Comparison of Elixhauser and Charlson Methods for Discriminative Performance in Mortality Risk in Patients with Schizophrenic Disorders

Tsai, Kuan-Yi; Hsieh, Kuan-Ying; Ou, Shu-Yu; Chou, Frank Huang-Chih; Chou, Yi-Pin

doi:10.3390/ijerph17072450

Cited by 8 publications

(13 citation statements)

References 42 publications

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“…We found that including comorbidities from the previous year when calculating the Charlson and Elixhauser score improved model fit when each was considered individually, implying superior control of confounding, compared with only using comorbidities recorded in the diagnostic-dominant episode. The Elixhauser score was more prognostic than the Charlson score when using one score to adjust for risk of 30-day mortality, as shown in previous studies (6,(18)(19)(20)(22)(23)(24)(25)(26)(27)(28)(29). However, we found that including both comorbidity scores together with their interaction, and incorporating non-linear effects, provided the best model fit.…”

Section: Discussionsupporting

confidence: 75%

“…The resulting modified single score (17) used diagnostic codes from the current admission with a "type" indicating preadmission comorbidity (analogous to those from the diagnosis-dominant episode), and comorbidities from all previous hospital admissions, implying other studies do the same. A systematic review of 54 papers prior to March 2011 found the Elixhauser score to generally be superior to Charlson (18); a conclusion echoed by studies since (6,(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30). However, none of these studies combined the scores in their validated forms in one model, and this does not seem to have been considered in the literature.…”

Section: Introductionmentioning

confidence: 99%

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Combining Charlson and Elixhauser scores with varying lookback predicated mortality better than using individual scores

Pritchard

Fawcett

Quan

et al. 2021

Journal of Clinical Epidemiology

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Objective: To investigate variation in the presence of secondary diagnosis codes in Charlson andElixhauser comorbidity scores and assess whether including a one-year lookback period improved prognostic adjustment by these scores individually, and combined, for 30-day mortality. Study Design and Setting:We analysed inpatient admissions from 01-Jan-2007 to 18-May-2018 in Oxfordshire, UK. Comorbidity scores were calculated using secondary diagnostic codes in the diagnostic-dominant episode, and primary and secondary codes from the year before. Associations between scores and 30-day mortality were investigated using Cox models with natural cubic splines for non-linearity, assessing fit using Akaike Information Criteria. Results:The one-year lookback improved model fit for Charlson and Elixhauser scores vs using diagnostic-dominant methods. Including both, and allowing non-linearity, improved model fit further. The diagnosis-dominant Charlson score and Elixhauser score using a 1-year lookback, and their interaction, provided the best comorbidity adjustment (reduction in AIC: 761 from best single score model). Conclusion:The Charlson and Elixhauser score calculated using primary and secondary diagnostic codes from 1-year lookback with secondary diagnostic codes from current episode improved individual predictive ability. Ideally, comorbidities should be adjusted for using both the Charlson (diagnostic-dominant) and Elixhauser (one-year lookback) scores, incorporating non-linearity and interactions for optimal confounding control.

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Section: Discussionsupporting

confidence: 75%

Section: Introductionmentioning

confidence: 99%

Combining Charlson and Elixhauser scores with varying lookback predicated mortality better than using individual scores

Pritchard

Fawcett

Quan

et al. 2021

Journal of Clinical Epidemiology

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“…We identified 59 studies for the topic of in-hospital mortality. 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 While the majority of studies used a retrospective cohort design, 11 used a prospective approach, 120 122 129 140 143 166 169 170 172 174 175 and two were meta-analysis studies. 154 …”

Section: Resultsmentioning

confidence: 99%

“…154 157 The majority of studies used EHR data, while 16 studies used some kind of registry data, 117 118 121 127 128 131 132 138 140 141 142 150 153 155 156 161 174 four studies used questionaries/surveys, 166 169 170 172 and two studies used administrative data. 134 167 With the emergence of public databases containing COVID-19 data, since 2020, many studies used these databases for their studies. 120 123 128 131 140 142 143 146 169 Study populations primarily comprised adults (sometimes limited to subpopulations such as those with chronic illnesses [e.g., Takada et al 164 and Sukmark et al 163 ]), but three studies included pediatric populations, 138 154 161 one study included newborns, 150 and two studies included elder patients aged over 65 years.…”

