OHDSI MEETINGS THIS WEEK
Patient-level prediction (western hemisphere workgroup meeting - Wednesday at 12pm EThttps://global.gotomeeting.com/join/972917661
Population-level estimation (eastern hemisphere) workgroup meeting - Wednesday at 3pm Hong Kong timehttps://meetings.webex.com/collabs/#/meetings/detail?uuid=M6WE9AOKFETH2VEFPVCZWWBIT0-D1JL&rnd=282455.55962
Architecture WG meeting - Thursday at 1pm EThttps://jjconferencing.webex.com/mw3000/mywebex/default.do?service=1&main_url=%2Fmc3000%2Fe.do%3Fsiteurl%3Djjconferencing%26AT%3DMI%26EventID%3D283835502%26MTID%3Dmb7e839a762fbdaab0608f27500679223%26Host%3DQUhTSwAAAAQxE5el-0hhEk3tW-04kHu5CmRCSYyMU3hFqUST5-t8WylwDpo-CNDTNHMT3KApphE5TP3u7lQA_6nW43osfk6S0%26FrameSet%3D2&siteurl=jjconferencing&nomenu=true
ANNOUNCEMENTS
2017 OHDSI Symposium - The date and location for the 2017 OHDSI Symposium has been confirmed! This year’s symposium will take place on Wednesday, October 18th at the Bethesda North Marriott. Full-day tutorial sessions will take place on October 19-20th. If you’re interested in attending, please save the date.
OHDSI F2F - Materials from the OHDSI F2F are now available here: https://www.ohdsi.org/past-events/
OHDSI collaborator meetings - We’re looking for presenters for our weekly community meetings. If you’re interested in sharing your working with the community, please let us know by replying to this thread, or by emailing me at beaton@ohdsi.org .
COMMUNITY PUBLICATIONS
Gender-based personalized pharmacotherapy: a systematic review.
MM Islam, U Iqbal, BA Walther, PA Nguyen, YJ Li, NK Dubey, TN Poly, JHB Masud, S Atique and S Syed-Abdul,
Archives of gynecology and obstetrics , Jun 2017
In general, male and female are prescribed the same amount of dosage even if most of the cases female required less dosage than male. Physicians are often facing problem on appropriate drug dosing, efficient treatment, and drug safety for a female in general. To identify and synthesize evidence about the effectiveness of gender-based therapy; provide the information to patients, providers, and health system intervention to ensure safety treatment; and minimize adverse effects.We performed a systematic review to evaluate the effect of gender difference on pharmacotherapy. Published articles from January 1990 to December 2015 were identified using specific term in MEDLINE (PubMed), EMBASE, and the Cochrane library according to search strategies that strengthen the reporting of observational and clinical studies.Twenty-six studies fulfilled the inclusion criteria for this systematic review, yielding a total of 6309 subjects. We observed that female generally has a lower the gastric emptying time, gastric PH, lean body mass, and higher plasma volume, BMI, body fat, as well as reduce hepatic clearance, difference in activity of Cytochrome P450 enzyme, and metabolize drugs at different rate compared with male. Other significant factors such as conjugation, protein binding, absorption, and the renal elimination could not be ignored. However, these differences can lead to adverse effects in female especially for the pregnant, post-menopausal, and elderly women.This systematic review provides an evidence for the effectiveness of dosage difference to ensure safety and efficient treatment. Future studies on the current topic are, therefore, recommended to reduce the adverse effect of therapy.
Exposure to statins is associated to fracture risk reduction in elderly people with cardiovascular disease: evidence from the AIFA-I-GrADE observational project.
