Artificial neural networks for predicting failure to survive following in-hospital cardiopulmonary resuscitation

Background. Neural networks are an artificial intelligence technique that uses a set of nonlinear equations to mimic the neuronal connections of biological systems. They have been shown to be useful for pattern recognition and outcome prediction applications, and have the potential to bring artificial intelligence techniques to the personal computers of practicing physicians, assisting them with a variety of medical decisions. It is proposed that such an artificial neural network can be trained, using information available at the time of admission to the hospital, to predict failure to survive following in-hospital cardiopulmonary resuscitation (CPR).

Methods. The age, sex, heart rate, and 21 other clinical variables were collected on a consecutive series of 218 adult patients undergoing CPR at a 295-bed public acute-care hospital. The data set was divided into two groups. A neural network was trained to predict failure to survive to discharge following CPR, using one group as the training set and the other as the testing set. The procedure was then reversed, and the results of the two networks were combined to form an aggregate network.

Results. The trained aggregate neural network had a sensitivity of 52.1% and a positive predictive value of 97% for the prediction of failure to survive following CPR. The relative risk of actually failing to survive to discharge following CPR for a patient predicted not to survive was 11.3 (95% CI 3.3 to 38.2).

Conclusions. Predicting failure to survive following CPR is but one possible application of neural network technology. It demonstrates how this technique can assist physicians in medical decision making. Future work should attempt to improve the positive predictive value of the neural network, to consider combining it with an expert system, and to compare it with other predictive tools. Once validated, the network can be distributed as a separate application for use by practicing physicians.

Key words. Neural networks (computer); resuscitation orders; cardiopulmonary resuscitation, survival.

The rate of survival to discharge following in-hospital cardiopulmonary resuscitation (CPR) has been reported to vary from 7%[1] to 24%[2] in published studies. In a recent meta-analysis of 14 studies,[3] this author found an average survival rate of 13.5%. While the analysis identified several patient subgroups with a low rate of survival to discharge following CPR, only patients with metastatic cancer were found to have a rate of survival to discharge of less than 1%.[3] The identification at hospital admission of additional patients with a negligible rate of survival to discharge following CPR would provide important prognostic information for discussions about do-not-resuscitate (DNR) orders.

George and colleagues[2] have proposed a Pre-Arrest Morbidity (PAM) index to prospectively identify patients with a negligible rate of survival to discharge following CPR. The index consists of 15 variables assigned 0, 1, or 3 points, with a score greater than 8 associated with failure to survive. Based on the results of the previously mentioned meta-analysis, the author has proposed a modified PAM index, hereafter referred to as the Prognosis After Resuscitation (PAR) score. It is easier to use than the...