Mortality Prediction in a Neurosurgical Intensive Care Unit

Aim: The ability to estimate factors influencing intensive care outcome, especially mortality, is highly important to patient care worldwide. Several clinical variables have been identified for the general intensive care unit (ICU) setting with high mortality predictive ability. However, the application of such predictors in the neurosurgical ICU setting is not yet established. The study was aimed to assess the predictive ability of the variables as sociated with mortality in a tertiary neurosurgical ICU. Material and methods: All neurosurgical patients admitted to ICU during a 5-month period in 2011 were recruited to the study (n = 258). A logistic regression model was used for data analysis and odds ratios were calculated for each predictor. Results: The observed hospital mortality rate was 3.49%. The four resulting predictors of mortality were: increased body temperature of 0.1 degrees C OR = 1.21, 95% CI 1.02-1.44; 1 mg/dl increase in blood glucose level OR = 0.93, 95% CI 0.87-0.99; one point increase on the Glasgow Coma Scale (GCS) eye subscale OR = 0.26, 95% CI 0.07-0.89; and 1 day increase in the length of stay prior to ICU admission OR = 1.14 (1.05-1.24). These predictors were put into a regression model and the area under the receiver operating characteristic curve (AUC) was 0.968, 95% CI 0.923-1.000. Conclusion: The body temperature, blood glucose level, GCS eye response and the length of hospital stay prior to ICU admission may be hospital mortality predictors in a neurosurgical ICU. Accordingly, a new predictive model should be developed.