Get 20M+ Full-Text Papers For Less Than $1.50/day. Subscribe now for You or Your Team.

Learn More →

Coding Algorithms for Defining Comorbidities in ICD-9-CM and ICD-10 Administrative Data

Coding Algorithms for Defining Comorbidities in ICD-9-CM and ICD-10 Administrative Data ORIGINAL ARTICLE Coding Algorithms for Defining Comorbidities in ICD-9-CM and ICD-10 Administrative Data Hude Quan, MD, PhD,*† Vijaya Sundararajan, MD, MPH, FACP,‡ Patricia Halfon, MD,§ Andrew Fong, BCOMM,* Bernard Burnand, MD, MPH,§ Jean-Christophe Luthi, MD, PhD,§ L. Duncan Saunders, MBBCh, PhD,¶ Cynthia A. Beck, MD, MASc,* Thomas E. Feasby, MD,** and William A. Ghali, MD, MPH,*†,†† Results: Among 56,585 patients in the ICD-9-CM data and 58,805 Objectives: Implementation of the International Statistical Classi- patients in the ICD-10 data, frequencies of the 17 Charlson comor- fication of Disease and Related Health Problems, 10th Revision bidities and the 30 Elixhauser comorbidities remained generally (ICD-10) coding system presents challenges for using administrative similar across algorithms. The new ICD-10 and enhanced ICD- data. Recognizing this, we conducted a multistep process to develop 9-CM coding algorithms either matched or outperformed the origi- ICD-10 coding algorithms to define Charlson and Elixhauser co- nal Deyo and Elixhauser ICD-9-CM coding algorithms in predicting morbidities in administrative data and assess the performance of the in-hospital mortality. The C-statistic was 0.842 for Deyo’s ICD- resulting algorithms. 9-CM coding algorithm, 0.860 for the ICD-10 coding algorithm, and Methods: ICD-10 coding algorithms were developed by “transla- 0.859 for the enhanced ICD-9-CM http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Medical Care Wolters Kluwer Health

Coding Algorithms for Defining Comorbidities in ICD-9-CM and ICD-10 Administrative Data

Medical Care , Volume 43 (11) – Nov 1, 2005

Loading next page...
 
/lp/wolters-kluwer-health/coding-algorithms-for-defining-comorbidities-in-icd-9-cm-and-icd-10-lj44SD2pBN

References

References for this paper are not available at this time. We will be adding them shortly, thank you for your patience.

ISSN
0025-7079
eISSN
1537-1948
DOI
10.1097/01.mlr.0000182534.19832.83
Publisher site
See Article on Publisher Site

Abstract

ORIGINAL ARTICLE Coding Algorithms for Defining Comorbidities in ICD-9-CM and ICD-10 Administrative Data Hude Quan, MD, PhD,*† Vijaya Sundararajan, MD, MPH, FACP,‡ Patricia Halfon, MD,§ Andrew Fong, BCOMM,* Bernard Burnand, MD, MPH,§ Jean-Christophe Luthi, MD, PhD,§ L. Duncan Saunders, MBBCh, PhD,¶ Cynthia A. Beck, MD, MASc,* Thomas E. Feasby, MD,** and William A. Ghali, MD, MPH,*†,†† Results: Among 56,585 patients in the ICD-9-CM data and 58,805 Objectives: Implementation of the International Statistical Classi- patients in the ICD-10 data, frequencies of the 17 Charlson comor- fication of Disease and Related Health Problems, 10th Revision bidities and the 30 Elixhauser comorbidities remained generally (ICD-10) coding system presents challenges for using administrative similar across algorithms. The new ICD-10 and enhanced ICD- data. Recognizing this, we conducted a multistep process to develop 9-CM coding algorithms either matched or outperformed the origi- ICD-10 coding algorithms to define Charlson and Elixhauser co- nal Deyo and Elixhauser ICD-9-CM coding algorithms in predicting morbidities in administrative data and assess the performance of the in-hospital mortality. The C-statistic was 0.842 for Deyo’s ICD- resulting algorithms. 9-CM coding algorithm, 0.860 for the ICD-10 coding algorithm, and Methods: ICD-10 coding algorithms were developed by “transla- 0.859 for the enhanced ICD-9-CM

Journal

Medical CareWolters Kluwer Health

Published: Nov 1, 2005

There are no references for this article.