Comparative evaluation of the clinical laboratory-based Intermountain risk score with the Charlson and Elixhauser comorbidity indices for mortality prediction
The Charlson and Elixhauser comorbidity indices are mortality predictors often used in clinical, administrative, and research applications. The Intermountain Mortality Risk Scores (IMRS) are validated mortality predictors that use all factors from the complete blood count and basic metabolic profile...
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| creator | Snow, Gregory L Bledsoe, Joseph R Butler, Allison Wilson, Emily L Rea, Susan Majercik, Sarah Anderson, Jeffrey L Horne, Benjamin D |
| description | The Charlson and Elixhauser comorbidity indices are mortality predictors often used in clinical, administrative, and research applications. The Intermountain Mortality Risk Scores (IMRS) are validated mortality predictors that use all factors from the complete blood count and basic metabolic profile. How IMRS, Charlson, and Elixhauser relate to each other is unknown.
All inpatient admissions except obstetric patients at Intermountain Healthcare's 21 adult care hospitals from 2010-2014 (N = 197,680) were examined in a observational cohort study. The most recent admission was a patient's index encounter. Follow-up to 2018 used hospital death records, Utah death certificates, and the Social Security death master file. Three Charlson versions, 8 Elixhauser versions, and 3 IMRS formulations were evaluated in Cox regression and the one of each that was most predictive was used in dual risk score mortality analyses (in-hospital, 30-day, 1-year, and 5-year mortality).
Indices with the strongest mortality associations and selected for dual score study were the age-adjusted Charlson, the van Walraven version of the acute Elixhauser, and the 1-year IMRS. For in-hospital mortality, Charlson (c = 0.719; HR = 4.75, 95% CI = 4.45, 5.07), Elixhauser (c = 0.783; HR = 5.79, CI = 5.41, 6.19), and IMRS (c = 0.821; HR = 17.95, CI = 15.90, 20.26) were significant predictors (p |
| doi_str_mv | 10.1371/journal.pone.0233495 |
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All inpatient admissions except obstetric patients at Intermountain Healthcare's 21 adult care hospitals from 2010-2014 (N = 197,680) were examined in a observational cohort study. The most recent admission was a patient's index encounter. Follow-up to 2018 used hospital death records, Utah death certificates, and the Social Security death master file. Three Charlson versions, 8 Elixhauser versions, and 3 IMRS formulations were evaluated in Cox regression and the one of each that was most predictive was used in dual risk score mortality analyses (in-hospital, 30-day, 1-year, and 5-year mortality).
Indices with the strongest mortality associations and selected for dual score study were the age-adjusted Charlson, the van Walraven version of the acute Elixhauser, and the 1-year IMRS. For in-hospital mortality, Charlson (c = 0.719; HR = 4.75, 95% CI = 4.45, 5.07), Elixhauser (c = 0.783; HR = 5.79, CI = 5.41, 6.19), and IMRS (c = 0.821; HR = 17.95, CI = 15.90, 20.26) were significant predictors (p<0.001) in univariate analyses. Dual score analysis of Charlson (HR = 1.79, CI = 1.66, 1.92) with IMRS (HR = 13.10, CI = 11.53, 14.87) and of Elixhauser (HR = 3.00, CI = 2.80, 3.21) with IMRS (HR = 11.42, CI = 10.09, 12.92) found significance for both scores in each model. Results were similar for 30-day, 1-year, and 5-year mortality.
IMRS provided the strongest ability to predict mortality, adding to and attenuating the predictive ability of the Charlson and Elixhauser indices whose mortality associations remained statistically significant. IMRS uses common, standardized, objective laboratory data and should be further evaluated for integration into mortality risk evaluations.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0233495</identifier><identifier>PMID: 32437416</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Age ; Biology and Life Sciences ; Biomedical laboratories ; Blood tests ; Codes ; Comorbidity ; Complete blood count ; Death ; Diagnosis ; Emergency medical care ; Hospital patients ; Hospitals ; Laboratories ; Medical laboratories ; Medical records ; Medicine ; Medicine and Health Sciences ; Metabolism ; Mortality ; Obstetrics ; Patient outcomes ; Patients ; People and places ; Physical Sciences ; Regression analysis ; Research and Analysis Methods ; Risk analysis ; Risk assessment ; Risk factors ; Salt ; Social security ; Statistical analysis ; Survival analysis</subject><ispartof>PloS one, 2020-05, Vol.15 (5), p.e0233495-e0233495</ispartof><rights>COPYRIGHT 2020 Public Library of Science</rights><rights>2020 Snow 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>2020 Snow et al 2020 Snow et al</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c585t-5051188f521530b4a081bcc0c75d509a0c095902b93c763a1a0b5e4eaecccb663</citedby><cites>FETCH-LOGICAL-c585t-5051188f521530b4a081bcc0c75d509a0c095902b93c763a1a0b5e4eaecccb663</cites><orcidid>0000-0002-2656-0263 ; 0000-0001-8530-1037 ; 0000-0003-1388-1612</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/PMC7241706/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC7241706/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,723,776,780,860,881,2096,2915,23845,27901,27902,53766,53768,79569,79570</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/32437416$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Snow, Gregory L</creatorcontrib><creatorcontrib>Bledsoe, Joseph R</creatorcontrib><creatorcontrib>Butler, Allison</creatorcontrib><creatorcontrib>Wilson, Emily L</creatorcontrib><creatorcontrib>Rea, Susan</creatorcontrib><creatorcontrib>Majercik, Sarah</creatorcontrib><creatorcontrib>Anderson, Jeffrey L</creatorcontrib><creatorcontrib>Horne, Benjamin D</creatorcontrib><title>Comparative evaluation of the clinical laboratory-based Intermountain risk score with the Charlson and Elixhauser comorbidity indices for mortality prediction</title><title>PloS one</title><addtitle>PLoS One</addtitle><description>The Charlson and Elixhauser comorbidity indices are mortality predictors often used in clinical, administrative, and research applications. The Intermountain Mortality Risk Scores (IMRS) are validated mortality predictors that use all factors from the complete blood count and basic metabolic profile. How IMRS, Charlson, and Elixhauser relate to each other is unknown.
