Weekly OHDSI Digest - 28Nov2016

MauraBeaton · November 28, 2016, 10:02pm

OHDSI MEETINGS THIS WEEK

Architecture Workgroup - Thursday at 1pm ET
Webex: https://jjconferencing.webex.com/mw3000/mywebex/default.do?service=1&main_url=%2Fmc3000%2Fe.do%3Fsiteurl%3Djjconferencing%26AT%3DMI%26EventID%3D283835502%26MTID%3Dmb7e839a762fbdaab0608f27500679223%26Host%3DQUhTSwAAAALyageAgcuAzkXBwXtvSQo68mr7eYO0HxmjwiiQlO24aYFcB8fE5PhzipREHF2PUFYlNiDTCigGeeVqVSb-RPTI0%26FrameSet%3D2&siteurl=jjconferencing&nomenu=true
Call-in toll-free number: 1-877-5659999 (US)
Call-in number: 1-617-9392838 (US)
Attendee access code: 498 480 37

Population-Level Estimation Workgroup - Wednesday at 2am ET, 8am CET
https://meetings.webex.com/collabs/#/meetings/detail?uuid=M6WE9AOKFETH2VEFPVCZWWBIT0-D1JL&rnd=626260.34165

ANNOUNCEMENTS

OHDSI Symposium Best Posters
The results are in! Congratulations to our best poster winners:

Methodological Research - Congrats to Christophe Lambert for his poster on Local Control for bias correction of time-to-event observational studies
Observational Data Management - Congrats to Christophe Lambert for his poster on Transforming the 2.33M-patient Medicare synthetic public use files to the OMOP CDMv5: ETL-CMS software and processed data available and feature-complete
Analytics, Technology and Infrastructure - Congrats to Sigfried Gold for his poster on CHRONOS: Cohort explora2on through individual patient profiles
Clinical Application - Clinical Characterization - Congrats to Rupa Makadia for her person on Using OHDSI tools to conduct clinical trial feasibility

COMMUNITY PUBLICATIONS

Statins and risk for new-onset diabetes mellitus: A real-world cohort study using a clinical research database.

ncbi.nlm.nih.gov

Statins and risk for new-onset diabetes mellitus: A real-world cohort study using a clinical research database.

D Yoon, SS Sheen, S Lee, YJ Choi, RW Park and HS Lim, Medicine, Nov 2016

Although concern regarding the increased risk for new-onset diabetes mellitus (NODM) after statin treatment has been raised, there has been a lack of evidence in real-world clinical practice, particularly in East Asians. We investigated whether statin use is associated with risk for NODM in Koreans. We conducted a retrospective cohort study using the clinical research database from electronic health records. The study cohort consisted of 8265 statin-exposed and 33,060 matched nonexposed patients between January 1996 and August 2013. Matching at a 1:4 ratio was performed using a propensity score based on age, gender, baseline glucose levels (mg/dL), and hypertension. The comparative risks for NODM with various statins (atorvastatin, fluvastatin, pitavastatin, pravastatin, rosuvastatin, and simvastatin) were estimated by both statin exposure versus matched nonexposed and within-class comparisons. The incidence of NODM among the statin-exposed group (6.000 per 1000 patient-years [PY]) was higher than that of the nonexposed group (3.244 per 1000 PY). The hazard ratio (HR) of NODM after statin exposure was 1.872 (95% confidence interval [CI], 1.432-2.445). Male gender (HR, 1.944; 95% CI, 1.497-2.523), baseline glucose per mg/dL (HR, 1.014; 95% CI, 1.013-1.016), hypertension (HR, 2.232; 95% CI, 1.515-3.288), and thiazide use (HR, 1.337; 95% CI, 1.081-1.655) showed an increased risk for NODM, while angiotensin-converting enzyme inhibitor or angiotensin II receptor blocker showed a decreased risk (HR, 0.774; 95% CI, 0.668-0.897). Atorvastatin-exposed patients showed a higher risk for NODM than their matched nonexposed counterparts (HR, 1.939; 95% CI, 1.278-2.943). However, the risk for NODM was not significantly different among statins in within-class comparisons. In conclusion, an increased risk for NODM was observed among statin users in a practical healthcare setting in Korea.

Computational Drug Target Screening through Protein Interaction Profiles.

ncbi.nlm.nih.gov

Computational Drug Target Screening through Protein Interaction Profiles.

