Coding algorithms for defining comorbidities in ICD-9-CM and ICD-10 administrative data

Department of Community Health Sciences, University of Calgary, Calgary, Alberta, Canada.
Medical Care (Impact Factor: 3.23). 12/2005; 43(11):1130-9.
Source: PubMed


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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    • "Information on comorbidities was generated from ICD-9-CM codes in the clinical database with coding algorithms as described by Quan et al. [17]. The CHA 2 DS 2 -VASc score and Charlson comorbidity index were also calculated for each patient for risk stratification [18] [19] [20]. "
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    ABSTRACT: Introduction Although there are many different antiarrhythmic drugs (AADs) approved for rhythm management of atrial fibrillation (AF), little comparative effectiveness data exist to guide drug selection. Methods We followed 5952 consecutive AF patients who were prescribed amiodarone (N = 2266), dronedarone (N = 488), dofetilide (N = 539), sotalol (N = 1718), or class 1C agents (N = 941) to the primary end point of AF recurrence. Results Median follow-up time was 18.2 months (range 0.1–101.6 months). Patients who were prescribed amiodarone had the highest, while patients on class 1C agents had the lowest baseline CHA2DS2-VASc score, Charlson comorbidity index, and burden of comorbid illnesses including coronary artery disease, congestive heart failure, diabetes mellitus, hyperlipidemia, chronic obstructive lung disease, chronic kidney disease, or cancer (p < 0.05 for all comparisons). After adjusting for baseline characteristics, using dronedarone as benchmark, amiodarone [hazard ratio (HR) 0.58, p < 0.001], class 1C agents (HR 0.70, p < 0.001), and sotalol (HR 0.79, p = 0.008), but not dofetilide (HR 0.87, p = 0.178) were associated with less AF recurrence. In addition, compared to dronedarone, amiodarone and class 1C agents were associated with lower rates of admissions for AF (HR 0.55, p < 0.001 for amiodarone; HR 0.71, p = 0.021 for class 1C agents) and all-cause mortality was lowest in patients treated with class 1C agents (HR 0.42, p = 0.018). The risk of stroke was similar among all groups. Conclusion Compared with dronedarone, amiodarone, class 1C agents, and sotalol are more effective for rhythm control, while dofetilide had similar efficacy. These findings have important implications for clinical practice.
    Journal of Cardiology 07/2015; DOI:10.1016/j.jjcc.2015.07.001 · 2.78 Impact Factor
    • "Finally, a revised version of the Charlson comorbidity index was used as generic marker of comorbidity (Quan et al., 2005). In diabetes patients, type 2 diabetes was not included in the calculation of the comorbidity index. "
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    ABSTRACT: It is unclear whether fracture risk is increased in newly diagnosed type 2 diabetes patients. In addition, fracture risk of various sites (hip, spine, upper extremity) was analysed. The study included 299,104 primary care patients from 1,072 practices who received a first type 2 diabetes diagnosis during the index period (01/2000-12/2013) (Disease Analyser, Germany). Furthermore, 299,104 non-diabetic controls were included after individual matching (1:1) to diabetes cases on age, sex, type of health insurance (private or statutory) and index date (visit at date of first diabetes diagnosis). Cumulative incidence of fractures was estimated for 10years after index date using product-limit methods. Hazard ratios were calculated using Cox regression models adjusting for comorbidity. Cumulative 10-year incidence of any, hip, spine, wrist/hand, forearm, and upper arm/shoulder fractures were 15.4%, 2.9%, 2.6%, 5.1%, 2.3%, and 2.3% in diabetes patients and 13.1%, 2.0%, 2.1%, 4.6%, 2.2%, and 1.7% in controls (log-rank test: all p<0.001, except wrist/hand p=0.56, forearm: p=0.54), respectively. In multivariate Cox regression models, newly diagnosed type 2 diabetes was related to an significantly increased risk of any fracture (adjusted hazard ratio, HR, 95% CI: 1.36, 1.32-1.40), as well as for hip (1.56, 1.45-1.67), spine (1.37, 1.28-1.47), wrist/hand (1.15, 1.03-1.27), forearm (1.12, 1.05-1.20), and upper arm/shoulder (1.61, 1.49-1.74) fractures. Already few years after diabetes diagnosis, type 2 diabetes patients more frequently experienced overall, hip, spine, and upper extremity fractures, respectively. The underlying mechanisms need to be further explored in order to prevent fractures among patients with type 2 diabetes. Copyright © 2015. Published by Elsevier Inc.
    Journal of Diabetes and its Complications 05/2015; 29(6). DOI:10.1016/j.jdiacomp.2015.05.007 · 3.01 Impact Factor
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    • "Patient characteristics included donor type (live, deceased), age (18–34 years, 35–49 years, 50–64 years, 65 years or more), gender (men, women), race/ethnicity (White, Black, Hispanic, and others), median household income quartiles by patient ZIP code ($1–$38999, $39000–$47999, $48000–$62999, $63000 or more), primary payer type (Medicare , Medicaid, private including HMO, and others), and Charlson-Deyo index to take into account potential effect of other comorbid conditions. The Charlson-Deyo index is a validated comorbidity measure for administrative data [15] [16] [17]. The index is comprised of 17 comorbidities, including myocardial infarction, congestive heart failure, peripheral vascular disease, cerebrovascular disease, dementia, chronic pulmonary disease, rheumatic disease, peptic ulcer disease, mild liver disease, diabetes with and without chronic complications, hemiplegia or paraplegia, renal disease, any malignancy (including lymphoma and leukemia, except malignant neoplasm of skin), moderate or severe liver disease, metastatic solid tumor, and human immunodeficiency virus/acquired immunodeficiency syndrome. "
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    ABSTRACT: Objective. Evaluate the prevalence and outcomes of urinary tract infection (UTI) among renal transplant recipients. Methods. A secondary analysis of the Nationwide Inpatient Sample 2009–2011 was conducted. Survey-weighted multivariable regression analyses were used to examine the impact of UTI on transplant complications, total charges, and length of stay. Results. A total of 1,044 renal transplant recipients, representing a population estimate of 49,862, were included in the study. UTI was most common in transplant recipients with hypertension (53%) and prevalence was noted to be 28.2 and 65.9 cases per 1,000 for men and women, respectively. UTI increased the likelihood of transplant complications (182% for men, 169% for women). Total charges were 28% higher among men as compared to 22% among women with UTI. Such infection also increased the length of stay by 87% among men and 74% among women. Discussion. UTI in renal transplant recipients was associated with prolonged length of stay, total charges, and increased odds of transplant complications. Interventions to prevent UTI among such patients should be a priority area for future research and practice.
    Journal of Transplantation 02/2015; 2015. DOI:10.1155/2015/854640
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