Literature DB >> 20838329

Severity of illness scoring systems in the intensive care unit.

Mark T Keegan1, Ognjen Gajic, Bekele Afessa.   

Abstract

OBJECTIVE: Adult intensive care unit prognostic models have been used for predicting patient outcome for three decades. The goal of this review is to describe the different versions of the main adult intensive care unit prognostic models and discuss their potential roles. DATA SOURCE: PubMed search and review of the relevant medical literature.
SUMMARY: The main prognostic models for assessing the overall severity of illness in critically ill adults are Acute Physiology and Chronic Health Evaluation, Simplified Acute Physiology Score, and Mortality Probability Model. Simplified Acute Physiology Score and Mortality Probability Model have been updated to their third versions and Acute Physiology and Chronic Health Evaluation to its fourth version. The development of prognostic models is usually followed by internal and external validation and performance assessment. Performance is assessed by area under the receiver operating characteristic curve for discrimination and Hosmer-Lemeshow statistic for calibration. The areas under the receiver operating characteristic curve of Simplified Acute Physiology Score 3, Acute Physiology and Chronic Health Evaluation IV, and Mortality Probability Model0 III were 0.85, 0.88, and 0.82, respectively, and all these three fourth-generation models had good calibration. The models have been extensively used for case-mix adjustment in clinical research and epidemiology, but their role in benchmarking, performance improvement, resource use, and clinical decision support has been less well studied.
CONCLUSIONS: The fourth-generation Acute Physiology and Chronic Health Evaluation, Simplified Acute Physiology Score 3, Acute Physiology and Chronic Health Evaluation IV, and Mortality Probability Model0 III adult prognostic models, perform well in predicting mortality. Future studies are needed to determine their roles for benchmarking, performance improvement, resource use, and clinical decision support.

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Year:  2011        PMID: 20838329     DOI: 10.1097/CCM.0b013e3181f96f81

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


  48 in total

1.  Development and cross-validation of the in-hospital mortality prediction in advanced cancer patients score: a preliminary study.

Authors:  David Hui; Kelly Kilgore; Bryan Fellman; Diana Urbauer; Stacy Hall; Julieta Fajardo; Wadih Rhondali; Jung Hun Kang; Egidio Del Fabbro; Donna Zhukovsky; Eduardo Bruera
Journal:  J Palliat Med       Date:  2012-06-04       Impact factor: 2.947

2.  Comparison of APACHE III, APACHE IV, SAPS 3, and MPM0III and influence of resuscitation status on model performance.

Authors:  Mark T Keegan; Ognjen Gajic; Bekele Afessa
Journal:  Chest       Date:  2012-10       Impact factor: 9.410

3.  An Evaluation of the Influence of Body Mass Index on Severity Scoring.

Authors:  Rodrigo Octavio Deliberato; Ary Serpa Neto; Matthieu Komorowski; David J Stone; Stephanie Q Ko; Lucas Bulgarelli; Carolina Rodrigues Ponzoni; Renato Carneiro de Freitas Chaves; Leo Anthony Celi; Alistair E W Johnson
Journal:  Crit Care Med       Date:  2019-02       Impact factor: 7.598

4.  Real-time mortality prediction in the Intensive Care Unit.

Authors:  Alistair E W Johnson; Roger G Mark
Journal:  AMIA Annu Symp Proc       Date:  2018-04-16

5.  Hospital mortality prediction for intermediate care patients: Assessing the generalizability of the Intermediate Care Unit Severity Score (IMCUSS).

Authors:  David N Hager; Varshitha Tanykonda; Zeba Noorain; Sarina K Sahetya; Catherine E Simpson; Juan Felipe Lucena; Dale M Needham
Journal:  J Crit Care       Date:  2018-05-19       Impact factor: 3.425

6.  Prognostic Evaluation of Mortality after Pediatric Resuscitation Assisted by Extracorporeal Life Support.

Authors:  Aurélie De Mul; Duy-Anh Nguyen; Carsten Doell; Marie-Hélène Perez; Vincenzo Cannizzaro; Oliver Karam
Journal:  J Pediatr Intensive Care       Date:  2018-07-11

7.  Learning a Severity Score for Sepsis: A Novel Approach based on Clinical Comparisons.

Authors:  Kirill Dyagilev; Suchi Saria
Journal:  AMIA Annu Symp Proc       Date:  2015-11-05

8.  Prognosis of patients with shock receiving vasopressors.

Authors:  Xue-Zhong Xing; Hai-Jun Wang; Chu-Lin Huang; Quan-Hui Yang; Shi-Ning Qu; Hao Zhang; Hao Wang; Yong Gao; Qing-Ling Xiao; Ke-Lin Sun
Journal:  World J Emerg Med       Date:  2013

9.  Subsequent infections in survivors of sepsis: epidemiology and outcomes.

Authors:  Tisha Wang; Ariss Derhovanessian; Sharon De Cruz; John A Belperio; Jane C Deng; Guy Soo Hoo
Journal:  J Intensive Care Med       Date:  2012-12-26       Impact factor: 3.510

Review 10.  Computerized decision support in adult and pediatric critical care.

Authors:  Cydni N Williams; Susan L Bratton; Eliotte L Hirshberg
Journal:  World J Crit Care Med       Date:  2013-11-04
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