{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T22:59:08Z","timestamp":1781650748906,"version":"3.54.5"},"reference-count":10,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2022,10,7]],"date-time":"2022-10-07T00:00:00Z","timestamp":1665100800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2022,10,7]],"date-time":"2022-10-07T00:00:00Z","timestamp":1665100800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/100005205","name":"Janssen Research and Development","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100005205","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Med Inform Decis Mak"],"abstract":"<jats:title>Abstract<\/jats:title><jats:sec>\n                <jats:title>Objectives<\/jats:title>\n                <jats:p>The Charlson comorbidity index (CCI), the most ubiquitous comorbid risk score, predicts one-year mortality among hospitalized patients and provides a single aggregate measure of patient comorbidity. The Quan adaptation of the CCI revised the CCI coding algorithm for applications to administrative claims data using the International Classification of Diseases (ICD). The purpose of the current study is to adapt and validate a coding algorithm for the CCI using the SNOMED CT standardized vocabulary, one of the most commonly used vocabularies for data collection in healthcare databases in the U.S.\n<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Methods<\/jats:title>\n                <jats:p>The SNOMED CT coding algorithm for the CCI was adapted through the direct translation of the Quan coding algorithms followed by manual curation by clinical experts. The performance of the SNOMED CT and Quan coding algorithms were compared in the context of a retrospective cohort study of inpatient visits occurring during the calendar years of 2013 and 2018 contained in two U.S. administrative claims databases. Differences in the CCI or frequency of individual comorbid conditions were assessed using standardized mean differences (SMD). Performance in predicting one-year mortality among hospitalized patients was measured based on the c-statistic of logistic regression models.\n<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>For each database and calendar year combination, no significant differences in the CCI or frequency of individual comorbid conditions were observed between vocabularies (SMD\u2009\u2264\u20090.10). Specifically, the difference in CCI measured using the SNOMED CT vs. Quan coding algorithms was highest in MDCD in 2013 (3.75 vs. 3.6; SMD\u2009=\u20090.03) and lowest in DOD in 2018 (3.93 vs. 3.86; SMD\u2009=\u20090.02). Similarly, as indicated by the c-statistic, there was no evidence of a difference in the performance between coding algorithms in predicting one-year mortality (SNOMED CT vs. Quan coding algorithms, range: 0.725\u20130.789 vs. 0.723\u20130.787, respectively). A total of 700 of 5,348 (13.1%) ICD code mappings were inconsistent between coding algorithms. The most common cause of discrepant codes was multiple ICD codes mapping to a SNOMED CT code (<jats:italic>n<\/jats:italic>\u2009=\u2009560) of which 213 were deemed clinically relevant thereby leading to information gain.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusion<\/jats:title>\n                <jats:p>The current study repurposed an important tool for conducting observational research to use the SNOMED CT standardized vocabulary.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1186\/s12911-022-02006-1","type":"journal-article","created":{"date-parts":[[2022,10,7]],"date-time":"2022-10-07T08:04:15Z","timestamp":1665129855000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Adaptation and validation of a coding algorithm for the Charlson Comorbidity Index in administrative claims data using the SNOMED CT standardized vocabulary"],"prefix":"10.1186","volume":"22","author":[{"given":"Stephen P.","family":"Fortin","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jenna","family":"Reps","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Patrick","family":"Ryan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,10,7]]},"reference":[{"key":"2006_CR1","doi-asserted-by":"publisher","first-page":"373","DOI":"10.1016\/0021-9681(87)90171-8","volume":"40","author":"ME Charlson","year":"1987","unstructured":"Charlson ME, Pompei P, Ales KL, MacKenzie CR. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis. 1987;40:373\u201383.","journal-title":"J Chronic Dis"},{"issue":"6","key":"2006_CR2","doi-asserted-by":"publisher","first-page":"613","DOI":"10.1016\/0895-4356(92)90133-8","volume":"45","author":"RA Deyo","year":"1992","unstructured":"Deyo RA, Cherkin DC, Ciol MA. Adapting a clinical comorbidity index for use with ICD-9-CM administrative databases. J Clin Epidemiol. 1992;45(6):613\u20139. https:\/\/doi.org\/10.1016\/0895-4356(92)90133-8 (PMID: 1607900).","journal-title":"J Clin Epidemiol"},{"issue":"10","key":"2006_CR3","doi-asserted-by":"publisher","first-page":"1075","DOI":"10.1016\/0895-4356(93)90103-8","volume":"46","author":"PS Romano","year":"1993","unstructured":"Romano PS, Roos LL, Jollis JG. Adapting a clinical comorbidity index for use with ICD-9-CM administrative data: differing perspectives. J Clin Epidemiol. 