Critical care medicine · 1992
Prediction criteria for successful weaning from respiratory support: statistical and connectionist analyses.
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Ashutosh K, Lee H, Mohan C K, Ranka S, Mehrotra K, Alexander C
Аннотация
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.
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.