Use of latent class analysis and patient reported outcome measures to identify distinct long COVID phenotypes: A longitudinal cohort study
We sought to 1) identify long COVID phenotypes based on patient reported outcome measures (PROMs) and 2) determine whether the phenotypes were associated with quality of life (QoL) and/or lung function. This was a longitudinal cohort study of hospitalized and non-hospitalized patients from March 202...
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| creator | Wong, Alyson W Tran, Karen C Binka, Mawuena Janjua, Naveed Z Sbihi, Hind Russell, James A Carlsten, Christopher Levin, Adeera Ryerson, Christopher J |
| description | We sought to 1) identify long COVID phenotypes based on patient reported outcome measures (PROMs) and 2) determine whether the phenotypes were associated with quality of life (QoL) and/or lung function.
This was a longitudinal cohort study of hospitalized and non-hospitalized patients from March 2020 to January 2022 that was conducted across 4 Post-COVID Recovery Clinics in British Columbia, Canada. Latent class analysis was used to identify long COVID phenotypes using baseline PROMs (fatigue, dyspnea, cough, anxiety, depression, and post-traumatic stress disorder). We then explored the association between the phenotypes and QoL (using the EuroQoL 5 dimensions visual analogue scale [EQ5D VAS]) and lung function (using the diffusing capacity of the lung for carbon monoxide [DLCO]).
There were 1,344 patients enrolled in the study (mean age 51 ±15 years; 780 [58%] were females; 769 (57%) were of a non-White race). Three distinct long COVID phenotypes were identified: Class 1) fatigue and dyspnea, Class 2) anxiety and depression, and Class 3) fatigue, dyspnea, anxiety, and depression. Class 3 had a significantly lower EQ5D VAS at 3 (50±19) and 6 months (54 ± 22) compared to Classes 1 and 2 (p |
| doi_str_mv | 10.1371/journal.pone.0286588 |
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This was a longitudinal cohort study of hospitalized and non-hospitalized patients from March 2020 to January 2022 that was conducted across 4 Post-COVID Recovery Clinics in British Columbia, Canada. Latent class analysis was used to identify long COVID phenotypes using baseline PROMs (fatigue, dyspnea, cough, anxiety, depression, and post-traumatic stress disorder). We then explored the association between the phenotypes and QoL (using the EuroQoL 5 dimensions visual analogue scale [EQ5D VAS]) and lung function (using the diffusing capacity of the lung for carbon monoxide [DLCO]).
There were 1,344 patients enrolled in the study (mean age 51 ±15 years; 780 [58%] were females; 769 (57%) were of a non-White race). Three distinct long COVID phenotypes were identified: Class 1) fatigue and dyspnea, Class 2) anxiety and depression, and Class 3) fatigue, dyspnea, anxiety, and depression. Class 3 had a significantly lower EQ5D VAS at 3 (50±19) and 6 months (54 ± 22) compared to Classes 1 and 2 (p<0.001). The EQ5D VAS significantly improved between 3 and 6 months for Class 1 (median difference of 6.0 [95% CI, 4.0 to 8.0]) and Class 3 (median difference of 5.0 [95% CI, 0 to 8.5]). There were no differences in DLCO between the classes.
