Effect of respiratory effort on target minute ventilation during Adaptive Support Ventilation

Marissa Su, Melina Simonpietri, Ehab G. Daoud

Abstract

Background

Adaptive support ventilation (ASV) is an intelligent mode of mechanical ventilation protocol which uses a closed-loop control between breaths. The algorithm states that for a given level of alveolar ventilation, there is a particular respiratory rate and tidal volume which achieve a lower work of breathing. The mode allows the clinician to set a desired minute ventilation percentage (MV%) while the ventilator automatically selects the target ventilatory pattern base on these inputs and feedback from the ventilator monitoring system. The goal is to minimize the work of breathing and reduce complications by allowing the ventilator to adjust the breath delivery taking into account the patient’s respiratory mechanics (Resistance, and Compliance). In this study we examine the effect of patients’ respiratory effort on target tidal volume (VT) and Minute Ventilation (V̇_e) during ASV using breathing simulator.

Methods

A bench study was performed by using the ASL 5000 breathing simulator to compare the target ventilator to actual VT and V̇_e value in simulated patients with various level of respiratory effort during ASV on the Hamilton G5 ventilator. The clinical scenario involves simulated adult male with IBW 70kg and normal lung mechanics: respiratory compliance of 70 mL/cm H₂O, and airway resistance of 9 cm H₂O/L/s. Simulated patients were subjected to five different level of muscle pressure (P_mus): 0 (Passive), -5, -10, -15, -25 (Active) cm H₂O at a set respiratory rate of 10 (below targeted VT) set at three different levels of minute ventilation goals: 100%, 200%, and 300%, with a PEEP of 5 cm H₂O. Fifty breaths were analyzed in every experiment. Means and standard deviations (SD) of variables were calculated. One way analysis of variants was done to compare the values. Pearson correlation coefficient test was used to calculate the correlation between the respiratory effort and the VT, V̇_e, and peak inspiratory pressure (PIP).

Results

The targeted VT and V̇_e were not significant in the passive patient when no effort was present, however were significantly higher in the active states at all levels of Pmus on the 100%, 200% and the 300 MV%. The VT and V̇_e increase correlated with the muscle effort in the 100 and 200 MV% but did not in the 300%.

Conclusions

Higher inspiratory efforts resulted in significantly higher VT and V̇_e than targeted ones. Estimating patients’ effort is important during setting ASV. Keywords: Mechanical ventilation, ASV, InteliVent, Pmus, tidal volume, percent minute ventilation

Keywords

Mechanical ventilation, ASV, InteliVent, Pmus, tidal volume, percent minute ventilation

References

1 . Chatburn RL, El-Khatib M, Mireles-Cabodevila E. A taxonomy for mechanical ventilation: 10 fundamental maxims. Respir Care 2014 ;59(11):1747-1763. https://doi.org/10.4187/respcare.03057 PMid:25118309

2. Otis AB, Fenn WO, Rahn H. Mechanics of breathing in man. J Appl Physiol 1950; 2:592-607. https://doi.org/10.1152/jappl.1950.2.11.592 PMid:15436363

3. Fernández J, Miguelena D, Mulett, H, et al. Adaptive support ventilation: State of the art review. Indian J Crit Care Med 2013; 17(1):16-22. https://doi.org/10.4103/0972-5229.112149 PMid:23833471 PMCid:PMC3701392

4. Wheatley D, Young, K. Adaptive support ventilation. What is it? Beneficial or not? J Mech Vent 2020; 2(1):34-44. https://doi.org/10.53097/JMV.10026

5. Arnal JM, Wysocki M, Nafati C, et al. Automatic selection of breathing pattern using adaptive support ventilation. Intensive Care Med 2008; 34:75-81. https://doi.org/10.1007/s00134-007-0847-0 PMid:17846747

6. Arnal JM, Garnero A, Saoli M, et al. Parameters for simulation of adult subjects during mechanical ventilation. Respir Care 2018; 63(2):158-168. https://doi.org/10.4187/respcare.05775 PMid:29042486

7. https://www.hamilton-medical.com/dam/jcr:f43f09a3-afe6-4de3-8312-0eb86dca8323/INTELLiVENT-ASV-brochure-en-689326.05.pdf. Accessed March 15, 2021.

8. ARDSnet. Ventilation with lower tidal volumes as compared with traditional tidal volumes for acute lung injury and the acute respiratory distress syndrome. N Engl J Med 2000; 342(18):1301-1308. https://doi.org/10.1056/NEJM200005043421801 PMid:10793162

9. Sulemanji D, Marchese A, Garbarini P, et al. Adaptive support ventilation: an appropriate mechanical ventilation strategy for acute respiratory distress syndrome? Anesthesiology 2009; 111(4):863-870. https://doi.org/10.1097/ALN.0b013e3181b55f8f PMid:19741490

10. Grinnan DC, Truwit JD. Clinical review: Respiratory mechanics in spontaneous and assisted ventilation. Crit Care 2005; 9:472. https://doi.org/10.1186/cc3516 PMid:16277736 PMCid:PMC1297597

11. Hamahata NT, Sato R, Yamasaki K, et al. Estimating actual inspiratory muscle pressure from airway occlusion pressure at 100 msec. J Mech Vent 2020; 1(1):8-13. https://doi.org/10.53097/JMV.10003

12. Schmidt M, Kindler F, Cecchini J, et al. Neurally adjusted ventilatory assist and proportional assist ventilation both improve patient-ventilator interaction. Crit Care. 2015;19(1):56. https://doi.org/10.1186/s13054-015-0763-6 PMid:25879592 PMCid:PMC4355459

13. Chanques G, Constantin JM, Devlin JW, et al. Analgesia and sedation in patients with ARDS. Intensive Care Med 2020; 46(12):2342-2356. https://doi.org/10.1007/s00134-020-06307-9 PMid:33170331 PMCid:PMC7653978

14. Esnault P, Cardinale M, Hraiech S, et al. High respiratory drive and excessive respiratory efforts predict relapse of respiratory failure in critically ill patients with COVID-19. Am J Respir Crit Care Med 202; 202(8):1173-1178. https://doi.org/10.1164/rccm.202005-1582LE PMid:32755309 PMCid:PMC7560807

15. Chatburn RL. Simulation-Based Evaluation of Mechanical Ventilators. Respir Care 2018; 63(7):936-940. https://doi.org/10.4187/respcare.06267 PMid:29941669