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The Charlson Index Is Insufficient to Control for Comorbidities in a National Trauma Registry
The Charlson Comorbidity Index (CCI) is frequently used to control for confounding by comorbidities in observational studies, but its performance as such has not been studied. We evaluated the performance of CCI and an alternative summary method, logistic principal component analysis (LPCA), to adju...
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| Published in: | The Journal of surgical research 2019-04, Vol.236, p.319-325 |
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| description | The Charlson Comorbidity Index (CCI) is frequently used to control for confounding by comorbidities in observational studies, but its performance as such has not been studied. We evaluated the performance of CCI and an alternative summary method, logistic principal component analysis (LPCA), to adjust for comorbidities, using as an example the association between insurance and mortality.
Using all admissions in the National Trauma Data Bank 2010-2015, we extracted mortality, payment method, and 36 International Classification of Disease, Ninth Revision–derived comorbidities. We estimated odds ratios (ORs) for the association between uninsured status and mortality before and after adjusting for CCI, LPCA, and separate covariates. We also calculated standardized mean differences (SMDs) of comorbidity variables before and after weighting the sample using inverse probability of treatment weights for CCI, LPCA, and separate covariates.
In 4,936,880 admissions, most (68.3%) had at least one comorbidity. Considerable imbalance was observed in the unweighted sample (mean SMD = 0.086, OR = 1.17), which was almost entirely eliminated by inverse probability of treatment weights on separate covariates (mean SMD = 0.012, OR = 1.36). The CCI performed similarly to the unweighted sample (mean SMD = 0.080, OR = 1.25), whereas two LPCA axes were better able to control for confounding (mean SMD = 0.04, OR = 1.31). Using covariate adjustment, the CCI accounted for 56.1% of observed confounding, whereas two LPCA axes accounted for 91.3%.
The use of the CCI to adjust for confounding may result in residual confounding, and alternative strategies should be considered. LPCA may be a viable alternative to adjusting for each comorbidity when samples are small or positivity assumptions are violated. |
| doi_str_mv | 10.1016/j.jss.2018.07.072 |
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Using all admissions in the National Trauma Data Bank 2010-2015, we extracted mortality, payment method, and 36 International Classification of Disease, Ninth Revision–derived comorbidities. We estimated odds ratios (ORs) for the association between uninsured status and mortality before and after adjusting for CCI, LPCA, and separate covariates. We also calculated standardized mean differences (SMDs) of comorbidity variables before and after weighting the sample using inverse probability of treatment weights for CCI, LPCA, and separate covariates.
In 4,936,880 admissions, most (68.3%) had at least one comorbidity. Considerable imbalance was observed in the unweighted sample (mean SMD = 0.086, OR = 1.17), which was almost entirely eliminated by inverse probability of treatment weights on separate covariates (mean SMD = 0.012, OR = 1.36). The CCI performed similarly to the unweighted sample (mean SMD = 0.080, OR = 1.25), whereas two LPCA axes were better able to control for confounding (mean SMD = 0.04, OR = 1.31). Using covariate adjustment, the CCI accounted for 56.1% of observed confounding, whereas two LPCA axes accounted for 91.3%.
The use of the CCI to adjust for confounding may result in residual confounding, and alternative strategies should be considered. LPCA may be a viable alternative to adjusting for each comorbidity when samples are small or positivity assumptions are violated.</description><identifier>ISSN: 0022-4804</identifier><identifier>EISSN: 1095-8673</identifier><identifier>DOI: 10.1016/j.jss.2018.07.072</identifier><identifier>PMID: 30694772</identifier><language>eng</language><publisher>United States: Elsevier Inc</publisher><subject>Adult ; Aged ; Bias ; Biostatistics ; Causal inference ; Comorbidity ; Confounding Factors, Epidemiologic ; Data Interpretation, Statistical ; Female ; Hospitalization - statistics & numerical data ; Humans ; Male ; Middle Aged ; Observational studies ; Observational Studies as Topic ; Odds Ratio ; Principal Component Analysis ; Registries - statistics & numerical data ; Retrospective Studies ; Risk Factors ; Trauma ; Trauma Centers - statistics & numerical data ; Trauma Severity Indices ; Wounds and Injuries - diagnosis ; Wounds and Injuries - epidemiology ; Wounds and Injuries - therapy</subject><ispartof>The Journal of surgical research, 2019-04, Vol.236, p.319-325</ispartof><rights>2018 Elsevier Inc.</rights><rights>Copyright © 2018 Elsevier Inc. All rights reserved.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c396t-fae7230f26c5c1ec115c501a8fbfc940943a5ae8fa6f005525014836491eb7b33</citedby><cites>FETCH-LOGICAL-c396t-fae7230f26c5c1ec115c501a8fbfc940943a5ae8fa6f005525014836491eb7b33</cites><orcidid>0000-0003-1603-2587</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>315,786,790,27957,27958</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/30694772$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Renson, Audrey</creatorcontrib><creatorcontrib>Bjurlin, Marc A.</creatorcontrib><title>The Charlson Index Is Insufficient to Control for Comorbidities in a National Trauma Registry</title><title>The Journal of surgical research</title><addtitle>J Surg Res</addtitle><description>The Charlson Comorbidity Index (CCI) is frequently used to control for confounding by comorbidities in observational studies, but its performance as such has not been studied. We evaluated the performance of CCI and an alternative summary method, logistic principal component analysis (LPCA), to adjust for comorbidities, using as an example the association between insurance and mortality.
