BACKGROUND AND OBJECTIVE: The ICD-9-CM adaptation of the Charlson comorbidity score has been a valuable resource for health services researchers. With the transition into ICD-10 coding worldwide, an ICD-10 version of the Deyo adaptation was developed and validated using population-based hospital data from Victoria, Australia. METHODS: The algorithm was translated from ICD-9-CM into ICD-10-AM (Australian modification) in a multistep process. After a mapping algorithm was used to develop an initial translation, these codes were manually examined by the coding experts and a general physician for face validity. Because the ICD-10 system is country specific, our goal was to keep many of the translated code at the three-digit level for generalizability of the new index. RESULTS: There appears to be little difference in the distribution of the Charlson Index score between the two versions. A strong association between increasing index scores and mortality exists: the area under the ROC curve is 0.865 for the last year using the ICD-9-CM version and remains high, at 0.855, for the ICD-10 version. CONCLUSION: This work represents the first rigorous adaptation of the Charlson comorbidity index for use with ICD-10 data. In comparison with a well-established ICD-9-CM coding algorithm, it yields closely similar prevalence and prognosis information by comorbidity category.
BACKGROUND AND OBJECTIVE: The ICD-9-CM adaptation of the Charlson comorbidity score has been a valuable resource for health services researchers. With the transition into ICD-10 coding worldwide, an ICD-10 version of the Deyo adaptation was developed and validated using population-based hospital data from Victoria, Australia. METHODS: The algorithm was translated from ICD-9-CM into ICD-10-AM (Australian modification) in a multistep process. After a mapping algorithm was used to develop an initial translation, these codes were manually examined by the coding experts and a general physician for face validity. Because the ICD-10 system is country specific, our goal was to keep many of the translated code at the three-digit level for generalizability of the new index. RESULTS: There appears to be little difference in the distribution of the Charlson Index score between the two versions. A strong association between increasing index scores and mortality exists: the area under the ROC curve is 0.865 for the last year using the ICD-9-CM version and remains high, at 0.855, for the ICD-10 version. CONCLUSION: This work represents the first rigorous adaptation of the Charlson comorbidity index for use with ICD-10 data. In comparison with a well-established ICD-9-CM coding algorithm, it yields closely similar prevalence and prognosis information by comorbidity category.
Authors: Dragos Serban; Simona Andreea Balasescu; Catalin Alius; Cristian Balalau; Alexandru Dan Sabau; Cristinel Dumitru Badiu; Bogdan Socea; Andra Maria Trotea; Ana Maria Dascalu; Ion Motofei; Valeriu Ardeleanu; Radu Iulian Spataru; Dan Sabau; Gabriel Catalin Smarandache Journal: Exp Ther Med Date: 2021-05-13 Impact factor: 2.447
Authors: S Iizaka; L Jiao; J Sugama; T Minematsu; M Oba; J Matsuo; K Tabata; T Sugiyama; H Sanada Journal: J Nutr Health Aging Date: 2012-01 Impact factor: 4.075
Authors: Lidvine Godaert; C Godard-Sebillotte; L Allard Saint-Albin; L Bousquet; I Bourdel-Marchasson; J-L Fanon; M Dramé Journal: Qual Life Res Date: 2017-09-01 Impact factor: 4.147