Patient Cohort Identification on Time Series Data Using the OMOP Common Data Model

Abstract

Background The identification of patient cohorts for recruiting patients into clinical trials requires an evaluation of study-specific inclusion and exclusion criteria. These criteria are specified depending on corresponding clinical facts. Some of these facts may not be present in the clinical source systems and need to be calculated either in advance or at cohort query runtime (so-called feasibility query). Objectives We use the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) as the repository for our clinical data. However, Atlas, the graphical user interface of OMOP, does not offer the functionality to perform calculations on facts data. Therefore, we were in search for a different approach. The objective of this study is to investigate whether the Arden Syntax can be used for feasibility queries on the OMOP CDM to enable on-the-fly calculations at query runtime, to eliminate the need to precalculate data elements that are involved with researchers' criteria specification. Methods We implemented a service that reads the facts from the OMOP repository and provides it in a form which an Arden Syntax Medical Logic Module (MLM) can process. Then, we implemented an MLM that applies the eligibility criteria to every patient data set and outputs the list of eligible cases (i.e., performs the feasibility query). Results The study resulted in an MLM-based feasibility query that identifies cases of overventilation as an example of how an on-the-fly calculation can be realized. The algorithm is split into two MLMs to provide the reusability of the approach. Conclusion We found that MLMs are a suitable technology for feasibility queries on the OMOP CDM. Our method of performing on-the-fly calculations can be employed with any OMOP instance and without touching existing infrastructure like the Extract, Transform and Load pipeline. Therefore, we think that it is a well-suited method to perform on-the-fly calculations on OMOP.

Generation of a Fast Healthcare Interoperability Resources (FHIR)-based Ontology for Federated Feasibility Queries in the Context of COVID-19: Feasibility Study

“…For proprietary, i2b2, and Observational Medical Outcomes Partnership (OMOP) data, solutions exist that provide researchers with an ontology-based UI [ 9 , 10 , 22 ]. These platforms can also be utilized for FHIR and openEHR data but require additional ETL processes [ 7 , 23 ]. The Leaf project [ 8 ] presents an alternative approach by using a model agnostic query system for medical data stored in Structured Query Language (SQL) databases.…”

NSAID use and clinical outcomes in COVID-19 patients: a 38-center retrospective cohort study

“…Patients with recorded use of aspirin and acetaminophen, which share some modes of action with NSAIDs, were excluded from the analysis. For each of the twelve NSAIDs and the excluded medications (aspirin and acetaminophen), we constructed a codeset containing concept IDs representing all formulations of the medications using ATLAS ( http://atlas-covid19.ohdsi.org/ ), the graphical user interface designed to construct cohorts and/or concept sets for the OMOP common data model [ 32 ]. Concept IDs for topical and ophthalmic NSAID preparations were excluded from these codesets.…”

NSAID use and clinical outcomes in COVID-19 patients: A 38-center retrospective cohort study

“…For each of the eight COX Inhibitors, we constructed a codeset containing concept IDs representing all formulations of the medications (see Supplemental Table S3) using ATLAS (http://atlas-covid19.ohdsi.org/), the graphical user interface for the OMOP common data model. 25 We explored the potential association between treatment with a pharmaceutical agent and severity of COVID-19. Two strategies were chosen to minimize the potential effects of confounding.…”

“…For each of the twelve NSAIDs and the excluded medications (aspirin and acetaminophen), we constructed a codeset containing concept IDs representing all formulations of the medications using ATLAS (http://atlas-covid19.ohdsi.org/), the graphical user interface designed to construct cohorts and/or concept sets for the OMOP common data model [32]. Concept IDs for topical and ophthalmic NSAID preparations were excluded from these codesets.…”