OHDSI MEETINGS THIS WEEK
OMOP CDM Oncology WG - Genomic Subgroup Meeting - Tuesday at 9am EThttps://us04web.zoom.us/j/412862164?pwd=NmpEWTdTQlB4N3VxT0tQRXdDWlg0dz09 https://www.ohdsi.org/web/wiki/doku.php?id=projects:workgroups:oncology-sg
OHDSI Community Call - Tuesday at 12pm EThttps://meetings.webex.com/collabs/#/meetings/detail?uuid=M59X2V1U61WC9ASID2Z5N3UT95-D1JL&rnd=96139.930901412523321531221112212141232121131213113112112121536 https://www.ohdsi.org/web/wiki/doku.php?id=projects:ohdsi_community
Oncology WG - Development Subgroup Meeting - Wednesday at 10am EThttps://www.ohdsi.org/web/wiki/doku.php?id=documentation:oncology:development_schedule https://www.ohdsi.org/web/wiki/doku.php?id=projects:workgroups:oncology-sg
NLP Working Group - Wednesday at 2pm EThttps://global.gotomeeting.com/join/707196421 https://www.ohdsi.org/web/wiki/doku.php?id=projects:workgroups:nlp-wg
OMOP CDM Oncology WG - CDM/Vocabulary Subgroup Meeting - Thursday at 10am EThttps://us04web.zoom.us/j/755053125?pwd=V0dOZVVnY3RMRWgxMVVGTDdVbnA1UT09 https://www.ohdsi.org/web/wiki/doku.php?id=projects:workgroups:oncology-sg
China WG - Friday at 10am EThttps://global.gotomeeting.com/join/721873621 https://www.ohdsi.org/web/wiki/doku.php?id=projects:workgroups:china-wg
You can find a full list of upcoming OHDSI meetings here: https://docs.google.com/document/d/1X0oa9R-V8cwpF1WQZDJOqcXZguPKRiCZ6XrQ2zXMiuQ/edit
ANNOUNCEMENTS
AMIA Abstract Submissions Due This Wednesday! AMIA’s 2020 Annual Symposium, set to take place Nov 14-18 in Chicago will be accepting abstracts until this Wednesday, March 11th. For more information, check out their call for participation:https://www.amia.org/amia2020/call-for-participation
Two OHDSI studies published in Lancet! Another OHDSI study has been published in Lancet! The EHDEN team’s Rheumatology paper is available here: https://www.thelancet.com/journals/lanrhe/article/PIIS2665-9913(19)30075-X/fulltext https://www.thelancet.com/journals/lancet/article/PIIS0140-6736(19)32317-7/fulltext https://www.ohdsi.org/ohdsi-news-updates/legend-hypertension-study/
I get nervous when I don’t get nervous. If I’m nervous I know I’m going to have a good show.
COMMUNITY PUBLICATIONS
Treatment Patterns for Chronic Comorbid Conditions in Patients With Cancer Using a Large-Scale Observational Data Network.
R Chen, P Ryan, K Natarajan, T Falconer, KD Crew, CG Reich, R Vashisht, G Randhawa, NH Shah and G Hripcsak,
JCO clinical cancer informatics , Mar 2020
Patients with cancer are predisposed to developing chronic, comorbid conditions that affect prognosis, quality of life, and mortality. While treatment guidelines and care variations for these comorbidities have been described for the general noncancer population, less is known about real-world treatment patterns in patients with cancer. We sought to characterize the prevalence and distribution of initial treatment patterns across a large-scale data network for depression, hypertension, and type II diabetes mellitus (T2DM) among patients with cancer.We used the Observational Health Data Sciences and Informatics network, an international collaborative implementing the Observational Medical Outcomes Partnership Common Data Model to standardize more than 2 billion patient records. For this study, we used 8 databases across 3 countries-the United States, France, and Germany-with 295,529,655 patient records. We identified patients with cancer using SNOMED (Systematized Nomenclature of Medicine) codes validated via manual review. We then characterized the treatment patterns of these patients initiating treatment of depression, hypertension, or T2DM with persistent treatment and at least 365 days of observation.Across databases, wide variations exist in treatment patterns for depression (n = 1,145,510), hypertension (n = 3,178,944), and T2DM (n = 886,766). When limited to 6-node (6-drug) sequences, we identified 61,052 unique sequences for depression, 346,067 sequences for hypertension, and 40,629 sequences for T2DM. These variations persisted across sites, databases, countries, and conditions, with the exception of metformin (73.8%) being the most common initial T2DM treatment. The most common initial medications were sertraline (17.5%) and escitalopram (17.5%) for depression and hydrochlorothiazide (20.5%) and lisinopril (19.6%) for hypertension.We identified wide variations in the treatment of common comorbidities in patients with cancer, similar to the general population, and demonstrate the feasibility of conducting research on patients with cancer across a large-scale observational data network using a common data model.
