- ICH GCP
- US Clinical Trials Registry
- Clinical Trial NCT05144607
Impact of Inspiratory Muscle Pressure Curves on the Ability of Professionals to Identify Patient-ventilator Asynchronies (Pmus)
Impact of the Display of Inspiratory Muscle Pressure Curves Estimated by Artificial Intelligence on the Ability of Health Care Professionals to Correctly Identify Patient-ventilator Asynchronies - Pmus Study
Study Overview
Status
Conditions
Intervention / Treatment
Detailed Description
The presence of asynchrony was associated with increased mechanical ventilation time, morbidity, reduced hospital discharge rate and IMV free time in the survival analysis, when compared to synchronous patients with the ventilator. Therefore, its correct detection is necessary to optimize ventilatory adjustments and provide a better outcome for patients in the ICU. Recently, the use of software has been proposed with the advantage of being a real-time, automatic and continuous analysis. However these need further studies.
The patient-ventilator interaction is the result of two different pressure systems, the Pmus performed by the patient through the activation of the respiratory muscles and the pressure provided by the mechanical ventilator (Pvent). The tracings, provided by the mechanical ventilator, of pressure and flow are considered a graphical representation of the interaction between Pmus and Pvent, and can exemplify the control of the respiratory cycle by the patient under the influence of the ventilator. The use of these to identify asynchrony, despite being visual and suitable for a logical interpretation, in clinical practice, proved to be dependent on characteristics inherent to the observer, such as length of experience and the presence of previous training. The average sensitivity to correctly identify asynchrony using this interpretation technique was described as 28%, even considering only experienced professionals.
The FlexiMag Max ventilator (Magnamed, São Paulo, Brazil) provides in its interface non-invasively estimated Pmus waveforms through an artificial intelligence algorithm. The Pmus estimate, as it represents the effort performed by the patient both in time and in intensity, when viewed simultaneously with the other waveforms displayed by the ventilator, should provide a more representative graphic portrait of the patient-ventilator interaction. However, the impact of using Pmus estimation on the observer's ability to correctly identify asynchrony has not been studied so far.
Hypothesis The display of the estimated muscle pressure waveform performed by the patient, simultaneously with the pressure and airway flow waveforms over time, will help healthcare professionals in intensive care units to identify patient-ventilator asynchrony.
Methods
Sample For each participant, a sensitivity value defined as the relative frequency of correct answers for all studied asynchronies will be calculated. After that, the average sensitivity (or average hit percentage) as the average of all participants in each group will apply. The sample size was defined based on a previous study, which had a mean sensitivity (or mean percentage of correct answers) of 28.0% with a standard deviation of 15%. In order to have an increase in the mean sensitivity of participants from 28 to 38% in the correct detection of asynchrony, with a power of 90% and a two-tailed significance level of 0.05, it will be necessary to include 49 participants per group, totaling 98 participants. The sample calculation statistics site was used (http://calculoamostral.bauru.usp.br/calculoamostral/).
Study Protocol The study will consist of a preparatory phase, to level participants in defining the different types of asynchronies through preliminary training. In this, a class on the subject will be given, designed for the study in question, synchronously using the Zoom ® tool. The class will be held at 6 different times, with a maximum of 20 participants per session, to allow at the end of each session to clarify doubts with the experts present who will be made available.
Upon completion of this step, participants will be cluster-randomized to the conventional group or Pmus group. Randomization will be stratified by length of experience in intensive care (less or more than five years). In the conventional group, two tracings will be displayed (pressure and flow) while in the Pmus group, three tracings will be displayed (pressure, flow and muscle pressure estimated through artificial intelligence algorithm).
Both groups will be exposed to the same simulated asynchrony scenarios using an active mechanical ventilation simulator (ASL 5000, IngMar Medical, Pittsburgh, Pennsylvania). A total of 49 scenarios will include synchronous, ineffective effort, auto-triggering, double-triggering, reverse-triggering, premature cycling, and late cycling situations. Groups of a maximum of 10 simultaneous participants will view the simulation of 49 scenarios in real time projected on the screen of an auditorium. Each scenario will be visible for one minute. At the end of this period, each participant must choose an alternative among the 7 available options that best signal the situation observed.
