title: Using patient admission characteristics alone to predict mortality of critically ill patients: A comparison of 3 prognostic scores.
abstract: OBJECTIVE
  This study compared the performance of 3 admission prognostic scores in predicting hospital mortality.
  
  
  METHODS
  Patient admission characteristics and hospital outcome of 9549 patients were recorded prospectively. The discrimination and calibration of the predicted risks of death derived from the Simplified Acute Physiology Score (SAPS III), Admission Mortality Prediction Model (MPM0 III), and admission Acute Physiology and Chronic Health Evaluation (APACHE) II were assessed by the area under the receiver operating characteristic curve and a calibration plot, respectively.
  
  
  RESULTS
  Of the 9549 patients included in the study, 1276 patients (13.3%) died after intensive care unit admission. Patient admission characteristics were significantly different between the survivors and nonsurvivors. All 3 prognostic scores had a reasonable ability to discriminate between the survivors and nonsurvivors (area under the receiver operating characteristic curve for SAPS III, 0.836; MPM0 III, 0.807; admission APACHE, 0.845), with best discrimination in emergency admissions. The SAPS III model had a slightly better calibration and overall performance (slope of calibration curve, 1.03; Brier score, 0.09; Nagelkerke R(2), 0.297) compared to the MPM0 III and admission APACHE II model.
  
  
  CONCLUSIONS
  All 3 intensive care unit admission prognostic scores had a good ability to predict hospital mortality of critically ill patients, with best discrimination in emergency admissions.
authors.affiliation: Department of Intensive Care, Royal Perth Hospital, Perth, Australia; School of Population Health, University of Western Australia, Perth, Australia; School of Veterinary & Life Sciences, Murdoch University, Perth, Australia. Electronic address: kwok.ho@health.wa.gov.au.
authors.forename: Kwok M
authors.initials: KM
authors.lastname: Ho
authors.affiliation: School of Nursing, Midwifery and Paramedicine, Curtin University, Perth, Australia. Electronic address: teresa.williams@curtin.edu.au.
authors.forename: Teresa A
authors.initials: TA
authors.lastname: Williams
authors.affiliation: Department of Intensive Care, Royal Perth Hospital, Perth, Australia. Electronic address: Yusra.Harahsheh@health.wa.gov.au.
authors.forename: Yusra
authors.initials: Y
authors.lastname: Harahsheh
authors.affiliation: Medicine, Surgery & Anesthesiology, Baystate Franklin Medical Center, Springfield, MA; Tufts University School of Medicine, Boston, MA. Electronic address: Thomas.Higgins@baystatehealth.org.
authors.forename: Thomas L
authors.initials: TL
authors.lastname: Higgins
mesh_terms: D018806:APACHE; D000328:Adult; D000368:Aged; D016638:Critical Illness / Q000401:mortality*; D005260:Female; D017052:Hospital Mortality; D006801:Humans; D007362:Intensive Care Units; D008297:Male; D008875:Middle Aged; D010343:Patient Admission; D011379:Prognosis; D012372:ROC Curve; D020379:Risk Adjustment; D012720:Severity of Illness Index
keywords: Outcome; Prediction; Prognosis; Risk adjustment; Severity of illness