Ehab G Daoud1, Reynaldo Katigbak2, Marcus Ottochian3. 1. Respiratory Care Program, Kapiolani Community College, Honolulu, Hawaii, and John A Burns School of Medicine, University of Hawaii Honolulu, Hawaii. ehab_daoud@hotmail.com. 2. Kaiser Permanente Respiratory Department, Kaiser Permanente, Honolulu, Hawaii. 3. Shock Trauma Center, University of Maryland, Baltimore, Maryland.
Abstract
BACKGROUND: New-generation ventilators display dynamic measures of respiratory mechanics, such as compliance, resistance, and auto-PEEP. Knowledge of the respiratory mechanics is paramount to clinicians at the bedside. These calculations are obtained automatically by using the least squares fitting method of the equation of motion. The accuracy of these calculations in static and dynamic conditions have not been fully validated or examined in different clinical conditions or various ventilator modes. METHODS: A bench study was performed by using a lung simulator to compare the ventilator automated calculations during passive and active conditions. Three clinical scenarios (normal, COPD, and ARDS) were simulated with known compliances and resistance set per respective condition: normal (compliance 50 mL/cm H2O, resistance 10 cm H2O/L/s), COPD (compliance 60 mL/cm H2O, resistance 22 cm H2O/L/s), and ARDS (compliance 30 mL/cm H2O, and resistance 13 cm H2O/L/s). Each scenario was subjected to 4 different muscle pressures (Pmus): 0, -5, -10, and -15 cm H2O. All the experiments were done using adaptive support ventilation. The resulting automated dynamic calculations of compliance and resistance were then compared based on the clinical scenarios. RESULTS: There was a small bias (average error) and level of agreement in the passive conditions in all the experiments; however, these errors and levels of agreement got progressively higher proportional to the increased Pmus. There was a strong positive correlation between Pmus and compliance measured as well as a strong negative correlation between Pmus and resistance measured. CONCLUSIONS: Automated displayed calculations of respiratory mechanics were not dependable or accurate in active breathing conditions. The calculations were clinically more reliable in passive conditions. We propose different methods of calculating Pmus, which, if incorporated into the calculations, would improve the accuracy of respiratory mechanics made via the least squares fitting method in actively breathing conditions.
BACKGROUND: New-generation ventilators display dynamic measures of respiratory mechanics, such as compliance, resistance, and auto-PEEP. Knowledge of the respiratory mechanics is paramount to clinicians at the bedside. These calculations are obtained automatically by using the least squares fitting method of the equation of motion. The accuracy of these calculations in static and dynamic conditions have not been fully validated or examined in different clinical conditions or various ventilator modes. METHODS: A bench study was performed by using a lung simulator to compare the ventilator automated calculations during passive and active conditions. Three clinical scenarios (normal, COPD, and ARDS) were simulated with known compliances and resistance set per respective condition: normal (compliance 50 mL/cm H2O, resistance 10 cm H2O/L/s), COPD (compliance 60 mL/cm H2O, resistance 22 cm H2O/L/s), and ARDS (compliance 30 mL/cm H2O, and resistance 13 cm H2O/L/s). Each scenario was subjected to 4 different muscle pressures (Pmus): 0, -5, -10, and -15 cm H2O. All the experiments were done using adaptive support ventilation. The resulting automated dynamic calculations of compliance and resistance were then compared based on the clinical scenarios. RESULTS: There was a small bias (average error) and level of agreement in the passive conditions in all the experiments; however, these errors and levels of agreement got progressively higher proportional to the increased Pmus. There was a strong positive correlation between Pmus and compliance measured as well as a strong negative correlation between Pmus and resistance measured. CONCLUSIONS: Automated displayed calculations of respiratory mechanics were not dependable or accurate in active breathing conditions. The calculations were clinically more reliable in passive conditions. We propose different methods of calculating Pmus, which, if incorporated into the calculations, would improve the accuracy of respiratory mechanics made via the least squares fitting method in actively breathing conditions.
Authors: José da Natividade Menezes Júnior; Ludmilla Mota Silva; Leonardo José Morais Santos; Helena França Correia; Wende Lopes; Virgínia Eugênia Pinheiro E Silva; Jorge Luis Motta Dos Anjos; Bruno Prata Martinez Journal: Rev Bras Ter Intensiva Date: 2020 Jul-Sep