Association between Use of Hydrochlorothiazide and Nonmelanoma Skin Cancer: Common Data Model Cohort Study in Asian Population

Although hydrochlorothiazide (HCTZ) has been suggested to increase skin cancer risk in white Westerners, there is scant evidence for the same in Asians. We analyzed the association between the use of hydrochlorothiazide and non-melanoma in the Asian population using the common data model. Methods: A retrospective multicenter observational study was conducted using a distributed research network to analyze the effect of HCTZ on skin cancer from 2004 to 2018. We performed Cox regression to evaluate the effects by comparing the use of HCTZ with other antihypertensive drugs. All analyses were re-evaluated using matched data using the propensity score matching (PSM). Then, the overall effects were evaluated by combining results with the meta-analysis. Results: Positive associations were observed in the use of HCTZ with high cumulative dose for non-melanoma skin cancer (NMSC) in univariate analysis prior to the use of PSM. Some negative associations were observed in the use of low and medium cumulative doses. Conclusion: Although many findings in our study were inconclusive, there was a non-significant association of a dose-response pattern with estimates increasing in cumulative dose of HCTZ. In particular, a trend with a non-significant positive association was observed with the high cumulative dose of HCTZ.

Download Full-text

Automated Production of Research Data Marts from a Canonical Fast Healthcare Interoperability Resource (FHIR) Data Repository: Applications to COVID-19 Research

10.1101/2021.03.11.21253384 ◽

2021 ◽

Author(s):

Leslie A Lenert ◽

Andrey V. Ilatovskiy ◽

James Agnew ◽

Patricia Rudsill ◽

Jeff Jacobs ◽

...

Keyword(s):

Clinical Data ◽

Data Model ◽

Assessment Tools ◽

Research Network ◽

Data Repository ◽

Common Data Model ◽

Real World Data ◽

Data Repositories ◽

Automated Production ◽

Healthcare Data

AbstractObjectiveObjective: The COVID-19 pandemic has enhanced the need for timely real-world data (RWD) for research. To meet this need, several large clinical consortia have developed networks for access to RWD from electronic health records (EHR), each with its own common data model (CDM) and custom pipeline for extraction, transformation, and load operations for production and incremental updating. However, the demands of COVID-19 research for timely RWD (e.g., 2-week delay) make this less feasible.Methods and MaterialsWe describe the use of the Fast Healthcare Interoperability Resource (FHIR) data model as a canonical model for representation of clinical data for automated transformation to the Patient-Centered Outcomes Research Network (PCORnet) and Observational Medical Outcomes Partnership (OMOP) CDMs and the near automated production of linked clinical data repositories (CDRs) for COVID-19 research using the FHIR subscription standard. The approach was applied to healthcare data from a large academic institution and was evaluated using published quality assessment tools.ResultsSix years of data (1.07M patients, 10.1M encounters, 137M laboratory results), were loaded into the FHIR CDR producing 3 linked real-time linked repositories: FHIR, PCORnet, and OMOP. PCORnet and OMOP databases were refined in subsequent post processing steps into production releases and met published quality standards. The approach greatly reduced CDM production efforts.ConclusionsFHIR and FHIR CDRs can play an important role in enhancing the availability of RWD from EHR systems. The above approach leverages 21st Century Cures Act mandated standards and could greatly enhance the availability of datasets for research.

Download Full-text

Fast Healthcare Interoperability Resources (FHIR) as a Meta Model to Integrate Common Data Models: Development of a Tool and Quantitative Validation Study

JMIR Medical Informatics ◽

10.2196/15199 ◽

2019 ◽

Vol 7 (4) ◽

pp. e15199

Author(s):

Emily Rose Pfaff ◽

James Champion ◽

Robert Louis Bradford ◽

Marshall Clark ◽

Hao Xu ◽

...

Keyword(s):

Clinical Data ◽

Data Model ◽

Research Collaboration ◽

Data Models ◽

Research Network ◽

Common Data Model ◽

Patient Centered ◽

Health Level 7 ◽

Value Sets ◽

Single Standard

Background In a multisite clinical research collaboration, institutions may or may not use the same common data model (CDM) to store clinical data. To overcome this challenge, we proposed to use Health Level 7’s Fast Healthcare Interoperability Resources (FHIR) as a meta-CDM—a single standard to represent clinical data. Objective In this study, we aimed to create an open-source application termed the Clinical Asset Mapping Program for FHIR (CAMP FHIR) to efficiently transform clinical data to FHIR for supporting source-agnostic CDM-to-FHIR mapping. Methods Mapping with CAMP FHIR involves (1) mapping each source variable to its corresponding FHIR element and (2) mapping each item in the source data’s value sets to the corresponding FHIR value set item for variables with strict value sets. To date, CAMP FHIR has been used to transform 108 variables from the Informatics for Integrating Biology & the Bedside (i2b2) and Patient-Centered Outcomes Research Network data models to fields across 7 FHIR resources. It is designed to allow input from any source data model and will support additional FHIR resources in the future. Results We have used CAMP FHIR to transform data on approximately 23,000 patients with asthma from our institution’s i2b2 database. Data quality and integrity were validated against the origin point of the data, our enterprise clinical data warehouse. Conclusions We believe that CAMP FHIR can serve as an alternative to implementing new CDMs on a project-by-project basis. Moreover, the use of FHIR as a CDM could support rare data sharing opportunities, such as collaborations between academic medical centers and community hospitals. We anticipate adoption and use of CAMP FHIR to foster sharing of clinical data across institutions for downstream applications in translational research.

