• Rui P Moreno, Philipp G H Metnitz, Barbara Metnitz, Peter Bauer, Susana Afonso de Carvalho, Anette Hoechtl, and SAPS 3 Investigators.
• Unidade de Cuidados Intensivos Polivalente, Hospital de St. António dos Capuchos, Centro Hospitalar de Lisboa Central, E. P. E., Lisboa, Portugal.
• J Crit Care. 2008 Sep 1;23(3):339-48.

ObjectiveThe objective of the study was to develop a model for estimating patient 28-day in-hospital mortality using 2 different statistical approaches.DesignThe study was designed to develop an outcome prediction model for 28-day in-hospital mortality using (a) logistic regression with random effects and (b) a multilevel Cox proportional hazards model.SettingThe study involved 305 intensive care units (ICUs) from the basic Simplified Acute Physiology Score (SAPS) 3 cohort.Patients And ParticipantsPatients (n = 17138) were from the SAPS 3 database with follow-up data pertaining to the first 28 days in hospital after ICU admission.InterventionsNone.Measurements And ResultsThe database was divided randomly into 5 roughly equal-sized parts (at the ICU level). It was thus possible to run the model-building procedure 5 times, each time taking four fifths of the sample as a development set and the remaining fifth as the validation set. At 28 days after ICU admission, 19.98% of the patients were still in the hospital. Because of the different sampling space and outcome variables, both models presented a better fit in this sample than did the SAPS 3 admission score calibrated to vital status at hospital discharge, both on the general population and in major subgroups.ConclusionsBoth statistical methods can be used to model the 28-day in-hospital mortality better than the SAPS 3 admission model. However, because the logistic regression approach is specifically designed to forecast 28-day mortality, and given the high uncertainty associated with the assumption of the proportionality of risks in the Cox model, the logistic regression approach proved to be superior.

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