title: Is the SAPS II score valid in surgical intensive care unit patients?
abstract: OBJECTIVE
  We investigated the performance of the simplified acute physiology score II (SAPS II) in a large cohort of surgical intensive care unit (ICU) patients and tested the hypothesis that customization of the score would improve the uniformity of fit in subgroups of surgical ICU patients.
  
  
  METHODS
  Retrospective analysis of prospectively collected data from all 12,938 patients admitted to a postoperative ICU between January 2004 and January 2009. Probabilities of hospital death were calculated for original and customized (C1-SAPS II and C2-SAPS II) scores. A priori subgroups were defined according to age, probability of death according to the SAPS II score, ICU length of stay (LOS), surgical procedures and type of admission.
  
  
  RESULTS
  The median ICU LOS was 1 (1-3) day. ICU and hospital mortality rates were 5.8% and 10.3%, respectively. Discrimination of the SAPS II was moderate [area under receiver operating characteristic curve (aROC) = 0.76 (0.75-0.78)], but calibration was poor. This model markedly overestimated hospital mortality rates [standardized mortality rate: 0.35 (0.33-0.37)]. First-level customization (C1-SAPS II) did not improve discrimination in the whole cohort or the subgroups, but calibration improved in some subgroups. Second-level customization (C2-SAPS II) improved discrimination in the whole cohort [aROC = 0.82 (0.79-0.85)] and most of the subgroups (aROC range 0.65-86). Calibration in this model (C2-SAPS II) improved in the whole cohort and in subgroups except in patients with ICU LOS 4-14 days and those undergoing neuro- or gastrointestinal surgery.
  
  
  CONCLUSIONS
  In this large cohort of surgical ICU patients, performance of the original SAPS II model was generally poor. Although second-level customization improved discrimination and calibration in the whole cohort and most of the subgroups, it failed to simultaneously improve calibration in the subgroups stratified according to the type of surgery, age or ICU LOS.
authors.affiliation: Department of Anaesthesiology and Intensive Care, Friedrich-Schiller-University Hospital, Jena, Germany. yasser.sakr@med.uni-jena.de
authors.forename: Yasser
authors.initials: Y
authors.lastname: Sakr
authors.forename: Juliana
authors.initials: J
authors.lastname: Marques
authors.forename: Stefan
authors.initials: S
authors.lastname: Mortsch
authors.forename: Matheus Demarchi
authors.initials: MD
authors.lastname: Gonsalves
authors.forename: Khosro
authors.initials: K
authors.lastname: Hekmat
authors.forename: Bjorn
authors.initials: B
authors.lastname: Kabisch
authors.forename: Matthias
authors.initials: M
authors.lastname: Kohl
authors.forename: Konrad
authors.initials: K
authors.lastname: Reinhart
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D019540:Area Under Curve; D019985:Benchmarking; D006305:Health Status Indicators; D017052:Hospital Mortality; D006801:Humans; D007362:Intensive Care Units / Q000706:statistics & numerical data*; D007902:Length of Stay / Q000706:statistics & numerical data; D008875:Middle Aged; D011182:Postoperative Care / Q000379:methods*; D011379:Prognosis; D011785:Quality Assurance, Health Care; D012372:ROC Curve; D012189:Retrospective Studies