Predicting the Outcome of Intensive Care Unit Patients

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 variables in the MLR model and, in addition, the variables necessary to evaluate the "subjective" models. The admission mortality prediction model MPM0(CPR) was validated on this new cohort of patients using goodness-of-fit tests. It was found that this model had excellent fit (p = .74). In addition, the overall correct classification for this model in the validation data set was 86.1%. The predictive values for dying and surviving, sensitivity, and specificity were 71.3%, 88.5%, 50.2%, and 95.0%, respectively. The direct comparison of MPM0(CPR) and the subjective systems based on the new cohort demonstrated that although all methods considered demonstrated comparable sensitivity, specificity, predictive values, and total correct classification rates, goodness-of-fit tests suggest that the probabilities of hospital mortality as produced by the statistical model best fit the observed mortality experience. One of the commonly used subjective models tended to overestimate the probabilities of hospital mortality, and the other tended to underestimate these probabilities. To be useful, a severity index should be based on simple calculations using readily available data, and be independent of medical treatment in the ICU. Given the widespread availability of microcomputers, the newly developed statistical model seems to satisfy these criteria. Key Words: Multiple logistic regressionMaximum likelihood estimateMortality prediction modelGoodness-of-fit testSensitivitySpecificityRelative risk