Dataset Information

Comparison of Elixhauser and Charlson Methods for Predicting Oral Cancer Survival.

ABSTRACT: Cancer survival correlates not only with the features of primary malignancy but also with the degree of underlying comorbidities. Of the multiple methods used for evaluating the impact of comorbidities on survival, the Charlson and Elixhauser methods are most common. This study compared these 2 comorbidity measures for predicting survival in oral cancer patients. Using the Taiwan National Health Insurance claims data (2008-2011), we acquired data regarding patients' characteristics, comorbidities, and survival from 3583 oral cancer patients. Comorbidity was classified according to both the Charlson comorbidity and Elixhauser comorbidity based on the International Classification of Diseases, 9th Revision. The Elixhauser comorbidity score and Charlson comorbidity score were also calculated. The prediction of survival was determined using measures of discrimination, including the Akaike information criterion and Harrell C (C-statistic). The mean age of the study cohort was 52?±?10 years, and 94.9% of the patients were male. The median follow-up time was 30.1 months, and the 3-year overall survival was 61.6%. Elixhauser comorbidity method added higher discrimination, compared with the Charlson comorbidity method (Harrell?C, 0.677 vs 0.651). Furthermore, the Elixhauser comorbidity score outperformed the Charlson comorbidity score in continuous variable (Harrell?C, 0.654 vs 0.646) and category (Harrell?C, 0.658 vs 0.645). The Elixhauser method is a superior comorbidity risk-adjustment model for oral cancer survival prediction. Utilization of the Elixhauser comorbidity method may be encouraged for risk adjustment in oral cancer study.

SUBMITTER: Chang HJ

PROVIDER: S-EPMC4998653 | biostudies-literature | 2016 Feb

REPOSITORIES: biostudies-literature

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Comparison of Elixhauser and Charlson Methods for Predicting Oral Cancer Survival.

Chang Heng-Jui HJ Chen Po-Chun PC Yang Ching-Chieh CC Su Yu-Chieh YC Lee Ching-Chih CC

Medicine 20160201 7

Cancer survival correlates not only with the features of primary malignancy but also with the degree of underlying comorbidities. Of the multiple methods used for evaluating the impact of comorbidities on survival, the Charlson and Elixhauser methods are most common. This study compared these 2 comorbidity measures for predicting survival in oral cancer patients. Using the Taiwan National Health Insurance claims data (2008-2011), we acquired data regarding patients' characteristics, comorbiditie ...[more]

PMID: 26886653

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Project description:BackgroundThe Charlson and Elixhauser Comorbidity Indices are the most widely used comorbidity assessment methods in medical research. Both methods are adapted for use with the International Classification of Diseases, which 10th revision (ICD-10) is used by over a hundred countries in the world. Available Charlson and Elixhauser Comorbidity Index calculating methods are limited to a few applications with command-line user interfaces, all requiring specific programming language skills. This study aims to use Microsoft Excel to develop a non-programming and ICD-10 based dataset calculator for Charlson and Elixhauser Comorbidity Index and to validate its results with R- and SAS-based methods.MethodsThe Excel-based dataset calculator was developed using the program's formulae, ICD-10 coding algorithms, and different weights of the Charlson and Elixhauser Comorbidity Index. Real, population-wide, nine-year spanning, index hip fracture data from the Estonian Health Insurance Fund was used for validating the calculator. The Excel-based calculator's output values and processing speed were compared to R- and SAS-based methods.ResultsA total of 11,491 hip fracture patients' comorbidities were used for validating the Excel-based calculator. The Excel-based calculator's results were consistent, revealing no discrepancies, with R- and SAS-based methods while comparing 192,690 and 353,265 output values of Charlson and Elixhauser Comorbidity Index, respectively. The Excel-based calculator's processing speed was slower but differing only from a few seconds up to four minutes with datasets including 6250-200,000 patients.ConclusionsThis study proposes a novel, validated, and non-programming-based method for calculating Charlson and Elixhauser Comorbidity Index scores. As the comorbidity calculations can be conducted in Microsoft Excel's simple graphical point-and-click interface, the new method lowers the threshold for calculating these two widely used indices.Trial registrationretrospectively registered.