Download Full-text

Data interchange using i2b2

Journal of the American Medical Informatics Association ◽

10.1093/jamia/ocv188 ◽

2016 ◽

Vol 23 (5) ◽

pp. 909-915 ◽

Cited By ~ 29

Author(s):

Jeffrey G Klann ◽

Aaron Abend ◽

Vijay A Raghavan ◽

Kenneth D Mandl ◽

Shawn N Murphy

Keyword(s):

Data Model ◽

Data Extraction ◽

Information Model ◽

Research Network ◽

Common Data Model ◽

Data Types ◽

Patient Centered ◽

Information Models ◽

National Patient ◽

Analytical Requirements

Abstract Objective Reinventing data extraction from electronic health records (EHRs) to meet new analytical needs is slow and expensive. However, each new data research network that wishes to support its own analytics tends to develop its own data model. Joining these different networks without new data extraction, transform, and load (ETL) processes can reduce the time and expense needed to participate. The Informatics for Integrating Biology and the Bedside (i2b2) project supports data network interoperability through an ontology-driven approach. We use i2b2 as a hub, to rapidly reconfigure data to meet new analytical requirements without new ETL programming. Materials and Methods Our 12-site National Patient-Centered Clinical Research Network (PCORnet) Clinical Data Research Network (CDRN) uses i2b2 to query data. We developed a process to generate a PCORnet Common Data Model (CDM) physical database directly from existing i2b2 systems, thereby supporting PCORnet analytic queries without new ETL programming. This involved: a formalized process for representing i2b2 information models (the specification of data types and formats); an information model that represents CDM Version 1.0; and a program that generates CDM tables, driven by this information model. This approach is generalizable to any logical information model. Results Eight PCORnet CDRN sites have implemented this approach and generated a CDM database without a new ETL process from the EHR. This enables federated querying within the CDRN and compatibility with the national PCORnet Distributed Research Network. Discussion We have established a way to adapt i2b2 to new information models without requiring changes to the underlying data. Eight Scalable Collaborative Infrastructure for a Learning Health System sites vetted this methodology, resulting in a network that, at present, supports research on 10 million patients’ data. Conclusion New analytical requirements can be quickly and cost-effectively supported by i2b2 without creating new data extraction processes from the EHR.

Download Full-text

International Comparison of Approaches to Common Data Models for Comparative Effectiveness Research

International Journal for Population Data Science ◽

10.23889/ijpds.v3i4.737 ◽

2018 ◽

Vol 3 (4) ◽

Author(s):

Adrian Levy ◽

Robert Platt ◽

Soko Setoguchi ◽

Jeffrey Brown ◽

Michael Paterson

Keyword(s):

Comparative Effectiveness Research ◽

Data Model ◽

Comparative Effectiveness ◽

Distributed Networks ◽

Network Governance ◽

Data Models ◽

Research Network ◽

Common Data Model ◽

Effectiveness Research ◽

The Us

Over the past decade, characterizing the safety and effectiveness of drugs has advanced through distributed networks of data repositories where investigators implement the same procedures to address the same topic using a common data model. Distributed networks for pharmacoepidemiology have now been established in the United States (US), Globally/Europe Canada, and Asian countries. Sentinel in the US was developed in response to legislation and is funded by the US Food and Drug Administration to address their safety queries. The Observational Medical Outcomes Partnership (OMOP) is an international collaborative with a growing European data network that developed a common data model through a public-private partnership. The Canadian Network of Observational Drug Effect Studies (CNODES) receives funding and study queries from Health Canada and dissemination is directly back to the regulator as well as through the peer-reviewed literature. The Asian Pharmacoepidemiology Network (AsPEN) is an investigator-initiated multi-national research network formed to support the safety and effectiveness assessment of medications and other therapeutics and to facilitate the prompt identification and validation of emerging safety issues among the countries in Asia and Pacific regions. While these networks have implemented two different common data models (CNODES with Sentinel, ASPEN with OMOP), each network differs from the others in the aims, stage and implementation, operational approach, data quality assurance mechanisms, funding, and dissemination. The objectives of this session are to compare and contrast the role and goals, design principles, implementation approaches, and analytic conventions and procedures between common data models implemented by SENTINEL, OMOP, CNODES, ands AsPEN. Divided into seven 15-minute segments the session begins with an overview of distributed networks of common data models for pharmacoepidemiology. In four slides, each presenter then characterizes their network by describing the following: number of data holders, lives covered, and records, data holdings, data access model, network governance. process for transforming a repository’s data into the common data model target audience(s), process of identifying queries and knowledge dissemination plan two key challenges faced by the network and the lessons learned In identifying similarities and meaningful differences between the networks, in the next segment the discussant will articulate the relative strengths of the different approaches taken. This will lead into the last segment in which the floor will be opened for questions and comments from the audience. The session would be of benefit to researchers seeking to better understand or join an existing distributed network as well as researchers interested in broadening their understanding of global comparative effectiveness research.

Download Full-text