Evaluation of a knowledge-based system providing ventilatory management and decision for extubation.

We evaluated whether a knowledge-based system (KBS) connected to a ventilator in pressure support mode could correctly predict the ability of patients to tolerate total withdrawal from ventilatory support. The KBS was designed to continuously adapt ventilatory assistance to the needs of the patient, to manage a strategy of gradually decreasing ventilatory assistance, and to indicate when the patient was able to breathe without assistance. Thirty-eight patients for whom weaning was being considered were evaluated using a conventional battery of parameters, including weaning criteria, tolerance of a T-piece trial, and outcome 48h after permanent withdrawal of ventilation. The results of this evaluation were compared with the suggestions made by the KBS at the end of a period of KBS-driven mechanical ventilation inserted in the conventional weaning procedure. The positive predictive value of the KBS was 89%, versus 77% for the conventional procedure and 81% for the rapid shallow breathing index alone. The KBS correctly predicted the course of five patients who tolerated a T-piece trial but required ventilation within 48 h. We conclude that our KBS ensured appropriate patient management during the weaning period and improved our ability to predict responses to weaning.

[1]  Ángel Viña,et al.  Guardian: A Prototype Intelligent Agent for Intensive-Care Monitoring , 1994, AAAI.

[2]  C Frostell,et al.  A knowledge-based support system for mechanical ventilation of the lungs. The KUSIVAR concept and prototype. , 1989, Computer methods and programs in biomedicine.

[3]  F Lemaire,et al.  Improved efficacy of spontaneous breathing with inspiratory pressure support. , 1987, The American review of respiratory disease.

[4]  Dean F. Sittig,et al.  Implementation of a computerized patient advice system using the HELP clinical information system. , 1989, Computers and biomedical research, an international journal.

[5]  D A Tong,et al.  Weaning patients from mechanical ventilation. A knowledge-based system approach. , 1990, Computer methods and programs in biomedicine.

[6]  J. L. Gall,et al.  A simplified acute physiology score for ICU patients , 1984, Critical care medicine.

[7]  Serdar Uckun,et al.  Intelligent system in patient monitoring and therapy management , 1994 .

[8]  Ching Y. Suen,et al.  Expert system evaluation techniques: a selected bibliography , 1991 .

[9]  P. Montravers,et al.  Nosocomial pneumonia in ventilated patients: a cohort study evaluating attributable mortality and hospital stay. , 1993, The American journal of medicine.

[10]  I. Grossbach-Landis,et al.  Weaning from mechanical ventilation , 2005, ERS practical Handbook of Invasive Mechanical Ventilation.

[11]  G. Foti,et al.  Effects of short-term oxygenation changes on acute lung injury patients undergoing pressure support ventilation. , 1993, Chest.

[12]  Jeanette X. Polaschek,et al.  The design and implementation of a ventilator-management advisor , 1993, Artif. Intell. Medicine.

[13]  L. Cinotti,et al.  Acute Left Ventricular Dysfunction during Unsuccessful Weaning from Mechanical Ventilation , 1988, Anesthesiology.

[14]  M. Habib,et al.  Pressure support compensation for inspiratory work due to endotracheal tubes and demand continuous positive airway pressure. , 1988, Chest.

[15]  D. Spiegelhalter,et al.  Evaluating medical expert systems: what to test and how? , 1990, Medical informatics = Medecine et informatique.

[16]  J. Strickland,et al.  A computer-controlled ventilator weaning system. , 1991, Chest.

[17]  M. Tobin,et al.  A prospective study of indexes predicting the outcome of trials of weaning from mechanical ventilation. , 1992, The New England journal of medicine.

[18]  W. Sanborn Inspiratory pressure support prevents diaphragmatic fatigue during weaning from mechanical ventilation. , 1989, The American review of respiratory disease.

[19]  M Dojat,et al.  A knowledge-based system for assisted ventilation of patients in intensive care units , 1992, International journal of clinical monitoring and computing.

[20]  S. Allen,et al.  The pattern of breathing during successful and unsuccessful trials of weaning from mechanical ventilation. , 2015, The American review of respiratory disease.

[21]  Edward H. Shortliffe,et al.  The adolescence of AI in medicine: will the field come of age in the '90s? , 1993, Artif. Intell. Medicine.

[22]  S Uckun,et al.  Intelligent systems in patient monitoring and therapy management. A survey of research projects. , 1994, International journal of clinical monitoring and computing.

[23]  M. Mathru,et al.  Pressure support. Changes in ventilatory pattern and components of the work of breathing. , 1991, Chest.

[24]  J. Marini,et al.  Impact of PEEP on lung mechanics and work of breathing in severe airflow obstruction. , 1989, Journal of applied physiology.

[25]  H. Lorino,et al.  Inspiratory pressure support compensates for the additional work of breathing caused by the endotracheal tube. , 1991, Anesthesiology.

[26]  N. MacIntyre,et al.  Respiratory function during pressure support ventilation. , 1986, Chest.

[27]  L. Brochard,et al.  Comparison of three methods of gradual withdrawal from ventilatory support during weaning from mechanical ventilation. , 1994, American journal of respiratory and critical care medicine.

[28]  B. Delafosse,et al.  Oxygen cost of breathing and diaphragmatic pressure-time index. Measurement in patients with COPD during weaning with pressure support ventilation. , 1990, Chest.

[29]  P. Macklem,et al.  The oxygen cost of breathing in patients with cardiorespiratory disease. , 2015, The American review of respiratory disease.