S Vilar, E Quezada, E Uriarte, S Costanzi, F Borges, D Viña and G Hripcsak, Scientific reports, 2016 15 11

The development of computational methods to discover novel drug-target interactions on a large scale is of great interest. We propose a new method for virtual screening based on protein interaction profile similarity to discover new targets for molecules, including existing drugs. We calculated Target Interaction Profile Fingerprints (TIPFs) based on ChEMBL database to evaluate drug similarity and generated new putative compound-target candidates from the non-intersecting targets in each pair of compounds. A set of drugs was further studied in monoamine oxidase B (MAO-B) and cyclooxygenase-1 (COX-1) enzyme through molecular docking and experimental assays. The drug ethoxzolamide and the natural compound piperlongumine, present in Piper longum L, showed hMAO-B activity with IC50 values of 25 and 65 μM respectively. Five candidates, including lapatinib, SB-202190, RO-316233, GW786460X and indirubin-3'-monoxime were tested against human COX-1. Compounds SB-202190 and RO-316233 showed a IC50 in hCOX-1 of 24 and 25 μM respectively (similar range as potent inhibitors such as diclofenac and indomethacin in the same experimental conditions). Lapatinib and indirubin-3'-monoxime showed moderate hCOX-1 activity (19.5% and 28% of enzyme inhibition at 25 μM respectively). Our modeling constitutes a multi-target predictor for large scale virtual screening with potential in lead discovery, repositioning and drug safety.

Clinical Research Informatics for Big Data and Precision Medicine.

ncbi.nlm.nih.gov

Clinical Research Informatics for Big Data and Precision Medicine.

C Weng and MG Kahn, Yearbook of medical informatics, Nov 2016 10

To reflect on the notable events and significant developments in Clinical Research Informatics (CRI) in the year of 2015 and discuss near-term trends impacting CRI.We selected key publications that highlight not only important recent advances in CRI but also notable events likely to have significant impact on CRI activities over the next few years or longer, and consulted the discussions in relevant scientific communities and an online living textbook for modern clinical trials. We also related the new concepts with old problems to improve the continuity of CRI research.The highlights in CRI in 2015 include the growing adoption of electronic health records (EHR), the rapid development of regional, national, and global clinical data research networks for using EHR data to integrate scalable clinical research with clinical care and generate robust medical evidence. Data quality, integration, and fusion, data access by researchers, study transparency, results reproducibility, and infrastructure sustainability are persistent challenges.The advances in Big Data Analytics and Internet technologies together with the engagement of citizens in sciences are shaping the global clinical research enterprise, which is getting more open and increasingly stakeholder-centered, where stakeholders include patients, clinicians, researchers, and sponsors.

Computerized Clinical Decision Support: Contributions from 2015.

ncbi.nlm.nih.gov

Computerized Clinical Decision Support: Contributions from 2015.

V Koutkias and J Bouaud, Yearbook of medical informatics, Nov 2016 10

To summarize recent research and select the best papers published in 2015 in the field of computerized clinical decision support for the Decision Support section of the IMIA yearbook.A literature review was performed by searching two bibliographic databases for papers related to clinical decision support systems (CDSSs) and computerized provider order entry (CPOE) systems. The aim was to identify a list of candidate best papers from the retrieved papers that were then peer-reviewed by external reviewers. A consensus meeting between the two section editors and the IMIA editorial team was finally conducted to conclude in the best paper selection.Among the 974 retrieved papers, the entire review process resulted in the selection of four best papers. One paper reports on a CDSS routinely applied in pediatrics for more than 10 years, relying on adaptations of the Arden Syntax. Another paper assessed the acceptability and feasibility of an important CPOE evaluation tool in hospitals outside the US where it was developed. The third paper is a systematic, qualitative review, concerning usability flaws of medication-related alerting functions, providing an important evidence-based, methodological contribution in the domain of CDSS design and development in general. Lastly, the fourth paper describes a study quantifying the effect of a complex, continuous-care, guideline-based CDSS on the correctness and completeness of clinicians' decisions.While there are notable examples of routinely used decision support systems, this 2015 review on CDSSs and CPOE systems still shows that, despite methodological contributions, theoretical frameworks, and prototype developments, these technologies are not yet widely spread (at least with their full functionalities) in routine clinical practice. Further research, testing, evaluation, and training are still needed for these tools to be adopted in clinical practice and, ultimately, illustrate the benefits that they promise.