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 Table of Contents  
ORIGINAL ARTICLE
Year : 2019  |  Volume : 3  |  Issue : 4  |  Page : 129-137

Patient-Ventilator asynchrony: Surveying the Knowledge of respiratory therapists in Saudi Arabia


Department of Respiratory Care, The Ministry of Health, Qatif Central Hospital, Qatif, Saudi Arabia

Date of Submission03-Oct-2019
Date of Decision15-Nov-2019
Date of Acceptance18-Nov-2019
Date of Web Publication18-Dec-2019

Correspondence Address:
Alawi Adnan Mohammed
Department of Respiratory Care, The Ministry of Health, Qatif Central Hospital, Qatif
Saudi Arabia
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/sccj.sccj_20_19

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  Abstract 


Objective: Patient-ventilator asynchrony (PVA) commonly occurs in critically ill patients, and it is connected with poor health outcomes. To prevent PVA, respiratory therapists (RTs) must have sufficient knowledge regarding respiratory physiology and mechanics and have the ability to understand ventilator graphics and patient signs and symptoms. However, little is known of the respiratory care practitioner's knowledge about PVA. The aim of this study is to assess the ability of RTs to identify and manage PVAs based on their years of experience, previous training, and characteristics of their clinical setting. Methodology: A study questionnaire was developed to examine the knowledge of RTs to identify PVA. This pilot survey was reviewed and tested by selected experts in the respiratory care field for appropriateness of questions and accuracy of the content. The final survey consisted of 33 items. This include six items on the respondent's demographic information, four on the previous PVA education, eight on the workplace policy and five ventilator screenshot to measure RTs' knowledge on waveform interpretation. Each screenshot had two open text questions asking about the possible causes and solutions for the identified asynchrony. Data were collected and managed using Qualtrics. Exploratory analysis using descriptive statistics was used to analyze the data. Results: A total of 118 recorded responses were received, and 79 participants completed the full survey. Overall, the ability to identify asynchronies on ventilator graph screenshots was poor. Only two RTs (1.7%) correctly detected all five types of asynchrony, whereas 14 (11.8%) identified four asynchronies, 31 (26.1%) recognized three asynchronies, 24 (20.2%) detected two asynchronies, 12 (10.1%) identified only one asynchrony, and 36 (30.3%) did not recognize any asynchronies. No statistically significant differences regarding previous training, years of experience, and work characteristics were observed. Conclusions: The overall knowledge regarding the identification of PVA among RTs is poor. Previous training, years of experience, and work characteristics were not an indicator to correctly identify PVAs.

Keywords: Assessment, identification, mechanical ventilation, patient-ventilator asynchrony, respiratory therapist


How to cite this article:
Mohammed AA. Patient-Ventilator asynchrony: Surveying the Knowledge of respiratory therapists in Saudi Arabia. Saudi Crit Care J 2019;3:129-37

How to cite this URL:
Mohammed AA. Patient-Ventilator asynchrony: Surveying the Knowledge of respiratory therapists in Saudi Arabia. Saudi Crit Care J [serial online] 2019 [cited 2020 Apr 2];3:129-37. Available from: http://www.sccj-sa.org/text.asp?2019/3/4/129/273454




  Introduction Top


Mechanical ventilation aims to enhance the ventilation and oxygenation of patients with diseases causing respiratory failure and decrease the work of breathing. Furthermore, it maintains adequate gas exchange and to off-load the respiratory muscles. To achieve this, harmonious interaction between the patient's respiratory system and the ventilator must be achieved. A mismatch between the patient's initiated breaths and the ventilator's assisted breaths is described as patient-ventilator asynchrony (PVA).[1] PVA can occur in any phase during assisted breath, which consists of four phases (trigger stage, flow/inhalation stage, cycling stage, and last is the exhalation stage). There are three main forms of ventilator asynchronies, trigger asynchronies, cycling asynchronies, and flow asynchronies. Several tools are available for the detection of PVA such as waveform analysis, measurement of the electrical activity of the diaphragm, and esophageal pressure. Waveform analysis is the most modality used due to its availability and safety since it is a noninvasive procedure. Recently, new modalities were introduced in some literature. These modalities use mathematical software that is programmed to detect different types of asynchrony. However, these modalities are rarely used because it's required specialized training and it is not cost-effective.

