OHDSI MEETINGS THIS WEEK
Gold Standard Phenotype Library WG meeting - No meeting this week, next meeting will be Tuesday, July 9th at 10am ET
Book of OHDSI WG meeting - Tuesday at 11am ET
OHDSI Community Call - No meeting this week, next meeting will be Tuesday, July 9th at 12pm ET
Common Data Model & Vocabulary WG meeting - No meeting this week, next meeting will be Tuesday, August 6th at 1pm ET
ATLAS workgroup meeting - No meeting this week, next meeting will be Wednesday, July 10th at 10am ET
ACHILLES 2.0 working group meeting - No meeting this week, next meeting will be Wednesday, July 10th at 2pm ET
You can find a full list of upcoming OHDSI meetings here:
OHDSI in Oxford - Read all about @Patrick_Ryan and @Rijnbeek’s OHDSI workshop at the Oxford Summer School session here: https://www.ohdsi.org/oxford-study/
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 firstname.lastname@example.org
2019 OHDSI Symposium - CREATIVE SUBMISSIONS - In addition to scientific submissions for the collaborator showcase, we’re also accepting 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:
The deadline for creative submission is 5pm ET on Monday, August 12th, 2019
2019 OHDSI Symposium - TUTORIALS Registration is now open for tutorals at this year’s OHDSI Symposium. Tutorials are set to take September 15th and 17th. More details about tutorials being offered is available here: https://www.ohdsi.org/tutorialworkshops2019/
Register for tutorials here: https://www.ohdsi.org/tutorialregistration2019/
I find only freedom in the realms of eccentricity.
David Bowie COMMUNITY PUBLICATIONS
A Data Element-Function Conceptual Model for Data Quality Checks.
JR Rogers, TJ Callahan, T Kang, A Bauck, R Khare, JS Brown, MG Kahn and C Weng,
EGEMS (Washington, DC), Apr 23 2019
In aggregate, existing data quality (DQ) checks are currently represented in heterogeneous formats, making it difficult to compare, categorize, and index checks. This study contributes a data element-function conceptual model to facilitate the categorization and indexing of DQ checks and explores the feasibility of leveraging natural language processing (NLP) for scalable acquisition of knowledge of common data elements and functions from DQ checks narratives.The model defines a "data element", the primary focus of the check, and a "function", the qualitative or quantitative measure over a data element. We applied NLP techniques to extract both from 172 checks for Observational Health Data Sciences and Informatics (OHDSI) and 3,434 checks for Kaiser Permanente's Center for Effectiveness and Safety Research (CESR).The model was able to classify all checks. A total of 751 unique data elements and 24 unique functions were extracted. The top five frequent data element-function pairings for OHDSI were Person-Count (55 checks), Insurance-Distribution (17), Medication-Count (16), Condition-Count (14), and Observations-Count (13); for CESR, they were Medication-Variable Type (175), Medication-Missing (172), Medication-Existence (152), Medication-Count (127), and Socioeconomic Factors-Variable Type (114).This study shows the efficacy of the data element-function conceptual model for classifying DQ checks, demonstrates early promise of NLP-assisted knowledge acquisition, and reveals the great heterogeneity in the focus in DQ checks, confirming variation in intrinsic checks and use-case specific "fitness-for-use" checks.
Penetration of new antidiabetic medications in East Asian countries and the United States: A cross-national
K Kubota, Y Kamijima, YH Kao Yang, S Kimura, E Chia-Cheng Lai, KKC Man, P Ryan, M Schuemie, P Stang, CC Su, ICK Wong, Y Zhang and S Setoguchi,
PloS one, 2018
The number of patients with diabetes is increasing particularly in Asia-Pacific region. Many of them are treated with antidiabetics. As the basis of the studies on the benefit and harm of antidiabetic drugs in the region, the information on patterns of market penetration of new classes of antidiabetic medications is important in providing context for subsequent research and analyzing and interpreting results.We compared penetration patterns of dipeptidyl peptidase-4 (DPP-4) inhibitors in Taiwan, Hong Kong, Japan, and the United States. We used the Taiwan National Health Insurance Research Database, a random sample of the Hong Kong Clinical Data Analysis and Reporting System, the Japan Medical Data Center database, and a 5% random sample of the US Medicare database converted to the Observational Medical Outcomes Partnership's Common Data Model to identify new users of oral antidiabetic medications. We standardized prevalence and incidence rates of medication use by age and sex to those in the 2010 Taiwanese population. We compared age, sex, comorbid conditions, and concurrent medications between new users of DPP-4 inhibitors and biguanides.Use of DPP-4 inhibitors 1 year after market entry was highest in Japan and lowest in Hong Kong. New users had more heart failure, hyperlipidemia, and renal failure than biguanide users in Taiwan, Hong Kong, and the United States while the proportions were similar in Japan. In a country with low penetration of DPP-4 inhibitors (eg, Hong Kong), users had diabetes with multiple comorbid conditions compared with biguanidine users. In a country with high penetration (eg, Japan), the proportion of users with comorbid conditions was similar to that of biguanide users.We observed a marked difference of the penetration patterns of newly marketed antidiabetics in different countries in Asia. Those results will provide the basic information useful in the future studies.
