Identifying Increased Risk of Readmission and In-hospital Mortality Using Hospital Administrative Data: The AHRQ Elixhauser Comorbidity Index.

doi:10.1097/MLR.0000000000000735

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Identifying Increased Risk of Readmission and In-hospital Mortality Using Hospital Administrative Data: The AHRQ Elixhauser Comorbidity Index.

Journal Article•DOI•

Identifying Increased Risk of Readmission and In-hospital Mortality Using Hospital Administrative Data: The AHRQ Elixhauser Comorbidity Index.

Brian J. Moore¹, Susan White, Raynard Washington, Natalia Coenen, Anne Elixhauser - Show less +1 more•Institutions (1)

Agency for Healthcare Research and Quality¹

01 Jul 2017-Medical Care (Med Care)-Vol. 55, Iss: 7, pp 698-705

TL;DR: These indices are effective methods to incorporate the influence of comorbid conditions in models designed to assess the risk of in-hospital mortality and readmission using administrative data with limited clinical information, especially when small samples sizes are an issue.

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Abstract: Objective:We extend the literature on comorbidity measurement by developing 2 indices, based on the Elixhauser Comorbidity measures, designed to predict 2 frequently reported health outcomes: in-hospital mortality and 30-day readmission in administrative data. The Elixhauser measures are commonly us

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Book Chapter•DOI•

Likelihood Modelling: Presentation of Multivariate Data for Clinical Use: The Framingham Study Risk Score Functions

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Lisa M. Sullivan¹, Joseph M. Massaro¹, Ralph B. D'Agostino¹•Institutions (1)

Boston University¹

30 Aug 2005

TL;DR: An effort to make available a tool for clinicians to aid in their decision-making process regarding treatment and to assist them in motivating patients toward healthy behaviours is made available.

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Abstract: The Framingham Heart Study has been a leader in the development and dissemination of multivariable statistical models to estimate the risk of coronary heart disease. These models quantify the impact of measurable and modifiable risk factors on the development of coronary heart disease and can be used to generate estimates of risk of coronary heart disease over a predetermined period, for example the next 10 years. We developed a system, which we call a points system, for making these complex statistical models useful to practitioners. The system is easy to use, it does not require a calculator or computer and it simplifies the estimation of risk based on complex statistical models. This system represents an effort to make available a tool for clinicians to aid in their decision-making process regarding treatment and to assist them in motivating patients toward healthy behaviours. The system is also readily available to patients who can easily estimate their own coronary heart disease risk and monitor this risk over time.

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545 citations

Journal Article•DOI•

Association of Race With Mortality Among Patients Hospitalized With Coronavirus Disease 2019 (COVID-19) at 92 US Hospitals.

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Baligh R. Yehia¹, Angela L. Winegar¹, Richard Fogel¹, Mohamad G. Fakih¹, Allison Ottenbacher¹, Christine Jesser¹, Angelo Bufalino¹, Ren-Huai Huang¹, Joseph Cacchione¹ - Show less +5 more•Institutions (1)

Ascension Health¹

03 Aug 2020

TL;DR: There was no difference in all-cause, in-hospital mortality between White and Black patients after adjusting for age, sex, insurance status, comorbidity, neighborhood deprivation, and site of care.

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Abstract: Importance While current reports suggest that a disproportionate share of US coronavirus disease 2019 (COVID-19) cases and deaths are among Black residents, little information is available regarding how race is associated with in-hospital mortality. Objective To evaluate the association of race, adjusting for sociodemographic and clinical factors, on all-cause, in-hospital mortality for patients with COVID-19. Design, Setting, and Participants This cohort study included 11 210 adult patients (age ≥18 years) hospitalized with confirmed severe acute respiratory coronavirus 2 (SARS-CoV-2) between February 19, 2020, and May 31, 2020, in 92 hospitals in 12 states: Alabama (6 hospitals), Maryland (1 hospital), Florida (5 hospitals), Illinois (8 hospitals), Indiana (14 hospitals), Kansas (4 hospitals), Michigan (13 hospitals), New York (2 hospitals), Oklahoma (6 hospitals), Tennessee (4 hospitals), Texas (11 hospitals), and Wisconsin (18 hospitals). Exposures Confirmed SARS-CoV-2 infection by positive result on polymerase chain reaction testing of a nasopharyngeal sample. Main Outcomes and Measures Death during hospitalization was examined overall and by race. Race was self-reported and categorized as Black, White, and other or missing. Cox proportional hazards regression with mixed effects was used to evaluate associations between all-cause in-hospital mortality and patient characteristics while accounting for the random effects of hospital on the outcome. Results Of 11 210 patients with confirmed COVID-19 presenting to hospitals, 4180 (37.3%) were Black patients and 5583 (49.8%) were men. The median (interquartile range) age was 61 (46 to 74) years. Compared with White patients, Black patients were younger (median [interquartile range] age, 66 [50 to 80] years vs 61 [46 to 72] years), were more likely to be women (2259 [49.0%] vs 2293 [54.9%]), were more likely to have Medicaid insurance (611 [13.3%] vs 1031 [24.7%]), and had higher median (interquartile range) scores on the Neighborhood Deprivation Index (−0.11 [−0.70 to 0.56] vs 0.82 [0.08 to 1.76]) and the Elixhauser Comorbidity Index (21 [0 to 44] vs 22 [0 to 46]). All-cause in-hospital mortality among hospitalized White and Black patients was 23.1% (724 of 3218) and 19.2% (540 of 2812), respectively. After adjustment for age, sex, insurance, comorbidities, neighborhood deprivation, and site of care, there was no statistically significant difference in risk of mortality between Black and White patients (hazard ratio, 0.93; 95% CI, 0.80 to 1.09). Conclusions and Relevance Although current reports suggest that Black patients represent a disproportionate share of COVID-19 infections and death in the United States, in this study, mortality for those able to access hospital care did not differ between Black and White patients after adjusting for sociodemographic factors and comorbidities.

