING THIS WEEK
CDM and Vocabulary WG meeting - Tuesday at 1pm ET
ATLAS workgroup meeting - Wednesday at 10am ET
ACHILLES 2.0 Work Group - Wednesday at 2pm ET
Toll Number: +1 (213) 204-2547
87454548# (Dial-in Number)
Psychiatry Work Group - Thursday at 8am ET
Population-Level Estimation WG (Eastern Hemisphere) - Thursday at 4pm in Korea
EHR Work Group Meeting - Friday at 10am ET
Metadata and Annotations WG meeting - Friday at 2pm ET
You can find a full list of upcoming OHDSI meetings here:
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 email@example.com
2019 OHDSI Symposium - Materials All materials from the 2019 OHDSI symposium, including presentation slides, handouts and posters are available here: https://www.ohdsi.org/2019-ohdsi-symposium-materials/
2019 OHDSI Symposium - Videos Videos from the 2019 OHDSI symposium, including presentations from the main symposium, tutorials and the Women in Real-World Analytics leadership forum are current in post-production and will be made available in October.
In the end, everything is a gag.
Charlie Chaplin COMMUNITY PUBLICATIONS
Measuring the Effectiveness of Safety Warnings on the Risk of Stroke in Older Antipsychotic Users: A Nationwide Cohort Study in Two Large Electronic Medical Records Databases in the United Kingdom and Italy.
J Sultana, A Fontana, F Giorgianni, S Tillati, C Cricelli, A Pasqua, E Patorno, C Ballard, M Sturkenboom and G Trifirò,
Drug safety, Sep 25 2019
Safety warnings relating to antipsychotic-associated stroke among older persons in the UK and Italy were issued. However, the impact of these safety warnings on stroke risk has not been measured to date.The aim of this study was to measure the change in stroke incidence after two safety warnings in both the UK and Italy.A cohort study was conducted using electronic medical records representative of the UK (The Health Improvement Network) and Italy (Health Search-IQVIA Health LPD), containing data on 11 million and 1 million patients, respectively. After each drug safety warning, elderly antipsychotic new initiators were propensity-score matched 1:1:1 on antipsychotic initiators before any safety warning. Stroke incidence within 6 months of antipsychotic initiation, using an intention-to-treat approach, was the main outcome.In the UK and Italy, 6342 and 7587 elderly antipsychotic initiators were identified, respectively. A 42% stroke incidence reduction was seen in the UK after the first safety warning [42.3 (95% confidence interval (CI) 35.2-50.8) vs. 24.4 [95% CI 19.0-31.2] events per 1000 person-years (PYs)], while there was a 60% stroke incidence reduction after the second warning (16.9 [95% CI 12.2-23.4] events per 1000 PYs) compared to before the first warning. There was no significant reduction in stroke incidence in Italy.Antipsychotic safety warnings were followed by a reduction in stroke incidence among older antipsychotic users in the UK, but not Italy.
Evaluation of Research Accessibility and Data Elements of HIV Registries.
CS Mayer, N Williams, KW Fung and V Huser,
Current HIV research, Sep 24 2019
Patient registries represent a long-term data collection system that is a platform for performing multiple research studies to generate real world evidence. Many of these registries use common data elements (CDEs) and link data from Electronic Health Records.This study evaluated HIV registry features that contribute to the registry's usability for retrospective analysis of existing registry data or new prospective interventional studies.We searched PubMed and ClinicalTrials.gov (CTG) to generate a list of HIV registries. We used the framework developed by the European Medical Agency (EMA) to evaluate the registries by determining the presence of key research features. These features included information about the registry, request and collaboration processes, and available data. We acquired data dictionaries and identified CDEs.We found 13 HIV registries that met our criteria, 11 through PubMed and 2 through CTG. The prevalence of the evaluated features ranged from all 13 (100%) having published key registry information to 0 having a research contract template. We analyzed 6 data dictionaries and identified 14 CDEs that were present in at least 4 of 6 (66.7%) registry data dictionaries.The importance of registries as platforms for research data is growing and the presence of certain features, including data dictionaries, contribute to the reuse and secondary research capabilities of a registry. We found some features such as collaboration policies were in a majority of registries while others, such as ethical support are in few and are more for future development.
Comparison of the cohort selection performance of Australian Medicines Terminology to Anatomical Therapeutic Chemical mappings.
GN Guo, J Jonnagaddala, S Farshid, V Huser, C Reich and ST Liaw,
Journal of the American Medical Informatics Association : JAMIA, Sep 23 2019
Electronic health records are increasingly utilized for observational and clinical research. Identification of cohorts using electronic health records is an important step in this process. Previous studies largely focused on the methods of cohort selection, but there is little evidence on the impact of underlying vocabularies and mappings between vocabularies used for cohort selection. We aim to compare the cohort selection performance using Australian Medicines Terminology to Anatomical Therapeutic Chemical (ATC) mappings from 2 different sources. These mappings were taken from the Observational Medical Outcomes Partnership Common Data Model (OMOP-CDM) and the Pharmaceutical Benefits Scheme (PBS) schedule.We retrieved patients from the electronic Practice Based Research Network data repository using 3 ATC classification groups (A10, N02A, N06A). The retrieved patients were further verified manually and pooled to form a reference standard which was used to assess the accuracy of mappings using precision, recall, and F measure metrics.The OMOP-CDM mappings identified 2.6%, 15.2%, and 24.4% more drugs than the PBS mappings in the A10, N02A and N06A groups respectively. Despite this, the PBS mappings generally performed the same in cohort selection as OMOP-CDM mappings except for the N02A Opioids group, where a significantly greater number of patients were retrieved. Both mappings exhibited variable recall, but perfect precision, with all drugs found to be correctly identified.We found that 1 of the 3 ATC groups had a significant difference and this affected cohort selection performance. Our findings highlighted that underlying terminology mappings can greatly impact cohort selection accuracy. Clinical researchers should carefully evaluate vocabulary mapping sources including methodologies used to develop those mappings.