There were 3 distinct long COVID phenotypes with different outcomes in QoL between 3 and 6 months after symptom onset. These phenotypes suggest that long COVID is a heterogeneous condition with distinct subpopulations who may have different outcomes and warrant tailored therapeutic approaches.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0286588</identifier><identifier>PMID: 37267379</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Analysis ; Anxiety ; Biology and Life Sciences ; Carbon monoxide ; Clinical outcomes ; Cohort analysis ; Cough ; COVID-19 ; Dyspnea ; Fatigue ; Illnesses ; Latent class analysis ; Long COVID ; Lungs ; Medicine and Health Sciences ; Mental depression ; Mental disorders ; Patients ; Phenotype ; Phenotypes ; Post traumatic stress disorder ; Posttraumatic stress disorder ; Psychological stress ; Quality of life ; Questionnaires ; Respiration ; Respiratory function ; Severe acute respiratory syndrome coronavirus 2 ; Social Sciences ; Statistical analysis ; Subpopulations</subject><ispartof>PloS one, 2023-06, Vol.18 (6), p.e0286588-e0286588</ispartof><rights>Copyright: © 2023 Wong 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 2023 Public Library of Science</rights><rights>2023 Wong 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>2023 Wong et al 2023 Wong et al</rights><rights>2023 Wong 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><citedby>FETCH-LOGICAL-c693t-ca4e393f700c7446776ba583177fa83fd08ae356858389c6264c7bcddccb201d3</citedby><cites>FETCH-LOGICAL-c693t-ca4e393f700c7446776ba583177fa83fd08ae356858389c6264c7bcddccb201d3</cites><orcidid>0000-0003-4910-1948</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/PMC10237387/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC10237387/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,864,885,2102,2928,23866,27924,27925,53791,53793</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/37267379$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Siddiqi, Tariq Jamal</contributor><creatorcontrib>Wong, Alyson W</creatorcontrib><creatorcontrib>Tran, Karen C</creatorcontrib><creatorcontrib>Binka, Mawuena</creatorcontrib><creatorcontrib>Janjua, Naveed Z</creatorcontrib><creatorcontrib>Sbihi, Hind</creatorcontrib><creatorcontrib>Russell, James A</creatorcontrib><creatorcontrib>Carlsten, Christopher</creatorcontrib><creatorcontrib>Levin, Adeera</creatorcontrib><creatorcontrib>Ryerson, Christopher J</creatorcontrib><title>Use of latent class analysis and patient reported outcome measures to identify distinct long COVID phenotypes: A longitudinal cohort study</title><title>PloS one</title><addtitle>PLoS One</addtitle><description>We sought to 1) identify long COVID phenotypes based on patient reported outcome measures (PROMs) and 2) determine whether the phenotypes were associated with quality of life (QoL) and/or lung function.
This was a longitudinal cohort study of hospitalized and non-hospitalized patients from March 2020 to January 2022 that was conducted across 4 Post-COVID Recovery Clinics in British Columbia, Canada. Latent class analysis was used to identify long COVID phenotypes using baseline PROMs (fatigue, dyspnea, cough, anxiety, depression, and post-traumatic stress disorder). We then explored the association between the phenotypes and QoL (using the EuroQoL 5 dimensions visual analogue scale [EQ5D VAS]) and lung function (using the diffusing capacity of the lung for carbon monoxide [DLCO]).
There were 1,344 patients enrolled in the study (mean age 51 ±15 years; 780 [58%] were females; 769 (57%) were of a non-White race). Three distinct long COVID phenotypes were identified: Class 1) fatigue and dyspnea, Class 2) anxiety and depression, and Class 3) fatigue, dyspnea, anxiety, and depression. Class 3 had a significantly lower EQ5D VAS at 3 (50±19) and 6 months (54 ± 22) compared to Classes 1 and 2 (p<0.001). The EQ5D VAS significantly improved between 3 and 6 months for Class 1 (median difference of 6.0 [95% CI, 4.0 to 8.0]) and Class 3 (median difference of 5.0 [95% CI, 0 to 8.5]). There were no differences in DLCO between the classes.
