Estimation of inspiratory muscle effort using three common indices in various respiratory models, a bench study

Joshua Hu, Osama Hasan, Kazushige Shiraishi, Yusuke Hirao, Ehab G Daoud

Abstract

Background

Liberation from mechanical ventilation is a complex therapeutic challenge in the intensive care unit. Estimating inspiratory effort during mechanical ventilation can mitigate lung and diaphragmatic injury, along with weakness and atrophy. During a spontaneous breathing trial, it can be critical to predict over or under assistance to guide safe liberation. While estimation of the inspiratory effort requires special equipment, many other indices have been developed to estimate patient effort, work, and actual muscle pressure.

In this bench study, we compare three commonly used maneuvers: airway occlusion at 100 msec (P0.1), airway pressure drop during full occlusion (Pocc), and pressure muscle index (PMI) for their accuracy in predicting the actual muscle effort.

Methods

A single active lung compartment using ASL5000 was modeled to simulate three common patient care scenarios, including “normal” (airway resistance 5 cm/l/s; compliance 60 ml/cm/H₂O), “restrictive” (airway resistance 10 cm/l/s; compliance 30 ml/cm/H₂O); and “obstructive” (airway resistance of 20 cm/l/s; compliance of 80 ml/cm/H₂O) with respiratory rate of 15/minute, inspiratory time of 1 second (10 % rise, 0% hold, and 10% release while exhalation is passive). A Bellavista 1000e ventilator was used for pressure support of 5 cmH₂O and positive end-expiratory pressure (PEEP) of 5 cmH₂O.

Each index was measured to the inputted Pmus, which ranged from 1 to 30 cmH₂O and increased by increments of 1. Results were analyzed using Pearson correlation and regression analysis to predict an associated formula. These were compared to the inputted Pmus using single factor ANOVA followed by post Hoc Tukey test. Formulas from the P0.1 and the Pocc were then compared against previously published equations using single factor ANOVA. Statistics were performed using SPSS 20. P < 0.05 was considered statistically significant.

Results

All three indices had strong correlations to Pmus, P0.1 [R 0.978, 95% CI 0.97, 0.99, P < 0.001], Pocc [R 0.999, 95% CI 1.1, 1.12, P < 0.001], and PMI [R 0.722, 95% CI 0.61, 0.81, P < 0.001]. The equations to estimate Pmus were: P0.1: 3.95 (P0.1) – 2.05; Pocc: 1.11 (Pocc) + 0.82; and PMI: 1.03 (PMI) + 8.26. A significant difference (P < 0.001) was observed when comparing the inputted Pmus with Pmus estimated from P0.1, Pocc, or PMI. Post hoc analysis showed no difference between Pmus to Pmus estimated from P0.1, Pmus to Pmus estimated from Pocc, and Pmus estimated from P0.1 and Pocc; while comparisons of Pmus estimated from PMI to those from the P0.1 and Pocc revealed significant differences (P < 0.001 and P < 0.001, respectively).

When comparing our formula for P0.1 to the previously published formula and the actual Pmus, no significant difference was observed (P 0.261), with post hoc tests revealing no significant differences between any pair. In contrast, a significant difference was found when comparing the formula for Pocc to the previously published formula and the actual Pmus (P < 0.001). Post hoc tests showed no difference between the new formula and Pmus (P 0.99), but a significant difference between Pmus and previous formula (P < 0.001).

Conclusions

While overall all three methods tested showed good correlation with the actual set Pmus, only P0.1 and the Pocc had strong correlation with the set Pmus in all three settings, suggesting that derived formulas can be useful to estimate muscle effort. PMI did not prove accurate, especially in obstructive scenarios, and may not be relied upon in practice.

