A Modification of the Elixhauser Comorbidity Measures Into a Point System for Hospital Death Using Administrative Data

By C. van Walraven, Peter C. Austin, Alison Jennings, Hude Quan, A. Forster. Medical Care, 2009

1,905 citations recorded by the source corpus.

Subfields: Epidemiology, General Health Professions

View Paper: https://www.semanticscholar.org/paper/f37738854b6a80f22d305cc789a6df95d1f9b7ad

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Citations in the corpus

comorbidity: An R package for computing comorbidity scores (2018, methodology): “Given that the version of the Elixhauser score studied by Moore et al . only included 29 comorbid conditions, we applied weights from van Walraven et al . for the remaining domains (van Walraven et al. 2009).”
Why summary comorbidity measures such as the Charlson Comorbidity Index and Elixhauser score work (2015, methodology): “As an additional example, we also investigate properties of the more recently developed Elixhauser score.(8) From the *Whiting School of Engineering Undergraduate Student, Johns Hopkins University, Baltimore, MD; Departments of wMedical Oncology; zSurgery, Fox Chase Cancer Center; yAcademic Affairs,”
Nationwide Trends in the Performance of Inpatient Hysterectomy in the United States (2013, background): “Patients were categorized based on the number of medical comorbidities into: zero, one, or two or more, as previously reported.(20) The hospitals in which patients were treated were characterized based on location (urban, rural), region of the country (northeast, midwest, west, south), size (small, ”
A combined comorbidity score predicted mortality in elderly patients better than existing scores (2011, methodology): “Nevertheless, using a new implementation of a single weighted numeric summary of the Elixhauser system, van Walraven et al [17] showed that it out-performed the Romano/ Charlson measure with Medicare weights derived by Schneeweiss et al [18] in discriminating in-hospital death.”
Time-uniform central limit theory and asymptotic confidence sequences (2021, neutral): “Baseline covariates X included for modelling consisted of the patients’ age and sex, whether they are diabetic, modified Elixhauser scores [54], and SOFA scores.”
Dynamic data during hypotensive episode improves mortality predictions among patients with sepsis and hypotension (2013, methodology): “Interestingly, while the genetic algorithm selected previously known predictors of mortality such as age, urine output, shock index, SOFA score and comorbid conditions as measured by the Van Walraven score (21), we also identified the change in serum chloride levels spanning the 24 hour interval bef”
Identifying Increased Risk of Readmission and In-hospital Mortality Using Hospital Administrative Data: The AHRQ Elixhauser Comorbidity Index (2017, methodology): “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 retain”
A data-driven approach to optimized medication dosing: a focus on heparin (2014, background): “nonwhite), the Elixhauser comorbidity index (an indicator of chronic illness), [13, 14] ICU care unit type (medical vs.”