A Simple System to Predict Mortality in Medical Intensive Care Unit
Bruna P. M. Rafacho *
Department of Internal Medicine, Botucatu Medical School, UNESP, Sao Paulo, Brazil.
Bertha F. Polegato
Department of Internal Medicine, Botucatu Medical School, UNESP, Sao Paulo, Brazil.
Roberto M. T. Inoue
Department of Internal Medicine, Botucatu Medical School, UNESP, Sao Paulo, Brazil.
Luciano N. Santos
Department of Internal Medicine, Botucatu Medical School, UNESP, Sao Paulo, Brazil.
Kurt Schnitz
Department of Internal Medicine, Botucatu Medical School, UNESP, Sao Paulo, Brazil.
Polyanne C. Garcia
Department of Internal Medicine, Botucatu Medical School, UNESP, Sao Paulo, Brazil.
Leonardo A. M. Zornoff
Department of Internal Medicine, Botucatu Medical School, UNESP, Sao Paulo, Brazil.
Marina P. Okoshi
Department of Internal Medicine, Botucatu Medical School, UNESP, Sao Paulo, Brazil.
Paula S. Azevedo
Department of Internal Medicine, Botucatu Medical School, UNESP, Sao Paulo, Brazil.
Daniella R. Duarte
Department of Internal Medicine, Botucatu Medical School, UNESP, Sao Paulo, Brazil.
Sergio A. R. Paiva
Department of Internal Medicine, Botucatu Medical School, UNESP, Sao Paulo, Brazil.
Marcos F. Minicucci
Department of Internal Medicine, Botucatu Medical School, UNESP, Sao Paulo, Brazil.
*Author to whom correspondence should be addressed.
Abstract
Background: Advances in critical care have increased survival chances and the demand for a scientific approach to outcome prediction. The present study aimed to investigate the associations of clinical information, demographic and laboratory data with mortality; and to elaborate and validate a regression equation for mortality prediction in a medical intensive care unit (ICU).
Methods: This study included 202 patients and took place in a medical ICU at the Botucatu Medical School Hospital, Brazil. In Phase 1, 123 patients admitted to ICU between September 2003 and October 2004 was retrospectively analyzed and allowed equation elaboration. In Phase 2, the mortality equation was prospectively applied in 79 patients consecutively admitted to ICU between August and December 2006.
Results: Among Phase 1 patients, 55% were males and mean age was 58±19 years. Mortality rate was 29%. Multivariate analysis revealed that shock (p=0.002) and hypoalbuminemia (p=0.024) were associated with higher mortality rate. When regression equation was applied in Phase 2 patients, higher equation values were shown for nonsurvivors (0.512; -1.008 -0.512) than for survivors (-1.008; -1.290 -1.008) (p=0.03). The equation also had good precision, 1.8% (IC95%; 1.1-4.7), and low bias, -3.1% (IC95%; -27.1 -20.8). Areas under the receiver operating characteristic (ROC) curve showed no statistical differences between APACHE II (0.75±0.06) and the equation (0.66±0.07) (p=0.27).
Conclusions: Our data suggest that a simple and accurate prognostic equation can be used to predict ICU mortality.
Keywords: Outcome, ICU, shock, hypoalbuminemia, mortality, APACHE II