A key issue that emerges from the literature is the importance of avoiding PVAs. Several studies showed that PVA is associated with significant patient discomfort, distress, and poor clinical outcomes.[2] According to Thille et al., 25% of patients on a mechanical ventilator had asynchrony, and this increased the time on mechanical ventilation three times more than patients without asynchrony.[3] Similar results were found by Chao et al. when examining 174 ventilated patients and found that patients with asynchronies required extended time on positive pressure ventilation (70 days, compared with 33 days for those without asynchrony).[4] Numerous factors must be considered while assessing PVAs including the timing, duration of observation, method of detection, disease, and modes of mechanical ventilation. In addition, respiratory therapists (RTs) must have extensive knowledge and understanding of respiratory mechanics, to help in the detection and management of each asynchrony appropriately.

Most studies in the current literature have examined the capacity of the health-care provider to detect PVA. Longhini et al. stated that the critical care doctors have limited ability to identify PVA.[5] Another study by Ramirez and Arellano showed that health-care providers with previous education on PVA score about four times higher than those who were not trained; however, experience and occupations were not related to their capability to detect PVA. Overall, little is known about respiratory care practitioner's knowledge in regard to PVA.[6] In addition, to my knowledge, most previous studies focused on the ability of health-care providers to identify PVA. However, this study will also investigate the level of confidence and the knowledge of RTs on how these asynchronies are managed after identification. Moreover, it will also investigate the barriers affecting the detection process.


  Methodology Top


A survey was developed to examine the ability of RTs in Saudi Arabia in their ability to identify and manage PVAs.

Study design

The first phase of the study involved the design of an initial survey for phase validation. This pilot survey was reviewed and tested by selected experts in the respiratory care field in Saudi Arabia and Australia for the appropriateness of questions and accuracy of the content.

The second phase of the study involved an exploratory survey of a larger scale of respiratory care practitioners in Saudi Arabia.

Tools

The study questionnaire was developed by the author in collaboration with experts in the field. After collecting and analyzing data and feedback from the first phase of the study, the survey was pilot tested for appropriateness and contents with selected experts in the respiratory care field.

The final survey consisted of 33 items. This include six items on the respondent's demographic information, four on the previous PVA education, eight on the workplace policy and five ventilator screenshot to measure RTs' knowledge on waveform interpretation. Each screenshot had two open text questions asking about the possible causes and solutions for the identified asynchrony. Eight items were related to workplace policy regarding ventilator management. Five images of ventilator screenshot items were included to assess RTs knowledge of waveform interpretation. Each screenshot had two open text questions asking about possible causes and solutions for the identified asynchrony. The five PVAs included were premature cycle, ineffective effort, breath stacking, or double trigger, ineffective flow, and auto-positive end-expiratory pressure (PEEP). Other types of asynchrony such as reverse triggering were excluded due to the difficulty of identifying asynchronies based on waveforms alone.

Methods

Ethics approval was obtained successfully on November 22, 2018, from the Institutional Review Board in King Fahad Medical City, Riyadh, KSA.

  • IRB Log Number: 18-615E


  • Department: External – University of Melbourne


  • Category Approval: Exempt.


Ethics approval was successfully obtained on January 17, 2019, from the Institutional Review Board in Qatif Central Hospital, Qatif, KSA (QCH-SREC0127).

The participants were informed that their confidentiality will be secure and participants' identification will be unknown. The data will be shared under the NHMRC guidelines, and there are no commercial issues only ethics relevant to publication.

Target population

For the survey validation phase, the targeted populations were health-care providers with expertise in dealing with ventilated patients such as intensive care unit (ICU) intensivists, neonatologists, nurses, leaders in respiratory care, senior and junior RTs, and well-known researchers in the respiratory care field.

