Literature DB >> 10767249

Effect of varying the case mix on the standardized mortality ratio and W statistic: A simulation study.

L G Glance1, T Osler, T Shinozaki.   

Abstract

OBJECTIVE: To evaluate the validity of using the standardized mortality ratio (SMR) and the W statistic as risk-adjusted measures of hospital mortality to judge ICU performance.
DESIGN: APACHE (acute physiology and chronic health evaluation) II data were collected prospectively from the surgical ICU (SICU) at a single institution using all adult admissions (n = 6806) over an 8-year period (excluding cardiac surgical patients, burn patients, and patients under 16 years of age). Using a computer simulation technique, virtual ICUs (VICUs) with mortality rates between 5% and 16% were constructed. After first dividing the original data set into deciles of risk, each VICU was constructed by randomly resampling between 10 and 680 patients from each decile. The SMR, W statistic, and Z statistic were calculated for 10,000 different case mixes.
SETTING: The SICU at a 450-bed teaching hospital. PATIENTS: A group of 6,806 adult patient admissions, excluding cardiac surgical patients and burn patients. MEASUREMENTS AND
RESULTS: VICUs were created from a data set of actual patients treated at one institution in order to test the hypothesis that the SMR and W statistic would remain invariant when applied to subsets of patients from a single institution. Instead, the SMR and W statistic were found to be very sensitive to changes in case mix. The SMR and W statistic were linear functions of the simulated ICU mortality rate.
CONCLUSION: This simulation demonstrates that the SMR and the W statistic based on APACHE II cannot be used to compare outcomes of ICUs. We have proposed a revision of the SMR that eliminates the effect of case mix and allows for more accurate comparisons of ICU performance.

Entities:  

Mesh:

Year:  2000        PMID: 10767249     DOI: 10.1378/chest.117.4.1112

Source DB:  PubMed          Journal:  Chest        ISSN: 0012-3692            Impact factor:   9.410


  10 in total

1.  Predictors of early postdischarge mortality in critically ill patients: a retrospective cohort study from the California Intensive Care Outcomes project.

Authors:  Eduard E Vasilevskis; Michael W Kuzniewicz; Brian A Cason; Rondall K Lane; Mitzi L Dean; Ted Clay; Deborah J Rennie; R Adams Dudley
Journal:  J Crit Care       Date:  2010-08-16       Impact factor: 3.425

Review 2.  Clinical review: scoring systems in the critically ill.

Authors:  Jean-Louis Vincent; Rui Moreno
Journal:  Crit Care       Date:  2010-03-26       Impact factor: 9.097

3.  International comparison of the performance of the paediatric index of mortality (PIM) 2 score in two national data sets.

Authors:  Stéphane Leteurtre; Bruno Grandbastien; Francis Leclerc; Roger Parslow
Journal:  Intensive Care Med       Date:  2012-05-09       Impact factor: 17.440

4.  Process monitoring in intensive care with the use of cumulative expected minus observed mortality and risk-adjusted P charts.

Authors:  Jerome G L Cockings; David A Cook; Rehana K Iqbal
Journal:  Crit Care       Date:  2006-02       Impact factor: 9.097

5.  Evaluation of Quality Indicators in an Indian Intensive Care Unit Using "CHITRA" Database.

Authors:  Kiran Kumar Gudivada; Bhuvana Krishna; Sampath Sriram
Journal:  Indian J Crit Care Med       Date:  2017-12

6.  Ranking hospitals when performance and risk factors are correlated: A simulation-based comparison of risk adjustment approaches for binary outcomes.

Authors:  Martin Roessler; Jochen Schmitt; Olaf Schoffer
Journal:  PLoS One       Date:  2019-12-04       Impact factor: 3.240

7.  Validation of a simplified risk prediction model using a cloud based critical care registry in a lower-middle income country.

Authors:  Bharath Kumar Tirupakuzhi Vijayaraghavan; Dilanthi Priyadarshini; Aasiyah Rashan; Abi Beane; Ramesh Venkataraman; Nagarajan Ramakrishnan; Rashan Haniffa
Journal:  PLoS One       Date:  2020-12-31       Impact factor: 3.240

8.  External validation of the Acute Physiology and Chronic Health Evaluation II in Korean intensive care units.

Authors:  Jae Yeol Kim; So Yeon Lim; Kyeongman Jeon; Younsuck Koh; Chae-Man Lim; Shin Ok Koh; Sungwon Na; Kyoung Min Lee; Byung Ho Lee; Jae-Young Kwon; Kook Hyun Lee; Seok-Hwa Yoon; Jisook Park; Gee Young Suh
Journal:  Yonsei Med J       Date:  2013-03-01       Impact factor: 2.759

9.  Optimal nutrition during the period of mechanical ventilation decreases mortality in critically ill, long-term acute female patients: a prospective observational cohort study.

Authors:  Rob J M Strack van Schijndel; Peter J M Weijs; Rixt H Koopmans; Hans P Sauerwein; Albertus Beishuizen; Armand R J Girbes
Journal:  Crit Care       Date:  2009-08-11       Impact factor: 9.097

10.  Direct risk standardisation: a new method for comparing casemix adjusted event rates using complex models.

Authors:  Jon Nicholl; Richard M Jacques; Michael J Campbell
Journal:  BMC Med Res Methodol       Date:  2013-10-29       Impact factor: 4.615

  10 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.