Literature DB >> 11057796

Effect of mortality rate on the performance of the Acute Physiology and Chronic Health Evaluation II: a simulation study.

L G Glance1, T M Osler, P Papadakos.   

Abstract

OBJECTIVE: To evaluate the impact of case mix variation on the performance of the Acute Physiology and Chronic Health Evaluation (APACHE) II using measures of calibration and discrimination.
DESIGN: APACHE II data were collected prospectively at the surgical intensive care unit of the University of Vermont on all adult admissions over an 8-yr period (excluding cardiac surgical patients, burn patients, and patients < 16 yrs of age). The original case mix was systematically varied to create 2,000 different case mixes ranging in mortality between 5% and 18% using a computer-intensive resampling algorithm. The area under the receiver operating characteristic curve and the Hosmer-Lemeshow C statistic were derived for each of the simulated case mixes with bootstrapping.
SETTING: The surgical intensive care unit at a 450-bed teaching hospital. PATIENTS: A group of 6,806 adult surgical patients excluding cardiac surgical patients and burn patients. MEASUREMENTS AND
RESULTS: Simulated data sets were created from a database of patients treated at a single institution to test the hypothesis that the performance of APACHE II is stable across a clinically reasonable range of mortality rates. The discrimination and calibration of APACHE II varied with case mix.
CONCLUSION: The discrimination of APACHE II is not independent of case mix. However, the variability of the Hosmer-Lemeshow statistic as a function of the case mix may simply reflect the limitations of this goodness of fit statistic to assess model calibration. Because the discrimination of APACHE II is a function of case mix, caution should be exercised when using APACHE II-based adjusted mortality rates to compare intensive care units with widely divergent case mixes.

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Year:  2000        PMID: 11057796     DOI: 10.1097/00003246-200010000-00008

Source DB:  PubMed          Journal:  Crit Care Med        ISSN: 0090-3493            Impact factor:   7.598


  5 in total

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Journal:  Intensive Care Med       Date:  2005-10-05       Impact factor: 17.440

2.  Intensive care medicine in 2050: statistical tools for development of prognostic models (why clinicians should not be ignored).

Authors:  Daniele Poole; Greta Carrara; Guido Bertolini
Journal:  Intensive Care Med       Date:  2017-06-02       Impact factor: 17.440

3.  External validation of the SAPS II, APACHE II and APACHE III prognostic models in South England: a multicentre study.

Authors:  Dieter H Beck; Gary B Smith; John V Pappachan; Brian Millar
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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.  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

  5 in total

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