Critical care nurses' knowledge and attitudes toward using ventilator waveform monitoring to detect patient-ventilator asynchrony: A cross-sectional online survey

Abstract

Background

Most mechanically ventilated patients will be exposed to some asynchrony with the ventilator. Inability to detect and manage patient-ventilator asynchrony (PVA) as early as possible may lead to poor outcomes, including, but not limited to, prolonged mechanical ventilation, extended ICU stay, and higher mortality. Critical care nurses as primary health care providers may achieve significant participation in the timely detection of PVA. Waveform monitoring is a non-invasive and reliable method, but it is also regarded as a challenging task even for skilled clinicians. The aim of the study is to assess the knowledge level and attitude of critical care nurses in Egypt regarding the use of ventilator waveform monitoring to detect PVA.

Methods

A cross-sectional online survey was carried out in three intensive care units in three hospitals in Alexandria, Egypt. The questionnaire comprised four parts to evaluate critical care nurses' level of knowledge and attitude regarding ventilator waveform monitoring and assessed their ability to detect PVA.

Results

Most nurses (88.1%) have poor knowledge levels and negative attitudes (93.1%) toward using waveform monitoring to detect PVA. A significant relationship was found between nurses' knowledge of ventilator waveform monitoring and previous training programs on mechanical ventilation (P = 0.031). In addition, a significant relationship between nurses' attitude toward ventilator waveform monitoring and their level of education (P = 0.002), and their attendance in previous courses containing waveform analysis (P = 0.020) was noted. A highly significant relation (P = 0.000) was detected between nurses' level of knowledge and their attitude toward ventilator waveform monitoring.

Conclusions

The majority of the critical care nurses have poor knowledge and negative attitudes regarding using ventilator waveform monitoring to detect PVA. Previous training on mechanical ventilation and attendance of courses on ventilator waveform analysis was found to be significantly correlated with nurses' level of knowledge and attitude regarding ventilator waveform monitoring.

Introduction

The most pervasive organ failure detected in critically ill patients is respiratory failure. The majority of these patients need mechanical ventilation (MV) to alleviate their work of breathing, maintain adequate gas exchange, and unburden their respiratory muscles⁽¹⁾. Consistent with a multinational survey involving approximately 5000 patients, the most common indications for MV are; acute respiratory failure (69%), coma (17%), chronic obstructive pulmonary disease (COPD) (3%), and neuromuscular disorders (2%)⁽²⁾. Assisted MV should be used early in order to eliminate the harmful effects of controlled ventilation, particularly the development of ventilator-induced diaphragmatic dysfunction, and to facilitate rapid weaning⁽³⁾. Effective MV necessitates the coordinated function of two pumps; the first is the MV, in which the settings are chosen by the clinician govern. The patient's respiratory system is the second pump, controlled by the neuromuscular system, lung compliance, and airway resistance. Presumably, these two pumps should work mutually, which is significant since synchrony between the ventilator and the patient maximizes patient comfort, decreases the work of breathing respiratory muscle fatigue, and enhances oxygenation and ventilation^{(4, 5)}.

In the 1970s, Patient-ventilator asynchrony (PVA) was described as "fighting the ventilator"⁽⁶⁾. Sassoon and Foster defined it as a mismatch between patient-initiated breaths and ventilator-assisted breaths⁽⁷⁾. PVA has recently been defined as the difference in inspiration and expiration timing between the patient and ventilator⁽⁸⁾. It can also be defined as a different time, flow, volume, or pressure demands of the patient's respiratory system and MV⁽⁹⁾. PVA risk factors are numerous, including patient-related factors such as anxiety, pain fever, and delirium. Diseases-related factors include high resistance (e.g., COPD), low compliance (e.g., ARDS), and decreased/increased respiratory drive because of central and neuromuscular disorders. Ventilator related factors as inappropriate ventilator settings of the trigger, rise time, level of pressure support, cycling, inspiratory flow, rate of respiration, tidal volume (VT), inspiratory time, and intrinsic positive end-expiratory pressure (auto-PEEP)^(9–11).