Using all admissions in the National Trauma Data Bank 2010-2015, we extracted mortality, payment method, and 36 International Classification of Disease, Ninth Revision–derived comorbidities. We estimated odds ratios (ORs) for the association between uninsured status and mortality before and after adjusting for CCI, LPCA, and separate covariates. We also calculated standardized mean differences (SMDs) of comorbidity variables before and after weighting the sample using inverse probability of treatment weights for CCI, LPCA, and separate covariates.
In 4,936,880 admissions, most (68.3%) had at least one comorbidity. Considerable imbalance was observed in the unweighted sample (mean SMD = 0.086, OR = 1.17), which was almost entirely eliminated by inverse probability of treatment weights on separate covariates (mean SMD = 0.012, OR = 1.36). The CCI performed similarly to the unweighted sample (mean SMD = 0.080, OR = 1.25), whereas two LPCA axes were better able to control for confounding (mean SMD = 0.04, OR = 1.31). Using covariate adjustment, the CCI accounted for 56.1% of observed confounding, whereas two LPCA axes accounted for 91.3%.
The use of the CCI to adjust for confounding may result in residual confounding, and alternative strategies should be considered. LPCA may be a viable alternative to adjusting for each comorbidity when samples are small or positivity assumptions are violated.</description><subject>Adult</subject><subject>Aged</subject><subject>Bias</subject><subject>Biostatistics</subject><subject>Causal inference</subject><subject>Comorbidity</subject><subject>Confounding Factors, Epidemiologic</subject><subject>Data Interpretation, Statistical</subject><subject>Female</subject><subject>Hospitalization - statistics & numerical data</subject><subject>Humans</subject><subject>Male</subject><subject>Middle Aged</subject><subject>Observational studies</subject><subject>Observational Studies as Topic</subject><subject>Odds Ratio</subject><subject>Principal Component Analysis</subject><subject>Registries - statistics & numerical data</subject><subject>Retrospective Studies</subject><subject>Risk Factors</subject><subject>Trauma</subject><subject>Trauma Centers - statistics & numerical data</subject><subject>Trauma Severity Indices</subject><subject>Wounds and Injuries - diagnosis</subject><subject>Wounds and Injuries - epidemiology</subject><subject>Wounds and Injuries - therapy</subject><issn>0022-4804</issn><issn>1095-8673</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><recordid>eNp9kE9r3DAQxUVJSbZpPkAvRcdcvB39sWWTU1madCEkUDbHImR51GixrUSSS_Pto2XTHgsPZoZ582B-hHxisGbAmi_79T6lNQfWrkEV8XdkxaCrq7ZR4oSsADivZAvyjHxIaQ9l7pQ4JWcCmk4qxVfk5-4R6ebRxDGFmW7nAf_QbSpNWpzz1uOcaQ50E-Ycw0hdiKWfQuz94LPHRP1MDb0z2YfZjHQXzTIZ-gN_-ZTjy0fy3pkx4cVbPScP1992m-_V7f3NdvP1trKia3LlDCouwPHG1pahZay2NTDTut7ZTkInhakNts40DqCueVnKVjSyY9irXohzcnnMfYrhecGU9eSTxXE0M4Ylac5UJ1vJBC9WdrTaGFKK6PRT9JOJL5qBPlDVe12o6gNVDarocPP5LX7pJxz-XfzFWAxXRwOWJ397jDod0FkcfESb9RD8f-JfAcqCh6s</recordid><startdate>201904</startdate><enddate>201904</enddate><creator>Renson, Audrey</creator><creator>Bjurlin, Marc A.</creator><general>Elsevier Inc</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0003-1603-2587</orcidid></search><sort><creationdate>201904</creationdate><title>The Charlson Index Is Insufficient to Control for Comorbidities in a National Trauma Registry</title><author>Renson, Audrey ; Bjurlin, Marc A.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c396t-fae7230f26c5c1ec115c501a8fbfc940943a5ae8fa6f005525014836491eb7b33</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Adult</topic><topic>Aged</topic><topic>Bias</topic><topic>Biostatistics</topic><topic>Causal inference</topic><topic>Comorbidity</topic><topic>Confounding Factors, Epidemiologic</topic><topic>Data Interpretation, Statistical</topic><topic>Female</topic><topic>Hospitalization - statistics & numerical data</topic><topic>Humans</topic><topic>Male</topic><topic>Middle Aged</topic><topic>Observational studies</topic><topic>Observational Studies as Topic</topic><topic>Odds Ratio</topic><topic>Principal Component Analysis</topic><topic>Registries - statistics & numerical data</topic><topic>Retrospective Studies</topic><topic>Risk Factors</topic><topic>Trauma</topic><topic>Trauma Centers - statistics & numerical data</topic><topic>Trauma Severity Indices</topic><topic>Wounds and Injuries - diagnosis</topic><topic>Wounds and Injuries - epidemiology</topic><topic>Wounds and Injuries - therapy</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Renson, Audrey</creatorcontrib><creatorcontrib>Bjurlin, Marc A.