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

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          Abstract

          Implementation of the International Statistical Classification of Disease and Related Health Problems, 10th Revision (ICD-10) coding system presents challenges for using administrative data. Recognizing this, we conducted a multistep process to develop ICD-10 coding algorithms to define Charlson and Elixhauser comorbidities in administrative data and assess the performance of the resulting algorithms. ICD-10 coding algorithms were developed by "translation" of the ICD-9-CM codes constituting Deyo's (for Charlson comorbidities) and Elixhauser's coding algorithms and by physicians' assessment of the face-validity of selected ICD-10 codes. The process of carefully developing ICD-10 algorithms also produced modified and enhanced ICD-9-CM coding algorithms for the Charlson and Elixhauser comorbidities. We then used data on in-patients aged 18 years and older in ICD-9-CM and ICD-10 administrative hospital discharge data from a Canadian health region to assess the comorbidity frequencies and mortality prediction achieved by the original ICD-9-CM algorithms, the enhanced ICD-9-CM algorithms, and the new ICD-10 coding algorithms. Among 56,585 patients in the ICD-9-CM data and 58,805 patients in the ICD-10 data, frequencies of the 17 Charlson comorbidities and the 30 Elixhauser comorbidities remained generally similar across algorithms. The new ICD-10 and enhanced ICD-9-CM coding algorithms either matched or outperformed the original Deyo and Elixhauser ICD-9-CM coding algorithms in predicting in-hospital mortality. The C-statistic was 0.842 for Deyo's ICD-9-CM coding algorithm, 0.860 for the ICD-10 coding algorithm, and 0.859 for the enhanced ICD-9-CM coding algorithm, 0.868 for the original Elixhauser ICD-9-CM coding algorithm, 0.870 for the ICD-10 coding algorithm and 0.878 for the enhanced ICD-9-CM coding algorithm. These newly developed ICD-10 and ICD-9-CM comorbidity coding algorithms produce similar estimates of comorbidity prevalence in administrative data, and may outperform existing ICD-9-CM coding algorithms.

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          Most cited references 5

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          Risk adjustment in outcome assessment: the Charlson comorbidity index.

          To measure the burden of comorbid diseases using the MED-ECHO database (Quebec), the so-called Charlson index was adapted to International Classification of Disease (ICD-9) codes. The resulting comorbidity index was applied to the study of inpatient death in a group of 62,456 patients having one of the following conditions: ischemic heart disease, congestive heart failure, stroke, or bacterial pneumonia. Multiple logistic regression was used to relate inpatient death to its predictors, including gender, principal diagnosis, age, and the comorbidity index. Various transformations of the comorbidity score were performed, and their effect on predictive accuracy was assessed. The comorbidity index was constantly and strongly associated with death. When gender, age, comorbidity and the principal diagnoses were taken into account, the area under the receiver-operating curve was 0.83. Therefore, the Charlson Index is a useful approach to risk adjustment in outcomes research from administrative databases.
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            Measuring potentially avoidable hospital readmissions.

            The objectives of this study were to develop a computerized method to screen for potentially avoidable hospital readmissions using routinely collected data and a prediction model to adjust rates for case mix. We studied hospital information system data of a random sample of 3,474 inpatients discharged alive in 1997 from a university hospital and medical records of those (1,115) readmitted within 1 year. The gold standard was set on the basis of the hospital data and medical records: all readmissions were classified as foreseen readmissions, unforeseen readmissions for a new affection, or unforeseen readmissions for a previously known affection. The latter category was submitted to a systematic medical record review to identify the main cause of readmission. Potentially avoidable readmissions were defined as a subgroup of unforeseen readmissions for a previously known affection occurring within an appropriate interval, set to maximize the chance of detecting avoidable readmissions. The computerized screening algorithm was strictly based on routine statistics: diagnosis and procedures coding and admission mode. The prediction was based on a Poisson regression model. There were 454 (13.1%) unforeseen readmissions for a previously known affection within 1 year. Fifty-nine readmissions (1.7%) were judged avoidable, most of them occurring within 1 month, which was the interval used to define potentially avoidable readmissions (n = 174, 5.0%). The intra-sample sensitivity and specificity of the screening algorithm both reached approximately 96%. Higher risk for potentially avoidable readmission was associated with previous hospitalizations, high comorbidity index, and long length of stay; lower risk was associated with surgery and delivery. The model offers satisfactory predictive performance and a good medical plausibility. The proposed measure could be used as an indicator of inpatient care outcome. However, the instrument should be validated using other sets of data from various hospitals.
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              Searching for an improved clinical comorbidity index for use with ICD-9-CM administrative data.

              We studied approaches to comorbidity risk adjustment by comparing two ICD-9-CM adaptations (Deyo, Dartmouth-Manitoba) of the Charlson comorbidity index applied to Massachusetts coronary artery bypass surgery data. We also developed a new comorbidity index by assigning study-specific weights to the original Charlson comorbidity variables. The 2 ICD-9-CM coding adaptations assigned identical Charlson comorbidity scores to 90% of cases, and specific comorbidities were largely found in the same cases (kappa values of 0.72-1.0 for 15 of 16 comorbidities). Meanwhile, the study-specific comorbidity index identified a 10% subset of patients with 15% mortality, whereas the 5% highest-risk patients according to the Charlson index had only 8% mortality (p = 0.01). A model using the new index to predict mortality had better validated performance than a model based on the original Charlson index (c = 0.74 vs. 0.70). Thus, in our population, the ICD-9-CM adaptation used to create the Charlson score mattered little, but using study-specific weights with the Charlson variables substantially improved the power of these data to predict mortality.
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                Author and article information

                Journal
                Medical Care
                Medical Care
                Ovid Technologies (Wolters Kluwer Health)
                0025-7079
                2005
                November 2005
                : 43
                : 11
                : 1130-1139
                Article
                10.1097/01.mlr.0000182534.19832.83
                16224307
                © 2005
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