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.

The Effectiveness of National Early Warning Score, Quick Sequential Organ Failure Assessment, Charlson Comorbidity Index, and Elixhauser Comorbidity Index Scores in Predicting Mortality Due to COVID-19 in Elderly Patients

“…Importantly, studies reveal that, although the sensitivity of q-SOFA is low, its specificity is high in general [31], and Zhou et al have shown that high q-SOFA was associated with high mortality [33,34,35], and both CCI and ECI can provide mortality prognosis based on the presence of comorbidities in the patient population. While available literature on acute and chronic conditions indicates that the ECI is statistically superior in predicting mortality [9], we found that q-SOFA more accurately predicted mortality than the ECI. Elixhauser et al have used comorbidities to evaluate in-hospital mortality, length of hospital stay, and estimated medical expenses [8], and a systematic analysis has indicated that the ECI can adequately predict in-hospital mortality [36].…”

“…Thus, early estimation of in-hospital mortality in patients admitted to the emergency department who may need intensive care would be beneficial and can also contribute to the planning of health services within the hospital under pandemic conditions. The Charlson Comorbidity Index (CCI) and the Elixhauser Comorbidity Index (ECI) [7,8,9] are the most commonly used tools for evaluating risk and inhospital mortality. The CCI is simple, easy, and accurate, and can predict long-term prognosis and survival by estimating the risk of mortality from a comorbid disease [10].…”

In-Hospital Mortality Disparities Among American Indian and Alaska Native, Black, and White Patients With COVID-19

“…This weighted approach has proven superior to unweighted ECI scores or other indexes at reducing type I errors and predicting odds of in-hospital mortality. 16 , 17 Application to COVID-19 interrogations have also proven effective. 18 , 19 , 20 …”

Factors Associated With Nonunion and Infection Following Ankle Arthrodesis Using a Large Claims Database: Who Has Elevated Risk?

“…The Elixhauser Comorbidity Index is composed of 31 comorbidities, compared with 17 in the Charlson Comorbidity Index, and covers both acute and chronic conditions. 6 , 32 The Charlson Comorbidity Index continues to be commonly used; however, the Elixhauser Comorbidity Index was used in the present study as it has yielded increased discrimination and has been shown to outperform the Charlson Comorbidity Index in predicting inpatient mortality in orthopaedic operations. 16 , 32 Smoking record was evaluated by any documented claim of tobacco use in the database.…”

“… 6 , 32 The Charlson Comorbidity Index continues to be commonly used; however, the Elixhauser Comorbidity Index was used in the present study as it has yielded increased discrimination and has been shown to outperform the Charlson Comorbidity Index in predicting inpatient mortality in orthopaedic operations. 16 , 32 Smoking record was evaluated by any documented claim of tobacco use in the database. Because of the nature of the database, this includes both patients who currently smoke or were a smoker at a time during which the database was established.…”