Project description:BACKGROUND:The Charlson and Elixhauser comorbidity indices are mortality predictors often used in clinical, administrative, and research applications. The Intermountain Mortality Risk Scores (IMRS) are validated mortality predictors that use all factors from the complete blood count and basic metabolic profile. How IMRS, Charlson, and Elixhauser relate to each other is unknown. METHODS:All inpatient admissions except obstetric patients at Intermountain Healthcare's 21 adult care hospitals from 2010-2014 (N = 197,680) were examined in a observational cohort study. The most recent admission was a patient's index encounter. Follow-up to 2018 used hospital death records, Utah death certificates, and the Social Security death master file. Three Charlson versions, 8 Elixhauser versions, and 3 IMRS formulations were evaluated in Cox regression and the one of each that was most predictive was used in dual risk score mortality analyses (in-hospital, 30-day, 1-year, and 5-year mortality). RESULTS:Indices with the strongest mortality associations and selected for dual score study were the age-adjusted Charlson, the van Walraven version of the acute Elixhauser, and the 1-year IMRS. For in-hospital mortality, Charlson (c = 0.719; HR = 4.75, 95% CI = 4.45, 5.07), Elixhauser (c = 0.783; HR = 5.79, CI = 5.41, 6.19), and IMRS (c = 0.821; HR = 17.95, CI = 15.90, 20.26) were significant predictors (p<0.001) in univariate analyses. Dual score analysis of Charlson (HR = 1.79, CI = 1.66, 1.92) with IMRS (HR = 13.10, CI = 11.53, 14.87) and of Elixhauser (HR = 3.00, CI = 2.80, 3.21) with IMRS (HR = 11.42, CI = 10.09, 12.92) found significance for both scores in each model. Results were similar for 30-day, 1-year, and 5-year mortality. CONCLUSIONS:IMRS provided the strongest ability to predict mortality, adding to and attenuating the predictive ability of the Charlson and Elixhauser indices whose mortality associations remained statistically significant. IMRS uses common, standardized, objective laboratory data and should be further evaluated for integration into mortality risk evaluations.

Project description:BackgroundUnderstanding how comorbidity measures contribute to patient mortality is essential both to describe patient health status and to adjust for risks and potential confounding. The Charlson and Elixhauser comorbidity indices are well-established for risk adjustment and mortality prediction. Still, a different set of comorbidity weights might improve the prediction of in-hospital mortality. The present study, therefore, aimed to derive a set of new Swiss Elixhauser comorbidity weightings, to validate and compare them against those of the Charlson and Elixhauser-based van Walraven weights in an adult in-patient population-based cohort of general hospitals.MethodsRetrospective analysis was conducted with routine data of 102 Swiss general hospitals (2012-2017) for 6.09 million inpatient cases. To derive the Swiss weightings for the Elixhauser comorbidity index, we randomly halved the inpatient data and validated the results of part 1 alongside the established weighting systems in part 2, to predict in-hospital mortality. Charlson and van Walraven weights were applied to Charlson and Elixhauser comorbidity indices. Derivation and validation of weightings were conducted with generalized additive models adjusted for age, gender and hospital types.ResultsOverall, the Elixhauser indices, c-statistic with Swiss weights (0.867, 95% CI, 0.865-0.868) and van Walraven's weights (0.863, 95% CI, 0.862-0.864) had substantial advantage over Charlson's weights (0.850, 95% CI, 0.849-0.851) and in the derivation and validation groups. The net reclassification improvement of new Swiss weights improved the predictive performance by 1.6% on the Elixhauser-van Walraven and 4.9% on the Charlson weights.ConclusionsAll weightings confirmed previous results with the national dataset. The new Swiss weightings model improved slightly the prediction of in-hospital mortality in Swiss hospitals. The newly derive weights support patient population-based analysis of in-hospital mortality and seek country or specific cohort-based weightings.

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Comparison of Elixhauser and Charlson Methods for Predicting Oral Cancer Survival.

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Comparison of Elixhauser and Charlson Methods for Predicting Oral Cancer Survival.

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