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.


METHODS
Retrospective development of predictive criteria and subsequent prospective testing of the same predictive criteria.


METHODS
Medical ICU of a 300-bed teaching Veterans Administration Hospital.


METHODS
Twenty-five ventilator-dependent elderly patients with acute respiratory failure.


METHODS
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.


CONCLUSIONS
Use of quadratic discriminant and neural network analyses could be useful in developing accurate predictive criteria for successful weaning based on simple bedside measurements.
Ashutosh
K
K
Department of Medicine, Veterans Administration Medical Center, Syracuse, New York 13210.
Lee
H
H
Mohan
C K
CK
Ranka
S
S
Mehrotra
K
K
Alexander
C
C
D000368:Aged; D016002:Discriminant Analysis; D006801:Humans; D016571:Neural Networks, Computer; D011379:Prognosis; D011446:Prospective Studies; D012129:Respiratory Function Tests / Q000706:statistics & numerical data; D012131:Respiratory Insufficiency / Q000453:epidemiology / Q000503:physiopathology / Q000628:therapy; D013997:Time Factors; D015300:Ventilator Weaning / Q000706:statistics & numerical data*