Keywords: Pmus, P0.1, P occlusion, PMI

References

1. Bertoni M, Spadaro S, Goligher EC. Monitoring patient respiratory effort during mechanical ventilation: lung and diaphragm-protective ventilation. Crit Care 2024; 28(1):94. https://doi.org/10.1186/s13054-020-2777-y PMID: 32204729 PMCID: PMC7092676

2. Chiumello D, Dres M, Camporota L. Lung and diaphragm protective ventilation guided by the esophageal pressure. Intensive Care Med 2022; 48(10):1302-1304. https://doi.org/10.1007/s00134-022-06814-x PMid:35906414

3. Sklienka P, Frelich M, Burša F. Patient self-inflicted lung injury-A narrative review of pathophysiology, early recognition, and management options. J Pers Med 2023;13(4):593. https://doi.org/10.3390/jpm13040593 PMid:37108979 PMCid:PMC10146629

4. Decramer M, Gayan-Ramirez G. Ventilator-induced diaphragmatic dysfunction: toward a better treatment? Am J Respir Crit Care Med 2004; 170(11):1141-1142. https://doi.org/10.1164/rccm.2409004 PMid:15563638

5. Powers SK, Shanely RA, Coombes JS, et al. Mechanical ventilation results in progressive contractile dysfunction in the diaphragm. J Appl Physiol (1985) 2002; 92(5):1851-1858. https://doi.org/10.1152/japplphysiol.00881.2001 PMid:11960933

6. Testelmans D, Maes K, Wouters P, et al. Rocuronium exacerbates mechanical ventilation-induced diaphragm dysfunction in rats. Crit Care Med 2006; 34(12):3018-3023. https://doi.org/10.1097/01.CCM.0000245783.28478.AD PMid:17012910

7. Petrof BJ, Jaber S, Matecki S. Ventilator-induced diaphragmatic dysfunction. Curr Opin Crit Care 2010;16(1):19-25. https://doi.org/10.1097/MCC.0b013e328334b166 PMid:19935062

8. Dres M, Goligher EC, Heunks LMA, et al. Critical illness-associated diaphragm weakness. Intensive Care Med 2017; 43(10):1441-1452. https://doi.org/10.1007/s00134-017-4928-4 PMid:28917004

9. Hamahata NT, Sato R, Daoud EG. Go with the flow-clinical importance of flow curves during mechanical ventilation: A narrative review. Can J Respir Ther 2020; 56:11-20. https://doi.org/10.29390/cjrt-2020-002 PMid:32844110 PMCid:PMC7427988

10. Telias I, Spadaro S. Techniques to monitor respiratory drive and inspiratory effort. Curr Opin Crit Care 2020; 26(1):3-10. https://doi.org/10.1097/MCC.0000000000000680 PMid:31764192

11. Umbrello M, Formenti P, Longhi D, et al. Diaphragm ultrasound as indicator of respiratory effort in critically ill patients undergoing assisted mechanical ventilation: a pilot clinical study. Crit Care 2015; 19(1):161. https://doi.org/10.1186/s13054-015-0894-9 PMid:25886857 PMCid:PMC4403842

12. Bellani G, Mauri T, Coppadoro A, et al. Estimation of patient’s inspiratory effort from the electrical activity of the diaphragm. Crit Care Med 2013; 41(6):1483-1491. https://doi.org/10.1097/CCM.0b013e31827caba0 PMid:23478659

13. Calzia E, Lindner KH, Witt S, et al. Pressure-time product and work of breathing during biphasic continuous positive airway pressure and assisted spontaneous breathing. Am J Respir Crit Care Med 1994; 150(4):904-910. https://doi.org/10.1164/ajrccm.150.4.7921461 PMid:7921461

14. Yoshida T, Brochard L. Esophageal pressure monitoring: why, when and how? Curr Opin Crit Care 2018; 24(3):216-222. https://doi.org/10.1097/MCC.0000000000000494 PMid:29601320

15. Karthika M, Al Enezi FA, Pillai LV, et al. Rapid shallow breathing index. Ann Thorac Med 2016; 11(3):167-176. https://doi.org/10.4103/1817-1737.176876 PMid:27512505 PMCid:PMC4966218

16. Albani F, Pisani L, Ciabatti G, et al. Flow Index: a novel, non-invasive, continuous, quantitative method to evaluate patient inspiratory effort during pressure support ventilation. Crit Care 2021; 25(1):196. https://doi.org/10.1186/s13054-021-03624-3 PMid:34099028 PMCid:PMC8182360

