RESEARCH OPPORTUNITIES
Generalizability Network Research Study @aaveritt ’s presentation from the July 10th community call: https://drive.google.com/file/d/1acWhR5FCsTMWL7hxmYCgVlUt6r1bBFm1/view?usp=sharing @aaveritt know by replying to her forum post:Generalizability, Applicability, and Replicability of RCTs: A Study
OHDSI MEETINGS THIS WEEK
OHDSI Community Call - Tuesday at 12pm EThttps://meetings.webex.com/collabs/#/meetings/detail?uuid=M59X2V1U61WC9ASID2Z5N3UT95-D1JL&rnd=811649.986822111212113
GIS working group meeting - Next Monday (September 17th) 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 Collaborator Showcase - NOTIFICATIONS SENT TO AUTHORS symposium@ohdsi.org . More information about the collaborator showcase, including presentation logistics and poster templates,is available here: https://www.ohdsi.org/collaborator-showcase/
Insanity doesn’t run in my family, it gallops.
COMMUNITY PUBLICATIONS
Choosing the Target Difference (“effect size”) for a Randomised Controlled Trial - DELTA2 Guidance
Association of Hemoglobin A1c Levels With Use of Sulfonylureas, Dipeptidyl Peptidase 4 Inhibitors, and Thiazolidinediones in Patients With Type 2 Diabetes Treated With Metformin
This multinational cohort study examines the association of second-line treatment (sulfonylureas, dipeptidyl peptidase 4 inhibitors [DPP-4], or thiazolidinediones) for type 2 diabetes after initial therapy with metformin with hemoglobin A1c (HbA1c)...
DELTA2 guidance on choosing the target difference and undertaking and reporting the sample size calculation for a randomised controlled trialhttps://eprints.soton.ac.uk/423152/
Methodological variations in lagged regression for detecting physiologic drug effects in EHR data.
ME Levine, DJ Albers and G Hripcsak,
Journal of biomedical informatics , Oct 2018
We studied how lagged linear regression can be used to detect the physiologic effects of drugs from data in the electronic health record (EHR). We systematically examined the effect of methodological variations ((i) time series construction, (ii) temporal parameterization, (iii) intra-subject normalization, (iv) differencing (lagged rates of change achieved by taking differences between consecutive measurements), (v) explanatory variables, and (vi) regression models) on performance of lagged linear methods in this context. We generated two gold standards (one knowledge-base derived, one expert-curated) for expected pairwise relationships between 7 drugs and 4 labs, and evaluated how the 64 unique combinations of methodological perturbations reproduce the gold standards. Our 28 cohorts included patients in the Columbia University Medical Center/NewYork-Presbyterian Hospital clinical database, and ranged from 2820 to 79,514 patients with between 8 and 209 average time points per patient. The most accurate methods achieved AUROC of 0.794 for knowledge-base derived gold standard (95%CI [0.741, 0.847]) and 0.705 for expert-curated gold standard (95% CI [0.629, 0.781]). We observed a mean AUROC of 0.633 (95%CI [0.610, 0.657], expert-curated gold standard) across all methods that re-parameterize time according to sequence and use either a joint autoregressive model with time-series differencing or an independent lag model without differencing. The complement of this set of methods achieved a mean AUROC close to 0.5, indicating the importance of these choices. We conclude that time-series analysis of EHR data will likely rely on some of the beneficial pre-processing and modeling methodologies identified, and will certainly benefit from continued careful analysis of methodological perturbations. This study found that methodological variations, such as pre-processing and representations, have a large effect on results, exposing the importance of thoroughly evaluating these components when comparing machine-learning methods.
Contributions from the 2017 Literature on Clinical Decision Support.
V Koutkias and J Bouaud,
Yearbook of medical informatics , Aug 2018
To summarize recent research and select the best papers published in 2017 in the field of computerized clinical decision support for the Decision Support section of the International Medical Informatics Association (IMIA) yearbook. A literature review was performed by searching two bibliographic databases for papers referring to clinical decision support systems (CDSSs). The aim was to identify a list of candidate best papers from the retrieved bibliographic records, which were then peer-reviewed by external reviewers. A consensus meeting of the IMIA editorial team finally selected the best papers on the basis of all reviews and the section editors' evaluation. Among the 1,194 retrieved papers, the entire review process resulted in the selection of four best papers. The first paper studies the impact of recency and of longitudinal extent of electronic health record (EHR) datasets used to train a data-driven predictive model of inpatient admission orders. The second paper presents a decision support tool for surgical team selection, relying on the history of surgical team members and the specific characteristics of the patient. The third paper compares three commercial drug-drug interaction knowledge bases, particularly against a reference list of highly-significant known interactions. The fourth paper focuses on supporting the diagnosis of postoperative delirium using an adaptation of the "anchor and learn" framework, which was applied in unstructured texts contained in EHRs.The conducted review illustrated also this year that research in the field of CDSS is very active. Of note is the increase in publications concerning data-driven CDSSs, as revealed by the review process and also reflected by the four papers that have been selected. This trend is in line with the current attention that "Big Data" and data-driven artificial intelligence have gained in the domain of health and CDSSs in particular.
