1. The Role of Physiological Models in Critiquing Mechanical Ventilation Treatments By Fleur T. Tehrani, PhD, PE Professor of Electrical Engineering California State University, Fullerton, CA, USA And Soraya Abbasi, MD Research Director, CHOP Newborn Care at Pennsylvania Hospital Associate Professor of Pediatrics, University of Pennsylvania School of Medicine, Philadelphia, PA, USA

2. Pennsylvania Hospital

5. Rising Preterm Delivery Rate 62,000 VLBW infants < 1500 gram 30,000 ELBW infants < 1000 grams are born annually in US 66 to 88% of ELBW infants survive

6. Respiratory Distress Syndrome Chronic Lung Disease

7. Why Decision Support Systems for Mechanical Ventilation? Assisted ventilation may lead to adverse consequences including lung injury Therefore clinicians will often select the lowest possible ventilatory support to maintain desired blood gases The accuracy of their choice remains unknown for minutes to hours until the next blood gases is obtained The ventilator setting is often readjusted for over or under estimation of the applied ventilatory support

8. Why Decision Support Systems for Mechanical Ventilation? Most ventilation modes are still open loop controlled Ventilation parameters need to be set carefully by considering many features of advanced ventilators as well as rapidly changing patient conditions Decision Support and Critiquing systems can be used as aides to clinicians to set ventilation parameters and avoid medical errors

9. What Kind of Critiquing System Was Used in this Study? The Critiquing system of this study is based on a mathematical model of neonatal respiratory system * The respiratory controller of the model was replaced by a positive pressure mechanical ventilator to simulate the effects of different ventilation parameters on the patient’s blood gases * T ehrani FT. Mathematical Analysis and Computer Simulation of the Respiratory System in the Newborn Infant. IEEE Transactions on Biomedical Engineering 1993; 40(5): 475-481

10. The block diagram of the model-based system

11. The model-based system was used to predict blood gases for an infant on IMV ventilation in a previous study * In two simulation experiments, the critiquing system simulated the effects of setting ventilation parameters according to the physician’s recommendations and according to the recommendations of another computerized system called FLEX (which is different from the model-based system proposed in this study) * Tehrani FT, Abbasi S. Evaluation of a Computerized System for Mechanical Ventilation of Infants. J of Clinical Monitoring and Computing 2009; 23: 93-104.

12. Simulation results of the model-based system by using the clinician’s set of ventilatory parameters for infant #5 in Reference #4

13. Simulation results of the model-based system by using the ventilation parameters recommended by a computerized system called FLEX, for infant #5 in Reference #4

14. The Trends of the results predicted by the model- based system were confirmed by the measurements reported in Reference #4

15. Summary Model-based critiquing system can analyze the input rapidly and: Critiques the recommended support level Predict the resultant blood gases for a selected support level Prevents the use of potentially lung damaging ventilatory support Prevents potential tissue damage from out of safe range blood gases and blood pH

16. CONCLUSION Model-based critiquing systems have the potential to be used as a helpful computational tools to determine better ventilatory treatment.

17. Thank you BW:528 gm GA: 23 5/7 wks

19. Therefore, by predicting different treatment outcomes based on patient’s blood gases, the model-based system could provide greater insight for clinicians and aid in making a more informed decision about the patient’s ventilation treatment at the bedside. Summary