Dataset Information

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

ABSTRACT:

Background

Understanding 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.

Methods

Retrospective 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.

Results

Overall, 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.

Conclusions

All 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.

SUBMITTER: Sharma N

PROVIDER: S-EPMC7786470 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature

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Publications

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

Sharma Narayan N Schwendimann René R Endrich Olga O Ausserhofer Dietmar D Simon Michael M

BMC health services research 20210106 1

<h4>Background</h4>Understanding 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 wei ...[more]

PMID: 33407455

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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:BackgroundSeveral different indices summarize patient comorbidity using health care data. An accurate index can be used to describe the risk profile of patients, and as an adjustment factor in analyses. How well these indices perform in persons with chronic kidney disease (CKD) is not well known.ObjectiveAssess the performance of 5 comorbidity indices at predicting mortality in 3 different patient groups with CKD: incident kidney transplant recipients, maintenance dialysis patients, and individuals with low estimated glomerular filtration rate (eGFR).DesignPopulation-based retrospective cohort study.SettingOntario, Canada, between 2004 and 2014.PatientsIndividuals at the time they first received a kidney transplant, received maintenance dialysis, or were confirmed to have an eGFR less than 45 mL/min per 1.73m2.MeasurementsFive comorbidity indices: Charlson comorbidity index, end-stage renal disease-modified Charlson comorbidity index, Johns Hopkins' Aggregated Diagnosis Groups score, Elixhauser score, and Wright-Khan index. Our primary outcome was 1-year all-cause mortality.MethodsComorbidity indices were estimated using information in the prior 2 years. Each group was randomly divided 100 times into derivation and validation samples. Model discrimination was assessed using median c-statistics from logistic regression models, and calibration was evaluated graphically.ResultsWe identified 4111 kidney transplant recipients, 23 897 individuals receiving maintenance dialysis, and 181 425 individuals with a low eGFR. Within 1 year, 108 (2.6%), 4179 (17.5%), and 17 898 (9.9%) in each group had died, respectively. In the validation sample, model discrimination was inadequate with median c-statistics less than 0.7 for all 5 comorbidity indices for all 3 groups. Calibration was also poor for all models.LimitationsThe study used administrative health care data so there is the potential for misclassification. Indices were modeled as continuous scores as opposed to indicators for individual conditions to limit overfitting.ConclusionsExisting comorbidity indices do not accurately predict 1-year mortality in patients with CKD. Current indices could be modified with additional risk factors to improve their performance in CKD, or a new index could be developed for this population.

Dataset Information

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

Background

Methods

Results

Conclusions

Publications

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

Similar Datasets

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