It is evident that different types of asynchronies occurred in a significant percentage of patients during MV. Leite et al. ⁽¹²⁾ illustrated that chance of PVA is higher by 10% in postoperative cardiac patients, especially when initially ventilated in volume-cycled ventilation (VCV), and this risk remained high in the subsequent pressure support ventilation (PSV) phases. Zhou et al.⁽¹³⁾ investigated the occurrence and complications of PVA. It was found that 24% of the studied mechanically ventilated patients had different types of PVA, with the highest percentage of occurrence being double triggering (13%). The second occurrence was both cycle mismatch (4%) and ineffective effort (4%); the least occurrence was delayed triggering (1%), double triggering occurred more frequently in volume cycled ventilation than in pressure cycled ventilation.

When the ventilator does not have the potential to detect patient effort, which is called trigger asynchrony, on the contrary, trigger asynchrony indicates that the patient's breathing rate is higher than the ventilator's rate, and the most ineffective efforts are detected during mechanical expiration. However, they will also occur during inspiration, ineffective trigger threshold caused by setting too high-pressure support, frequency, inspiratory time, and tidal volume⁽¹⁴⁾. Reverse Trigger is often misidentified as a double trigger or auto-trigger, but it is neither. Mechanisms responsible for the reverse trigger are thoracic or diaphragmatic stretching receptors and spinal reflex⁽¹⁵⁾.

Auto triggering occurs when a mechanical breath is delivered to the patient without exerting any inspiratory effort, which can be caused by a high inspiratory trigger sensitivity, air leaks in the endotracheal tube cuff, ventilator circuit, chest tube, and flow oscillations⁽¹⁶⁾. Double triggering indicates two consequent inspirations with short expiratory time, where the patient triggers the primary cycle. Risk factors of double triggering are high respiratory drive, low tidal volume, neural inspiratory time⁽¹⁷⁾.

Flow asynchronies imply that the delivery of a mandatory breath does not correspond to the requirements of the patient. It is classified into an inadequate flow and a high flow. The inadequate flow is caused by high rise time in pressure targeted breath, high demand diseases, such as fever, sepsis, pain, anxiety, hypoxia, and hypercarbia⁽¹⁸⁾. High flow is less common and caused by low rise time in pressure targeted breath and high flow in volume targeted breath.

Cycling Asynchrony is classed into early cycling and delayed cycling. when the allotted time to the mechanical breath is not enough for the patient's inspiratory effort, the problem of early cycling occurs while delayed cycling happens when the mechanical breath has a longer time in comparison to the patient's inspiratory effort⁽¹⁹⁾. The problems arise from cycling too prematurely as the patient continues to be in the inspiratory phase. Still, the ventilator has been switched to expiration, which may cause double triggering and increased work of breathing⁽²⁰⁾. Patient-ventilator asynchrony can cause a variety of unfavorable outcomes, including increased work of breathing, patient discomfort, alveolar overdistension, lung injury, sleep disorders, unnecessary use of sedation, modifying the result of weaning, as well as a more extended stay in ICU and long term MV^{(21, 22)}.Health care professionals, especially critical care nurses who are in charge of patient monitoring and care for 24 hours, should bear in mind to ensure that the patient receives suitable ventilation requirements and prevent worse outcomes related to PVA^{(1, 17)}.

Consequently, the prevention, early detection, and management of PVAs are recognized as vital actions during both invasive and non-invasive ventilation (NIV)^{(17, 23, 24)}. There are several tools to assess asynchrony. Measurement of diaphragmatic electrical activity by electromyography and esophageal pressure measurement is considered the method of choice for detecting asynchrony^{(18, 25)}. Nevertheless, they are invasive and expensive; therefore, their availability during daily clinical practice is restricted. Waveform analysis from mechanical ventilation is a non-invasive method to determine patient-ventilator asynchronies. Ventilator waveforms provide a graphical explanation of how a patient's breath is delivered. Ventilator waveforms do not necessitate extra equipment and are readily available for real-time. The ability of the health care providers, especially critical care nurses, to detect PVA timely and accurately can improve critically ill patients’ outcomes^{(23, 26)}.

