OHDSI MEETINGS THIS WEEK
OHDSI Community Call - Tuesday at 12pm EThttps://meetings.webex.com/collabs/#/meetings/detail?uuid=M59X2V1U61WC9ASID2Z5N3UT95-D1JL&rnd=811649.98682211121
CDM and Vocabulary Development WG - Tuesday at 1pm EThttps://jjconferencing.webex.com/mw3100/mywebex/default.do?service=1&main_url=%2Fmc3100%2Fe.do%3Fsiteurl%3Djjconferencing%26AT%3DMI%26EventID%3D594628767%26MTID%3Dma32e5ce14c744149f041412fc99040f1%26Host%3DQUhTSwAAAASivUFA_FkARnj1Rsomn5oGEyPnH_1DRqyEcN7d_RZgyd5IeFbkG8Fv57-6VVN7TcHkgAKX8O8kxRQnXWqZY8PV0%26FrameSet%3D2&siteurl=jjconferencing&nomenu=true
Western Hemisphere Methods workgroup meeting - Thursday at 12pm EThttps://meetings.webex.com/collabs/#/meetings/detail?uuid=M3T9BZV9RSB6YNDM8WDDZMI19D-D1JL&rnd=630501.91919
Architecture Working Group - Thursday at 10am EThttps://jjconferencing.webex.com/mw3100/mywebex/default.do?service=1&siteurl=jjconferencing&nomenu=true&main_url=%2Fmc3100%2Fe.do%3Fsiteurl%3Djjconferencing%26AT%3DMI%26EventID%3D610982452%26UID%3D501476547%26Host%3DQUhTSwAAAAQu4P1o9qm71JJ1Zj4-uvZbjQttsCinu71JCRxBAHAXnzjjRAiTspTzU9ojLmjMF4CcTBWw4zn1dqYPTWu5vJ9_0%26FrameSet%3D2%26MTID%3Dm3e1ceeca56f1e94c9fcf1ae98c10e02e
GIS working group meeting - Next Monday (June 11th) at 10am ET
Simple, modern video meetings for the global workforce. Join from anywhere, including your desktop, browser, mobile device, or video room device.
ANNOUNCEMENTS
2018 OHDSI Symposium - REGISTER NOW https://www.ohdsi.org/symposium-registration-2/ https://www.ohdsi.org/tutorial-workshops/
2018 OHDSI Symposium - CALL FOR PARTICIPATION https://www.ohdsi.org/collaborator-showcase/
China Hack-a-thon - Materials from the 2018 China Hack-a-thon are now online: https://www.ohdsi.org/past-events/
2018 China Symposium https://www.ohdsi.org/events/2018-china-symposium/
Atheism is a non-prophet organization.
COMMUNITY PUBLICATIONS
Mo1024 - Low Rates of Screening for Celiac Disease Among Family Members; Analysis of Algorithm-Identified Familial Relationshipshttps://www.sciencedirect.com/science/article/pii/S0016508518323850
Discovering associations between adverse drug events using pattern structures and ontologies.
G Personeni, E Bresso, MD Devignes, M Dumontier, M Smaïl-Tabbone and A Coulet,
Journal of biomedical semantics , Aug 22 2017
Patient data, such as electronic health records or adverse event reporting systems, constitute an essential resource for studying Adverse Drug Events (ADEs). We explore an original approach to identify frequently associated ADEs in subgroups of patients.Because ADEs have complex manifestations, we use formal concept analysis and its pattern structures, a mathematical framework that allows generalization using domain knowledge formalized in medical ontologies. Results obtained with three different settings and two different datasets show that this approach is flexible and allows extraction of association rules at various levels of generalization.The chosen approach permits an expressive representation of a patient ADEs. Extracted association rules point to distinct ADEs that occur in a same group of patients, and could serve as a basis for a recommandation system. The proposed representation is flexible and can be extended to make use of additional ontologies and various patient records.
Empowering genomic medicine by establishing critical sequencing result data flows: the eMERGE example.
