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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| Veröffentlicht in: | PloS one 2025-01, Vol.20 (1), p.e0310634 |
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| creator | Rao, Gowtham A Shoaibi, Azza Makadia, Rupa Hardin, Jill Swerdel, Joel Weaver, James Voss, Erica A Conover, Mitchell M Fortin, Stephen Sena, Anthony G Knoll, Chris Hughes, Nigel Gilbert, James P Blacketer, Clair Andryc, Alan DeFalco, Frank Molinaro, Anthony Reps, Jenna Schuemie, Martijn J Ryan, Patrick B |
| description | 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. |
| doi_str_mv | 10.1371/journal.pone.0310634 |
| format | Article |
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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.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0310634</identifier><identifier>PMID: 39820599</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Algorithms ; Alzheimer Disease - diagnosis ; Alzheimer Disease - epidemiology ; Alzheimer's disease ; Biology and Life Sciences ; Chronic conditions ; Cohort Studies ; Computer and Information Sciences ; Data sources ; Diabetes ; Diagnosis ; Female ; Genotype & phenotype ; Humans ; Incidence ; Information Sources ; Laboratory tests ; Lupus Erythematosus, Systemic - diagnosis ; Lupus Erythematosus, Systemic - epidemiology ; Male ; Medical records ; Medicine and Health Sciences ; Neurodegenerative diseases ; Open source software ; Phenotype ; Phenotypes ; Population ; Population studies ; Public domain ; Public software ; Source code ; Systemic lupus erythematosus ; Technology application ; Trends</subject><ispartof>PloS one, 2025-01, Vol.20 (1), p.e0310634</ispartof><rights>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.</rights><rights>COPYRIGHT 2025 Public Library of Science</rights><rights>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.</rights><rights>2025 Rao et al 2025 Rao et al</rights><rights>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.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c4874-61a6a975aba5b45cc53070fb80ab0e228cdf66927accb6814819814d60e369893</cites><orcidid>0000-0003-2682-2187 ; 0000-0001-8630-5347 ; 0000-0003-2294-3459 ; 0000-0002-6976-2594 ; 0000-0002-4949-7236 ; 0000-0003-0755-5191 ; 0000-0002-2970-0778 ; 0000-0002-0817-5361</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC11737733/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC11737733/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,723,776,780,860,881,2096,2915,23845,27901,27902,53766,53768,79342,79343</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/39820599$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Rao, Gowtham A</creatorcontrib><creatorcontrib>Shoaibi, Azza</creatorcontrib><creatorcontrib>Makadia, Rupa</creatorcontrib><creatorcontrib>Hardin, Jill</creatorcontrib><creatorcontrib>Swerdel, Joel</creatorcontrib><creatorcontrib>Weaver, James</creatorcontrib><creatorcontrib>Voss, Erica A</creatorcontrib><creatorcontrib>Conover, Mitchell M</creatorcontrib><creatorcontrib>Fortin, Stephen</creatorcontrib><creatorcontrib>Sena, Anthony G</creatorcontrib><creatorcontrib>Knoll, Chris</creatorcontrib><creatorcontrib>Hughes, Nigel</creatorcontrib><creatorcontrib>Gilbert, James P</creatorcontrib><creatorcontrib>Blacketer, Clair</creatorcontrib><creatorcontrib>Andryc, Alan</creatorcontrib><creatorcontrib>DeFalco, Frank</creatorcontrib><creatorcontrib>Molinaro, Anthony</creatorcontrib><creatorcontrib>Reps, Jenna</creatorcontrib><creatorcontrib>Schuemie, Martijn J</creatorcontrib><creatorcontrib>Ryan, Patrick B</creatorcontrib><title>CohortDiagnostics: Phenotype evaluation across a network of observational data sources using population-level characterization</title><title>PloS one</title><addtitle>PLoS One</addtitle><description>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.