The scenarios will be performed simulating the patient-ventilator interaction using the active servo lung 5000 (ASL 5000, IngMar Medical, Pittsburgh, Pennsylvania) connected to the FlexiMag Max mechanical ventilator (Magmamed, São Paulo, Brazil).
The ASL 5000 will play the role of the patient in the interaction, as this, through the digital control of a piston, allows the simulation of different levels of patient efforts under different mechanical conditions of the respiratory system. The 49 proposed scenarios will be elaborated through the combination of effortlessness, low and high effort under the condition of normal, restrictive and obstructive respiratory mechanics, defined by the combination of resistance and compliance of the respiratory system.
The role of the ventilator in the interaction will be played by the FlexiMag Max adjusted in the assist-controlled ventilation modes for volume and pressure and in the spontaneous pressure support mode.
The interaction will include synchronous and asynchronous tracings. Asynchronies will be simulated based on descriptions previously published in the literature, as shown in the table below.
Types of asynchronies Definition Ineffective effort. Presence of effort (Pmus) without ventilator triggering
Double triggering. Two ventilator cycles triggered by a single effort
Auto triggering. None patient effort (Pmus) with ventilator triggering
Reverse triggering. Pmus follows the controlled (or auto-triggered) cycle with a fixed frequency and delay. May or may not generate double cycle
Premature cycling. Inspiratory time too short compared to the patient, defined as cycling to the expiratory phase before peak Pmus.
Delayed cycling. Inspiratory time too long in relation to the patient: defined as cycling to the expiratory phase after the end of the effort (Pmus).
Statistical analysis
Usual descriptive analysis will be performed. Variables with normal distribution will be described using mean and standard deviation and compared between groups using the Student t test. Variables with non-normal distribution will be described as median and interquartile range and will be compared between groups using the Mann-Whitney test. Categorical variables will be described as absolute and relative frequency and compared using the chi-square test. Data normality will be verified by the Shapiro-Wilk test. For each participant, means of sensitivity, specificity, positive predictive value and negative predictive value of asynchrony detection will be calculated. The means of these variables will be compared between the participants of the conventional group and the Pmus group using the t test, or the Mann-Whitney test as indicated. It will be verified whether there is heterogeneity of effect regarding previous experience in intensive care.
The level of statistical significance will be set at 0.05 two-tailed. The R 3.3.2 software (www.r-project.org) will be used. The collected data will be stored in google forms - without identification.
Expected results If the hypothesis is confirmed, a superiority in the Pmus group compared to the conventional group is expected, with a difference of 10 percentage points in the mean sensitivity, in correctly identifying the different types of patient-ventilator asynchrony per participant.
Ethical aspects The study will be submitted to the Ethics Committee of the institution where it will be carried out. Participants will be included in the study after signing an informed consent form. The study will be entirely simulation-based and will not involve patient participation.
Study Type
Enrollment (Actual)
Phase
- Not Applicable
Contacts and Locations
Study Locations
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São Paulo, Brazil, 01308-000
- Hospital Sirio Libanes
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Participation Criteria
Eligibility Criteria
Ages Eligible for Study
Accepts Healthy Volunteers
Genders Eligible for Study
Description
Inclusion Criteria:
- Healthcare professionals (physicians and respiratory therapists) who work in intensive care units
Exclusion Criteria:
- refusal to participate
Study Plan
How is the study designed?
Design Details
- Primary Purpose: Diagnostic
- Allocation: Randomized
- Interventional Model: Parallel Assignment
- Masking: None (Open Label)
Arms and Interventions
Participant Group / Arm |
Intervention / Treatment |
---|---|
No Intervention: Control group
Detection of patient-ventilator asynchronies through visual inspection of pressure and flow waveforms.
