Accuracy of the Ventilator Automated Displayed Respiratory Mechanics in Passive and Active Breathing Conditions: A Bench Study

被引:9
作者
Daoud, Ehab G. [1 ,2 ]
Katigbak, Reynaldo [3 ]
Ottochian, Marcus [4 ]
机构
[1] Kapiolani Community Coll, 4303 Diamond Head Rd, Honolulu, HI 96816 USA
[2] Univ Hawaii, John A Burns Sch Med, Honolulu, HI 96822 USA
[3] Kaiser Permanente, Kaiser Permanente Resp Dept, Honolulu, HI USA
[4] Univ Maryland, Shock Trauma Ctr, Baltimore, MD 21201 USA
关键词
least squares fitting method; lung model; muscle pressure; respiratory mechanics; simulation models; PRESSURE; SIMULATION;
D O I
10.4187/respcare.06422
中图分类号
R4 [临床医学];
学科分类号
1002 ; 100602 ;
摘要
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 mlJcm H2O, and resistance 13 cm H2O/L/s). Each scenario was subjected to 4 different muscle pressures (P-mus): 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 P-mus. There was a strong positive correlation between P-mus and compliance measured as well as a strong negative correlation between P-mus 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 P-mus, which, if incorporated into the calculations, would improve the accuracy of respiratory mechanics made via the least squares fitting method in actively breathing conditions. (C) 2019 Daedalus Enterprises
引用
收藏
页码:1555 / 1560
页数:6
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