Prediction criteria for successful weaning from respiratory support: Statistical and connectionist analyses

K. Ashutosh, H. Lee, C. K. Mohan, S. Ranka, K. Mehrotra, C. Alexander

Research output: Contribution to journalArticlepeer-review

33 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)1295-1301
Number of pages7
JournalCritical Care Medicine
Issue number9
StatePublished - 1992


  • maximum voluntary ventilation
  • mechanical ventilation
  • neural network
  • respiratory failure
  • respiratory rate
  • statistics
  • tidal volume
  • vital capacity
  • weaning

ASJC Scopus subject areas

  • Critical Care and Intensive Care Medicine


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