OHDSI MEETINGS THIS WEEK
Please note: Due to the COVID-19 outbreak some OHDSI meetings may be cancelled or postponed. Below is a list of meetings that are currently still planned to occur, but be sure to keep an eye out on the forum and wiki for the latest updates on WG meetings
Oncology WG - Genomic Subgroup Meeting - Tuesday 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://columbiacuimc.zoom.us/j/945377669 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
Psychiatry WG Meeting - Thursday at 8am EThttps://zoom.us/j/240721098?pwd=OHhYaWNlelJ6cUlYaG1yYTk1QUlkZz09 https://www.ohdsi.org/web/wiki/doku.php?id=projects:workgroups:psychiatry
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
EHR WG Meeting - Friday at 10am EThttps://ucdenver.zoom.us/j/4984831362 https://www.ohdsi.org/web/wiki/doku.php?id=projects:workgroups:ehr-wg
You can find a full list of upcoming OHDSI meetings here: https://docs.google.com/document/d/1X0oa9R-V8cwpF1WQZDJOqcXZguPKRiCZ6XrQ2zXMiuQ/edit
ANNOUNCEMENTS
New Teleconferencing Details for OHDSI Community Calls - Our weekly OHDSI community calls, which take place every Tuesday at noon will no longer be hosted via webex. For the time being, these calls will be hosted via Zoom. The details to join are:Zoom Meeting URL: https://columbiacuimc.zoom.us/j/945377669
OHDSI F2F & 2020 US Symposium To do our part in reducing the spread of COVID-19 we have decided not to hold a F2F meeting this summer. However, planning for the 2020 U.S. Symposium, which will take place October 18-21st, is underway, and we will be sharing details when they become available.
COVID-19 Virtual Study-a-thon - To contribute to the COVID-19 response, the OHDSI community hosted a virtual study-a-thon on March 26-29th. For videos from this event, check out this forum post: FINAL GLOBAL UPDATE: #OHDSICOVID19 Study-A-Thon (video link posted) https://www.ohdsi.org/covid-19-updates/
Solitude sometimes is the best society.
COMMUNITY PUBLICATIONS
Clinical data quality: a data life cycle perspective.
C Weng,
Biostatistics & epidemiology , 2020
Clinical data is the staple of modern learning health systems. It promises to accelerate biomedical discovery and improves the efficiency of clinical and translational research but is also fraught with significant data quality issues. This paper aims to provide a life cycle perspective of clinical data quality issues along with recommendations for establishing appropriate expectations for research based on real-world clinical data and best practices for reusing clinical data as a secondary data source.
Methotrexate and relative risk of dementia amongst patients with rheumatoid arthritis: a multi-national multi-database case-control study.
D Newby, D Prieto-Alhambra, T Duarte-Salles, D Ansell, L Pedersen, J van der Lei, M Mosseveld, P Rijnbeek, G James, M Alexander, P Egger, J Podhorna, R Stewart, G Perera, P Avillach, S Grosdidier, S Lovestone and AJ Nevado-Holgado,
Alzheimer's research & therapy , Apr 2020 06
Inflammatory processes have been shown to play a role in dementia. To understand this role, we selected two anti-inflammatory drugs (methotrexate and sulfasalazine) to study their association with dementia risk.A retrospective matched case-control study of patients over 50 with rheumatoid arthritis (486 dementia cases and 641 controls) who were identified from electronic health records in the UK, Spain, Denmark and the Netherlands. Conditional logistic regression models were fitted to estimate the risk of dementia.Prior methotrexate use was associated with a lower risk of dementia (OR 0.71, 95% CI 0.52-0.98). Furthermore, methotrexate use with therapy longer than 4 years had the lowest risk of dementia (odds ratio 0.37, 95% CI 0.17-0.79). Sulfasalazine use was not associated with dementia (odds ratio 0.88, 95% CI 0.57-1.37).Further studies are still required to clarify the relationship between prior methotrexate use and duration as well as biological treatments with dementia risk.