F Rea, S Bonassi, C Vitale, G Trifirò, S Cascini, G Roberto, A Chinellato, E Lucenteforte, A Mugelli and G Corrao,
Pharmacoepidemiology and drug safety , Jul 2017
Conflicting findings were observed from clinical trials and observational studies evaluating the association between the use of statins and the risk of fracture. A case-control study nested into a cohort of elderly patients on treatment with statins for cardiovascular secondary prevention was performed on this issue.The cohort was formed by 13 875 individuals aged ≥65 years from several Italian health units receiving statins after hospital discharge for cardiovascular outcomes. From this cohort, 964 patients who experienced fracture were identified (i.e., cases). Up to five controls were randomly selected for each case from the underlying cohort. Conditional logistic regression was used to model the risk of fracture associated with adherence to statins, which was measured from the proportion of days covered (PDC) by treatment. A set of sensitivity analyses was performed in order to account for sources of systematic uncertainty.Compared with patients with low adherence (PDC ≤ 40%), those on intermediate (PDC 41-80%) and high (PDC > 80%) adherence exhibited a risk reduction of 21% (95% confidence interval 6% to 23%) and 25% (7% to 40%). Similar effects were observed among patients younger and older than 80 years, as well as among men, while there was no evidence that adherence to statins affected the risk of fracture among women. Sensitivity analyses revealed that the associations were consistent and robust.Use of statins for secondary cardiovascular prevention is associated with fracture risk reduction in elderly people. Further studies are required to better clarify the statin-fracture association in postmenopausal women. Copyright © 2017 John Wiley & Sons, Ltd.
Attribute Based Access Control for Healthcare Resourceshttp://dl.acm.org/citation.cfm?id=3041055
Risk Prediction for Ischemic Stroke and Transient Ischemic Attack in Patients Without Atrial Fibrillation: A Retrospective Cohort Study
EHR-Based Phenotyping: Bulk Learning and Evaluation
PH Chiu and G Hripcsak,
Journal of biomedical informatics , 2017 06
In data-driven phenotyping, a core computational task is to identify medical concepts and their variations from sources of electronic health records (EHR) to stratify phenotypic cohorts. A conventional analytic framework for phenotyping largely uses a manual knowledge engineering approach or a supervised learning approach where clinical cases are represented by variables encompassing diagnoses, medicinal treatments and laboratory tests, among others. In such a framework, tasks associated with feature engineering and data annotation remain a tedious and expensive exercise, resulting in poor scalability. In addition, certain clinical conditions, such as those that are rare and acute in nature, may never accumulate sufficient data over time, which poses a challenge to establishing accurate and informative statistical models. In this paper, we use infectious diseases as the domain of study to demonstrate a hierarchical learning method based on ensemble learning that attempts to address these issues through feature abstraction. We use a sparse annotation set to train and evaluate many phenotypes at once, which we call bulk learning. In this batch-phenotyping framework, disease cohort definitions can be learned from within the abstract feature space established by using multiple diseases as a substrate and diagnostic codes as surrogates. In particular, using surrogate labels for model training renders possible its subsequent evaluation using only a sparse annotated sample. Moreover, statistical models can be trained and evaluated, using the same sparse annotation, from within the abstract feature space of low dimensionality that encapsulates the shared clinical traits of these target diseases, collectively referred to as the bulk learning set.
A longitudinal analysis of data quality in a large pediatric data research network.
R Khare, L Utidjian, BJ Ruth, MG Kahn, E Burrows, K Marsolo, N Patibandla, H Razzaghi, R Colvin, D Ranade, M Kitzmiller, D Eckrich and LC Bailey,
Journal of the American Medical Informatics Association : JAMIA , Nov 2017 01
PEDSnet is a clinical data research network (CDRN) that aggregates electronic health record data from multiple children's hospitals to enable large-scale research. Assessing data quality to ensure suitability for conducting research is a key requirement in PEDSnet. This study presents a range of data quality issues identified over a period of 18 months and interprets them to evaluate the research capacity of PEDSnet.Results were generated by a semiautomated data quality assessment workflow. Two investigators reviewed programmatic data quality issues and conducted discussions with the data partners' extract-transform-load analysts to determine the cause for each issue.The results include a longitudinal summary of 2182 data quality issues identified across 9 data submission cycles. The metadata from the most recent cycle includes annotations for 850 issues: most frequent types, including missing data (>300) and outliers (>100); most complex domains, including medications (>160) and lab measurements (>140); and primary causes, including source data characteristics (83%) and extract-transform-load errors (9%).The longitudinal findings demonstrate the network's evolution from identifying difficulties with aligning the data to a common data model to learning norms in clinical pediatrics and determining research capability.While data quality is recognized as a critical aspect in establishing and utilizing a CDRN, the findings from data quality assessments are largely unpublished. This paper presents a real-world account of studying and interpreting data quality findings in a pediatric CDRN, and the lessons learned could be used by other CDRNs.
Thanks in advance!