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Experimental: Pmus group
Detection of patient-ventilator asynchronies through visual inspection of estimated inspiratory muscle pressure curves, in addition to pressure and flow waveforms.
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The intervention will be the display of an additional curve - the estimated inspiratory muscle pressure waveform generated using an artificial intelligence algorithm.
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What is the study measuring?
Primary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
---|---|---|
Ability of ICU health care professionals to detect patient-ventilator asynchrony
Time Frame: Immediately after the completion of the test sessions
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The mean sensitivity to detect asynchronies, calculated for each healthcare professional, will be compared between the groups.
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Immediately after the completion of the test sessions
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Secondary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
---|---|---|
Other measures of diagnostic ability
Time Frame: Immediately after the completion of the test sessions
|
Specificity, positive and negative predictive values
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Immediately after the completion of the test sessions
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Collaborators and Investigators
Sponsor
Investigators
- Principal Investigator: Eduardo LV Costa, Hospital Sirio-Libanes
Publications and helpful links
General Publications
- Akoumianaki E, Maggiore SM, Valenza F, Bellani G, Jubran A, Loring SH, Pelosi P, Talmor D, Grasso S, Chiumello D, Guerin C, Patroniti N, Ranieri VM, Gattinoni L, Nava S, Terragni PP, Pesenti A, Tobin M, Mancebo J, Brochard L; 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 Mar 1;189(5):520-31. doi: 10.1164/rccm.201312-2193CI.
- Levine S, Nguyen T, Taylor N, Friscia ME, Budak MT, Rothenberg P, Zhu J, Sachdeva R, Sonnad S, Kaiser LR, Rubinstein NA, Powers SK, Shrager JB. Rapid disuse atrophy of diaphragm fibers in mechanically ventilated humans. N Engl J Med. 2008 Mar 27;358(13):1327-35. doi: 10.1056/NEJMoa070447.
- Thille AW, Rodriguez P, Cabello B, Lellouche F, Brochard L. Patient-ventilator asynchrony during assisted mechanical ventilation. Intensive Care Med. 2006 Oct;32(10):1515-22. doi: 10.1007/s00134-006-0301-8. Epub 2006 Aug 1.
- Vassilakopoulos T, Petrof BJ. Ventilator-induced diaphragmatic dysfunction. Am J Respir Crit Care Med. 2004 Feb 1;169(3):336-41. doi: 10.1164/rccm.200304-489CP. No abstract available.
- Kondili E, Prinianakis G, Alexopoulou C, Vakouti E, Klimathianaki M, Georgopoulos D. Respiratory load compensation during mechanical ventilation--proportional assist ventilation with load-adjustable gain factors versus pressure support. Intensive Care Med. 2006 May;32(5):692-9. doi: 10.1007/s00134-006-0110-0. Epub 2006 Mar 8.
- Tobin MJ, Laghi F, Jubran A. Narrative review: ventilator-induced respiratory muscle weakness. Ann Intern Med. 2010 Aug 17;153(4):240-5. doi: 10.7326/0003-4819-153-4-201008170-00006.
- Powers SK, Kavazis AN, Levine S. Prolonged mechanical ventilation alters diaphragmatic structure and function. Crit Care Med. 2009 Oct;37(10 Suppl):S347-53. doi: 10.1097/CCM.0b013e3181b6e760.
- Jubran A. Critical illness and mechanical ventilation: effects on the diaphragm. Respir Care. 2006 Sep;51(9):1054-61; discussion 1062-4.
- Laghi F, Tobin MJ. Disorders of the respiratory muscles. Am J Respir Crit Care Med. 2003 Jul 1;168(1):10-48. doi: 10.1164/rccm.2206020.
- Goligher EC, Brochard LJ, Reid WD, Fan E, Saarela O, Slutsky AS, Kavanagh BP, Rubenfeld GD, Ferguson ND. Diaphragmatic myotrauma: a mediator of prolonged ventilation and poor patient outcomes in acute respiratory failure. Lancet Respir Med. 2019 Jan;7(1):90-98. doi: 10.1016/S2213-2600(18)30366-7. Epub 2018 Nov 16.