S Aronson, L Babb, D Ames, RA Gibbs, E Venner, JJ Connelly, K Marsolo, C Weng, MS Williams, AL Hartzler, WH Liang, JD Ralston, EB Devine, S Murphy, CG Chute, PJ Caraballo, IJ Kullo, RR Freimuth, LV Rasmussen, FH Wehbe, JF Peterson, JR Robinson, K Wiley and C Overby Taylor,
Journal of the American Medical Informatics Association : JAMIA , Oct 2018 01
The eMERGE Network is establishing methods for electronic transmittal of patient genetic test results from laboratories to healthcare providers across organizational boundaries. We surveyed the capabilities and needs of different network participants, established a common transfer format, and implemented transfer mechanisms based on this format. The interfaces we created are examples of the connectivity that must be instantiated before electronic genetic and genomic clinical decision support can be effectively built at the point of care. This work serves as a case example for both standards bodies and other organizations working to build the infrastructure required to provide better electronic clinical decision support for clinicians.
Automated Metabolic Phenotyping of Cytochrome Polymorphisms Using PubMed Abstract Mining.
L Chen, C Friedman and J Finkelstein,
AMIA ... Annual Symposium proceedings. AMIA Symposium , 2017
Pharmacogenetics-related publications, which are increasing rapidly, provide important new pharmacogenetics knowledge. Automated approaches to extract information of new alleles and to identify their impact on metabolic phenotypes from publications are urgently needed to facilitate personalized medicine and improve clinical outcomes. Cytochrome polymorphisms, responsible for a wide variation of drug pharmacodynamics, individual efficacy and adverse effects, have significant potential for optimizing drug therapy. A few studies have addressed specialized efforts to automatically extract cytochrome polymorphisms and their characterizations regarding metabolic phenotypes from the literature. In this paper, we present a novel rule-based text-mining system to extract metabolic phenotypes of polymorphisms from PubMed abstracts with a focus on cytochrome P450. This system is promising as it achieved a precision of 85.71% in a preliminary proof-of-concept evaluation and is expected to automatically provide up-to-date metabolic information for cytochrome polymorphisms, which is critical to advance personalized medicine and improve clinical care.
Cancer recording in patients with and without type 2 diabetes in the Clinical Practice Research Datalink primary care data and linked hospital admission data: a cohort study.
R Williams, TP van Staa, AM Gallagher, T Hammad, HGM Leufkens and F de Vries,
BMJ open , May 26 2018
Conflicting results from studies using electronic health records to evaluate the associations between type 2 diabetes and cancer fuel concerns regarding potential biases. This study aimed to describe completeness of cancer recording in UK primary care data linked to hospital admissions records.Patients aged 40+ years with insulin or oral antidiabetic prescriptions in Clinical Practice Research Datalink (CPRD) primary care without type 1 diabetes were matched by age, sex and general practitioner practice to non-diabetics. Those eligible for linkage to Hospital Episode Statistics Admitted Patient Care (HES APC), and with follow-up during April 1997-December 2006 were included.Cancer recording and date of first record of cancer were compared. Characteristics of patients with cancer most likely to have the diagnosis recorded only in a single data source were assessed. Relative rates of cancer estimated from the two datasets were compared.53 585 patients with type 2 diabetes matched to 47 435 patients without diabetes were included.Of all cancers (excluding non-melanoma skin cancer) recorded in CPRD, 83% were recorded in HES APC. 94% of cases in HES APC were recorded in CPRD. Concordance was lower when restricted to same-site cancer records, and was negatively associated with increasing age. Relative rates for cancer were similar in both datasets.Good concordance in cancer recording was found between CPRD and HES APC among type 2 diabetics and matched controls. Linked data may reduce misclassification and increase case ascertainment when analysis focuses on site-specific cancers.
Prediction of Recurrent Clostridium Difficile Infection Using Comprehensive Electronic Medical Records in an Integrated Healthcare Delivery System.
