Literature DB >> 16769457

Adjustment of intensive care unit outcomes for severity of illness and comorbidity scores.

Monica Norena1, Hubert Wong, Willie D Thompson, Sean P Keenan, Peter M Dodek.   

Abstract

PURPOSE: Comparison of outcomes among intensive care units (ICUs) requires adjustment for patient variables. Severity of illness scores are associated with hospital mortality, but administrative databases rarely include the elements of these scores. However, these databases include the elements of comorbidity scores. The purpose of this study was to compare the value of these scores as adjustment variables in statistical models of hospital mortality and hospital and ICU length of stay after adjustment for other covariates.
MATERIALS AND METHODS: We used multivariable regression to study 1808 patients admitted to a 13-bed medical-surgical ICU in a 400-bed tertiary hospital between December 1998 and August 2003.
RESULTS: For all patients, after adjusting for age, sex, major clinical category, source of admission, and socioeconomic determinants of health, we found that Acute Physiology and Chronic Health Evaluation (APACHE) II and comorbidity scores were significantly associated with hospital mortality and that comorbidity but not APACHE II was significantly associated with hospital length of stay. Separate analysis of hospital survivors and nonsurvivors showed that both APACHE II and comorbidity scores were significantly associated with hospital length of stay and APACHE II score was associated with ICU length of stay.
CONCLUSION: The value of APACHE II and comorbidity scores as adjustment variables depends on the outcome and population of interest.

Entities:  

Mesh:

Year:  2006        PMID: 16769457     DOI: 10.1016/j.jcrc.2005.11.011

Source DB:  PubMed          Journal:  J Crit Care        ISSN: 0883-9441            Impact factor:   3.425


  9 in total

1.  Pneumothorax after insertion of central venous catheters in the intensive care unit: association with month of year and week of month.

Authors:  Najib T Ayas; Monica Norena; Hubert Wong; Dean Chittock; Peter M Dodek
Journal:  Qual Saf Health Care       Date:  2007-08

2.  Comparison of Charlson comorbidity index with SAPS and APACHE scores for prediction of mortality following intensive care.

Authors:  Steffen Christensen; Martin Berg Johansen; Christian Fynbo Christiansen; Reinhold Jensen; Stanley Lemeshow
Journal:  Clin Epidemiol       Date:  2011-06-17       Impact factor: 4.790

3.  The Prognostic Value of the Charlson's Comorbidity Index in Patients with Prolonged Acute Mechanical Ventilation: A Single Center Experience.

Authors:  Seung Eon Song; Sang Hee Lee; Eun-Jung Jo; Jung Seop Eom; Jeong Ha Mok; Mi-Hyun Kim; Ki Uk Kim; Min Ki Lee; Kwangha Lee
Journal:  Tuberc Respir Dis (Seoul)       Date:  2016-10-05

4.  Respiratory mechanics, ventilator-associated pneumonia and outcomes in intensive care unit.

Authors:  Kelser de Souza Kock; Rosemeri Maurici
Journal:  World J Crit Care Med       Date:  2018-02-04

5.  Charlson comorbidity index derived from chart review or administrative data: agreement and prediction of mortality in intensive care patients.

Authors:  Knut Stavem; Henrik Hoel; Stein Arve Skjaker; Rolf Haagensen
Journal:  Clin Epidemiol       Date:  2017-06-02       Impact factor: 4.790

6.  Critical care at the end of life: a population-level cohort study of cost and outcomes.

Authors:  Dipayan Chaudhuri; Peter Tanuseputro; Brent Herritt; Gianni D'Egidio; Mathieu Chalifoux; Kwadwo Kyeremanteng
Journal:  Crit Care       Date:  2017-05-31       Impact factor: 9.097

7.  A comparison between the APACHE II and Charlson Index Score for predicting hospital mortality in critically ill patients.

Authors:  Susan Quach; Deirdre A Hennessy; Peter Faris; Andrew Fong; Hude Quan; Christopher Doig
Journal:  BMC Health Serv Res       Date:  2009-07-30       Impact factor: 2.655

8.  Long-term outcomes and healthcare utilization following critical illness--a population-based study.

Authors:  A D Hill; R A Fowler; R Pinto; M S Herridge; B H Cuthbertson; D C Scales
Journal:  Crit Care       Date:  2016-03-31       Impact factor: 9.097

9.  Administrative and Claims Data Help Predict Patient Mortality in Intensive Care Units by Logistic Regression: A Nationwide Database Study.

Authors:  Yu-Ting Hsu; Yi-Ting He; Chien-Kun Ting; Mei-Yung Tsou; Gau-Jun Tang; Christy Pu
Journal:  Biomed Res Int       Date:  2020-02-25       Impact factor: 3.411

  9 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.