</description><subject>Algorithms</subject><subject>Alzheimer Disease - diagnosis</subject><subject>Alzheimer Disease - epidemiology</subject><subject>Alzheimer's disease</subject><subject>Biology and Life Sciences</subject><subject>Chronic conditions</subject><subject>Cohort Studies</subject><subject>Computer and Information Sciences</subject><subject>Data sources</subject><subject>Diabetes</subject><subject>Diagnosis</subject><subject>Female</subject><subject>Genotype & phenotype</subject><subject>Humans</subject><subject>Incidence</subject><subject>Information Sources</subject><subject>Laboratory tests</subject><subject>Lupus Erythematosus, Systemic - diagnosis</subject><subject>Lupus Erythematosus, Systemic - epidemiology</subject><subject>Male</subject><subject>Medical records</subject><subject>Medicine and Health Sciences</subject><subject>Neurodegenerative diseases</subject><subject>Open source software</subject><subject>Phenotype</subject><subject>Phenotypes</subject><subject>Population</subject><subject>Population studies</subject><subject>Public domain</subject><subject>Public software</subject><subject>Source code</subject><subject>Systemic lupus erythematosus</subject><subject>Technology 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P</au><au>Blacketer, Clair</au><au>Andryc, Alan</au><au>DeFalco, Frank</au><au>Molinaro, Anthony</au><au>Reps, Jenna</au><au>Schuemie, Martijn J</au><au>Ryan, Patrick B</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>CohortDiagnostics: Phenotype evaluation across a network of observational data sources using population-level characterization</atitle><jtitle>PloS one</jtitle><addtitle>PLoS One</addtitle><date>2025-01-16</date><risdate>2025</risdate><volume>20</volume><issue>1</issue><spage>e0310634</spage><pages>e0310634-</pages><issn>1932-6203</issn><eissn>1932-6203</eissn><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.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>39820599</pmid><doi>10.1371/journal.pone.0310634</doi><tpages>e0310634</tpages><orcidid>https://orcid.org/0000-0003-2682-2187</orcidid><orcidid>https://orcid.org/0000-0001-8630-5347</orcidid><orcidid>https://orcid.org/0000-0003-2294-3459</orcidid><orcidid>https://orcid.org/0000-0002-6976-2594</orcidid><orcidid>https://orcid.org/0000-0002-4949-7236</orcidid><orcidid>https://orcid.org/0000-0003-0755-5191</orcidid><orcidid>https://orcid.org/0000-0002-2970-0778</orcidid><orcidid>https://orcid.org/0000-0002-0817-5361</orcidid><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 1932-6203 |
| ispartof | PloS one, 2025-01, Vol.20 (1), p.e0310634 |
| issn | 1932-6203 1932-6203 |
| language | eng |
| recordid | cdi_plos_journals_3156419035 |
| source | MEDLINE; DOAJ Directory of Open Access Journals; Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals; PubMed Central; Free Full-Text Journals in Chemistry; Public Library of Science (PLoS) |
| subjects | Algorithms Alzheimer Disease - diagnosis Alzheimer Disease - epidemiology Alzheimer's disease Biology and Life Sciences Chronic conditions Cohort Studies Computer and Information Sciences Data sources Diabetes Diagnosis Female Genotype & phenotype Humans Incidence Information Sources Laboratory tests Lupus Erythematosus, Systemic - diagnosis Lupus Erythematosus, Systemic - epidemiology Male Medical records Medicine and Health Sciences Neurodegenerative diseases Open source software Phenotype Phenotypes Population Population studies Public domain Public software Source code Systemic lupus erythematosus Technology application Trends |
| title | CohortDiagnostics: Phenotype evaluation across a network of observational data sources using population-level characterization |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-11T02%3A05%3A11IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-gale_plos_&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=CohortDiagnostics:%20Phenotype%20evaluation%20across%20a%20network%20of%20observational%20data%20sources%20using%20population-level%20characterization&rft.jtitle=PloS%20one&rft.au=Rao,%20Gowtham%20A&rft.date=2025-01-16&rft.volume=20&rft.issue=1&rft.spage=e0310634&rft.pages=e0310634-&rft.issn=1932-6203&rft.eissn=1932-6203&rft_id=info:doi/10.1371/journal.pone.0310634&rft_dat=%3Cgale_plos_%3EA823804730%3C/gale_plos_%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=3156419035&rft_id=info:pmid/39820599&rft_galeid=A823804730&rft_doaj_id=oai_doaj_org_article_cebba8004a044fdd94a60fecdc29755e&rfr_iscdi=true |