title: Severity of illness scoring systems in the intensive care unit.
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.
  
  
  METHODS
  PubMed search and review of the relevant medical literature.
  
  
  CONCLUSIONS
  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.
authors.affiliation: Department of Anesthesiology, Mayo Clinic, Rochester, MN, USA.
authors.forename: Mark T
authors.initials: MT
authors.lastname: Keegan
authors.forename: Ognjen
authors.initials: O
authors.lastname: Gajic
authors.forename: Bekele
authors.initials: B
authors.lastname: Afessa
mesh_terms: D018806:APACHE; D019985:Benchmarking; D002423:Cause of Death; D003422:Critical Care / Q000379:methods; D016638:Critical Illness / Q000401:mortality* / Q000628:therapy; D005260:Female; D017052:Hospital Mortality / Q000639:trends*; D006801:Humans; D007362:Intensive Care Units; D008297:Male; D015233:Models, Statistical; D012720:Severity of Illness Index; D016019:Survival Analysis; D014481:United States