- Demoule A, Molinari N, Jung B, Prodanovic H, Chanques G, Matecki S, Mayaux J, Similowski T, Jaber S. Patterns of diaphragm function in critically ill patients receiving prolonged mechanical ventilation: a prospective longitudinal study. Ann Intensive Care. 2016 Dec;6(1):75. doi: 10.1186/s13613-016-0179-8. Epub 2016 Aug 5.
- Dres M, Rittayamai N, Brochard L. Monitoring patient-ventilator asynchrony. Curr Opin Crit Care. 2016 Jun;22(3):246-53. doi: 10.1097/MCC.0000000000000307.
- Nava S, Bruschi C, Fracchia C, Braschi A, Rubini F. Patient-ventilator interaction and inspiratory effort during pressure support ventilation in patients with different pathologies. Eur Respir J. 1997 Jan;10(1):177-83. doi: 10.1183/09031936.97.10010177.
- Pohlman MC, McCallister KE, Schweickert WD, Pohlman AS, Nigos CP, Krishnan JA, Charbeneau JT, Gehlbach BK, Kress JP, Hall JB. Excessive tidal volume from breath stacking during lung-protective ventilation for acute lung injury. Crit Care Med. 2008 Nov;36(11):3019-23. doi: 10.1097/CCM.0b013e31818b308b.
- Pham T, Telias I, Piraino T, Yoshida T, Brochard LJ. Asynchrony Consequences and Management. Crit Care Clin. 2018 Jul;34(3):325-341. doi: 10.1016/j.ccc.2018.03.008.
- Sottile PD, Albers D, Smith BJ, Moss MM. Ventilator dyssynchrony - Detection, pathophysiology, and clinical relevance: A Narrative review. Ann Thorac Med. 2020 Oct-Dec;15(4):190-198. doi: 10.4103/atm.ATM_63_20. Epub 2020 Oct 10.
- Mellott KG, Grap MJ, Munro CL, Sessler CN, Wetzel PA, Nilsestuen JO, Ketchum JM. Patient ventilator asynchrony in critically ill adults: frequency and types. Heart Lung. 2014 May-Jun;43(3):231-43. doi: 10.1016/j.hrtlng.2014.02.002.
- Fabry B, Guttmann J, Eberhard L, Bauer T, Haberthur C, Wolff G. An analysis of desynchronization between the spontaneously breathing patient and ventilator during inspiratory pressure support. Chest. 1995 May;107(5):1387-94. doi: 10.1378/chest.107.5.1387.
- Holanda MA, Vasconcelos RDS, Ferreira JC, Pinheiro BV. Patient-ventilator asynchrony. J Bras Pneumol. 2018 Jul-Aug;44(4):321-333. doi: 10.1590/S1806-37562017000000185. Epub 2018 Jul 6. Erratum In: J Bras Pneumol. 2018 Sep 03;:
- Murias G, Lucangelo U, Blanch L. Patient-ventilator asynchrony. Curr Opin Crit Care. 2016 Feb;22(1):53-9. doi: 10.1097/MCC.0000000000000270.
- Colombo D, Cammarota G, Alemani M, Carenzo L, Barra FL, Vaschetto R, Slutsky AS, Della Corte F, Navalesi P. Efficacy of ventilator waveforms observation in detecting patient-ventilator asynchrony. Crit Care Med. 2011 Nov;39(11):2452-7. doi: 10.1097/CCM.0b013e318225753c.
- Gilstrap D, MacIntyre N. Patient-ventilator interactions. Implications for clinical management. Am J Respir Crit Care Med. 2013 Nov 1;188(9):1058-68. doi: 10.1164/rccm.201212-2214CI.
- Chatburn RL, Mireles-Cabodevila E. 2019 Year in Review: Patient-Ventilator Synchrony. Respir Care. 2020 Apr;65(4):558-572. doi: 10.4187/respcare.07635.
