Predicting the Outcome of Intensive Care Unit Patients

Abstract

Abstract Statisticians are being asked with increasing frequency to develop models for occurrences in medical environments. Until recently, only subjective models were available to predict mortality for patients in an intensive care unit (ICU). These models were based on variables and associated weights determined by panels of medical “experts.” This article shows how multiple logistic regression (MLR) can be used to develop an objective model for prediction of hospital mortality among ICU patients. An MLR model to be applied when a patient is admitted to the ICU was developed on 737 ICU patients. The final model is based on the following variables: presence of coma or deep stupor at admission, emergency admission, cancer part of present problem, probable infection, cardiopulmonary resuscitation (CPR) prior to admission, age, and systolic blood pressure at admission. To validate this model, a new cohort of 1,997 consecutive ICU patients was entered into the study. Information was collected for the variabl...