Empirical assessment of case-based methods for drug safety alert identification in the French National Healthcare System database (SNDS): Methodology of the ALCAPONE project.
NH Thurin, R Lassalle, M Schuemie, M Pénichon, JJ Gagne, JA Rassen, J Benichou, A Weill, P Blin, N Moore and C Droz-Perroteau,
Pharmacoepidemiology and drug safetyREFERENCES , Mar 2020 04
To introduce the methodology of the ALCAPONE project.The French National Healthcare System Database (SNDS), covering 99% of the French population, provides a potentially valuable opportunity for drug safety alert generation. ALCAPONE aimed to assess empirically in the SNDS case-based designs for alert generation related to four health outcomes of interest.ALCAPONE used a reference set adapted from observational medical outcomes partnership (OMOP) and Exploring and Understanding Adverse Drug Reactions (EU-ADR) project, with four outcomes-acute liver injury (ALI), myocardial infarction (MI), acute kidney injury (AKI), and upper gastrointestinal bleeding (UGIB)-and positive and negative drug controls. ALCAPONE consisted of four main phases: (1) data preparation to fit the OMOP Common Data Model and select the drug controls; (2) detection of the selected controls via three case-based designs: case-population, case-control, and self-controlled case series, including design variants (varying risk window, adjustment strategy, etc.); (3) comparison of design variant performance (area under the ROC curve, mean square error, etc.); and (4) selection of the optimal design variants and their calibration for each outcome.Over 2009-2014, 5225 cases of ALI, 354 109 MI, 12 633 AKI, and 156 057 UGIB were identified using specific definitions. The number of detectable drugs ranged from 61 for MI to 25 for ALI. Design variants generated more than 50 000 points estimates. Results by outcome will be published in forthcoming papers.ALCAPONE has shown the interest of the empirical assessment of pharmacoepidemiological approaches for drug safety alert generation and may encourage other researchers to do the same in other databases.
Incidence, risk factors and re-exacerbation rate of severe asthma exacerbations in a multinational, multidatabase pediatric cohort study.
M Engelkes, EJ Baan, MAJ de Ridder, E Svensson, D Prieto-Alhambra, F Lapi, C Giaquinto, G Picelli, N Boudiaf, F Albers, LA Evitt, S Cockle, E Bradford, MK Van Dyke, R Suruki, P Rijnbeek, MCJM Sturkenboom, HM Janssens and KMC Verhamme,
Pediatric allergy and immunology : official publication of the European Society of Pediatric Allergy and Immunology , Mar 2020 01
There are sparse real world data on severe asthma exacerbations (SAE) in children. This multinational cohort study assessed the incidence and risk factors of SAE and the incidence of asthma related rehospitalization in asthmatic children.Asthma patients 5-17 years with ≥1 year of follow-up were identified in six European electronic databases from the Netherlands, Italy, UK, Denmark and Spain in 2008-2013. Asthma was defined as ≥1 asthma specific disease code within 3 months of prescriptions/dispensing of asthma medication. Severe asthma was defined as high-dosed inhaled corticosteroids plus a second controller. SAE was defined by systemic corticosteroids, emergency department visit and/or hospitalization all for reason of asthma. Risk factors for SAE were estimated by Poisson regression analyses.The cohort consisted of 212,060 paediatric asthma patients contributing to 678,625 patient years (PY). SAE rates ranged between 17-198/1,000 PY and were higher in severe asthma, and highest in severe asthma patients with a history of exacerbations. Prior SAE (Incidence Rate Ratio 3-45) and younger age increased the SAE risk in all countries, whereas obesity, atopy and GERD were a risk factor in some but not all countries. Rehospitalization rates were up to 79% within 1 year.In a real world setting, SAE rates were highest in children with severe asthma with a history of exacerbations. Many severe asthma patients were rehospitalized within 1 year. Asthma management focusing on prevention of SAE is important to reduce the burden of asthma.
