title: Mortality prediction using SAPS II: an update for French intensive care units.
abstract: BACKGROUND
  The standardized mortality ratio (SMR) is commonly used for benchmarking intensive care units (ICUs). Available mortality prediction models are outdated and must be adapted to current populations of interest. The objective of this study was to improve the Simplified Acute Physiology Score (SAPS) II for mortality prediction in ICUs, thereby improving SMR estimates.
  
  
  METHODS
  A retrospective data base study was conducted in patients hospitalized in 106 French ICUs between 1 January 1998 and 31 December 1999. A total of 77,490 evaluable admissions were split into a training set and a validation set. Calibration and discrimination were determined for the original SAPS II, a customized SAPS II and an expanded SAPS II developed in the training set by adding six admission variables: age, sex, length of pre-ICU hospital stay, patient location before ICU, clinical category and whether drug overdose was present. The training set was used for internal validation and the validation set for external validation.
  
  
  RESULTS
  With the original SAPS II calibration was poor, with marked underestimation of observed mortality, whereas discrimination was good (area under the receiver operating characteristic curve 0.858). Customization improved calibration but had poor uniformity of fit; discrimination was unchanged. The expanded SAPS II exhibited good calibration, good uniformity of fit and better discrimination (area under the receiver operating characteristic curve 0.879). The SMR in the validation set was 1.007 (confidence interval 0.985-1.028). Some ICUs had better and others worse performance with the expanded SAPS II than with the customized SAPS II.
  
  
  CONCLUSIONS
  The original SAPS II model did not perform sufficiently well to be useful for benchmarking in France. Customization improved the statistical qualities of the model but gave poor uniformity of fit. Adding simple variables to create an expanded SAPS II model led to better calibration, discrimination and uniformity of fit, producing a tool suitable for benchmarking.
authors.affiliation: Unit of Medical intensive, Hôpital Saint Louis, Paris, France. jr.legall@sls.ap-hop-paris.fr
authors.forename: Jean Roger
authors.initials: JR
authors.lastname: Le Gall
authors.forename: Anke
authors.initials: A
authors.lastname: Neumann
authors.forename: François
authors.initials: F
authors.lastname: Hemery
authors.forename: Jean Pierre
authors.initials: JP
authors.lastname: Bleriot
authors.forename: Jean Pierre
authors.initials: JP
authors.lastname: Fulgencio
authors.forename: Bernard
authors.initials: B
authors.lastname: Garrigues
authors.forename: Christian
authors.initials: C
authors.lastname: Gouzes
authors.forename: Eric
authors.initials: E
authors.lastname: Lepage
authors.forename: Pierre
authors.initials: P
authors.lastname: Moine
authors.forename: Daniel
authors.initials: D
authors.lastname: Villers
mesh_terms: D000328:Adult; D019985:Benchmarking / Q000379:methods*; D005260:Female; D005544:Forecasting / Q000379:methods; D005602:France / Q000453:epidemiology; D017052:Hospital Mortality / Q000639:trends*; D006801:Humans; D007362:Intensive Care Units / Q000706:statistics & numerical data*; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D012372:ROC Curve; D015203:Reproducibility of Results; D012189:Retrospective Studies