For the second phase, the targeted subjects were all respiratory care practitioners in Saudi Arabia. RTs were the study subjects because they are substantially more expert with mechanical ventilation.

The anticipated sample size was 137 based on a 95% confidence level, a margin error of 0.8, and a population size around 1500.

Inclusion and exclusion criteria for the exploratory survey were as follows:

Eligible male or female RTs, 18 years and older, academic or nonacademic RTs, and work experience with ventilated patients.

Recruitment

For the survey validation phase, purposive sampling with maximum variation was used to recruit the participants. This means that the primary investigator selected participants with different characteristics to ensure that high variability was present in the collected data.

People who agreed to participate were asked to provide their E-mails. Then, an E-mail containing the survey in a word document (for more accessible comments) was sent to those participants. In 2 weeks, 11 replies were received, and the final survey version was tailored according to the feedback received.

For the second phase (exploratory survey), the participants were recruited through social media advertisements such as Facebook, WhatsApp groups, and Twitter. The snowball technique also used to hire more RTs who may have missed these invitations.

Data collection

Qualtrics website was the platform used for this survey study.

Statistical analysis plan

Exploratory analysis using descriptive statistics (frequencies for categorical variables and means and standard deviations for continuous variables) was calculated for relevant questions.

The analysis mainly aimed to explore whether the mean number of asynchronies (based on the sum of all correct responses to the multiple-choice questions) detected varied according to the participant characteristics (e.g., previous training and years of experience). This was done through conducting independent-samples t-tests and one-way ANOVA test.

Follow-up analysis using Chi-square tests of independence aimed to determine whether the understanding of specific asynchronies was related to participant characteristics.

Chi-square (goodness-of-fit test) was used on each of the five waveform questions to determine whether there was a relationship between correct identification of asynchrony and participant characteristics. To analyze the qualitative variables (previous training in PVA), RTs were categorized as received previous training and not received previous training. To analyze the experience level of the RTs, the number of years of experience was categorized as <5 years and 5 years or more. For analyzing RTs' education level, the categories were undergraduate degrees and graduate degrees. In addition, the number of ventilator checks was categorized as once per shift, two times per shift, and three times per shift.

The sum of correct responses to the multiple-choice questions was very mildly positively skewed, so initially, both parametric and nonparametric forms of the independent-samples t-test were undertaken. However, the parametric and nonparametric analysis yielded the same result, and so, for ease of interpretation, parametric tests are reported.

For qualitative questions (open text answers) relating to participants workplace policy regarding PVA and their answers about the possible causes and management strategies (q17, q18, q20, q21, q23, q24, q26, q27, q29, q30, q32, and q33), correct answers were coded and the percentages of each similar codes were calculated. For all the analyses performed, P < 0.05 was considered statistically significant. Jamovi version 0.9.6.8 (University of Newcastle, New South Wales, Newcastle) and STATA 15.1 (StataCorp, Texas, USA) were used for data analysis in consultation with data analysis specialists.


  Results Top


Characteristics of the sample

A total of 118 responses were recorded. The general characteristics of the participants according to their previous training on PVA and clinical workplace setting are summarized in [Table 1] and [Table 2].
Table 1: Training characteristics

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Table 2: Workplace characteristics

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The number of male RTs was approximately double the number of female therapists (77 males, 65.3%, and 39 females, 33.1%). The majority of participants held undergraduate (bachelor or diploma) degrees (110 undergraduates, 91.5%, vs. ten graduates, 8.5%). Most of the therapists (103 of the total respondents) worked in critical care units (89%). Nearly half of the participants (49%) had worked as a RT for more than 5 years. With regard to specific education received on PVAs in their undergraduate degree, 51% had received focused lectures on PVA and approximately 41% indicated that they only had 1–4 h of lectures on PVA, whereas 47% of the participants said that they could not remember and only 11% said that they had more than 5 h of lectures. In terms of PVA policies in the participant's workplace, 60% responded that there is no specific policy for asynchrony detection and management in their setting.

