Coding algorithms for defining Charlson and Elixhauser co-morbidities in Read-coded databases

Abstract

Background Comorbidity measures, such as the Charlson Comorbidity Index (CCI) and Elixhauser Method (EM), are frequently used for risk-adjustment by healthcare researchers. This study sought to create CCI and EM lists of Read codes, which are standard terminology used in some large primary care databases. It also aimed to describe and compare the predictive properties of the CCI and EM amongst patients with hip fracture (and matched controls) in a large primary care administrative dataset. Methods Two researchers independently screened 111,929 individual Read codes to populate the 17 CCI and 31 EM comorbidity categories. Patients with hip fractures were identified (together with age- and sex-matched controls) from UK primary care practices participating in the Clinical Practice Research Datalink (CPRD). The predictive properties of both comorbidity measures were explored in hip fracture and control populations using logistic regression models fitted with 30- and 365-day mortality as the dependent variables together with tests of equality for Receiver Operating Characteristic (ROC) curves. Results There were 5832 CCI and 7156 EM comorbidity codes. The EM improved the ability of a logistic regression model (using age and sex as covariables) to predict 30-day mortality (AUROC 0.744 versus 0.686). The EM alone also outperformed the CCI (0.696 versus 0.601). Capturing comorbidities over a prolonged period only modestly improved the predictive value of either index: EM 1-year look-back 0.645 versus 5-year 0.676 versus complete record 0.695 and CCI 0.574 versus 0.591 versus 0.605. Conclusions The comorbidity code lists may be used by future researchers to calculate CCI and EM using records from Read coded databases. The EM is preferable to the CCI but only marginal gains should be expected from incorporating comorbidities over a period longer than 1 year. Electronic supplementary material The online version of this article (10.1186/s12874-019-0753-5) contains supplementary material, which is available to authorized users.

COVID-19 – exploring the implications of long-term condition type and extent of multimorbidity on years of life lost: a modelling study

“…Therefore, we modelled two scenarios: independence between age and multimorbidity count (i.e. no correlation between age and multimorbidity count among people dying of COVID- 19), and a positive association between age and multimorbidity count. To inform the latter, we examined data within SAIL for 145 patients who had influenza recorded as the cause of death in their death certificate in 2011.…”

“…Individuals were considered to have a LTC if they had a relevant diagnostic code entered prior to 31 st December 2011. Relevant codes were identified from the Charlson comorbidity index and the Elixhauser comorbidity index 16,17 , which had established algorithms for identification from ICD-10 codes 18 , and have been adapted for using Read codes in primary care 19 . Code lists are available in the supplementary material 15 .…”

What factors increase the risk of complications in SARS-CoV-2 positive patients? A cohort study in a nationwide Israeli health organization

“…MHS registries used are: Cardiovascular diseases (including ischemic heart disease, congestive heart failure, peripheral vascular disease, cerebrovascular disease, and other cardiovascular diseases) [9], diabetes [10,11], hypertension [12], osteoporosis [13], chronic kidney disease [14], cognitive disorders, mental illness [15], cancer, immunosuppression, weight disorders (obesity, overweight and underweight), smoking, and nursing home. For other conditions, we relied on previously grouped lists of diagnosis codes (Read codes or International Classification of Diseases, ICD, codes, ninth revision) [16][17][18]: Deficiency anemia, Fluid and electrolyte disorders, chronic obstructive pulmonary disease (COPD), chronic pulmonary disease, neurological disorders, end stage renal disease, rheumatoid arthritis, paralysis, hip fracture, lymphoma, aspiration pneumonia, pleural effusion, respiratory failure, and alcohol consumption.…”

Comparing Charlson and Elixhauser comorbidity indices with different weightings to predict in-hospital mortality: an analysis of national inpatient data

“…Critical health outcomes often require effective risk adjustment based on patient characteristics. This is especially true for comorbidities [1,2], which function as major predictors of mortality [3]. Over one-third of hospitalized patients have at least one comorbidity; two-thirds of those over 65 [4,2] and three-quarters of those over 85 have at least two [5].…”