Weekly OHDSI Digest - 23Jan2017

MauraBeaton · January 23, 2017, 4:52pm

OHDSI MEETINGS THIS WEEK

Population-Level Estimation work group (Eastern Hemisphere) - Wednesday at 12pm ET
URL: https://meetings.webex.com/collabs/meetings/join?uuid=M6WE9AOKFETH2VEFPVCZWWBIT0-D1JL

Patient-level prediction WG meeting - Wednesday at 12pm ET
URL: https://global.gotomeeting.com/join/972917661
Join by phone:
United States: +1 (571) 317-3116
Netherlands: +31 (0) 208 084 055
Access Code: 972-917-661
Other country numbers can be found here: http://www.ohdsi.org/web/wiki/doku.php?id=projects:workgroups:patient-level_prediction

Architechture WG meeting - 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%3DQUhTSwAAAAJ_RbVdkwLKAs2Z53zdqlJeWVp4xaaQjWtL7vrvJELJ3uGlhajeNGJ28I1CbKPYdpaOXXf6ejxpu0qTT0CRvAW60%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

ANNOUNCEMENTS

OHDSI F2F - Registration is now open for the OHDSI F2F to take place on March 17-18th at GeorgiaTech in Atlanta. Register here:
http://www.ohdsi.org/events/2017-ohdsi-collaborator-face-to-face/

‘Judge of a man by his questions rather than by his answers.’

Voltaire

COMMUNITY PUBLICATIONS

Drug Repurposing Hypothesis Generation Using the “RE:fine Drugs” System.

ncbi.nlm.nih.gov

Drug Repurposing Hypothesis Generation Using the "RE:fine Drugs" System.

K Regan, S Moosavinasab, P Payne and S Lin, Journal of visualized experiments : JoVE, 2016 12 11

The promise of drug repurposing is that existing drugs may be used for new disease indications in order to curb the high costs and time for approval. The goal of computational methods for drug repurposing is to enable solutions for safer, cheaper and faster drug discovery. Towards this end, we developed a novel method that integrates genetic and clinical phenotype data from large-scale GWAS and PheWAS studies with detailed drug information on the concept of transitive Drug-Gene-Disease triads. We created "RE:fine Drugs," a freely available, interactive dashboard that automates gene, disease and drug-based searches to identify drug repurposing candidates. This web-based tool supports a user-friendly interface that includes an array of advanced search and export options. Results can be prioritized in a variety of ways, including but not limited to, biomedical literature support, strength and statistical significance of GWAS and/or PheWAS associations, disease indications and molecular drug targets. Here we provide a protocol that illustrates the functionalities available in the "RE:fine Drugs" system and explores the different advanced options through a case study.

Incretin-based therapy and acute cholecystitis: a review of case reports and EudraVigilance spontaneous adverse drug reaction reporting database.

ncbi.nlm.nih.gov

Incretin-based therapy and acute cholecystitis: a review of case reports and EudraVigilance spontaneous adverse drug reaction reporting database.

V Pizzimenti, A Giandalia, D Cucinotta, GT Russo, M Smits, PM Cutroneo and G Trifirò, Journal of clinical pharmacy and therapeutics, Apr 2016

Glucagon-like peptide-1 (GLP-1) receptor agonists delay gastric and bowel emptying. A similar inhibitory effect of GLP-1 on gallbladder motility has been suggested, possibly leading to an increased risk of cholecystitis related to incretin-based medications, which include GLP-1 antagonists. Our objective was to review evidence in EudraVigilance, the European spontaneous reporting database and the scientific literature on this issue.Increasing evidence suggests an association of incretins with gallbladder adverse events. Pharmacovigilance data from EudraVigilance includes 200 serious ADR reports concerning cholecystitis related to the use of incretin-based therapies. Several mechanisms may explain this increased risk of cholecystitis, including rapid weight loss, inhibition of gallbladder contraction and emptying, reduced bile acids production, modulation of inflammation.The available data suggest the possibility of gallbladder disease in diabetic subjects treated with incretins and highlight the importance of evaluating risk factors for cholelithiasis and gallbladder diseases in patients with diabetes before starting this therapy.

Prediction of black box warning by mining patterns of Convergent Focus Shift in clinical trial study populations using linked public data.

ncbi.nlm.nih.gov

Prediction of black box warning by mining patterns of Convergent Focus Shift in clinical trial study populations using linked public data.

