Literature DB >> 23194207

Performance of APACHE III over time in Australia and New Zealand: a retrospective cohort study.

E Paul1, M Bailey, A Van Lint, V Pilcher.   

Abstract

The Acute Physiology and Chronic Health Evaluation (APACHE) III-j model has been used for benchmarking intensive care unit (ICU) outcomes in Australia and New Zealand for over a decade. This study assessed performance of the APACHE III-j model in adult patients admitted to Australasian ICUs during a ten-year period. Data were extracted from the Australian and New Zealand Intensive Care Society Adult Patient Database. Performance of APACHE III-j at different time points and within different age strata was evaluated by dividing the whole cohort into five 'two-year' groups. Calibration and discrimination were assessed by the Brier score, Hosmer-Lemeshow C and H statistics, Standardised Mortality Ratio (SMR), Cox calibration regression, calibration curves and area under the receiver operating characteristic curve (AUROC). Model performance within diagnostic categories was evaluated. Between 1 January 2000 and 31 December 2009, 558,585 ICU admissions which met inclusion criteria were included in the analysis. The mean (standard deviation) age was 60.8 (18.4) years and 58.3% were male. Overall observed mortality was 12.6%. The mean (standard deviation) APACHE III-j predicted mortality was 14.5% (21.8). Although discrimination (as measured by AUROC) was preserved over time, all other markers of model performance showed deterioration. There was a significant decrease in SMR in eight of ten most common diagnoses examined. This study demonstrates that performance of APACHE III-j model has deteriorated in Australasian hospitals and there is now a clear need for an updated modelling approach to improve mortality prediction, performance monitoring and quality of research undertaken in Australian and New Zealand ICUs.

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Year:  2012        PMID: 23194207     DOI: 10.1177/0310057X1204000609

Source DB:  PubMed          Journal:  Anaesth Intensive Care        ISSN: 0310-057X            Impact factor:   1.669


  11 in total

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2.  The Global Open Source Severity of Illness Score (GOSSIS).

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3.  Developing and Validating Multi-Modal Models for Mortality Prediction in COVID-19 Patients: a Multi-center Retrospective Study.

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Journal:  J Digit Imaging       Date:  2022-07-05       Impact factor: 4.903

4.  Evaluation of APACHE-IV Predictive Scoring in Surgical Abdominal Sepsis: A Retrospective Cohort Study.

Authors:  Tiffany Chan; Michael S Bleszynski; Andrzej K Buczkowski
Journal:  J Clin Diagn Res       Date:  2016-03-01

5.  A study on the efficacy of APACHE-IV for predicting mortality and length of stay in an intensive care unit in Iran.

Authors:  Mohammad Ghorbani; Haleh Ghaem; Abbas Rezaianzadeh; Zahra Shayan; Farid Zand; Reza Nikandish
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6.  Characterising risk of in-hospital mortality following cardiac arrest using machine learning: A retrospective international registry study.

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Review 7.  The Role of Oliguria and the Absence of Fluid Administration and Balance Information in Illness Severity Scores.

Authors:  Neil J Glassford; Rinaldo Bellomo
Journal:  Korean J Crit Care Med       Date:  2017-05-31

8.  Calibration drift in regression and machine learning models for acute kidney injury.

Authors:  Sharon E Davis; Thomas A Lasko; Guanhua Chen; Edward D Siew; Michael E Matheny
Journal:  J Am Med Inform Assoc       Date:  2017-11-01       Impact factor: 4.497

9.  Increased insulin resistance in intensive care: longitudinal retrospective analysis of glycaemic control patients in a New Zealand ICU.

Authors:  Jennifer L Knopp; J Geoffrey Chase; Geoffrey M Shaw
Journal:  Ther Adv Endocrinol Metab       Date:  2021-05-31       Impact factor: 3.565

10.  A scoping review of registry captured indicators for evaluating quality of critical care in ICU.

Authors:  Issrah Jawad; Sumayyah Rashan; Chathurani Sigera; Jorge Salluh; Arjen M Dondorp; Rashan Haniffa; Abi Beane
Journal:  J Intensive Care       Date:  2021-08-05
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