17. 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

18. Telias I, Junhasavasdikul D, Rittayamai N, et al. Airway occlusion pressure as an estimate of respiratory drive and inspiratory effort during assisted ventilation. Am J Respir Crit Care Med 2020; 201(9):1086-1098. https://doi.org/10.1164/rccm.201907-1425OC PMid:32097569

19. Conti G, Antonelli M, Arzano S, et al. Measurement of occlusion pressures in critically ill patients. Crit Care 1997; 1(3):89-93. https://doi.org/10.1186/cc110 PMid:11094467 PMCid:PMC137221

20. Bertoni M, Telias I, Urner M, et al. A novel non-invasive method to detect excessively high respiratory effort and dynamic transpulmonary driving pressure during mechanical ventilation. Crit Care 2019; 23(1):346. https://doi.org/10.1186/s13054-019-2617-0 PMid:31694692 PMCid:PMC6836358

21. Foti G, Cereda M, Banfi G, et al. End-inspiratory airway occlusion: a method to assess the pressure developed by inspiratory muscles in patients with acute lung injury undergoing pressure support. Am J Respir Crit Care Med 1997; 156(4 Pt 1):1210-1216. https://doi.org/10.1164/ajrccm.156.4.96-02031 PMid:9351624

22. Anand A. The forgotten tale of spontaneous plateau pressure. J Mech Vent 2023; 4(3):115-118. https://doi.org/10.53097/JMV.10084

23. Hasan O, Hirao Y, Shiraishi K, et al. Estimating inspiratory muscle effort during pressure support ventilation: A comparison of common indices. CHEST 2024; 166(4):A2773. https://doi.org/10.1016/j.chest.2024.06.1681

24. Arnal Jean-Michel. Monitoring respiratory mechanics in mechanically ventilated patients. Hamilton Medical. Published: 6/17/2020. Accessed: 11/5/2024. https://www.hamilton-medical.com/en_US/Article-page~knowledge-base~6e39d4bb-1ab7-4c46-bc18-83f3e77897f9~Monitoring-respiratory-mechanics-in-mechanically-ventilated-patients~.html

25. Goligher EC, Dres M, Patel BK, et al. Lung and diaphragm-protective ventilation. Am J Respir Crit Care Med 2020 ;202(7):950-961. https://doi.org/10.1164/rccm.202003-0655CP PMid:32516052 PMCid:PMC7710325

26. Fajardo-Campoverdi A, González-Castro A, Adasme-Jeria R, et al. Mechanical ventilator release protocol. recommendation based on a review of the evidence. J Mech Vent 2023; 4(1):31-42. https://doi.org/10.53097/JMV.10072

27. Sato R, Hasegawa D, Hamahata NT, et al. The predictive value of airway occlusion pressure at 100 msec (P0.1) on successful weaning from mechanical ventilation: A systematic review and meta-analysis. J Crit Care 2021; 63:124-132. https://doi.org/10.1016/j.jcrc.2020.09.030 PMid:33012587

28. Sassoon CS, Te TT, Mahutte CK, et al. Airway occlusion pressure. An important indicator for successful weaning in patients with chronic obstructive pulmonary disease. Am Rev Respir Dis 1987; 135(1):107-113. https://doi.org/10.1164/arrd.1987.135.1.107 PMID: 3800139

29. Gao R, Zhou JX, Yang YL, et al. Use of pressure muscle index to predict the contribution of patient’s inspiratory effort during pressure support ventilation: a prospective physiological study. Front Med (Lausanne) 2024; 11:1390878. https://doi.org/10.3389/fmed.2024.1390878 PMid:38737762 PMCid:PMC11082330

30. de Vries HJ, Tuinman PR, Jonkman AH, et al. Performance of noninvasive airway occlusion maneuvers to assess lung stress and diaphragm effort in mechanically ventilated critically ill patients. Anesthesiology 2023; 138:274-288. https://doi.org/10.1097/ALN.0000000000004467 PMid:36520507

31. Docci M, Foti G, Brochard L, et al. Pressure support, patient effort and tidal volume: a conceptual model for a non linear interaction. Crit Care 2024; 28:358. https://doi.org/10.1186/s13054-024-05144-2 PMid:39506755 PMCid:PMC11539557