All inpatient admissions except obstetric patients at Intermountain Healthcare's 21 adult care hospitals from 2010-2014 (N = 197,680) were examined in a observational cohort study. The most recent admission was a patient's index encounter. Follow-up to 2018 used hospital death records, Utah death certificates, and the Social Security death master file. Three Charlson versions, 8 Elixhauser versions, and 3 IMRS formulations were evaluated in Cox regression and the one of each that was most predictive was used in dual risk score mortality analyses (in-hospital, 30-day, 1-year, and 5-year mortality).
Indices with the strongest mortality associations and selected for dual score study were the age-adjusted Charlson, the van Walraven version of the acute Elixhauser, and the 1-year IMRS. For in-hospital mortality, Charlson (c = 0.719; HR = 4.75, 95% CI = 4.45, 5.07), Elixhauser (c = 0.783; HR = 5.79, CI = 5.41, 6.19), and IMRS (c = 0.821; HR = 17.95, CI = 15.90, 20.26) were significant predictors (p<0.001) in univariate analyses. Dual score analysis of Charlson (HR = 1.79, CI = 1.66, 1.92) with IMRS (HR = 13.10, CI = 11.53, 14.87) and of Elixhauser (HR = 3.00, CI = 2.80, 3.21) with IMRS (HR = 11.42, CI = 10.09, 12.92) found significance for both scores in each model. Results were similar for 30-day, 1-year, and 5-year mortality.
IMRS provided the strongest ability to predict mortality, adding to and attenuating the predictive ability of the Charlson and Elixhauser indices whose mortality associations remained statistically significant. IMRS uses common, standardized, objective laboratory data and should be further evaluated for integration into mortality risk evaluations.</description><subject>Age</subject><subject>Biology and Life Sciences</subject><subject>Biomedical laboratories</subject><subject>Blood tests</subject><subject>Codes</subject><subject>Comorbidity</subject><subject>Complete blood count</subject><subject>Death</subject><subject>Diagnosis</subject><subject>Emergency medical care</subject><subject>Hospital patients</subject><subject>Hospitals</subject><subject>Laboratories</subject><subject>Medical laboratories</subject><subject>Medical records</subject><subject>Medicine</subject><subject>Medicine and Health Sciences</subject><subject>Metabolism</subject><subject>Mortality</subject><subject>Obstetrics</subject><subject>Patient outcomes</subject><subject>Patients</subject><subject>People and places</subject><subject>Physical Sciences</subject><subject>Regression analysis</subject><subject>Research and Analysis Methods</subject><subject>Risk analysis</subject><subject>Risk assessment</subject><subject>Risk factors</subject><subject>Salt</subject><subject>Social security</subject><subject>Statistical analysis</subject><subject>Survival 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evaluation of the clinical laboratory-based Intermountain risk score with the Charlson and Elixhauser comorbidity indices for mortality prediction</title><author>Snow, Gregory L ; Bledsoe, Joseph R ; Butler, Allison ; Wilson, Emily L ; Rea, Susan ; Majercik, Sarah ; Anderson, Jeffrey L ; Horne, Benjamin D</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c585t-5051188f521530b4a081bcc0c75d509a0c095902b93c763a1a0b5e4eaecccb663</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Age</topic><topic>Biology and Life Sciences</topic><topic>Biomedical laboratories</topic><topic>Blood tests</topic><topic>Codes</topic><topic>Comorbidity</topic><topic>Complete blood count</topic><topic>Death</topic><topic>Diagnosis</topic><topic>Emergency medical care</topic><topic>Hospital patients</topic><topic>Hospitals</topic><topic>Laboratories</topic><topic>Medical laboratories</topic><topic>Medical records</topic><topic>Medicine</topic><topic>Medicine and Health Sciences</topic><topic>Metabolism</topic><topic>Mortality</topic><topic>Obstetrics</topic><topic>Patient outcomes</topic><topic>Patients</topic><topic>People and places</topic><topic>Physical Sciences</topic><topic>Regression analysis</topic><topic>Research and Analysis Methods</topic><topic>Risk analysis</topic><topic>Risk assessment</topic><topic>Risk factors</topic><topic>Salt</topic><topic>Social security</topic><topic>Statistical analysis</topic><topic>Survival analysis</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Snow, Gregory L</creatorcontrib><creatorcontrib>Bledsoe, Joseph R</creatorcontrib><creatorcontrib>Butler, Allison</creatorcontrib><creatorcontrib>Wilson, Emily L</creatorcontrib><creatorcontrib>Rea, Susan</creatorcontrib><creatorcontrib>Majercik, Sarah</creatorcontrib><creatorcontrib>Anderson, Jeffrey 