One question on the survey asked participants to estimate the percentage of ventilated patients and the percentage of patients experiencing PVA in each participant's setting. The results showed that most participants (36%) said that the percentage of ventilated patients was between 50% and 75%. Furthermore, 38% of the respondents estimated that the percentage of patients with PVA in their clinical setting was between 25% and 49% [Table 2]. These numbers align with those reported in the published literature and reported by Blokpoel et al., Garofalo et al., and Thille et al.[3],[7],[8] For the patient-to-RT ratio, approximately 82% of the respondents reported that they had six patients or more under their care. Moreover, approximately 60% responded that they do not have an internal policy or guidelines regarding asynchrony management.{Table 2}

Examination of the previous training on PVA in the past 12 months was one of the main emphases in this study. Of the RTs who participated in this survey, 67% responded that they received previous training, 25% indicated that they received training during hospital orientation, 22% during clinical instruction in their workplace, 22% in seminars or conferences, and only 18% received training through online courses or literature [Table 1].{Table 1}

Correct identification of asynchrony by respiratory therapists

To assess the ability of RTs to identify PVA correctly, participants were provided with five ventilator screenshots (premature cycle, ineffective effort, breath stacking, inadequate flow, and auto-PEEP) and asked to identify the type of asynchrony presented. Remarkably, few participants identified four or more of the different asynchronies. Only 2 RTs (1.7%) correctly detected all five types of asynchrony, whereas 14 (11.8%) identified four asynchronies, 31 (26.1%) recognized three asynchronies, 24 (20.2%) detected two asynchronies, 12 (10.1%) identified only one asynchrony, and 36 (30.3%) did not recognize any asynchronies [Table 3].
Table 3: Number of correctly identified asynchronies

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Eighty-six percent of the participants indicated that they had high confidence regarding PVA identification [Table 1]; however, based on the result, only two therapists (1.7%) could identify the five asynchronies. There was a variation in the degree to which specific asynchronies were identified. The most recognized asynchrony was breath stacking or double trigger by 65 (80.3%) RTs, followed by auto-PEEP which was identified by 43 (55.8%), ineffective effort by 43 (50%), inadequate flow by 30 (38%), and premature cycling was detected by 36 (37.1%) RTs.

Is previous training, years of experience, and frequency of vent check related to correctly identifying asynchronies

An independent-samples t-test was conducted to explore the relationship between the mean number of asynchronies detected and whether the RT had received previous training on PVA. For therapists that received previous training, the average number of asynchronies detected was 2.05, whereas for those that had not received training on PVA, the mean was 1.60 [Table 4] and [Table 5]. However, the Independent-samples t-test was not statistically significant; t(106) = 1.45, P = 0.150; thus, the average number of correctly identified asynchronies did not differ for participants with and without previous training on PVA.
Table 4: Comparison between respiratory therapists who had received training versus those who had not according to the number of correctly identified asynchronies

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Table 5: Comparison between years of experience as respiratory therapist to the number of correctly identified asynchronies

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No significant difference was observed when the mean number of correctly identified asynchronies was compared between groups with <5 years and more than 5 years' experience. For therapists with <5 years' experience, the mean number of asynchronies detected was 1.92, whereas it was 1.83 for those with more than 5 years' experience. The independent-samples t-test was not statistically significant; t (116) =0.321, P = 0.749. Thus, the average number of correctly identified asynchronies did not differ for participants with different experience levels [Table 5].

