CohortDiagnostics: Phenotype evaluation across a network of observational data sources using population-level characterization
This paper introduces a novel framework for evaluating phenotype algorithms (PAs) using the open-source tool, Cohort Diagnostics. The method is based on several diagnostic criteria to evaluate a patient cohort returned by a PA. Diagnostics include estimates of incidence rate, index date entry code b...
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| Published in | PloS one Vol. 20; no. 1; p. e0310634 |
|---|---|
| Main Authors | , , , , , , , , , , , , , , , , , , , |
| Format | Journal Article |
| Language | English |
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United States
Public Library of Science
16.01.2025
Public Library of Science (PLoS) |
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| Abstract | This paper introduces a novel framework for evaluating phenotype algorithms (PAs) using the open-source tool, Cohort Diagnostics.
The method is based on several diagnostic criteria to evaluate a patient cohort returned by a PA. Diagnostics include estimates of incidence rate, index date entry code breakdown, and prevalence of all observed clinical events prior to, on, and after index date. We test our framework by evaluating one PA for systemic lupus erythematosus (SLE) and two PAs for Alzheimer's disease (AD) across 10 different observational data sources.
By utilizing CohortDiagnostics, we found that the population-level characteristics of individuals in the cohort of SLE closely matched the disease's anticipated clinical profile. Specifically, the incidence rate of SLE was consistently higher in occurrence among females. Moreover, expected clinical events like laboratory tests, treatments, and repeated diagnoses were also observed. For AD, although one PA identified considerably fewer patients, absence of notable differences in clinical characteristics between the two cohorts suggested similar specificity.
We provide a practical and data-driven approach to evaluate PAs, using two clinical diseases as examples, across a network of OMOP data sources. Cohort Diagnostics can ensure the subjects identified by a specific PA align with those intended for inclusion in a research study.
Diagnostics based on large-scale population-level characterization can offer insights into the misclassification errors of PAs. |
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| AbstractList | This paper introduces a novel framework for evaluating phenotype algorithms (PAs) using the open-source tool, Cohort Diagnostics. The method is based on several diagnostic criteria to evaluate a patient cohort returned by a PA. Diagnostics include estimates of incidence rate, index date entry code breakdown, and prevalence of all observed clinical events prior to, on, and after index date. We test our framework by evaluating one PA for systemic lupus erythematosus (SLE) and two PAs for Alzheimer's disease (AD) across 10 different observational data sources. By utilizing CohortDiagnostics, we found that the population-level characteristics of individuals in the cohort of SLE closely matched the disease's anticipated clinical profile. Specifically, the incidence rate of SLE was consistently higher in occurrence among females. Moreover, expected clinical events like laboratory tests, treatments, and repeated diagnoses were also observed. For AD, although one PA identified considerably fewer patients, absence of notable differences in clinical characteristics between the two cohorts suggested similar specificity. We provide a practical and data-driven approach to evaluate PAs, using two clinical diseases as examples, across a network of OMOP data sources. Cohort Diagnostics can ensure the subjects identified by a specific PA align with those intended for inclusion in a research study. Diagnostics based on large-scale population-level characterization can offer insights into the misclassification errors of PAs. This paper introduces a novel framework for evaluating phenotype algorithms (PAs) using the open-source tool, Cohort Diagnostics.OBJECTIVEThis paper introduces a novel framework for evaluating phenotype algorithms (PAs) using the open-source tool, Cohort Diagnostics.The method is based on several diagnostic criteria to evaluate a patient cohort returned by a PA. Diagnostics include estimates of incidence rate, index date entry code breakdown, and prevalence of all observed clinical events prior to, on, and after index date. We test our framework by evaluating one PA for systemic lupus erythematosus (SLE) and two PAs for Alzheimer's disease (AD) across 10 different observational data