</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><jtitle>The Journal of surgical research</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Renson, Audrey</au><au>Bjurlin, Marc A.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>The Charlson Index Is Insufficient to Control for Comorbidities in a National Trauma Registry</atitle><jtitle>The Journal of surgical research</jtitle><addtitle>J Surg Res</addtitle><date>2019-04</date><risdate>2019</risdate><volume>236</volume><spage>319</spage><epage>325</epage><pages>319-325</pages><issn>0022-4804</issn><eissn>1095-8673</eissn><notes>ObjectType-Article-2</notes><notes>SourceType-Scholarly Journals-1</notes><notes>ObjectType-Undefined-1</notes><notes>ObjectType-Feature-3</notes><notes>content type line 23</notes><abstract>The Charlson Comorbidity Index (CCI) is frequently used to control for confounding by comorbidities in observational studies, but its performance as such has not been studied. We evaluated the performance of CCI and an alternative summary method, logistic principal component analysis (LPCA), to adjust for comorbidities, using as an example the association between insurance and mortality.
Using all admissions in the National Trauma Data Bank 2010-2015, we extracted mortality, payment method, and 36 International Classification of Disease, Ninth Revision–derived comorbidities. We estimated odds ratios (ORs) for the association between uninsured status and mortality before and after adjusting for CCI, LPCA, and separate covariates. We also calculated standardized mean differences (SMDs) of comorbidity variables before and after weighting the sample using inverse probability of treatment weights for CCI, LPCA, and separate covariates.
In 4,936,880 admissions, most (68.3%) had at least one comorbidity. Considerable imbalance was observed in the unweighted sample (mean SMD = 0.086, OR = 1.17), which was almost entirely eliminated by inverse probability of treatment weights on separate covariates (mean SMD = 0.012, OR = 1.36). The CCI performed similarly to the unweighted sample (mean SMD = 0.080, OR = 1.25), whereas two LPCA axes were better able to control for confounding (mean SMD = 0.04, OR = 1.31). Using covariate adjustment, the CCI accounted for 56.1% of observed confounding, whereas two LPCA axes accounted for 91.3%.
The use of the CCI to adjust for confounding may result in residual confounding, and alternative strategies should be considered. LPCA may be a viable alternative to adjusting for each comorbidity when samples are small or positivity assumptions are violated.</abstract><cop>United States</cop><pub>Elsevier Inc</pub><pmid>30694772</pmid><doi>10.1016/j.jss.2018.07.072</doi><tpages>7</tpages><orcidid>https://orcid.org/0000-0003-1603-2587</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | Adult Aged Bias Biostatistics Causal inference Comorbidity Confounding Factors, Epidemiologic Data Interpretation, Statistical Female Hospitalization - statistics & numerical data Humans Male Middle Aged Observational studies Observational Studies as Topic Odds Ratio Principal Component Analysis Registries - statistics & numerical data Retrospective Studies Risk Factors Trauma Trauma Centers - statistics & numerical data Trauma Severity Indices Wounds and Injuries - diagnosis Wounds and Injuries - epidemiology Wounds and Injuries - therapy |
| title | The Charlson Index Is Insufficient to Control for Comorbidities in a National Trauma Registry |
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