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248 citations

Additional excerpts

...Comorbidities ECI, median (IQR)c 32 (15-54) 32 (15-54) 34 (19-55) 30 (3-47)...
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Journal Article•DOI•

comorbidity: An R package for computing comorbidity scores

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Alessandro Gasparini

30 Mar 2018

TL;DR: Comorbidity scores are extensively used in observational medical research studies to avoid potential bias when the burden of disease could be confounding the association of interest.

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Abstract: Comorbidity scores are extensively used in observational medical research studies to avoid potential bias when the burden of disease could be confounding the association of interest. This is of primary importance, given the increasing availability and use of administrative data (such as medical records and insurance claims) for research purposes. Several comorbidity scores have been proposed throughout the years; however, two of them are used most frequently in practice: the Charlson comorbidity index (Charlson et al. 1987) and the Elixhauser comorbidity index (Elixhauser et al. 1998). The Charlson comorbidity index defines a set of comorbid conditions using International Classification of Disease (ICD) diagnostic codes. Each comorbid condition has an associated weight, and the sum of all weights results in a single comorbidity score per patient. The current version of the Charlson score includes 17 comorbidities. Similarly, the Elixhauser comorbidity index is based on ICD diagnostic codes and includes 31 comorbidities. In origin, the Elixhauser index score was based on the cumulative number of conditions present; since then, several weighting systems accounting for the increase or decrease in mortality risk associated with each condition have been proposed and used in practice (van Walraven et al. 2009; Moore et al. 2017).

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168 citations

Cites background or methods from "Identifying Increased Risk of Readm..."

...In origin, the Elixhauser index score was based on the cumulative number of conditions present; since then, several weighting systems accounting for the increase or decrease in mortality risk associated with each condition have been proposed and used in practice (van Walraven et al. 2009; Moore et al. 2017)....
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...…for the increase or decrease in mortality risk associated with each condition have been proposed and used in practice (van Walraven et al. 2009; Moore et al. 2017). comorbidity is an R package that allows computing comorbidity scores in an easy and straightforward way. comorbidity is available…...
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...The weighting system implemented for the Charlson score is based on the original paper by Charlson et al. (Charlson et al. 1987); conversely, we implemented the weighting system proposed by Moore et al. for the Elixhauser score (Moore et al. 2017)....
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Journal Article•DOI•

Prevalence of Antibiotic-Resistant Pathogens in Culture-Proven Sepsis and Outcomes Associated With Inadequate and Broad-Spectrum Empiric Antibiotic Use.

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Chanu Rhee¹, Chanu Rhee², Sameer S Kadri³, John P. Dekker³, Robert L. Danner³, Huai-Chun Chen, David Fram, Fang Zhang², Rui Wang², Michael Klompas², Michael Klompas¹ - Show less +7 more•Institutions (3)

Brigham and Women's Hospital¹, Harvard University², National Institutes of Health³

01 Apr 2020

TL;DR: In this study, broad-spectrum antibiotics were frequently administered to patients with community-onset sepsis without resistant organisms, and these therapies were associated with worse outcomes.