There were 3 distinct long COVID phenotypes with different outcomes in QoL between 3 and 6 months after symptom onset. These phenotypes suggest that long COVID is a heterogeneous condition with distinct subpopulations who may have different outcomes and warrant tailored therapeutic approaches.</description><subject>Analysis</subject><subject>Anxiety</subject><subject>Biology and Life Sciences</subject><subject>Carbon monoxide</subject><subject>Clinical outcomes</subject><subject>Cohort analysis</subject><subject>Cough</subject><subject>COVID-19</subject><subject>Dyspnea</subject><subject>Fatigue</subject><subject>Illnesses</subject><subject>Latent class analysis</subject><subject>Long COVID</subject><subject>Lungs</subject><subject>Medicine and Health Sciences</subject><subject>Mental depression</subject><subject>Mental disorders</subject><subject>Patients</subject><subject>Phenotype</subject><subject>Phenotypes</subject><subject>Post traumatic stress disorder</subject><subject>Posttraumatic stress disorder</subject><subject>Psychological stress</subject><subject>Quality of life</subject><subject>Questionnaires</subject><subject>Respiration</subject><subject>Respiratory function</subject><subject>Severe acute respiratory syndrome coronavirus 2</subject><subject>Social Sciences</subject><subject>Statistical analysis</subject><subject>Subpopulations</subject><issn>1932-6203</issn><issn>1932-6203</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><sourceid>DOA</sourceid><recordid>eNqNk9uO0zAQhiMEYpeFN0BgCQnBRYsPiQ_coKqcKq1UCdi9tRzHab1K42A7iL4CT43TZlcN2guUCzsz3_wT_85k2XME54gw9O7G9b5VzbxzrZlDzGnB-YPsHAmCZxRD8vBkf5Y9CeEGwoJwSh9nZ4RhyggT59mfq2CAq0Gjomkj0I0KAaikuw922FSgU9EOKW8656OpgOujdjsDdkaF3psAogO2Soit96CyIdpWR9C4dgOW6-vVR9BtTevivjPhPVgcEjb2lU1NgHbbJApCet8_zR7Vqgnm2bheZFefP_1Yfp1drr-slovLmaaCxJlWuSGC1AxCzfKcMkZLVXCCGKsVJ3UFuTKkoDzFuNAU01yzUleV1iWGqCIX2cujbte4IEcbg8QcJ7-YyEUiVkeicupGdt7ulN9Lp6w8BJzfSOWj1Y2RQhdMY50rTHSOCiFKwagqCwF5qSDlSevD2K0vd6bSySevmonoNNPardy4XxJBTBjhLCm8GRW8-9mbEOXOBm2aRrXG9YcPTySjCCX01T_o_ccbqY1KJ7Bt7VJjPYjKBStQgSEUQ9v5PVR6KrOzOv10tU3xScHbSUFiovkdN6oPQa6-f_t_dn09ZV-fsFujmrgNrumjdW2YgvkR1N6F4E195zKCcpiZWzfkMDNynJlU9uL0hu6KboeE_AXBCxLZ</recordid><startdate>20230602</startdate><enddate>20230602</enddate><creator>Wong, 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of latent class analysis and patient reported outcome measures to identify distinct long COVID phenotypes: A longitudinal cohort study</title><author>Wong, Alyson W ; Tran, Karen C ; Binka, Mawuena ; Janjua, Naveed Z ; Sbihi, Hind ; Russell, James A ; Carlsten, Christopher ; Levin, Adeera ; Ryerson, Christopher J</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c693t-ca4e393f700c7446776ba583177fa83fd08ae356858389c6264c7bcddccb201d3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Analysis</topic><topic>Anxiety</topic><topic>Biology and Life Sciences</topic><topic>Carbon monoxide</topic><topic>Clinical outcomes</topic><topic>Cohort analysis</topic><topic>Cough</topic><topic>COVID-19</topic><topic>Dyspnea</topic><topic>Fatigue</topic><topic>Illnesses</topic><topic>Latent class analysis</topic><topic>Long COVID</topic><topic>Lungs</topic><topic>Medicine and Health 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Jamal</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Use of latent class analysis and patient reported outcome measures to identify distinct long COVID phenotypes: A longitudinal cohort study</atitle><jtitle>PloS one</jtitle><addtitle>PLoS One</addtitle><date>2023-06-02</date><risdate>2023</risdate><volume>18</volume><issue>6</issue><spage>e0286588</spage><epage>e0286588</epage><pages>e0286588-e0286588</pages><issn>1932-6203</issn><eissn>1932-6203</eissn><abstract>We sought to 1) identify long COVID phenotypes based on patient reported outcome measures (PROMs) and 2) determine whether the phenotypes were associated with quality of life (QoL) and/or lung function.