1993;46(10):1075\u20139. https:\/\/doi.org\/10.1016\/0895-4356(93)90103-8 (PMID: 8410092).","journal-title":"J Clin Epidemiol"},{"issue":"12","key":"2006_CR4","doi-asserted-by":"publisher","first-page":"1429","DOI":"10.1016\/s0895-4356(96)00271-5","volume":"49","author":"W D'Hoore","year":"1996","unstructured":"D\u2019Hoore W, Bouckaert A, Tilquin C. Practical considerations on the use of the Charlson comorbidity index with administrative data bases. J Clin Epidemiol. 1996;49(12):1429\u201333. https:\/\/doi.org\/10.1016\/s0895-4356(96)00271-5 (PMID: 8991959).","journal-title":"J Clin Epidemiol"},{"issue":"11","key":"2006_CR5","doi-asserted-by":"publisher","first-page":"1130","DOI":"10.1097\/01.mlr.0000182534.19832.83","volume":"43","author":"H Quan","year":"2005","unstructured":"Quan H, Sundararajan V, Halfon P, Fong A, Burnand B, Luthi JC, Saunders LD, Beck CA, Feasby TE, Ghali WA. Coding algorithms for defining comorbidities in ICD-9-CM and ICD-10 administrative data. Med Care. 2005;43(11):1130\u20139. https:\/\/doi.org\/10.1097\/01.mlr.0000182534.19832.83 (PMID: 16224307).","journal-title":"Med Care"},{"issue":"1","key":"2006_CR6","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1186\/s12874-019-0753-5","volume":"19","author":"D Metcalfe","year":"2019","unstructured":"Metcalfe D, Masters J, Delmestri A, et al. Coding algorithms for defining Charlson and Elixhauser co-morbidities in Read-coded databases. BMC Med Res Methodol. 2019;19(1):115. https:\/\/doi.org\/10.1186\/s12874-019-0753-5.","journal-title":"BMC Med Res Methodol"},{"key":"2006_CR7","unstructured":"Fortin SP. Predictive performance of the Charlson Comorbidity Index: SNOMED CT disease hierarchy versus international classification of diseases. OHDSI; 2021."},{"key":"2006_CR8","unstructured":"Viernes B, Lynch KE, Robison B, Gatsby E, DuVall SL, Matheny ME. SNOMED CT disease hierarchies and the Charlson comorbidity index (CCI): an analysis of OHDSI methods for determining CCI. OHDSI; 2020."},{"issue":"10211","key":"2006_CR9","doi-asserted-by":"publisher","first-page":"1816","DOI":"10.1016\/S0140-6736(19)32317-7.PMID:31668726;PMCID:PMC6924620","volume":"394","author":"MA Suchard","year":"2019","unstructured":"Suchard MA, Schuemie MJ, Krumholz HM, You SC, Chen R, Pratt N, Reich CG, Duke J, Madigan D, Hripcsak G, Ryan PB. Comprehensive comparative effectiveness and safety of first-line antihypertensive drug classes: a systematic, multinational, large-scale analysis. Lancet. 2019;394(10211):1816\u201326. https:\/\/doi.org\/10.1016\/S0140-6736(19)32317-7.PMID:31668726;PMCID:PMC6924620.","journal-title":"Lancet"},{"key":"2006_CR10","unstructured":"OHDSI. The book of OHDSI: observational health data sciences and informatics. OHDSI; 2019."}],"updated-by":[{"DOI":"10.1186\/s12911-023-02205-4","type":"correction","label":"Correction","source":"publisher","updated":{"date-parts":[[2023,6,15]],"date-time":"2023-06-15T00:00:00Z","timestamp":1686787200000}}],"container-title":["BMC Medical Informatics and Decision Making"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12911-022-02006-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12911-022-02006-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12911-022-02006-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,6,15]],"date-time":"2023-06-15T14:03:00Z","timestamp":1686837780000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcmedinformdecismak.biomedcentral.com\/articles\/10.1186\/s12911-022-02006-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,7]]},"references-count":10,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2022,12]]}},"alternative-id":["2006"],"URL":"https:\/\/doi.org\/10.1186\/s12911-022-02006-1","relation":{"correction":[{"id-type":"doi","id":"10.1186\/s12911-023-02205-4","asserted-by":"object"}]},"ISSN":["1472-6947"],"issn-type":[{"value":"1472-6947","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,10,7]]},"assertion":[{"value":"25 August 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 September 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 October 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 June 2023","order":4,"name":"change_date","label":"Change Date","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"Correction","order":5,"name":"change_type","label":"Change Type","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"A Correction to this paper has been published:","order":6,"name":"change_details","label":"Change Details","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"https:\/\/doi.org\/10.1186\/s12911-023-02205-4","URL":"https:\/\/doi.org\/10.1186\/s12911-023-02205-4","order":7,"name":"change_details","label":"Change Details","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Pursuant to Title 45 Code of Federal Regulations, Part 46 of the United States, specifically 45 CFR 46.104 (d)(4), retrospective analyses conducted in the DOD and MDCD are considered exempt from informed consent and institutional review board (IRB) approval in the United States. All methods were carried out in accordance with relevant guidelines and regulations.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and informed consent"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"All authors are employees of Janssen Research and Development, LLC, a subsidiary of Johnson and Johnson. Stephen Fortin, Jenna Reps, and Patrick Ryan own stock in Johnson & Johnson.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}},{"value":"Review history available in Additional file .","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Review History"}}],"article-number":"261"}}