Ehab G Daoud
Cite
Daoud EG. Esophageal pressure guided closed-loop ventilation: A theoretical framework toward precision mechanical ventilation. J Mech Vent 2026; 7(2):78-87.
Abstract
Background
Esophageal balloon manometry is a surrogate for pleural pressure and has been in clinical use for decades. The main advantages for its use are to partition the total lung and chest wall mechanics, thus providing transpulmonary inspiratory, expiratory, and driving pressure monitoring as the true stress delivered to the lung, and the capability to measure the transpulmonary mechanical power as the true energy delivered to inflate the lungs. Thus, it can provide more insight into lung and diaphragmatic protective ventilation than total airway pressure alone. Its capability as the gold standard to measure patient effort, muscle pressure, and to diagnose patient-ventilator interactions and dyssynchronies adds further value to its usage.
Proposed Framework
As the paradigm shifts toward personalized lung and diaphragm-protective ventilation, alongside increased recognition of ventilator-induced and patient self-inflicted lung injury (VILI and P-SILI respectively), using esophageal balloon manometry seems to be the ultimate tool to achieve these goals. In this paper, we introduce an automated theoretical closed-loop ventilation mode that is based on adjusting the inspiratory and expiratory transpulmonary pressures to a safe zone to provide true individualized lung and diaphragmatic protective ventilation, while simultaneously adjusting the triggering, maintaining, and cycling of breaths according to the patient’s neural time and effort, potentially detecting and eliminating dyssynchronies.
Clinical Implications
This adaptive mode aims to optimize the balance between lung recruitment, overdistension, and respiratory muscle unloading, addressing VILI, P-SILI, ventilator induced diaphragm dysfunction (VIDD) and dyssynchronies.
Conclusion
The theoretical esophageal pressure guided closed-loop ventilation based on continuous input and feedback from the esophageal balloon represents a physiologically attractive framework toward individualized lung and diaphragm protective ventilation can conceptually lead to safer personalized ventilation, significantly lessen dyssynchronies, and potentially improve mortality outcomes in acute respiratory failure. However, this concept remains theoretical and unvalidated. Future studies are required before claims regarding feasibility, safety and outcome benefits can be made.
Keywords: Esophageal balloon manometry, Transpulmonary pressures, dyssynchronies, closed-loop ventilation.
References
| 1. Shokry M, Yamasaki K, Daoud EG. Can you calculate the total respiratory, lung, and chest wall respiratory mechanics? J Mech Vent 2020; 1(1):24-25. https://doi.org/10.53097/JMV.10007 | |||
| 2. Shokry M, Simonpietri M, Yamasaki K, Daoud EG. Calculating the work of breathing during mechanical ventilation. J Mech Vent 2021; 2(2):71-72. https://doi.org/10.53097/JMV.10025 | |||
| 3. Grace Hofmann, Lutana Haan, Jeff Anderson. Esophageal pressure measurements in patients with acute respiratory distress syndrome. Crit Care Nurse 2016; 36(5):27-35. https://doi.org/10.4037/ccn2016917 PMid:27694355 | |||
| 4. Jonkman AH, Telias I, Spinelli E, et al. The oesophageal balloon for respiratory monitoring in ventilated patients: updated clinical review and practical aspects. Eur Respir Rev 2023; 32(168):220186. https://doi.org/10.1183/16000617.0186-2022 PMid:37197768 PMCid:PMC10189643 | |||
| 5. Pham T, Telias I, Beitler JR. Esophageal manometry. Respir Care 2020; 65(6):772-792. https://doi.org/10.4187/respcare.07425 PMid:32457170 PMCid:PMC7362579 | |||
| 6. Subirà C, de Haro C, Magrans R, et al. Minimizing asynchronies in mechanical ventilation: current and future trends. Respir Care 2018; 63(4):464-478. https://doi.org/10.4187/respcare.05949 PMid:29487094 | |||
| 7. de Bie MJ, Rietveld PJ, van der Velde-Quist F, et al. The association between patient-ventilator asynchrony and clinical outcomes in mechanically ventilated patients: A systematic review. Crit Care Med 2025; 53(11):e2261-e2270. https://doi.org/10.1097/CCM.0000000000006816 PMid:40793855 PMCid:PMC12577656 | |||
| 8. Ongsupankul S, Capirig CJ, Daoud EG. Bridging the gap: Enhancing synchrony in mechanical ventilation. J Mech Vent 2025; 6(1):32-43. https://doi.org/10.53097/JMV.10120 | |||