This was a longitudinal cohort study of hospitalized and non-hospitalized patients from March 2020 to January 2022 that was conducted across 4 Post-COVID Recovery Clinics in British Columbia, Canada. Latent class analysis was used to identify long COVID phenotypes using baseline PROMs (fatigue, dyspnea, cough, anxiety, depression, and post-traumatic stress disorder). We then explored the association between the phenotypes and QoL (using the EuroQoL 5 dimensions visual analogue scale [EQ5D VAS]) and lung function (using the diffusing capacity of the lung for carbon monoxide [DLCO]).
There were 1,344 patients enrolled in the study (mean age 51 ±15 years; 780 [58%] were females; 769 (57%) were of a non-White race). Three distinct long COVID phenotypes were identified: Class 1) fatigue and dyspnea, Class 2) anxiety and depression, and Class 3) fatigue, dyspnea, anxiety, and depression. Class 3 had a significantly lower EQ5D VAS at 3 (50±19) and 6 months (54 ± 22) compared to Classes 1 and 2 (p<0.001). The EQ5D VAS significantly improved between 3 and 6 months for Class 1 (median difference of 6.0 [95% CI, 4.0 to 8.0]) and Class 3 (median difference of 5.0 [95% CI, 0 to 8.5]). There were no differences in DLCO between the classes.
There were 3 distinct long COVID phenotypes with different outcomes in QoL between 3 and 6 months after symptom onset. These phenotypes suggest that long COVID is a heterogeneous condition with distinct subpopulations who may have different outcomes and warrant tailored therapeutic approaches.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>37267379</pmid><doi>10.1371/journal.pone.0286588</doi><tpages>e0286588</tpages><orcidid>https://orcid.org/0000-0003-4910-1948</orcidid><oa>free_for_read</oa></addata></record> |
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| identifier | ISSN: 1932-6203 |
| ispartof | PloS one, 2023-06, Vol.18 (6), p.e0286588-e0286588 |
| issn | 1932-6203 1932-6203 |
| language | eng |
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| source | DOAJ Directory of Open Access Journals; Public Library of Science (PLoS) Journals Open Access; EZB-FREE-00999 freely available EZB journals; PubMed Central; Free Full-Text Journals in Chemistry |
| subjects | Analysis Anxiety Biology and Life Sciences Carbon monoxide Clinical outcomes Cohort analysis Cough COVID-19 Dyspnea Fatigue Illnesses Latent class analysis Long COVID Lungs Medicine and Health Sciences Mental depression Mental disorders Patients Phenotype Phenotypes Post traumatic stress disorder Posttraumatic stress disorder Psychological stress Quality of life Questionnaires Respiration Respiratory function Severe acute respiratory syndrome coronavirus 2 Social Sciences Statistical analysis Subpopulations |
| title | Use of latent class analysis and patient reported outcome measures to identify distinct long COVID phenotypes: A longitudinal cohort study |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-21T11%3A46%3A21IST&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=Use%20of%20latent%20class%20analysis%20and%20patient%20reported%20outcome%20measures%20to%20identify%20distinct%20long%20COVID%20phenotypes:%20A%20longitudinal%20cohort%20study&rft.jtitle=PloS%20one&rft.au=Wong,%20Alyson%20W&rft.date=2023-06-02&rft.volume=18&rft.issue=6&rft.spage=e0286588&rft.epage=e0286588&rft.pages=e0286588-e0286588&rft.issn=1932-6203&rft.eissn=1932-6203&rft_id=info:doi/10.1371/journal.pone.0286588&rft_dat=%3Cgale_plos_%3EA751520097%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=2821937949&rft_id=info:pmid/37267379&rft_galeid=A751520097&rft_doaj_id=oai_doaj_org_article_9c57c2c4a23c41599b976ab5908ba068&rfr_iscdi=true |