sources.MATERIALS AND METHODSThe method is based on several diagnostic criteria to evaluate a patient cohort returned by a PA. Diagnostics include estimates of incidence rate, index date entry code breakdown, and prevalence of all observed clinical events prior to, on, and after index date. We test our framework by evaluating one PA for systemic lupus erythematosus (SLE) and two PAs for Alzheimer's disease (AD) across 10 different observational data sources.By utilizing CohortDiagnostics, we found that the population-level characteristics of individuals in the cohort of SLE closely matched the disease's anticipated clinical profile. Specifically, the incidence rate of SLE was consistently higher in occurrence among females. Moreover, expected clinical events like laboratory tests, treatments, and repeated diagnoses were also observed. For AD, although one PA identified considerably fewer patients, absence of notable differences in clinical characteristics between the two cohorts suggested similar specificity.RESULTSBy utilizing CohortDiagnostics, we found that the population-level characteristics of individuals in the cohort of SLE closely matched the disease's anticipated clinical profile. Specifically, the incidence rate of SLE was consistently higher in occurrence among females. Moreover, expected clinical events like laboratory tests, treatments, and repeated diagnoses were also observed. For AD, although one PA identified considerably fewer patients, absence of notable differences in clinical characteristics between the two cohorts suggested similar specificity.We provide a practical and data-driven approach to evaluate PAs, using two clinical diseases as examples, across a network of OMOP data sources. Cohort Diagnostics can ensure the subjects identified by a specific PA align with those intended for inclusion in a research study.DISCUSSIONWe provide a practical and data-driven approach to evaluate PAs, using two clinical diseases as examples, across a network of OMOP data sources. Cohort Diagnostics can ensure the subjects identified by a specific PA align with those intended for inclusion in a research study.Diagnostics based on large-scale population-level characterization can offer insights into the misclassification errors of PAs.CONCLUSIONDiagnostics based on large-scale population-level characterization can offer insights into the misclassification errors of PAs. ObjectiveThis paper introduces a novel framework for evaluating phenotype algorithms (PAs) using the open-source tool, Cohort Diagnostics.Materials and methodsThe method is based on several diagnostic criteria to evaluate a patient cohort returned by a PA. Diagnostics include estimates of incidence rate, index date entry code breakdown, and prevalence of all observed clinical events prior to, on, and after index date. We test our framework by evaluating one PA for systemic lupus erythematosus (SLE) and two PAs for Alzheimer’s disease (AD) across 10 different observational data sources.ResultsBy utilizing CohortDiagnostics, we found that the population-level characteristics of individuals in the cohort of SLE closely matched the disease’s anticipated clinical profile. Specifically, the incidence rate of SLE was consistently higher in occurrence among females. Moreover, expected clinical events like laboratory tests, treatments, and repeated diagnoses were also observed. For AD, although one PA identified considerably fewer patients, absence of notable differences in clinical characteristics between the two cohorts suggested similar specificity.DiscussionWe provide a practical and data-driven approach to evaluate PAs, using two clinical diseases as examples, across a network of OMOP data sources. Cohort Diagnostics can ensure the subjects identified by a specific PA align with those intended for inclusion in a research study.ConclusionDiagnostics based on large-scale population-level characterization can offer insights into the misclassification errors of PAs. Objective This paper introduces a novel framework for evaluating phenotype algorithms (PAs) using the open-source tool, Cohort Diagnostics. Materials and methods The method is based on several diagnostic criteria to evaluate a patient cohort returned by a PA. Diagnostics include estimates of incidence rate, index date entry code breakdown, and prevalence of all observed clinical events prior to, on, and after index date. We test our framework by evaluating one PA for systemic lupus erythematosus (SLE) and two PAs for Alzheimer's disease (AD) across 10 different observational data sources. Results By utilizing CohortDiagnostics, we found that the population-level characteristics of