Literature DB >> 33506478

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

Christian Maier1, Lorenz A Kapsner2, Sebastian Mate2, Hans-Ulrich Prokosch1,2, Stefan Kraus3.   

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. Thieme. All rights reserved.

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Year:  2021        PMID: 33506478      PMCID: PMC7840432          DOI: 10.1055/s-0040-1721481

Source DB:  PubMed          Journal:  Appl Clin Inform        ISSN: 1869-0327            Impact factor:   2.342


  24 in total

1.  Using Arden Syntax to identify registry-eligible very low birth weight neonates from the Electronic Health Record.

Authors:  Indra Neil Sarkar; Elizabeth S Chen; Paul T Rosenau; Matthew B Storer; Beth Anderson; Jeffrey D Horbar
Journal:  AMIA Annu Symp Proc       Date:  2014-11-14

2.  Generalizing the Arden Syntax to a Common Clinical Application Language.

Authors:  Stefan Kraus
Journal:  Stud Health Technol Inform       Date:  2018

3.  Incrementally Transforming Electronic Medical Records into the Observational Medical Outcomes Partnership Common Data Model: A Multidimensional Quality Assurance Approach.

Authors:  Kristine E Lynch; Stephen A Deppen; Scott L DuVall; Benjamin Viernes; Aize Cao; Daniel Park; Elizabeth Hanchrow; Kushan Hewa; Peter Greaves; Michael E Matheny
Journal:  Appl Clin Inform       Date:  2019-10-23       Impact factor: 2.342

4.  Mapping the Entire Record-An Alternative Approach to Data Access from Medical Logic Modules.

Authors:  Stefan Kraus; Dennis Toddenroth; Martin Staudigel; Wolfgang Rödle; Philipp Unberath; Lena Griebel; Hans-Ulrich Prokosch; Sebastian Mate
Journal:  Appl Clin Inform       Date:  2020-05-13       Impact factor: 2.342

Review 5.  Writing Arden Syntax Medical Logic Modules.

Authors:  G Hripcsak
Journal:  Comput Biol Med       Date:  1994-09       Impact factor: 4.589

6.  Decision support for clinical trial eligibility determination in breast cancer.

Authors:  L Ohno-Machado; S J Wang; P Mar; A A Boxwala
Journal:  Proc AMIA Symp       Date:  1999

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Authors:  George Hripcsak; Jon D Duke; Nigam H Shah; Christian G Reich; Vojtech Huser; Martijn J Schuemie; Marc A Suchard; Rae Woong Park; Ian Chi Kei Wong; Peter R Rijnbeek; Johan van der Lei; Nicole Pratt; G Niklas Norén; Yu-Chuan Li; Paul E Stang; David Madigan; Patrick B Ryan
Journal:  Stud Health Technol Inform       Date:  2015

8.  Analysis of eligibility criteria complexity in clinical trials.

Authors:  Jessica Ross; Samson Tu; Simona Carini; Ida Sim
Journal:  Summit Transl Bioinform       Date:  2010-03-01

9.  Secondary Use of EHR: Data Quality Issues and Informatics Opportunities.

Authors:  Taxiarchis Botsis; Gunnar Hartvigsen; Fei Chen; Chunhua Weng
Journal:  Summit Transl Bioinform       Date:  2010-03-01

10.  Automatic trial eligibility surveillance based on unstructured clinical data.

Authors:  Stéphane M Meystre; Paul M Heider; Youngjun Kim; Daniel B Aruch; Carolyn D Britten
Journal:  Int J Med Inform       Date:  2019-05-23       Impact factor: 4.730

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

Authors:  Justin T Reese; Ben Coleman; Lauren Chan; Hannah Blau; Tiffany J Callahan; Luca Cappelletti; Tommaso Fontana; Katie R Bradwell; Nomi L Harris; Elena Casiraghi; Giorgio Valentini; Guy Karlebach; Rachel Deer; Julie A McMurry; Melissa A Haendel; Christopher G Chute; Emily Pfaff; Richard Moffitt; Heidi Spratt; Jasvinder A Singh; Christopher J Mungall; Andrew E Williams; Peter N Robinson
Journal:  Virol J       Date:  2022-05-15       Impact factor: 5.913

Review 4.  Gaps in standards for integrating artificial intelligence technologies into ophthalmic practice.

Authors:  Sally L Baxter; Aaron Y Lee
Journal:  Curr Opin Ophthalmol       Date:  2021-09-01       Impact factor: 4.299

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

Authors:  Lorenz Rosenau; Raphael W Majeed; Josef Ingenerf; Alexander Kiel; Björn Kroll; Thomas Köhler; Hans-Ulrich Prokosch; Julian Gruendner
Journal:  JMIR Med Inform       Date:  2022-04-27

6.  Metformin is Associated with Reduced COVID-19 Severity in Patients with Prediabetes.

Authors:  Lauren E Chan; Elena Casiraghi; Bryan Laraway; Ben Coleman; Hannah Blau; Adnin Zaman; Nomi Harris; Kenneth Wilkins; Michael Gargano; Giorgio Valentini; David Sahner; Melissa Haendel; Peter N Robinson; Carolyn Bramante; Justin Reese
Journal:  medRxiv       Date:  2022-08-30
  6 in total

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