Eldho Paul1, Michael Bailey, David Pilcher. 1. Australian and New Zealand Intensive Care Research Centre, Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia. Electronic address: eldho.paul@monash.edu.
Abstract
PURPOSE: The purpose of this study is to develop and validate a new mortality prediction model (Australian and New Zealand Risk of Death [ANZROD]) for Australian and New Zealand intensive care units (ICUs) and compare its performance with the existing Acute Physiology and Chronic Health Evaluation (APACHE) III-j. MATERIALS AND METHODS: All ICU admissions from 2004 to 2009 were extracted from the Australian and New Zealand Intensive Care Society Adult Patient Database. Hospital mortality was modeled using logistic regression with training (two third) and validation (one third) data sets. Predictor variables included APACHE III score components, source of admission to ICU and hospital, lead time, elective surgery, treatment limitation, ventilation status, and APACHE III diagnoses. Model performance was assessed by standardized mortality ratio, Hosmer-Lemeshow C and H statistics, Brier score, Cox calibration regression, area under the receiver operating characteristic curve, and calibration curves. RESULTS: There were 456605 patients available for model development and validation. Observed mortality was 11.3%. Performance measures (standardized mortality ratio, Hosmer-Lemeshow C and H statistics, and receiver operating characteristic curve) for the ANZROD and APACHE III-j model in the validation data set were 1.01, 104.9 and 111.4, and 0.902; 0.84, 1596.6 and 2087.3, and 0.885, respectively. CONCLUSIONS: The ANZROD has better calibration; discrimination compared with the APACHE III-j. Further research is required to validate performance over time and in specific subgroups of ICU population.
PURPOSE: The purpose of this study is to develop and validate a new mortality prediction model (Australian and New Zealand Risk of Death [ANZROD]) for Australian and New Zealand intensive care units (ICUs) and compare its performance with the existing Acute Physiology and Chronic Health Evaluation (APACHE) III-j. MATERIALS AND METHODS: All ICU admissions from 2004 to 2009 were extracted from the Australian and New Zealand Intensive Care Society Adult Patient Database. Hospital mortality was modeled using logistic regression with training (two third) and validation (one third) data sets. Predictor variables included APACHE III score components, source of admission to ICU and hospital, lead time, elective surgery, treatment limitation, ventilation status, and APACHE III diagnoses. Model performance was assessed by standardized mortality ratio, Hosmer-Lemeshow C and H statistics, Brier score, Cox calibration regression, area under the receiver operating characteristic curve, and calibration curves. RESULTS: There were 456605 patients available for model development and validation. Observed mortality was 11.3%. Performance measures (standardized mortality ratio, Hosmer-Lemeshow C and H statistics, and receiver operating characteristic curve) for the ANZROD and APACHE III-j model in the validation data set were 1.01, 104.9 and 111.4, and 0.902; 0.84, 1596.6 and 2087.3, and 0.885, respectively. CONCLUSIONS: The ANZROD has better calibration; discrimination compared with the APACHE III-j. Further research is required to validate performance over time and in specific subgroups of ICU population.
Authors: Brenda M Vincent; Daniel Molling; Gabriel J Escobar; Timothy P Hofer; Theodore J Iwashyna; Vincent X Liu; Amy K Rosen; Andrew M Ryan; Sarah Seelye; Wyndy L Wiitala; Hallie C Prescott Journal: Med Care Date: 2021-12-01 Impact factor: 2.983
Authors: Manoj Saxena; Paul Young; David Pilcher; Michael Bailey; David Harrison; Rinaldo Bellomo; Simon Finfer; Richard Beasley; Jonathan Hyam; David Menon; Kathryn Rowan; John Myburgh Journal: Intensive Care Med Date: 2015-02-03 Impact factor: 17.440
Authors: David Pilcher; Laura Gladkis; Byron Arcia; Michael Bailey; David Cook; Yael Cass; Helen Opdam Journal: Transplantation Date: 2015-10 Impact factor: 4.939