A one-way ANOVA test was conducted to examine if there was a difference between the average number of correctly identified asynchronies according to the number of ventilator checks done by the RT. There were no statistically significant differences between group means as determined by one-way ANOVA (F(1, 114) = 0.103, P = 0.749). The mean number of asynchronies identified did not statically differ according to the number of checks (1.22 for once per shift, 2.06 for twice per shift, and 1.80 for three times per shift). Thus, the average number of correctly identified asynchronies did not differ for participants with the self-reported frequency of vent checks done by RTs [Table 6].
Table 6: Comparison between the number of vent checks performed by respiratory therapists and the number of correctly identified asynchronies

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Association between previous training and correctly identifying each asynchrony

[Figure 1] shows the association between having received previous training and the ability to detect different types of asynchrony.
Figure 1: Percentage of correctly identified asynchronies based on previous training on patient-ventilator asynchrony

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A Chi-square test of independence was conducted to see if previous training was associated with detecting each asynchrony. For example, of the overall RTs who identified premature cycle (n = 38), the percentage of RTs who correctly identified premature cycle was higher in the trained group (48%) compared to the untrained group (20%).

Correlations between the years of experience and asynchrony

[Figure 2] shows the association between years of experience as a RT and the ability to detect different types of asynchrony. The tests were not statistically significant with P value more than 0.05 for all PVA types. As [Figure 2] shows, the percentage of RTs correctly identifying each asynchrony did not differ for participants with different experience levels.
Figure 2: Percentage of correctly identified asynchronies based on years of experience

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{Figure 2}

Association between number of ventilator checks performed by the respiratory therapists and correctly identifying each asynchrony

[Figure 3] shows the association between the number of ventilator checks performed by the RTs and the ability to detect different types of asynchrony. As [Figure 3] shows, RTs who were used to do more than one ventilator check per shift tended to identify more asynchrony compared with RTs who only checked once per shift. However, we observed significant results only with participants who identified breath stacking; χ2(2) = 14.5, P < 0.001.
Figure 3: Percentage of correctly identified asynchronies based number of ventilator checks performed by respiratory therapists per shift

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Qualitative data

For qualitative questions (open text answers) relating to participants' workplace policy regarding PVA and their answers about the possible causes and management strategies (q17, q18, q20, q21, q23, q24, q26, q27, q29, q30, q32, and q33), answers were coded and the percentages of each similar codes were calculated. In q17, for example, participants were asked to identify the available PVA detection tools in their workplace. The majority identified waveforms as the only available tools with a different definition, so these answers were coded as a waveform.

Approximately 52% indicated that they do not have any detection tools (such as diaphragmatic electromyography and other dedicated software) in their workplace to detect PVA, whereas 47% said that the only asynchrony detection tool available in their workplace is the ventilator screen [Table 2].

An open text question asked respondents to report on the barriers they experienced in asynchrony detection. The lack of knowledge and experience in PVA was reported by 28% of the RTs, 14% reported uncooperative doctors with the titration of sedation, setting change, and lack of authorities, and 10% identified said lack of staff. However, 45% of the respondents said that they did not have any problems detecting PVAs [Table 7]. Although the number of correctly identified asynchrony is limited [Table 3], those who did identify it had a good ability to understand the causes to manage.
Table 7: Difficulties faced by respiratory therapists in their workplace

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  Discussion Top


The aim of this study was to survey the knowledge of respiratory care practitioners in Saudi Arabia about their ability to identify PVA based on previous training, years of experience, and work characteristics. Also, it aims to measure the depth and intensity of PVA education and to explore the difficulties and insufficiencies faced RTS to identify and manage PVA.

The participants in the current study self-reported that the rate of asynchronies in Saudi Arabia ranged between 24% and 49%. This is consistent with what was reported in previous research.[4],[2],[5] The main finding in this study is that RTs ability to detect PVA is limited. Eighty-six percent of the participants indicated that they had high confidence regarding PVA identification [Table 2]; however, based on the result, only two therapists (1.7%) could identify the five asynchronies (premature cycle, ineffective efforts, breath stacking, inadequate flow, and auto-PEEP). This is a crucial issue where the majority are overconfident in their ability to identify and manage PVA. This high confidence may steer the therapists' attention away from improving their knowledge regarding PVA.