GJ Escobar, JM Baker, P Kipnis, JD Greene, TC Mast, SB Gupta, N Cossrow, V Mehta, V Liu and ER Dubberke,
Infection control and hospital epidemiology , 2017 10
BACKGROUND Predicting recurrent Clostridium difficile infection (rCDI) remains difficult.We employed a retrospective cohort design. Granular electronic medical record (EMR) data had been collected from patients hospitalized at 21 Kaiser Permanente Northern California hospitals. The derivation dataset (2007-2013) included data from 9,386 patients who experienced incident CDI (iCDI) and 1,311 who experienced their first CDI recurrences (rCDI). The validation dataset (2014) included data from 1,865 patients who experienced incident CDI and 144 who experienced rCDI. Using multiple techniques, including machine learning, we evaluated more than 150 potential predictors. Our final analyses evaluated 3 models with varying degrees of complexity and 1 previously published model. RESULTS Despite having a large multicenter cohort and access to granular EMR data (eg, vital signs, and laboratory test results), none of the models discriminated well (c statistics, 0.591-0.605), had good calibration, or had good explanatory power. CONCLUSIONS Our ability to predict rCDI remains limited. Given currently available EMR technology, improvements in prediction will require incorporating new variables because currently available data elements lack adequate explanatory power. Infect Control Hosp Epidemiol 2017;38:1196-1203.
Finding factors that predict treatment-resistant depression: Results of a cohort study.
MS Cepeda, J Reps and P Ryan,
Depression and anxiety , Jul 2018
Treatment for depressive disorders often requires subsequent interventions. Patients who do not respond to antidepressants have treatment-resistant depression (TRD). Predicting who will develop TRD may help healthcare providers make more effective treatment decisions. We sought to identify factors that predict TRD in a real-world setting using claims databases.A retrospective cohort study was conducted in a US claims database of adult subjects with newly diagnosed and treated depression with no mania, dementia, and psychosis. The index date was the date of antidepressant dispensing. The outcome was TRD, defined as having at least three distinct antidepressants or one antidepressant and one antipsychotic within 1 year after the index date. Predictors were age, gender, medical conditions, medications, and procedures 1 year before the index date.Of 230,801 included patients, 10.4% developed TRD within 1 year. TRD patients at baseline were younger; 10.87% were between 18 and 19 years old versus 7.64% in the no-TRD group, risk ratio (RR) = 1.42 (95% confidence interval [CI] 1.37-1.48). TRD patients were more likely to have an anxiety disorder at baseline than non-TRD patients, RR = 1.38 (95% CI 1.35-1.14). At 3.68, fatigue had the highest RR (95% CI 3.18-4.25). TRD patients had substance use disorders, psychiatric conditions, insomnia, and pain more often at baseline than non-TRD patients.Ten percent of subjects newly diagnosed and treated for depression developed TRD within a year. They were younger and suffered more frequently from fatigue, substance use disorders, anxiety, psychiatric conditions, insomnia, and pain than non-TRD patients.
Disease Heritability Inferred from Familial Relationships Reported in Medical Records.
FCG Polubriaginof, R Vanguri, K Quinnies, GM Belbin, A Yahi, H Salmasian, T Lorberbaum, V Nwankwo, L Li, MM Shervey, P Glowe, I Ionita-Laza, M Simmerling, G Hripcsak, S Bakken, D Goldstein, K Kiryluk, EE Kenny, J Dudley, DK Vawdrey and NP Tatonetti,
Cell , Jun 2018 14
Heritability is essential for understanding the biological causes of disease but requires laborious patient recruitment and phenotype ascertainment. Electronic health records (EHRs) passively capture a wide range of clinically relevant data and provide a resource for studying the heritability of traits that are not typically accessible. EHRs contain next-of-kin information collected via patient emergency contact forms, but until now, these data have gone unused in research. We mined emergency contact data at three academic medical centers and identified 7.4 million familial relationships while maintaining patient privacy. Identified relationships were consistent with genetically derived relatedness. We used EHR data to compute heritability estimates for 500 disease phenotypes. Overall, estimates were consistent with the literature and between sites. Inconsistencies were indicative of limitations and opportunities unique to EHR research. These analyses provide a validation of the use of EHRs for genetics and disease research.
Cardiovascular Disease Risk Varies by Birth Month in Canineshttps://www.nature.com/articles/s41598-018-25199-w