H Ma and C Weng, Journal of biomedical informatics, Apr 2016

To link public data resources for predicting post-marketing drug safety label changes by analyzing the Convergent Focus Shift patterns among drug testing trials.We identified 256 top-selling prescription drugs between 2003 and 2013 and divided them into 83 BBW drugs (drugs with at least one black box warning label) and 173 ROBUST drugs (drugs without any black box warning label) based on their FDA black box warning (BBW) records. We retrieved 7499 clinical trials that each had at least one of these drugs for intervention from the ClinicalTrials.gov. We stratified all the trials by pre-marketing or post-marketing status, study phase, and study start date. For each trial, we retrieved drug and disease concepts from clinical trial summaries to model its study population using medParser and SNOMED-CT. Convergent Focus Shift (CFS) pattern was calculated and used to assess the temporal changes in study populations from pre-marketing to post-marketing trials for each drug. Then we selected 68 candidate drugs, 18 with BBW warning and 50 without, that each had at least nine pre-marketing trials and nine post-marketing trials for predictive modeling. A random forest predictive model was developed to predict BBW acquisition incidents based on CFS patterns among these drugs. Pre- and post-marketing trials of BBW and ROBUST drugs were compared to look for their differences in CFS patterns.Among the 18 BBW drugs, we consistently observed that the post-marketing trials focused more on recruiting patients with medical conditions previously unconsidered in the pre-marketing trials. In contrast, among the 50 ROBUST drugs, the post-marketing trials involved a variety of medications for testing their associations with target intervention(s). We found it feasible to predict BBW acquisitions using different CFS patterns between the two groups of drugs. Our random forest predictor achieved an AUC of 0.77. We also demonstrated the feasibility of the predictor for identifying long-term BBW acquisition events without compromising prediction accuracy.This study contributes a method for post-marketing pharmacovigilance using Convergent Focus Shift (CFS) patterns in clinical trial study populations mined from linked public data resources. These signals are otherwise unavailable from individual data resources. We demonstrated the added value of linked public data and the feasibility of integrating ClinicalTrials.gov summaries and drug safety labels for post-marketing surveillance. Future research is needed to ensure better accessibility and linkage of heterogeneous drug safety data for efficient pharmacovigilance.

Multivariate analysis of the population representativeness of related clinical studies.

ncbi.nlm.nih.gov

Multivariate analysis of the population representativeness of related clinical studies.

Z He, P Ryan, J Hoxha, S Wang, S Carini, I Sim and C Weng, Journal of biomedical informatics, Apr 2016

To develop a multivariate method for quantifying the population representativeness across related clinical studies and a computational method for identifying and characterizing underrepresented subgroups in clinical studies.We extended a published metric named Generalizability Index for Study Traits (GIST) to include multiple study traits for quantifying the population representativeness of a set of related studies by assuming the independence and equal importance among all study traits. On this basis, we compared the effectiveness of GIST and multivariate GIST (mGIST) qualitatively. We further developed an algorithm called "Multivariate Underrepresented Subgroup Identification" (MAGIC) for constructing optimal combinations of distinct value intervals of multiple traits to define underrepresented subgroups in a set of related studies. Using Type 2 diabetes mellitus (T2DM) as an example, we identified and extracted frequently used quantitative eligibility criteria variables in a set of clinical studies. We profiled the T2DM target population using the National Health and Nutrition Examination Survey (NHANES) data.According to the mGIST scores for four example variables, i.e., age, HbA1c, BMI, and gender, the included observational T2DM studies had superior population representativeness than the interventional T2DM studies. For the interventional T2DM studies, Phase I trials had better population representativeness than Phase III trials. People at least 65years old with HbA1c value between 5.7% and 7.2% were particularly underrepresented in the included T2DM trials. These results confirmed well-known knowledge and demonstrated the effectiveness of our methods in population representativeness assessment.mGIST is effective at quantifying population representativeness of related clinical studies using multiple numeric study traits. MAGIC identifies underrepresented subgroups in clinical studies. Both data-driven methods can be used to improve the transparency of design bias in participation selection at the research community level.

Engaging hospitalized patients in clinical care: Study protocol for a pragmatic randomized controlled trial.

ncbi.nlm.nih.gov

Engaging hospitalized patients in clinical care: Study protocol for a pragmatic randomized controlled trial.

R Masterson Creber, J Prey, B Ryan, I Alarcon, M Qian, S Bakken, S Feiner, G Hripcsak, F Polubriaginof, S Restaino, R Schnall, P Strong and D Vawdrey, Contemporary clinical trials, Mar 2016

Patients who are better informed and more engaged in their health care have higher satisfaction with health care and better health outcomes. While patient engagement has been a focus in the outpatient setting, strategies to engage inpatients in their care have not been well studied. We are undertaking a study to assess how patients' information needs during hospitalization can be addressed with health information technologies. To achieve this aim, we developed a personalized inpatient portal that allows patients to see who is on their care team, monitor their vital signs, review medications being administered, review current and historical lab and test results, confirm allergies, document pain scores and send questions and comments to inpatient care providers. The purpose of this paper is to describe the protocol for the study.This pragmatic randomized controlled trial will enroll 426 inpatient cardiology patients at an urban academic medical center into one of three arms receiving: 1) usual care, 2) iPad with general internet access, or 3) iPad with access to the personalized inpatient portal. The primary outcome of this trial is patient engagement, which is measured through the Patient Activation Measure. To assess scalability and potential reach of the intervention, we are partnering with a West Coast community hospital to deploy the patient engagement technology in their environment with an additional 160 participants.This study employs a pragmatic randomized control trial design to test whether a personalized inpatient portal will improve patient engagement. If the study is successful, continuing advances in mobile computing technology should make these types of interventions available in a variety of clinical care delivery settings.