Development of prediction model for outcome on discharge of intensive care unit patients in Philippine General Hospital using polytomous logistic regression - Catapang, Sotera M.
Prognostic assessment for important clinical events has been very useful in monitoring resource utilization and improving quality assessment. To obtain these assessments, a "predictive instrument/tool" is neede which is either a scoring system or a predictive model.A statistical method, a polytomous approach is presented to derive prediction model so as to classify an outcome with 3 categories. The method was applied to ICU patients of the Philippine General Hospital which involves 3 categories of patients (dea, unimproved, improved) at discharge. A total of 351 patients for model development and 163 patients for model validation. There were 4 models derived, the first model consisted of 8 risk factors and one interaction term obtained from the univariate and bivariate analysis respectively. Using the hierarchical backward elimination procedure, the final model which consisted of 5 risk factors (level of conciousness, need for mechanical ventilation, overt gastrointestinal bleeding, respiratory rate, age) and the interaction term between respiratory rate and age was obtained. The likelihood ratio test was used as a joint test to determine a general risk relationship with the 5 risk factors without specifying the disease subgroups involved. To test for the equality of risk coefficients for the dead group and unimproved group, Wald test was used. The result was that all the 5 predictors were associated with the dead group and only 2 predictors (level of consciousness and need for mechanical ventilation) were associated with the unimproved group. Overt gastrointestinal bleeding, respiratory rate, age and the interaction term between respiratory rate and age were not associated with the unimproved group.In development and validation samples, the 4 derived models fitted well (goodness-of-fit). The overall percentage of correct classification for each of the 4 derived models was 72, with a higher correct classification for the improved and the dead groups. However, the unimproved group has a very small percentage of correct classification.Polytomous logistic regression then, was able to:1. estimate simultaneously the different parameters of the dead group and those of the unimproved group; and2. test the hypothesis that the risk factors for the dead group are different from those of the unimproved group.