OHDSI MEETINGS THIS WEEK
OHDSI Community Call - Tuesday at 12pm ET
US TOLL: +1-415-655-0001
Meeting Number: 199 982 907
CDM Survey Data Discussion - Tuesday at 1pm ET
More details: Survey data discussion (CDM Working Group)
Patient-level prediction (Western hemisphere) workgroup meeting - Wednesday at 12pm ET
Population-Level Estimation (Eastern hemisphere) workgroup meeting - Wednesday at 3pm Hong Kong time
Architecture WG meeting - Thursday at 1pm ET
GIS working group meeting - Monday (September 25th) at 10am ET
2017 OHDSI Symposium Registration - Registration for this year’s symposium is filling up quickly. If you haven’t registered yet, we recommend you do so soon! Register here: https://www.ohdsi.org/symposium-registration/
PLEASE NOTE: Registration is for the main symposium only (set to take place on October 18th). Registration for tutorials is full.
Tutorial Registration - Tutorial registration is now closed and selected participants have been notified of their status. If you have registered for a tutorial but have not heard about your participants status, please email firstname.lastname@example.org
Call for Sponsorship - To fund the 2017 symposium we need to raise $200K to cover operating costs of venue rental, lunch, audio/visuals and recordings fees. We’re reaching out to the entire community for sponsors who want to support this important event and allow us to build upon the success of past symposia. More details: OHDSI Symposium 2017 - call for sponsorship
Updating - Leading up to the symposium, we’ll be updating collaborator profiles and our data network list. If you have updates you’d like made to your OHDSI profile, or have updated information about your database, please email OHDSI.org email@example.com with the changes.
The past cannot be cured. COMMUNITY PUBLICATIONS
Risk of Lower Extremity Amputations in Patients With Type 2 Diabetes Mellitus Treated With SGLT2 Inhibitors in the United States: A Retrospective Cohort Study
Dynamic-ETL: a hybrid approach for health data extraction, transformation and loading.
TC Ong, MG Kahn, BM Kwan, T Yamashita, E Brandt, P Hosokawa, C Uhrich and LM Schilling,
BMC medical informatics and decision making, Sep 2017 13
Electronic health records (EHRs) contain detailed clinical data stored in proprietary formats with non-standard codes and structures. Participating in multi-site clinical research networks requires EHR data to be restructured and transformed into a common format and standard terminologies, and optimally linked to other data sources. The expertise and scalable solutions needed to transform data to conform to network requirements are beyond the scope of many health care organizations and there is a need for practical tools that lower the barriers of data contribution to clinical research networks.We designed and implemented a health data transformation and loading approach, which we refer to as Dynamic ETL (Extraction, Transformation and Loading) (D-ETL), that automates part of the process through use of scalable, reusable and customizable code, while retaining manual aspects of the process that requires knowledge of complex coding syntax. This approach provides the flexibility required for the ETL of heterogeneous data, variations in semantic expertise, and transparency of transformation logic that are essential to implement ETL conventions across clinical research sharing networks. Processing workflows are directed by the ETL specifications guideline, developed by ETL designers with extensive knowledge of the structure and semantics of health data (i.e., "health data domain experts") and target common data model.D-ETL was implemented to perform ETL operations that load data from various sources with different database schema structures into the Observational Medical Outcome Partnership (OMOP) common data model. The results showed that ETL rule composition methods and the D-ETL engine offer a scalable solution for health data transformation via automatic query generation to harmonize source datasets.D-ETL supports a flexible and transparent process to transform and load health data into a target data model. This approach offers a solution that lowers technical barriers that prevent data partners from participating in research data networks, and therefore, promotes the advancement of comparative effectiveness research using secondary electronic health data.
Dipeptidyl Peptidase-4 Inhibitors and Risk of Heart Failure in Patients With Type 2 Diabetes Mellitus: A Population-Based Cohort Study.
YG Kim, D Yoon, S Park, SJ Han, DJ Kim, KW Lee, RW Park and HJ Kim,
Circulation. Heart failure, Sep 2017
The association between dipeptidyl-peptidase IV inhibitors (DPP-4i) and heart failure (HF) remains unclear. In 1 randomized controlled trial and some observational studies, DPP-4i reportedly increased the risk of HF, but 2 other randomized controlled trials and observational studies have shown no such risk. Here, we evaluated the risk of HF and cardiovascular outcomes of DPP-4i compared with sulfonylureas.A population-based retrospective cohort study was conducted using the Korean Health Insurance Review and Assessment Service database from January 1, 2009, to December 31, 2015. Incident users of sulfonylurea and DPP-4i who were not prescribed the comparator drug in the year before treatment initiation were included. DPP-4i-treated and sulfonylurea-treated patients were matched on propensity score, calculated with >40 variables. The risk of hospitalization for HF was evaluated with a Cox proportional hazards model in 255 691 matched pairs. Analyses were conducted in the total patient population and in both strata divided by the presence of cardiovascular disease during the baseline period. The hazard ratios (HRs) of hospitalization for HF for DPP-4i-treated patients were 0.78 (95% confidence interval [CI], 0.67-0.86) in all of the patients, 0.77 (95% CI, 0.68-0.79) in patients with baseline cardiovascular disease, and 0.71 (95% CI, 0.56-0.90) in patients without baseline cardiovascular disease compared with HRs for sulfonylurea-treated patients. Sitagliptin and linagliptin showed statistically lower risk for hospitalization for HF (HR, 0.76; 95% CI, 0.67-0.86 for sitagliptin-prescribed patients; HR, 0.74; 95% CI, 0.59-0.92 for linagliptin-prescribed patients) than for sulfonylurea. The HRs for hospitalization for myocardial infarction and stroke with the use of a DPP-4i versus sulfonylurea were HR, 0.76 (95% CI, 0.67-0.87) and HR, 0.63 (95% CI, 0.60-0.67), respectively.Our findings suggest that DPP-4i use did not increase the risk of HF compared with sulfonylurea. In addition, the risks for cardiovascular outcomes were not elevated in DPP-4i-treated patients compared with sulfonylurea-treated patients.
A conceptual framework for evaluating data suitability for observational studies