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L</au><au>Rea, Susan</au><au>Majercik, Sarah</au><au>Anderson, Jeffrey L</au><au>Horne, Benjamin D</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Comparative evaluation of the clinical laboratory-based Intermountain risk score with the Charlson and Elixhauser comorbidity indices for mortality prediction</atitle><jtitle>PloS one</jtitle><addtitle>PLoS One</addtitle><date>2020-05-21</date><risdate>2020</risdate><volume>15</volume><issue>5</issue><spage>e0233495</spage><epage>e0233495</epage><pages>e0233495-e0233495</pages><issn>1932-6203</issn><eissn>1932-6203</eissn><abstract>The Charlson and Elixhauser comorbidity indices are mortality predictors often used in clinical, administrative, and research applications. The Intermountain Mortality Risk Scores (IMRS) are validated mortality predictors that use all factors from the complete blood count and basic metabolic profile. How IMRS, Charlson, and Elixhauser relate to each other is unknown.
All inpatient admissions except obstetric patients at Intermountain Healthcare's 21 adult care hospitals from 2010-2014 (N = 197,680) were examined in a observational cohort study. The most recent admission was a patient's index encounter. Follow-up to 2018 used hospital death records, Utah death certificates, and the Social Security death master file. Three Charlson versions, 8 Elixhauser versions, and 3 IMRS formulations were evaluated in Cox regression and the one of each that was most predictive was used in dual risk score mortality analyses (in-hospital, 30-day, 1-year, and 5-year mortality).
Indices with the strongest mortality associations and selected for dual score study were the age-adjusted Charlson, the van Walraven version of the acute Elixhauser, and the 1-year IMRS. For in-hospital mortality, Charlson (c = 0.719; HR = 4.75, 95% CI = 4.45, 5.07), Elixhauser (c = 0.783; HR = 5.79, CI = 5.41, 6.19), and IMRS (c = 0.821; HR = 17.95, CI = 15.90, 20.26) were significant predictors (p<0.001) in univariate analyses. Dual score analysis of Charlson (HR = 1.79, CI = 1.66, 1.92) with IMRS (HR = 13.10, CI = 11.53, 14.87) and of Elixhauser (HR = 3.00, CI = 2.80, 3.21) with IMRS (HR = 11.42, CI = 10.09, 12.92) found significance for both scores in each model. Results were similar for 30-day, 1-year, and 5-year mortality.
IMRS provided the strongest ability to predict mortality, adding to and attenuating the predictive ability of the Charlson and Elixhauser indices whose mortality associations remained statistically significant. IMRS uses common, standardized, objective laboratory data and should be further evaluated for integration into mortality risk evaluations.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>32437416</pmid><doi>10.1371/journal.pone.0233495</doi><orcidid>https://orcid.org/0000-0002-2656-0263</orcidid><orcidid>https://orcid.org/0000-0001-8530-1037</orcidid><orcidid>https://orcid.org/0000-0003-1388-1612</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | Age Biology and Life Sciences Biomedical laboratories Blood tests Codes Comorbidity Complete blood count Death Diagnosis Emergency medical care Hospital patients Hospitals Laboratories Medical laboratories Medical records Medicine Medicine and Health Sciences Metabolism Mortality Obstetrics Patient outcomes Patients People and places Physical Sciences Regression analysis Research and Analysis Methods Risk analysis Risk assessment Risk factors Salt Social security Statistical analysis Survival analysis |
| title | Comparative evaluation of the clinical laboratory-based Intermountain risk score with the Charlson and Elixhauser comorbidity indices for mortality prediction |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-21T19%3A48%3A43IST&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=Comparative%20evaluation%20of%20the%20clinical%20laboratory-based%20Intermountain%20risk%20score%20with%20the%20Charlson%20and%20Elixhauser%20comorbidity%20indices%20for%20mortality%20prediction&rft.jtitle=PloS%20one&rft.au=Snow,%20Gregory%20L&rft.date=2020-05-21&rft.volume=15&rft.issue=5&rft.spage=e0233495&rft.epage=e0233495&rft.pages=e0233495-e0233495&rft.issn=1932-6203&rft.eissn=1932-6203&rft_id=info:doi/10.1371/journal.pone.0233495&rft_dat=%3Cgale_plos_%3EA624573587%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=2405599692&rft_id=info:pmid/32437416&rft_galeid=A624573587&rft_doaj_id=oai_doaj_org_article_a6225b2e3ce34b31b75934748c1fa22c&rfr_iscdi=true |