| 9. Sottile PD, Albers D, Higgins C, et al. The association between ventilator dyssynchrony, delivered tidal volume, and sedation using a novel automated ventilator dyssynchrony detection algorithm. Crit Care Med 2018; 46(2):e151-e157. https://doi.org/10.1097/CCM.0000000000002849 PMid:29337804 PMCid:PMC5772880 | |||
| 10. Luo XY, He X, Zhou YM, et al. Patient-ventilator asynchrony in acute brain-injured patients: a prospective observational study. Ann Intensive Care 2020; 10(1):144. https://doi.org/10.1186/s13613-020-00763-8 PMid:33074406 PMCid:PMC7570406 | |||
| 11. Mireles-Cabodevila E, Siuba MT, Chatburn RL. A taxonomy for patient-ventilator interactions and a method to read ventilator waveforms. Respir Care 2022; 67(1):129-148. https://doi.org/10.4187/respcare.09316 PMid:34470804 | |||
| 12. Ramirez II, Arellano DH, Adasme RS, et al. Ability of ICU health-care professionals to identify patient-ventilator asynchrony using waveform analysis. Respir Care 2017; 62(2):144-149. https://doi.org/10.4187/respcare.04750 PMid:28108684 | |||
| 13. Bellani G, Laffey JG, Pham T, et al; LUNG SAFE Investigators; ESICM Trials Group. Epidemiology, Patterns of Care, and mortality for patients with acute respiratory distress syndrome in intensive care units in 50 countries. JAMA 2016; 315(8):788-800. https://doi.org/10.1001/jama.2016.0291 PMid:26903337 | |||
| 14. Gattinoni L, Giosa L, Bonifazi M, et all. Targeting transpulmonary pressure to prevent ventilator-induced lung injury. Expert Rev Respir Med 2019; 13(8):737-746. https://doi.org/10.1080/17476348.2019.1638767 PMid:31274034 | |||
| 15. Dostal P, Dostalova V. Practical aspects of esophageal pressure monitoring in patients with acute respiratory distress syndrome. Journal of Personalized Medicine. 2023; 13(1):136. https://doi.org/10.3390/jpm13010136 PMid:36675797 PMCid:PMC9867326 | |||
| 16. Williams EC, Motta-Ribeiro GC, Vidal Melo MF. Driving pressure and transpulmonary pressure: How do we guide safe mechanical ventilation? Anesthesiology 2019; 131(1):155-163. https://doi.org/10.1097/ALN.0000000000002731 PMid:31094753 PMCid:PMC6639048 | |||
| 17. Baedorf Kassis E, Talmor D. Clinical application of esophageal manometry: how I do it. Crit Care 2021; 25(1):6. https://doi.org/10.1186/s13054-020-03453-w PMid:33402179 PMCid:PMC7786919 | |||
| 18. Talmor D, Sarge T, Malhotra A, et al. Mechanical ventilation guided by esophageal pressure in acute lung injury. N Engl J Med 2008; 359:2095-2104. https://doi.org/10.1056/NEJMoa0708638 PMid:19001507 PMCid:PMC3969885 | |||
| 19. Beitler JR, Sarge T, Banner-Goodspeed VM, et al. Effect of titrating positive end-expiratory pressure (peep) with an esophageal pressure-guided strategy vs an empirical high peep-fio2 strategy on death and days free from mechanical ventilation among patients with acute respiratory distress syndrome: A randomized clinical trial. JAMA 2019; 321(9):846-857. https://doi.org/10.1001/jama.2019.0555 PMid:30776290 PMCid:PMC6439595 | |||
| 20. Baedorf Kassis E, Loring SH, Talmor D. Should we titrate peep based on end-expiratory transpulmonary pressure? yes. Ann Transl Med 2018; 6(19):390. https://doi.org/10.21037/atm.2018.06.35 PMid:30460264 PMCid:PMC6212356 | |||
| 21. Sarge T, Baedorf-Kassis E, Banner-Goodspeed V, et al; EPVent-2 Study Group. Effect of esophageal pressure-guided positive end-expiratory pressure on survival from acute respiratory distress syndrome: A risk-based and mechanistic reanalysis of the EPVent-2 trial. Am J Respir Crit Care Med 2021; 204(10):1153-1163. https://doi.org/10.1164/rccm.202009-3539OC PMid:34464237 PMCid:PMC8759303 | |||
| 22. Coppola S, Caccioppola A, Froio S, et al. Effect of mechanical power on intensive care mortality in ARDS patients. Crit Care 2020; 24(1):246. https://doi.org/10.1186/s13054-020-02963-x PMid:32448389 PMCid:PMC7245621 | |||
| 23. Soydan E, Demirel O, Yay ED, et al. Association of compliance-normalized airway and transpulmonary mechanical power with mortality in PARDS. Eur J Pediatr 2026; 185(6):393. https://doi.org/10.1007/s00431-026-07048-5 PMid:42133083 | |||
| 24. De Oliveira B, Aljaberi N, Taha A, et al. Patient-ventilator dyssynchrony in critically ill patients. J Clin Med 2021; 10(19):4550. https://doi.org/10.3390/jcm10194550 PMid:34640566 PMCid:PMC8509510 | |||
| 25. Valdez C, Sarani B. Proportional assist ventilation. Curr Probl Surg 2013; 50(10):484-488. https://doi.org/10.1067/j.cpsurg.2013.08.013 PMid:24156847 | |||