This result is consistent with previous studies which found that the overall ability of the health-care provider to recognize asynchronies using waveform analysis is poor.[5],[9],[10] Similarly, 36 (30%) RTs did not recognize any of the five asynchronies in this survey which is higher than what previous studies reported (17% in Ramirez and Arellano study) and (20% in Younes et al. study).[6],[11] On the other hand, a recent pilot study reported that the knowledge and attitude about PVA among critical care professionals in Brazil are high.[12] This poor result maybe because we examined knowledge based only on screenshot analysis of ventilator waveforms, or it might be due to the fact that learning and interpreting PVA is not a simple task even for an expert RT. However, as participants reported in the survey, the only method of detection available in their clinical setting is the waveform analysis from the ventilator screens. Using waveform analysis to identify PVA is very helpful and is a crucial proficiency that all RTs must acquire to improve the interaction between the patient and the ventilator and to limit the complication of mechanical ventilation.[6] In addition, the RT needs to enhance their knowledge about respiratory mechanics and physiology.

The most recognized asynchrony was the breath stacking or double trigger with about 80% of therapists able to identify it correctly. This could be because a double trigger can be detected easily by the appearance of two assisted breaths without expiration. This was supported by the findings of Ramirez and Arellano who reported that double trigger was the most detected asynchrony in their study (64%).[6]

In previous studies by Fusi et al., Lynch-Smith et al., and Ramirez and Arellano, the researchers reported that the health-care provider with previous training on ventilator asynchronies could significantly recognize asynchronies better than those who did not receive any training.[6],[13],[14] In this study, we could not observe the same findings. This study showed that there is no statistically significant association between RTs who had previous training on PVA and the number of identified asynchronies; P = 0.150. This is attributed to the relatively small sample size in this study. Furthermore, in this study, the timeframe between the last training and participating in this survey is unknown which could cause a recall bias. Other studies performed a pretest and posttest which gave them a better chance of finding an association.

The number of years of experience as an RT and different work characteristics did not show any relationship with P = 0.838 and 0.288, respectively. This is supported by the findings from Colombo et al., Ramirez and Arellano, and Younes et al., where researchers found no association between the years of experience in the ICUs and the number of correctly detected asynchronies.[6],[10],[11] This study hypothesized that experience would be the main indicator of asynchrony detection, but we could not observe any association. This would be due to the different clinical practices with different responsibilities in each clinical setting with a variety of ventilator types as respondents indicate in the survey.

Apart from training and education, clinical setting characteristics play an important role in the process of asynchrony recognition. The number of ventilator checks done by the RTs can somewhat help the therapist to identify more PVA [Table 7]. RTs who were used to do more than one ventilator check per shift tended to identify more asynchrony compared with RTs who only checked once per shift (67%) versus once per shift (8%). However, we observed significant results only with participants who identified breath stacking; X2 (2) =14.5, P < 0.001. Frequency of assessing ventilated patients may help therapist to become more familiar with any small changes in the ventilator graphics.

An investigation of the difficulties was faced by the RTs in recognition of PVA in their clinical setting. This study found that the main barriers were lack of knowledge and experience (29%), uncooperative ICU doctors or limited RTs' responsibilities to change the ventilator parameters (5%), lack of staff or overload (8%), type of ventilator (4%), and level of sedation (9%). In terms of overload, the majority of therapists (83%) indicated that they oversaw the care of 6 patients or more per shift. This number exceeds the international recommendation of the RT-to-ventilated patient ratio of 1:4 or 1:5 according to the Canadian teaching hospitals.[15] The higher patient load that imposed on the RT in Saudi Arabian hospitals may explain the poor results in identifying PVA and may be due to inadequate time to carefully assess each individual ventilated patient. In terms of the type of ventilator mode, a recent study by Alabdrabalameer et al. showed that the number of asynchronies varied with the type of ventilator mode.[16] The researchers found that the asynchrony index was less in adaptive support ventilation mode than another mode like pressure-regulated volume control.