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Abstract: Importance Broad-spectrum antibiotics are recommended for all patients with suspected sepsis to minimize the risk of undertreatment. However, little is known regarding the net prevalence of antibiotic-resistant pathogens across all patients with community-onset sepsis or the outcomes associated with unnecessarily broad empiric treatment. Objective To elucidate the epidemiology of antibiotic-resistant pathogens and the outcomes associated with both undertreatment and overtreatment in patients with culture-positive community-onset sepsis. Design, Setting, and Participants This cohort study included 17 430 adults admitted to 104 US hospitals between January 2009 and December 2015 with sepsis and positive clinical cultures within 2 days of admission. Data analysis took place from January 2018 to December 2019. Exposures Inadequate empiric antibiotic therapy (ie, ≥1 pathogen nonsusceptible to all antibiotics administered on the first or second day of treatment) and unnecessarily broad empiric therapy (ie, active against methicillin-resistantStaphylococcus aureus[MRSA]; vancomycin-resistantEnterococcus[VRE]; ceftriaxone-resistant gram-negative [CTX-RO] organisms, includingPseudomonas aeruginosa; or extended-spectrum β-lactamase [ESBL] gram-negative organisms when none of these were isolated). Main Outcomes and Measures Prevalence and empiric treatment rates for antibiotic-resistant organisms and associations of inadequate and unnecessarily broad empiric therapy with in-hospital mortality were assessed, adjusting for baseline characteristics and severity of illness. Results Of 17 430 patients with culture-positive community-onset sepsis (median [interquartile range] age, 69 [57-81] years; 9737 [55.9%] women), 2865 (16.4%) died in the hospital. The most common culture-positive sites were urine (9077 [52.1%]), blood (6968 [40.0%]), and the respiratory tract (2912 [16.7%]). The most common pathogens wereEscherichia coli(5873 [33.7%]),S aureus(3706 [21.3%]), andStreptococcusspecies (2361 [13.5%]). Among 15 183 cases in which all antibiotic-pathogen susceptibility combinations could be calculated, most (12 398 [81.6%]) received adequate empiric antibiotics. Empiric therapy targeted resistant organisms in 11 683 of 17 430 cases (67.0%; primarily vancomycin and anti-Pseudomonalβ-lactams), but resistant organisms were uncommon (MRSA, 2045 [11.7%]; CTX-RO, 2278 [13.1%]; VRE, 360 [2.1%]; ESBLs, 133 [0.8%]). The net prevalence for at least 1 resistant gram-positive organism (ie, MRSA or VRE) was 13.6% (2376 patients), and for at least 1 resistant gram-negative organism (ie, CTX-RO, ESBL, or CRE), it was 13.2% (2297 patients). Both inadequate and unnecessarily broad empiric antibiotics were associated with higher mortality after detailed risk adjustment (inadequate empiric antibiotics: odds ratio, 1.19; 95% CI, 1.03-1.37;P = .02; unnecessarily broad empiric antibiotics: odds ratio, 1.22; 95% CI, 1.06-1.40;P = .007). Conclusions and Relevance In this study, most patients with community-onset sepsis did not have resistant pathogens, yet broad-spectrum antibiotics were frequently administered. Both inadequate and unnecessarily broad empiric antibiotics were associated with higher mortality. These findings underscore the need for better tests to rapidly identify patients with resistant pathogens and for more judicious use of broad-spectrum antibiotics for empiric sepsis treatment.

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164 citations

Journal Article•DOI•

Risk factors for discontinuation of buprenorphine treatment for opioid use disorders in a multi-state sample of Medicaid enrollees

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Hillary Samples¹, Arthur Robin Williams², Mark Olfson², Stephen Crystal³•Institutions (3)

Columbia University¹, Columbia University Medical Center², Rutgers University³

01 Dec 2018-Journal of Substance Abuse Treatment

TL;DR: There is a need to implement treatment models that more effectively address barriers to treatment retention for Medicaid beneficiaries with OUD treated with buprenorphine following treatment initiation to identify risk factors for early discontinuation.

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117 citations

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References

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A new method of classifying prognostic comorbidity in longitudinal studies: Development and validation☆

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Mary E. Charlson¹, Peter Pompei¹, Kathy L. Ales¹, C. Ronald MacKenzie¹•Institutions (1)

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"Identifying Increased Risk of Readm..." refers methods in this paper

...Previous research on comorbidity indices focused on predicting mortality in small study populations.(10,11) The Charlson Comorbidity index was designed to predict mortality within 1 year for patients with breast cancer being considered for clinical trials, and subsequent work modified the Charlson index focusing on mortality as the outcome....
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Abstract: \"A new edition of the definitive guide to logistic regression modeling for health science and other applicationsThis thoroughly expanded Third Edition provides an easily accessible introduction to the logistic regression (LR) model and highlights the power of this model by examining the relationship between a dichotomous outcome and a set of covariables. Applied Logistic Regression, Third Edition emphasizes applications in the health sciences and handpicks topics that best suit the use of modern statistical software. The book provides readers with state-of-the-art techniques for building, interpreting, and assessing the performance of LR models. New and updated features include: A chapter on the analysis of correlated outcome data. A wealth of additional material for topics ranging from Bayesian methods to assessing model fit Rich data sets from real-world studies that demonstrate each method under discussion. Detailed examples and interpretation of the presented results as well as exercises throughout Applied Logistic Regression, Third Edition is a must-have guide for professionals and researchers who need to model nominal or ordinal scaled outcome variables in public health, medicine, and the social sciences as well as a wide range of other fields and disciplines\"--

