Modeling in-hospital patient survival during the first 28 days after intensive care unit admission: a prognostic model for clinical trials in general critically ill patients.
OBJECTIVE
The objective of the study was to develop a model for estimating patient 28-day in-hospital mortality using 2 different statistical approaches.


METHODS
The 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.


METHODS
The study involved 305 intensive care units (ICUs) from the basic Simplified Acute Physiology Score (SAPS) 3 cohort.


METHODS
Patients (n = 17138) were from the SAPS 3 database with follow-up data pertaining to the first 28 days in hospital after ICU admission.


METHODS
None.


RESULTS
The 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.


CONCLUSIONS
Both 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.
Moreno
Rui P
RP
Unidade de Cuidados Intensivos Polivalente, Hospital de St. António dos Capuchos, Centro Hospitalar de Lisboa Central, E. P. E., Lisboa, Portugal.
Metnitz
Philipp G H
PG
Metnitz
Barbara
B
Bauer
Peter
P
Afonso de Carvalho
Susana
S
Hoechtl
Anette
A
D000368:Aged; D002986:Clinical Trials as Topic / Q000706:statistics & numerical data*; D016638:Critical Illness / Q000401:mortality*; D005260:Female; D017052:Hospital Mortality; D006801:Humans; D007362:Intensive Care Units / Q000706:statistics & numerical data*; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D011379:Prognosis; D018570:Risk Assessment; D012720:Severity of Illness Index; D013997:Time Factors