Impact of Inspiratory Muscle Pressure Curves on the Ability of Professionals to Identify Patient-ventilator Asynchronies

Study Overview
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Brief Summary
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Patient-ventilator asynchronies can occur as a result of a mismatch between neural (patient) and ventilator inspiratory and expiratory phases. Sensitivity of this visual analysis, even when performed by experts in the field, is low, around 28% in one landmark publication. The impact of the display of Pmus together with the other ventilator waveforms on the ability of health-care professionals to identify asynchronies has not been tested so far. OBJECTIVES: To compare the sensitivity and specificity of the detection of patient-ventilator asynchrony by health professionals through visual inspection of the ventilator waveforms (conventional group) with the sensitivity and specificity of health professionals who have available, in addition to these ventilator waveforms, also the estimated inspiratory muscle pressure curve (Pmus group). METHODS: Participants will analyze 49 consecutive different scenarios of mechanical ventilation generated in a simulator. Intensive care unit physicians and respiratory therapist will be invited to participate and after the inclusion will be randomized to one of two groups: 1) the control group will inspect pressure and flow curves and 2) the Pmus group will inspect pressure, flow, and Pmus curves. Before the start of the study, all participants will have a 30-min training session to homogenize their concepts on the definitions of the different types of asynchrony. Subsequently, the participants will be randomized to the conventional group or Pmus group. Participants will be designated to watch different sessions, in groups of at most 20 individuals, according to their randomization. In these sessions, recorded ventilator waveforms will be projected to a large screen for 30 seconds. A still image containing a few ventilatory cycles will remain visible for another 30 seconds when participants will have to choose which asynchrony (if any) the participants can see on the screen. Sessions of the Pmus group will display, in addition to pressure and flow, the estimated muscle pressure curves. The main outcome is the asynchrony detection rate (sensitivity). It will be also compared specificity, positive and negative predictive values for asynchrony detection. Statistical significance will be set at an alpha level of 0.05. The sample size was estimated in 98 participants based on the expectation of a 10 percentage points difference in the sensitivity between groups.

Detailed Description
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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.

Official Title
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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

Conditions
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Mechanical Ventilation Complication

Intervention / Treatment
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* Other: Muscle Pressure curve (Pmus)


Participation Criteria
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Eligibility Criteria
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Inclusion Criteria: Healthcare professionals (physicians and respiratory therapists) who work in intensive care units Exclusion Criteria: refusal to participate

Ages Eligible for Study
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Minimum Age: 18 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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Accepts Healthy Volunteers

Study Plan
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How is the study designed?
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Design Details

Primary Purpose: Diagnostic
Allocation: Randomized
Intervention Model: Parallel Assignment
Interventional Model Description: Participants will be randomized to one of two groups: control group or Pmus group. The control group will watch recorded ventilator waveforms (pressure and flow), and the Pmus group will watch, in addition to pressure and flow, estimated muscle pressure curves based on artificial intelligence. For each of 49 scenarios recorded from simulated conditions using the ASL-5000 simulator, participants will have to select among multiple choices if there was patient-ventilator asynchrony.
Masking: None (Open Label)


Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| No Intervention: Control group<br>Detection of patient-ventilator asynchronies through visual inspection of pressure and flow waveforms. | |
| Experimental: Pmus group<br>Detection of patient-ventilator asynchronies through visual inspection of estimated inspiratory muscle pressure curves, in addition to pressure and flow waveforms. | Other: Muscle Pressure curve (Pmus)<br>* The intervention will be the display of an additional curve - the estimated inspiratory muscle pressure waveform generated using an artificial intelligence algorithm.<br>|

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Ability of ICU health care professionals to detect patient-ventilator asynchrony | The mean sensitivity to detect asynchronies, calculated for each healthcare professional, will be compared between the groups. | Immediately after the completion of the test sessions | 

Secondary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Other measures of diagnostic ability | Specificity, positive and negative predictive values | Immediately after the completion of the test sessions |