PREDICTION CRITERIA FOR SUCCESSFUL WEANING FROM RESPIRATORY SUPPORT - STATISTICAL AND CONNECTIONIST ANALYSES

Objective: To develop predictive criteria for successful weaning of patients from mechanical assistance to ventilation, based on simple clinical tests using discriminant analyses and neural network systems. Design: Retrospective development of predictive criteria and subsequent prospective testing of the same predictive criteria. Setting: Medical ICU of a 300-bed teaching Veterans Administration Hospital. Patients: Twenty-five ventilator-dependent elderly patients with acute respiratory failure. Interventions: Routine measurements of negative inspiratory force, tidal volume, minute ventilation, respiratory rate, vital capacity, and maximum voluntary ventilation, followed by a weaning trial. Success or failure in 21 efforts was analyzed by a linear and quadratic discriminant model and neural network formulas to develop prediction criteria. The criteria developed were tested for predictive power prospectively in nine trials in six patients. Results: The statistical and neural network analyses predicted the success or failure of weaning within 90% to 100% accuracy. Conclusion: Use of quadratic discriminant and neural network analyses could be useful in developing accurate predictive criteria for successful weaning based on simple bedside measurements.