A Novel Stakeholder Engagement Approach for Patient-centered Outcomes Research.
KK Kim, D Khodyakov, K Marie, H Taras, D Meeker, HO Campos and L Ohno-Machado,
Medical care, 2018 10
The engagement of patients and other stakeholders is a critical element in the design of patient-centered outcomes research studies. However, methodology for scalable engagement in research management particularly activities such as operationalization of principles and setting of priorities is not well-developed. The objective of this study is to describe a novel approach for scalable stakeholder engagement in research aligned with the Patient-Centered Outcomes Research Institute (PCORI) engagement principles, which was evaluated in a national clinical data research network.Patient, patient advocate, clinician, and researcher stakeholders were recruited from clinical sites, as well as social media sites related to the 3 conditions of focus, heart failure, obesity, and Kawasaki disease. The engagement strategy was designed, implemented, and mapped to the PCORI engagement principles. Evaluation included internal assessment and quantitative measures of online engagement.We operationalized the PCORI principles with 12 stakeholder engagement strategies and convened stakeholder advisory boards and online research prioritization panels to determine research priorities in a rigorous, deliberative process. A total of 46 advisors (20 patients) and 339 panelists (159 patients) actively participated. There were not significant differences between patients and clinicians in level of online engagement. Nonetheless, while patients reported a slightly greater challenge with following online discussion, they overall had a more favorable opinion about use of the online format.An efficient way to engage large numbers of representative stakeholders in research is a necessary first step to assure the public of trustworthy use of data networks for health research. This paper describes a comprehensive approach to engagement in patient-centered outcomes research management that informs ongoing development of rigorous methodologies in this area.
Comprehensive comparison of monotherapies for psychiatric hospitalization risk in bipolar disorders.
A Nestsiarovich, AJ Mazurie, NG Hurwitz, B Kerner, SJ Nelson, AS Crisanti, M Tohen, RL Krall, DJ Perkins and CG Lambert,
Bipolar disorders, 2018 12
This study compared 29 drugs for risk of psychiatric hospitalization in bipolar disorders, addressing the evidence gap on the >50 drugs used by US patients for treatment.The Truven Health Analytics MarketScan® database was used to identify 190 894 individuals with bipolar or schizoaffective disorder who filled a prescription for one of 29 drugs of interest: lithium, first- or second-generation antipsychotics, mood-stabilizing anticonvulsants, and antidepressants. Competing risks regression survival analysis was used to compare drugs for risk of psychiatric hospitalization, adjusting for patient age, sex, comorbidities, and pretreatment medications. Other competing risks were ending monotherapy and non-psychiatric hospitalization.Three drugs were associated with significantly lower risk of psychiatric hospitalization than lithium: valproate (relative risk [RR] = 0.80, P = 3.20 × 10-4 ), aripiprazole (RR = 0.80, P = 3.50 × 10-4 ), and bupropion (RR = 0.80, P = 2.80 × 10-4 ). Eight drugs were associated with significantly higher risk of psychiatric hospitalization: haloperidol (RR = 1.57, P = 9.40 × 10-4 ), clozapine (RR = 1.52, P = .017), fluoxetine (RR = 1.17, P = 3.70 × 10-3 ), sertraline (RR = 1.17, P = 3.20 × 10-3 ), citalopram (RR = 1.14, P = .013), duloxetine (RR = 1.24, P = 5.10 × 10-4 ), venlafaxine (RR = 1.33; P = 1.00 × 10-6 ), and ziprasidone (RR = 1.25; P = 6.20 × 10-3 ).This largest reported retrospective observational study on bipolar disorders pharmacotherapy to date demonstrates that the majority of patients end monotherapy within 2 months after treatment start. The risk of psychiatric hospitalization varied almost two-fold across individual medications. The data add to the evidence favoring lithium and mood stabilizer use in short-term bipolar disorder management. The findings that the dopaminergic drugs aripiprazole and bupropion had better outcomes than other members of their respective classes and that antidepressant outcomes may vary by baseline mood polarity merit further investigation.