Section: Resultsmentioning

confidence: 99%

Data Science Trends Relevant to Nursing Practice: A Rapid Review of the 2020 Literature

et al. 2022

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Background The term “data science” encompasses several methods, many of which are considered cutting edge and are being used to influence care processes across the world. Nursing is an applied science and a key discipline in health care systems in both clinical and administrative areas, making the profession increasingly influenced by the latest advances in data science. The greater informatics community should be aware of current trends regarding the intersection of nursing and data science, as developments in nursing practice have cross-professional implications. Objectives This study aimed to summarize the latest (calendar year 2020) research and applications of nursing-relevant patient outcomes and clinical processes in the data science literature. Methods We conducted a rapid review of the literature to identify relevant research published during the year 2020. We explored the following 16 topics: (1) artificial intelligence/machine learning credibility and acceptance, (2) burnout, (3) complex care (outpatient), (4) emergency department visits, (5) falls, (6) health care–acquired infections, (7) health care utilization and costs, (8) hospitalization, (9) in-hospital mortality, (10) length of stay, (11) pain, (12) patient safety, (13) pressure injuries, (14) readmissions, (15) staffing, and (16) unit culture. Results Of 16,589 articles, 244 were included in the review. All topics were represented by literature published in 2020, ranging from 1 article to 59 articles. Numerous contemporary data science methods were represented in the literature including the use of machine learning, neural networks, and natural language processing. Conclusion This review provides an overview of the data science trends that were relevant to nursing practice in 2020. Examinations of such literature are important to monitor the status of data science's influence in nursing practice.

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“…One year after the implementation of the Charlson Comorbidity Index, researcher Elixhauser and colleagues introduced a new model for estimating the comorbidity index, The Elixhauser Comorbidity Index (ECI). The ECI is suitable for studying data from administrative databases, such as the hospitalization database, and it includes 31 comorbidities ( 7 , 8 ).…”

Section: Introductionmentioning

confidence: 99%

Performance of Charlson and Elixhauser Comorbidity Index to Predict in-Hospital Mortality in Patients with Stroke in Sumadija and Western Serbia

Bajić¹,

Galic²,

Mihailović³

et al. 2021

ijph

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Background: Comorbidities are major predictors of in-hospital mortality in stroke patients. The Charlson comorbidity index (CCI) and the Elikhauser comorbidity index (ECI) are scoring systems for classifying comorbidities. We aimed to compare the performance of the CCI and ECI to predict in-hospital mortality in stroke patients. Methods: We included patients hospitalized for stroke in the Clinical Center of Kragujevac, Serbia for the last 7 years. Hospitalizations caused by stroke, were identified by the International Classification of Diseases-10 (ICD-10) codes I60.0 - I69.9. All patients were divided into two cohorts: Alive cohort (n=3297) and Mortality cohort (n=978). Results: There were significant associations between higher CCIS and increased risk of in-hospital mortality (HR = 1.07, 95% CI = 1.01–1.12) and between higher ECIS and increased risk of in-hospital mortality (HR = 1.04, 95% CI = 0.99–1.09). Almost 2/3 patients (66.9%) had comorbidities included in the CCI score and 1/3 patients (30.2%) had comorbidities included in the ECI score. The statistically significant higher CCI score (t = -3.88, df = 1017.96, P <0.01) and ECI score (t = -6.7, df = 1447.32, P <0.01) was in the mortality cohort. Area Under the Curve for ECI score was 0.606 and for CCI score was 0.549. Conclusion: Both, the CCI and the ECI can be used as scoring systems for classifying comorbidities in the administrative databases, but the model’s ECI Score had a better discriminative performance of in-hospital mortality in the stroke patients than the CCI Score model.

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Comparison of Elixhauser and Charlson Methods for Discriminative Performance in Mortality Risk in Patients with Schizophrenic Disorders

Cited by 8 publications

References 42 publications

Combining Charlson and Elixhauser scores with varying lookback predicated mortality better than using individual scores

Combining Charlson and Elixhauser scores with varying lookback predicated mortality better than using individual scores

Data Science Trends Relevant to Nursing Practice: A Rapid Review of the 2020 Literature

Performance of Charlson and Elixhauser Comorbidity Index to Predict in-Hospital Mortality in Patients with Stroke in Sumadija and Western Serbia

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