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Adapting a clinical comorbidity index for use with ICD-9-CM administrative databases

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Richard A. Deyo¹, Daniel C. Cherkin², Daniel C. Cherkin³, Daniel C. Cherkin¹, Marcia A. Ciol² - Show less +1 more•Institutions (3)

University of Washington¹, Veterans Health Administration², Group Health Cooperative³

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TL;DR: It is concluded that the adapted comorbidity index will be useful in studies of disease outcome and resource use employing administrative databases.

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9,805 citations

"Identifying Increased Risk of Readm..." refers methods in this paper

...Previous research on comorbidity indices focused on predicting mortality in small study populations.(10,11) The Charlson Comorbidity index was designed to predict mortality within 1 year for patients with breast cancer being considered for clinical trials, and subsequent work modified the Charlson index focusing on mortality as the outcome....
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Journal Article•DOI•

Comorbidity measures for use with administrative data.

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Anne Elixhauser, Claudia A Steiner, D R Harris¹, Rosanna M. Coffey•Institutions (1)

Westat¹

01 Jan 1998-Medical Care

TL;DR: The present method addresses some of the limitations of previous measures and produces an expanded set of comorbidities that easily is applied without further refinement to administrative data for a wide range of diseases.

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Abstract: Objectives.This study attempts to develop a comprehensive set of comorbidity measures for use with large administrative inpatient datasets.Methods.The study involved clinical and empirical review of comorbidity measures, development of a framework that attempts to segregate comorbidities from other

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8,138 citations

"Identifying Increased Risk of Readm..." refers methods in this paper

...The Elixhauser comorbidity measures were developed in 1998 for use with hospital administrative discharge data as a set of clinical conditions that exist before hospital admission, are not related to the principal diagnosis, and are likely to be a significant factor influencing mortality and resource use in the hospital.(1) Numerous studies, mostly clinical in nature, have demonstrated advantages of using the Elixhauser comorbidities to identify increased mortality or hospital readmission risk....
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Journal Article•DOI•

A modification of the Elixhauser comorbidity measures into a point system for hospital death using administrative data.

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Carl van Walraven, Peter C. Austin, Alison Jennings, Hude Quan, Alan J. Forster - Show less +1 more

01 Jun 2009-Medical Care

TL;DR: The Elixhauser comorbidity system can be condensed to a single numeric score that summarizes disease burden and is adequately discriminative for death in hospital when analyzing administrative data.

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Abstract: Background:Comorbidity measures are necessary to describe patient populations and adjust for confounding. In direct comparisons, studies have found the Elixhauser comorbidity system to be statistically slightly superior to the Charlson comorbidity system at adjusting for comorbidity. However, the El

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1,499 citations

"Identifying Increased Risk of Readm..." refers background or methods in this paper

...We used the backward stepwise models thereafter for consistency with the previous literature on the topic.(4,5) For bootstrapped mortality models, acquired immune deficiency syndrome, rheumatoid arthritis, uncomplicated diabetes, peptic ulcer disease, hypothyroidism, and valvular disease were retained in <20% of the stepwise models....
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...The first to create a comorbidity index based on the 30 original Elixhauser measures to predict in-hospital mortality were van Walraven et al.4 They found that the derived comorbidity score was significantly associated with in-hospital mortality, but the data were obtained from a single hospital and pooled over a long time frame (1996–2008) to increase sample size....
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...The first to create a comorbidity index based on the 30 original Elixhauser measures to predict in-hospital mortality were van Walraven et al.(4) They found that the derived comorbidity score was significantly associated with in-hospital mortality, but the data were obtained from a single hospital and pooled over a long time frame (1996–2008) to increase sample size....
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...The Elixhauser comorbidities only recently became available as an index.(4,5) An index provides researchers with assigned weights for diseases from which a single comorbidity score may be implemented in risk-adjustment methodologies and predictive models....
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...We converted the parameter estimates in the final logistic regression models to indices following the methods of Sullivan et al(22) and van Walraven et al.(4) The point value for each comorbidity was calculated as the value of its regression coefficient divided by the absolute value of the regression coefficient for the comorbidity with the smallest absolute value, rounded to the nearest integer....
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