Clinical Concept Value Sets and Interoperability in Health Data Analytics.
S Gold, A Batch, R McClure, G Jiang, H Kharrazi, R Saripalle, V Huser, C Weng, N Roderer, A Szarfman, N Elmqvist and D Gotz,
AMIA ... Annual Symposium proceedings. AMIA Symposium, 2018
This paper focuses on value sets as an essential component in the health analytics ecosystem. We discuss shared repositories of reusable value sets and offer recommendations for their further development and adoption. In order to motivate these contributions, we explain how value sets fit into specific analytic tasks and the health analytics landscape more broadly; their growing importance and ubiquity with the advent of Common Data Models, Distributed Research Networks, and the availability of higher order, reusable analytic resources like electronic phenotypes and electronic clinical quality measures; the formidable barriers to value set reuse; and our introduction of a concept-agnostic orientation to vocabulary collections. The costs of ad hoc value set management and the benefits of value set reuse are described or implied throughout. Our standards, infrastructure, and design recommendations are not systematic or comprehensive but invite further work to support value set reuse for health analytics. The views represented in the paper do not necessarily represent the views of the institutions or of all the co-authors.
Association of Hemoglobin A1c Levels With Use of Sulfonylureas, Dipeptidyl Peptidase 4 Inhibitors, and Thiazolidinediones in Patients With Type 2 Diabetes Treated With Metformin: Analysis From the Observational Health Data Sciences and Informatics Initiative.
R Vashisht, K Jung, A Schuler, JM Banda, RW Park, S Jin, L Li, JT Dudley, KW Johnson, MM Shervey, H Xu, Y Wu, K Natrajan, G Hripcsak, P Jin, M Van Zandt, A Reckard, CG Reich, J Weaver, MJ Schuemie, PB Ryan, A Callahan and NH Shah,
JAMA network open, 2018 08 03
Consensus around an efficient second-line treatment option for type 2 diabetes (T2D) remains ambiguous. The availability of electronic medical records and insurance claims data, which capture routine medical practice, accessed via the Observational Health Data Sciences and Informatics network presents an opportunity to generate evidence for the effectiveness of second-line treatments.To identify which drug classes among sulfonylureas, dipeptidyl peptidase 4 (DPP-4) inhibitors, and thiazolidinediones are associated with reduced hemoglobin A1c (HbA1c) levels and lower risk of myocardial infarction, kidney disorders, and eye disorders in patients with T2D treated with metformin as a first-line therapy.Three retrospective, propensity-matched, new-user cohort studies with replication across 8 sites were performed from 1975 to 2017. Medical data of 246 558 805 patients from multiple countries from the Observational Health Data Sciences and Informatics (OHDSI) initiative were included and medical data sets were transformed into a unified common data model, with analysis done using open-source analytical tools. Participants included patients with T2D receiving metformin with at least 1 prior HbA1c laboratory test who were then prescribed either sulfonylureas, DPP-4 inhibitors, or thiazolidinediones. Data analysis was conducted from 2015 to 2018.Treatment with sulfonylureas, DPP-4 inhibitors, or thiazolidinediones starting at least 90 days after the initial prescription of metformin.The primary outcome is the first observation of the reduction of HbA1c level to 7% of total hemoglobin or less after prescription of a second-line drug. Secondary outcomes are myocardial infarction, kidney disorder, and eye disorder after prescription of a second-line drug.A total of 246 558 805 patients (126 977 785 women [51.5%]) were analyzed. Effectiveness of sulfonylureas, DPP-4 inhibitors, and thiazolidinediones prescribed after metformin to lower HbA1c level to 7% or less of total hemoglobin remained indistinguishable in patients with T2D. Patients treated with sulfonylureas compared with DPP-4 inhibitors had a small increased consensus hazard ratio of myocardial infarction (1.12; 95% CI, 1.02-1.24) and eye disorders (1.15; 95% CI, 1.11-1.19) in the meta-analysis. Hazard of observing kidney disorders after treatment with sulfonylureas, DPP-4 inhibitors, or thiazolidinediones was equally likely.The examined drug classes did not differ in lowering HbA1c and in hazards of kidney disorders in patients with T2D treated with metformin as a first-line therapy. Sulfonylureas had a small, higher observed hazard of myocardial infarction and eye disorders compared with DPP-4 inhibitors in the meta-analysis. The OHDSI collaborative network can be used to conduct a large international study examining the effectiveness of second-line treatment choices made in clinical management of T2D.
Selecting comparators for drugs with multiple indications and complex treatment patterns: Example selecting comparators for daratumumab in multiple myeloma
Comparing record linkage software programs and algorithms using real-world data
Linkage of medical databases, including insurer claims and electronic health records (EHRs), is increasingly common. However, few studies have investigated the behavior and output of linkage software. To determine how linkage quality is affected by...