Glioblastome Multiforme: A Bibliometric Analysis.
M Akmal, N Hasnain, A Rehan, U Iqbal, S Hashmi, K Fatima, MZ Farooq, F Khosa, J Siddiqi and MK Khan,
World neurosurgery , Apr 2020
Bibliometric analyses are widely used to gauge the scholarly impact of any scientific publication. We conducted a bibliometric analysis of the 100 most influential articles on glioblastoma multiforme (GBM). We searched Scopus using the keywords "Glioblastoma multiforme," "GBM," Glioblastoma," and "Grade IV glioma." A list of the top 100 articles was prepared. The articles were sorted according to the number of citations. A detailed analysis was carried out to identify the characteristics of the most influential studies. The 100 most cited articles in the field were published over 38 years between 1978 and 2018, with the maximum number of articles published in the 10-year period from 2001 to 2010. The total number of citations for 100 articles was 148,594 and 4.8% were self-citations. Citations ranged from 9624 to 617, with a median of 935 (interquartile range, 906). The top cited articles originated from 22 countries, with the greatest contributions from the United States. Nature made the greatest contribution to the research on GBM, with a total of 14 articles, and Cancer Cell and New England Journal of Medicine were the second biggest contributors. Fifty-seven studies focused on the pathogenesis of GBM. There were 12 authors who had ≥5 articles in the top 100 citation list. Only 31% of the articles were funded by public and private sector organizations. Our analysis highlights the characteristics of the most influential articles on GBM and provides valuable insight into the research that has been conducted in this field.
Development and validation of a prognostic model predicting symptomatic hemorrhagic transformation in acute ischemic stroke at scale in the OHDSI network.
Q Wang, JM Reps, KF Kostka, PB Ryan, Y Zou, EA Voss, PR Rijnbeek, R Chen, GA Rao, H Morgan Stewart, AE Williams, RD Williams, M Van Zandt, T Falconer, M Fernandez-Chas, R Vashisht, SR Pfohl, NH Shah, SN Kasthurirathne, SC You, Q Jiang, C Reich and Y Zhou,
PloS one , 2020
Hemorrhagic transformation (HT) after cerebral infarction is a complex and multifactorial phenomenon in the acute stage of ischemic stroke, and often results in a poor prognosis. Thus, identifying risk factors and making an early prediction of HT in acute cerebral infarction contributes not only to the selections of therapeutic regimen but also, more importantly, to the improvement of prognosis of acute cerebral infarction. The purpose of this study was to develop and validate a model to predict a patient's risk of HT within 30 days of initial ischemic stroke.We utilized a retrospective multicenter observational cohort study design to develop a Lasso Logistic Regression prediction model with a large, US Electronic Health Record dataset which structured to the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM). To examine clinical transportability, the model was externally validated across 10 additional real-world healthcare datasets include EHR records for patients from America, Europe and Asia.In the database the model was developed, the target population cohort contained 621,178 patients with ischemic stroke, of which 5,624 patients had HT within 30 days following initial ischemic stroke. 612 risk predictors, including the distance a patient travels in an ambulance to get to care for a HT, were identified. An area under the receiver operating characteristic curve (AUC) of 0.75 was achieved in the internal validation of the risk model. External validation was performed across 10 databases totaling 5,515,508 patients with ischemic stroke, of which 86,401 patients had HT within 30 days following initial ischemic stroke. The mean external AUC was 0.71 and ranged between 0.60-0.78.A HT prognostic predict model was developed with Lasso Logistic Regression based on routinely collected EMR data. This model can identify patients who have a higher risk of HT than the population average with an AUC of 0.78. It shows the OMOP CDM is an appropriate data standard for EMR secondary use in clinical multicenter research for prognostic prediction model development and validation. In the future, combining this model with clinical information systems will assist clinicians to make the right therapy decision for patients with acute ischemic stroke.