| 26. Suarez-Sipmann F, Pérez Márquez M, González Arenas P. Nuevos modos de ventilación: NAVA [New modes of ventilation: NAVA]. Med Intensiva 2008; 32(8):398-403. https://doi.org/10.1016/S0210-5691(08)75711-5 PMid:19055933 | |||
| 27. Arnal JM, Daoud EG. Guidelines on setting the target minute ventilation in Adaptive Support Ventilation. J Mech Vent 2021; 2(3):80-85. https://doi.org/10.53097/JMV.10029 | |||
| 28. Renaud Y, Auroi J, Cabrio D, et al. Patient-ventilator synchrony under non-invasive ventilation is improved by an automated real time waveform analysis algorithm: a bench study. Intensive Care Med Exp 2025; 13(1):16. https://doi.org/10.1186/s40635-025-00726-y PMid:39937374 PMCid:PMC11822138 | |||
| 29. Morinishi K, Itagaki T, Akimoto Y, et al. Effects of trigger algorithms on trigger performance and patient-ventilator synchrony. Respir Care 2025; 70(10):1285-1293. https://doi.org/10.1089/respcare.12694 PMid:40329919 | |||
| 30. Nakornnoi B, Tscheikuna J, Rittayamai N. The effects of real-time waveform analysis software on patient ventilator synchronization during pressure support ventilation: a randomized crossover physiological study. BMC Pulm Med 2024; 24(1):212. https://doi.org/10.1186/s12890-024-03039-0 PMid:38693506 PMCid:PMC11064376 | |||
| 31. Shokry M, Yamasaki K. Ineffective trigger, the always missed sign. J Mech Vent 2020; 1(2):57-58. https://doi.org/10.53097/JMV.10014 | |||
| 32. Thille AW, Akoumianaki E, Hernández G. Patient-ventilator asynchrony: physiological causes and clinical aspects. Intensive Care Med 2026; 52(3):578-582. https://doi.org/10.1007/s00134-026-08312-w PMid:41632215 | |||
| 33. Gallardo A. Rethinking double triggering: A phenotype rather than a patient-ventilator dyssynchrony? J Mech Vent 2026;7(1):27-31. https://doi.org/10.53097/JMV.10146 | |||
| 34. Baedorf Kassis E, Su HK, Graham AR, et al. Reverse trigger phenotypes in acute respiratory distress syndrome. Am J Respir Crit Care Med 2021; 203(1):67-77. https://doi.org/10.1164/rccm.201907-1427OC PMid:32809842 PMCid:PMC7781129 | |||
| 35. Gallardo A, Sayat MC, Silvero A, et al. Classifying Reverse triggering breaths: A clinically oriented approach. J Mech Vent 2025; 6(4):201-207. https://doi.org/10.53097/JMV.10141 | |||
| 36. Perez V, Pasco J. Identifying asynchronies: work shifting and double triggering. J Mech Vent 2022; 3(4):190-194. https://doi.org/10.53097/JMV.10066 | |||
| 37. Akoumianaki E, Maggiore SM, Valenza F, et al; PLUG Working Group (Acute Respiratory Failure Section of the European Society of Intensive Care Medicine). The application of esophageal pressure measurement in patients with respiratory failure. Am J Respir Crit Care Med 2014; 189(5):520-531. https://doi.org/10.1164/rccm.201312-2193CI PMid:24467647 | |||
| 38. de Vries H, Jonkman A, Shi ZH, et al. Assessing breathing effort in mechanical ventilation: physiology and clinical implications. Ann Transl Med 2018; 6(19):387. https://doi.org/10.21037/atm.2018.05.53 PMid:30460261 PMCid:PMC6212364 | |||
| 39. Aliverti A, Carlesso E, Dellacà R, et al. Chest wall mechanics during pressure support ventilation. Crit Care 2006; 10(2):R54. https://doi.org/10.1186/cc4867 PMid:16584534 PMCid:PMC1550890 | |||
| 40. Natalini G, Buizza B, Granato A, et al. Non-invasive assessment of respiratory muscle activity during pressure support ventilation: accuracy of end-inspiration occlusion and least square fitting methods. J Clin Monit Comput 2021; 35(4):913-921. https://doi.org/10.1007/s10877-020-00552-5 PMid:32617847 PMCid:PMC7330529 | |||
| 41. van Oosten JP, Akoumianaki E, Jonkman AH. Monitoring respiratory muscles effort during mechanical ventilation. Curr Opin Crit Care 2025; 31(1):12-20. https://doi.org/10.1097/MCC.0000000000001229 PMid:39560150 PMCid:PMC11676600 | |||
| 42. Hu J, Hasan O, Shiraishi K, et al. Comparison of estimation of inspiratory muscle effort using three common indices in various respiratory models, a bench study. J Mech Vent 2024; 5(4):119-125. https://doi.org/10.53097/JMV.10111 | |||
| 43. Goligher EC, Jonkman AH, Dianti J, et al. Clinical strategies for implementing lung and diaphragm-protective ventilation: avoiding insufficient and excessive effort. Intensive Care Med 2020; 46(12):2314-2326. https://doi.org/10.1007/s00134-020-06288-9 PMid:33140181 PMCid:PMC7605467 |
