articleIntensive Care MedicineAug 16, 2005HYBRID OA

SAPS 3—From evaluation of the patient to evaluation of the intensive care unit. Part 2: Development of a prognostic model for hospital mortality at ICU admission

Hospital Santo António dos Capuchos · Vienna General Hospital · +9 more institutions

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Abstract

Objective

To develop a model to assess severity of illness and predict vital status at hospital discharge based on ICU admission data.

Design

Prospective multicentre, multinational cohort study. PATIENTS AND SETTING: A total of 16,784 patients consecutively admitted to 303 intensive care units from 14 October to 15 December 2002. MEASUREMENTS AND RESULTS: ICU admission data (recorded within +/-1 h) were used, describing: prior chronic conditions and diseases; circumstances related to and physiologic derangement at ICU admission. Selection of variables for inclusion into the model used different complementary strategies. For cross-validation, the model-building procedure was run five times, using randomly selected four fifths of the sample as a development- and the remaining fifth as validation-set. Logistic regression methods were then used to reduce complexity of the model. Final estimates of regression coefficients were determined by use of multilevel logistic regression. Variables selection and weighting were further checked by bootstraping (at patient level and at ICU level). Twenty variables were selected for the final model, which exhibited good discrimination (aROC curve 0.848), without major differences across patient typologies. Calibration was also satisfactory (Hosmer-Lemeshow goodness-of-fit test H=10.56, p=0.39, C=14.29, p=0.16). Customized equations for major areas of the world were computed and demonstrate a good overall goodness-of-fit.

Citation impact

1,414
total citations
FWCI
15.27
Percentile
100%
References
37
Citations per year

Authors

11

Topics & keywords

Keywords
  • Medicine
  • Logistic regression
  • Intensive care unit
  • Goodness of fit
  • SAPS II
  • Intensive care
  • Anesthesiology
  • APACHE II
UN Sustainable Development Goals
  • Reduced inequalities
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Funding