Literature DB >> 22033059

Caution when using prognostic models: a prospective comparison of 3 recent prognostic models.

Antonio Paulo Nassar1, Amilcar Oshiro Mocelin, André Luiz Baptiston Nunes, Fabio Poianas Giannini, Leonardo Brauer, Fabio Moreira Andrade, Carlos Augusto Dias.   

Abstract

PURPOSE: Prognostic models have been developed to estimate mortality and to compare outcomes in different intensive care units. However, these models need to be validated before their use in different populations. In this study, we assessed the performance of 3 recently developed general prognostic models (Acute Physiologic and Chronic Health Evaluation [APACHE] IV, Simplified Acute Physiology Score [SAPS] 3 and Mortality Probability Model III [MPM(0)-III]) in a population admitted at 3 medical-surgical Brazilian intensive care units.
MATERIALS AND METHODS: All patients admitted from July 2008 to December 2009 were evaluated for inclusion in the study. Standardized mortality ratios were calculated for all models. Calibration was assessed by the Hosmer-Lemeshow goodness-of-fit test. Discrimination was evaluated using the area under the receiver operator curve.
RESULTS: A total of 5780 patients were included. Inhospital mortality was 9.1%. Discrimination was very good for all models (area under the receiver operator curve for APACHE IV, SAPS 3 and MPM(0)-III was 0.883, 0.855 and 0.840, respectively). APACHE IV showed better discrimination than SAPS 3 and MPM(0)-III (P < .001 for both comparisons). All models calibrated poorly and overestimated hospital mortality (Hosmer-Lemeshow statistic was 53.7, 134.2, 226.6 for APACHE IV, MPM(0)-III, and SAPS 3, respectively; P < .001 for all).
CONCLUSIONS: In this study, all models showed poor calibration, while discrimination was very good for all of them. As this has been a common finding in validation studies, caution is warranted when using prognostic models for benchmarking.
Copyright © 2012 Elsevier Inc. All rights reserved.

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Year:  2011        PMID: 22033059     DOI: 10.1016/j.jcrc.2011.08.016

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


  26 in total

1.  Calibration strategies to validate predictive models: is new always better?

Authors:  Nicolás Serrano
Journal:  Intensive Care Med       Date:  2012-05-15       Impact factor: 17.440

2.  Validation of the APACHE IV model and its comparison with the APACHE II, SAPS 3, and Korean SAPS 3 models for the prediction of hospital mortality in a Korean surgical intensive care unit.

Authors:  Hannah Lee; Yoon-Jung Shon; Hyerim Kim; Hyesun Paik; Hee-Pyoung Park
Journal:  Korean J Anesthesiol       Date:  2014-08-26

3.  Mortality prediction in intensive care units with the Super ICU Learner Algorithm (SICULA): a population-based study.

Authors:  Romain Pirracchio; Maya L Petersen; Marco Carone; Matthieu Resche Rigon; Sylvie Chevret; Mark J van der Laan
Journal:  Lancet Respir Med       Date:  2014-11-24       Impact factor: 30.700

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

Authors:  Jesse D Raffa; Alistair E W Johnson; Zach O'Brien; Tom J Pollard; Roger G Mark; Leo A Celi; David Pilcher; Omar Badawi
Journal:  Crit Care Med       Date:  2022-03-25       Impact factor: 9.296

5.  The Applicability of Commonly Used Severity of Illness Scores to Tropical Infections in Australia.

Authors:  Kris Salaveria; Simon Smith; Yu-Hsuan Liu; Richard Bagshaw; Markus Ott; Alexandra Stewart; Matthew Law; Angus Carter; Josh Hanson
Journal:  Am J Trop Med Hyg       Date:  2021-10-18       Impact factor: 3.707

6.  Prediction of ICU mortality in critically ill children : Comparison of SOFA, GCS, and FOUR score.

Authors:  Jamileh Ramazani; Mohammad Hosseini
Journal:  Med Klin Intensivmed Notfmed       Date:  2018-10-01       Impact factor: 0.840

7.  Performance of three prognostic models in patients with cancer in need of intensive care in a medical center in China.

Authors:  XueZhong Xing; Yong Gao; HaiJun Wang; ChuLin Huang; ShiNing Qu; Hao Zhang; Hao Wang; KeLin Sun
Journal:  PLoS One       Date:  2015-06-25       Impact factor: 3.240

8.  Sequential oxygenation index and organ dysfunction assessment within the first 3 days of mechanical ventilation predict the outcome of adult patients with severe acute respiratory failure.

Authors:  Hsu-Ching Kao; Ting-Yu Lai; Heui-Ling Hung; Yu-Mu Chen; Po-An Chou; Chin-Chou Wang; Meng-Chih Lin; Wen-Feng Fang
Journal:  ScientificWorldJournal       Date:  2013-02-18

9.  APACHE IV is superior to MELD scoring system in predicting prognosis in patients after orthotopic liver transplantation.

Authors:  Yueyun Hu; Xianling Zhang; Yuan Liu; Jun Yan; Tiehua Li; Ailing Hu
Journal:  Clin Dev Immunol       Date:  2013-11-18

Review 10.  Year in review 2012: Critical Care--management.

Authors:  Abhijit Duggal; Gordon Rubenfeld
Journal:  Crit Care       Date:  2013-11-22       Impact factor: 9.097

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