Another significant finding is the lack of policy and tools for asynchrony detection and management in the participant's hospital. Approximately 60% indicated that there is no specific policy for asynchrony identification and management, and the only tool available in their setting for asynchrony detection is the analysis of the waveform from the ventilator screen.[6] Even if interpreting ventilator waveforms offers some pros like its availability and applicability at the bedside without the need for additional devices such as electromyography, it has been reported that it has some limitations. For example, PVA can occur at any time and requires continuous monitoring by the therapist in order to detect every asynchrony. Furthermore, some asynchronies such as reverse triggering are difficult to identify by ventilator graphs only. Reverse triggering was defined first by Akoumianaki et al. in deeply sedated patients as inspiratory patient efforts that appear after every mechanical inflation, which can occur in any stage of patient breath.[17] Sometimes, it can trigger the ventilator, and it appears like a double trigger. Interestingly, a high percentage of the participants in this study mistakenly selected a reverse trigger for most of the waveform questions.

This study resulted in the development of a survey to examine RTs' knowledge in collaboration with academics, experts, and leaders in respiratory care fields.

Second, the research was communicated early which enables it to reach a wider audience, and multiple comments received that the topic triggers the attention among RT in Saudi Arabia about the importance of detecting and managing ventilator asynchronies for better patient outcomes. Third, up to my knowledge, this is the first study assessing the knowledge of respiratory care practitioners in Saudi Arabia regarding PVAs.

This study could be expanded in the future by modifying the survey to include alternative measures than waveforms, possibly patient scenario, and video recording.

Study limitations

First, this study was constructed only on analyzing screenshots of ventilator graphs without any other visual sign, parameters, and setting such as respiratory rate and tidal volume. Second, the RTs were asked to answer multiple-choice questions which can bias the results by the element of predicting. Third, since this study only targeted RTs in Saudi Arabia, we cannot assume generalizability for our results. Future research with a higher sample size could be helpful to see the relationship.

Recommendation

Continuous education on PVAs, especially on types and different causes for each type, is very important which can aid RTs to understand the different kinds of asynchronies clearly and to have a strong idea on how to manage PVA for every specific patient condition.

Shortage of resources and special education courses directed for ventilator asynchrony were barriers identified by this study. The respiratory departments and respiratory degree providers must tackle this wide knowledge gap by offering more courses or programs specifically on PVA.


  Conclusions Top


Mechanical ventilation is an essential treatment modality in patients with critical conditions. However, if PVA develops, it can cause patient discomfort, lung injuries, and poor outcomes; lengthen the duration of hospital stay; and increase the mortality rates. PVA is reported on an average of 25%–49% in Saudi Arabian hospitals. Therefore, identifying and managing ventilator asynchronies is believed to be a crucial skill that every RT must acquire in order to limit its complication. Yet, the studies on the capacity of health-care providers to detect asynchrony are relatively limited. Recognizing ventilator asynchronies is not an easy task as this study identified the wide knowledge gap in asynchrony detection among RT in Saudi Arabia. PVA can be improved by the use of proper modes of ventilation that matches the ventilatory support with the patient demands. Patient-ventilator asynchrony can be improved by the use of proper modes of ventilation that match the ventilatory support with the patient demands. and bystrengthening our understanding of respiratory mechanics and respiratory physiology. The lack of staff and overload imposed on RTs in their clinical setting, lack of knowledge and training courses and resources about PVA, and working with difficult ICU doctors were the main strains faced by RTs which affect their ability to accurately detect and manage ventilator asynchronies.

Acknowledgment

First and foremost, I would like to thank my supervisors Dr. Anita Horvath and Dr. Kate Reid for their time, guidance, encouragement, and understanding through my master's study.

I would like to thank Dr. Charles Melpas and Dr. Mohammed Alrammadan for their support in the statistical analysis of the survey.

I would also like to thank Mr. Hassan Al Gazwi for his time and advice whenever needed from the start of the project.

Financial support and sponsorship

Nil.

Conflicts of interest

There are no conflicts of interest.



 
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    Figures

  [Figure 1], [Figure 2], [Figure 3]
 
 
    Tables

  [Table 1], [Table 2], [Table 3], [Table 4], [Table 5], [Table 6], [Table 7]



 

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