individuals in the cohort of SLE closely matched the disease's anticipated clinical profile. Specifically, the incidence rate of SLE was consistently higher in occurrence among females. Moreover, expected clinical events like laboratory tests, treatments, and repeated diagnoses were also observed. For AD, although one PA identified considerably fewer patients, absence of notable differences in clinical characteristics between the two cohorts suggested similar specificity. Discussion We provide a practical and data-driven approach to evaluate PAs, using two clinical diseases as examples, across a network of OMOP data sources. Cohort Diagnostics can ensure the subjects identified by a specific PA align with those intended for inclusion in a research study. Conclusion Diagnostics based on large-scale population-level characterization can offer insights into the misclassification errors of PAs. Objective This paper introduces a novel framework for evaluating phenotype algorithms (PAs) using the open-source tool, Cohort Diagnostics. Materials and methods The method is based on several diagnostic criteria to evaluate a patient cohort returned by a PA. Diagnostics include estimates of incidence rate, index date entry code breakdown, and prevalence of all observed clinical events prior to, on, and after index date. We test our framework by evaluating one PA for systemic lupus erythematosus (SLE) and two PAs for Alzheimer’s disease (AD) across 10 different observational data sources. Results By utilizing CohortDiagnostics, we found that the population-level characteristics of individuals in the cohort of SLE closely matched the disease’s anticipated clinical profile. Specifically, the incidence rate of SLE was consistently higher in occurrence among females. Moreover, expected clinical events like laboratory tests, treatments, and repeated diagnoses were also observed. For AD, although one PA identified considerably fewer patients, absence of notable differences in clinical characteristics between the two cohorts suggested similar specificity. Discussion We provide a practical and data-driven approach to evaluate PAs, using two clinical diseases as examples, across a network of OMOP data sources. Cohort Diagnostics can ensure the subjects identified by a specific PA align with those intended for inclusion in a research study. Conclusion Diagnostics based on large-scale population-level characterization can offer insights into the misclassification errors of PAs. This paper introduces a novel framework for evaluating phenotype algorithms (PAs) using the open-source tool, Cohort Diagnostics. The method is based on several diagnostic criteria to evaluate a patient cohort returned by a PA. Diagnostics include estimates of incidence rate, index date entry code breakdown, and prevalence of all observed clinical events prior to, on, and after index date. We test our framework by evaluating one PA for systemic lupus erythematosus (SLE) and two PAs for Alzheimer's disease (AD) across 10 different observational data sources. By utilizing CohortDiagnostics, we found that the population-level characteristics of individuals in the cohort of SLE closely matched the disease's anticipated clinical profile. Specifically, the incidence rate of SLE was consistently higher in occurrence among females. Moreover, expected clinical events like laboratory tests, treatments, and repeated diagnoses were also observed. For AD, although one PA identified considerably fewer patients, absence of notable differences in clinical characteristics between the two cohorts suggested similar specificity. We provide a practical and data-driven approach to evaluate PAs, using two clinical diseases as examples, across a network of OMOP data sources. Cohort Diagnostics can ensure the subjects identified by a specific PA align with those intended for inclusion in a research study. Diagnostics based on large-scale population-level characterization can offer insights into the misclassification errors of PAs. |
| Audience | Academic |
| Author | Fortin, Stephen Knoll, Chris Molinaro, Anthony Gilbert, James P. Andryc, Alan Blacketer, Clair DeFalco, Frank Voss, Erica A. Conover, Mitchell M. Makadia, Rupa Hardin, Jill Weaver, James Shoaibi, Azza Schuemie, Martijn J. Swerdel, Joel Ryan, Patrick B. Hughes, Nigel Rao, Gowtham A. Sena, Anthony G. Reps, Jenna |
| AuthorAffiliation | 2 OHDSI Collaborators, Observational Health Data Sciences and Informatics (OHDSI), New York, NY, United States of America 3 Department of Biostatistics, University of California, Los Angeles, CA, United States of America 4 Department of Biomedical Informatics, Columbia University, New York, NY, United States of America University of Siena: Universita degli Studi di Siena, ITALY 1 Observational Health Data Analytics, Janssen Research and Development, LLC, Titusville, NJ, United States of America |