- Schreiber A, Bertoni M, Goligher EC. Avoiding Respiratory and Peripheral Muscle Injury During Mechanical Ventilation: Diaphragm-Protective Ventilation and Early Mobilization. Crit Care Clin. 2018 Jul;34(3):357-381. doi: 10.1016/j.ccc.2018.03.005.
- Sinderby C, Liu S, Colombo D, Camarotta G, Slutsky AS, Navalesi P, Beck J. An automated and standardized neural index to quantify patient-ventilator interaction. Crit Care. 2013 Oct 16;17(5):R239. doi: 10.1186/cc13063.
- Chappell HW. Nurses' commitment makes a difference in discharge planning. Interview by Nancy Prewitt. Ky Nurse. 1989 May-Jun;37(3):13-4. No abstract available.
- de Wit M, Miller KB, Green DA, Ostman HE, Gennings C, Epstein SK. Ineffective triggering predicts increased duration of mechanical ventilation. Crit Care Med. 2009 Oct;37(10):2740-5. doi: 10.1097/ccm.0b013e3181a98a05.
- Blanch L, Villagra A, Sales B, Montanya J, Lucangelo U, Lujan M, Garcia-Esquirol O, Chacon E, Estruga A, Oliva JC, Hernandez-Abadia A, Albaiceta GM, Fernandez-Mondejar E, Fernandez R, Lopez-Aguilar J, Villar J, Murias G, Kacmarek RM. Asynchronies during mechanical ventilation are associated with mortality. Intensive Care Med. 2015 Apr;41(4):633-41. doi: 10.1007/s00134-015-3692-6. Epub 2015 Feb 19.
- Blanch L, Sales B, Montanya J, Lucangelo U, Garcia-Esquirol O, Villagra A, Chacon E, Estruga A, Borelli M, Burgueno MJ, Oliva JC, Fernandez R, Villar J, Kacmarek R, Murias G. Validation of the Better Care(R) system to detect ineffective efforts during expiration in mechanically ventilated patients: a pilot study. Intensive Care Med. 2012 May;38(5):772-80. doi: 10.1007/s00134-012-2493-4. Erratum In: Intensive Care Med. 2013 Feb;39(2):341.
- Kondili E, Prinianakis G, Georgopoulos D. Patient-ventilator interaction. Br J Anaesth. 2003 Jul;91(1):106-19. doi: 10.1093/bja/aeg129. No abstract available.
- Ramirez II, Arellano DH, Adasme RS, Landeros JM, Salinas FA, Vargas AG, Vasquez FJ, Lobos IA, Oyarzun ML, Restrepo RD. Ability of ICU Health-Care Professionals to Identify Patient-Ventilator Asynchrony Using Waveform Analysis. Respir Care. 2017 Feb;62(2):144-149. doi: 10.4187/respcare.04750. Epub 2016 Oct 25.
- Parthasarathy S, Jubran A, Tobin MJ. Assessment of neural inspiratory time in ventilator-supported patients. Am J Respir Crit Care Med. 2000 Aug;162(2 Pt 1):546-52. doi: 10.1164/ajrccm.162.2.9901024.
- Bellani G, Mauri T, Coppadoro A, Grasselli G, Patroniti N, Spadaro S, Sala V, Foti G, Pesenti A. Estimation of patient's inspiratory effort from the electrical activity of the diaphragm. Crit Care Med. 2013 Jun;41(6):1483-91. doi: 10.1097/CCM.0b013e31827caba0.
Study record dates
Study Major Dates
Study Start (Actual)
Primary Completion (Actual)
Study Completion (Actual)
Study Registration Dates
First Submitted
First Submitted That Met QC Criteria
First Posted (Actual)
Study Record Updates
Last Update Posted (Actual)
Last Update Submitted That Met QC Criteria
Last Verified
More Information
Terms related to this study
Additional Relevant MeSH Terms
Other Study ID Numbers
- AVAP-NG 2219
Drug and device information, study documents
Studies a U.S. FDA-regulated drug product
Studies a U.S. FDA-regulated device product
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