| AuthorAffiliation_xml | – name: 3 Department of Biostatistics, University of California, Los Angeles, CA, United States of America – name: 4 Department of Biomedical Informatics, Columbia University, New York, NY, United States of America – name: 1 Observational Health Data Analytics, Janssen Research and Development, LLC, Titusville, NJ, United States of America – name: 2 OHDSI Collaborators, Observational Health Data Sciences and Informatics (OHDSI), New York, NY, United States of America – name: University of Siena: Universita degli Studi di Siena, ITALY |
| Author_xml | – sequence: 1 givenname: Gowtham A. orcidid: 0000-0002-4949-7236 surname: Rao fullname: Rao, Gowtham A. – sequence: 2 givenname: Azza orcidid: 0000-0002-6976-2594 surname: Shoaibi fullname: Shoaibi, Azza – sequence: 3 givenname: Rupa surname: Makadia fullname: Makadia, Rupa – sequence: 4 givenname: Jill orcidid: 0000-0003-2682-2187 surname: Hardin fullname: Hardin, Jill – sequence: 5 givenname: Joel surname: Swerdel fullname: Swerdel, Joel – sequence: 6 givenname: James orcidid: 0000-0003-0755-5191 surname: Weaver fullname: Weaver, James – sequence: 7 givenname: Erica A. surname: Voss fullname: Voss, Erica A. – sequence: 8 givenname: Mitchell M. surname: Conover fullname: Conover, Mitchell M. – sequence: 9 givenname: Stephen surname: Fortin fullname: Fortin, Stephen – sequence: 10 givenname: Anthony G. orcidid: 0000-0001-8630-5347 surname: Sena fullname: Sena, Anthony G. – sequence: 11 givenname: Chris surname: Knoll fullname: Knoll, Chris – sequence: 12 givenname: Nigel surname: Hughes fullname: Hughes, Nigel – sequence: 13 givenname: James P. orcidid: 0000-0003-2294-3459 surname: Gilbert fullname: Gilbert, James P. – sequence: 14 givenname: Clair surname: Blacketer fullname: Blacketer, Clair – sequence: 15 givenname: Alan surname: Andryc fullname: Andryc, Alan – sequence: 16 givenname: Frank surname: DeFalco fullname: DeFalco, Frank – sequence: 17 givenname: Anthony surname: Molinaro fullname: Molinaro, Anthony – sequence: 18 givenname: Jenna orcidid: 0000-0002-2970-0778 surname: Reps fullname: Reps, Jenna – sequence: 19 givenname: Martijn J. orcidid: 0000-0002-0817-5361 surname: Schuemie fullname: Schuemie, Martijn J. – sequence: 20 givenname: Patrick B. surname: Ryan fullname: Ryan, Patrick B. |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/39820599$$D View this record in MEDLINE/PubMed |
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| Copyright | Copyright: © 2025 Rao et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. COPYRIGHT 2025 Public Library of Science 2025 Rao et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. 2025 Rao et al 2025 Rao et al 2025 Rao et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
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| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 Competing Interests: All authors were employees of Janssen Research & Development, LLC, and shareholders of Johnson & Johnson (J&J) stock at the time of manuscript conceptualization, drafting, editing, and initial approval for submission. All authors except AM continue to be employees of J&J. Author AM changed affiliation after the initial submission and is currently affiliated with VNS Health. This competing interest does not alter our adherence to PLOS ONE policies on sharing data and materials. |
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The method is based on... Objective This paper introduces a novel framework for evaluating phenotype algorithms (PAs) using the open-source tool, Cohort Diagnostics. Materials and... This paper introduces a novel framework for evaluating phenotype algorithms (PAs) using the open-source tool, Cohort Diagnostics. The method is based on... ObjectiveThis paper introduces a novel framework for evaluating phenotype algorithms (PAs) using the open-source tool, Cohort Diagnostics.Materials and... This paper introduces a novel framework for evaluating phenotype algorithms (PAs) using the open-source tool, Cohort Diagnostics.OBJECTIVEThis paper introduces... Objective This paper introduces a novel framework for evaluating phenotype algorithms (PAs) using the open-source tool, Cohort Diagnostics. Materials and... |
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| Title | CohortDiagnostics: Phenotype evaluation across a network of observational data sources using population-level characterization |
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