Waveform analysis by visual inspection will be a reliable, non-invasive, and substantial tool for detecting patient-ventilator asynchrony⁽²⁷⁾. It is essential for ICU staff to integrate their knowledge of pulmonary physiology with adequate knowledge regarding ventilator waveform in order to be able to interpret if critically ill patients interact appropriately or poorly with the ventilator for early detection of PVAs^(27–29). Since critical care nurses are the professional figure who spends the majority of their working time at the bedside, they have the chance to achieve significant participation in the timely identification of PVA. Hence, ventilator waveform analysis has been identified as a skill that needs a properly trained professional^{(17, 30)}. Consequently, the question is whether the critical care nurses can identify PVA through waveform analysis.

Up to now, the number of nurse researchers who handled the patient-ventilator interactions is limited. Enrico et al.⁽¹⁾ conducted a review and investigated the level of nursing skills to discover patient-ventilator asynchrony. They found that published research that assessed knowledge and skills of nurses related to the ventilator's waveform monitoring are very limited. The few available pieces of research point out that nurses rarely participate in ventilator waveform analysis^{(17, 30, 31)}. The suggested causes for this were related to multifaceted monitoring, inadequate suitable educational courses, and limited resources and instructive tools. In the end, they concluded that ventilator graphic monitoring might be a difficult practice, especially for ICU nurses. In addition, they recommended that postgraduate university courses cover the knowledge and skills needed to manage patient-ventilator asynchrony adequately.

Recently, a universal study was conducted by Ramirez et al. ⁽³²⁾ through an online survey to investigate the main factors associated with proper recognition and management of PVA. The results revealed that proper recognition and management of PVA were associated significantly with specific training programs in MV, the number of ICU beds, and the number of recognized PVAs. Several studies were conducted on mechanically ventilated patients in Egypt. Nonetheless, no study has been conducted on this issue. Hence, this study was performed to assess the knowledge level and attitude of critical care nurses in Egypt regarding the use of ventilator waveform monitoring to detect PVA.

Aim of the Study:

The study aimed to assess the knowledge and attitudes of critical care nurses in Egypt towards the use of ventilator waveform monitoring to detect patient-ventilator asynchrony. 

Research question:

• What is the level of knowledge and attitudes towards the use of ventilator waveform monitoring among critical care nurses in Egypt?
• Can critical care nurses in Egypt detect patient-ventilator asynchrony through ventilator waveform analysis?

Materials And Method

Research design: A cross-sectional online survey was adopted in this study.

Subjects and Setting: The study sample was chosen from three General Intensive Care Units (ICUs) in three hospitals, Alexandria, Egypt. All nurses in the three ICUs were targeted in our sample. Of the 137 critical care nurses, 101 agreed to participate in the study. All participants provided their informed consent for participation in this study. The ICU nurses of both sexes who provided direct care for mechanically ventilated patients and voluntarily agreed to participate in the study were included in the current study while ICU nurses who are not bedside nurses were excluded from the study. 

Tool of data collection: Ventilator waveform monitoring questionnaire

The researcher developed this questionnaire after reviewing related literature^{(1, 17, 30)}to evaluate critical care nurses' level of knowledge and attitudes regarding ventilator waveform monitoring, as well as to assess their ability to detect PVA through ventilator waveform analysis. This tool consists of four parts. 

Part one: Demographic data and clinical experiences

This section includes 16 questions, in which participants were asked to state their sex, age, level of education, profession, years of experience, type of ICU, and working shift and to specify whether they had prior training in mechanical ventilation. If they received prior training in mechanical ventilation, they had to answer seven questions to identify the number of attended courses, name of attended courses, name of training institutions, time of last training course, if the training courses' curriculum contained waveform analysis, and if the trained nurses tried to analyze the ventilator waveform before.

Part two: ICU nurses' responses regarding ICU protocol

This part includes three questions, in which critical care nurses are asked both multiple-choice and 'yes" or "no" questions to determine whether there is a written protocol for the care of mechanically ventilated patients in the ICUs where they work. These questions also determine if ICU protocol includes ventilator graphics monitoring as part of the care of mechanically ventilated patients and who's responsible for monitoring ventilator graphics.

Part three: Ventilator waveform analysis

This part assessed the level of knowledge of the critical care nurses regarding ventilator waveform monitoring and their ability to detect PVA through ventilator waveform analysis. It included four pictures, three of them depicted common types of PVAs (Ineffective triggering, double triggering, and auto-triggering), and the fourth demonstrated normal ventilator waveform. The respondents were asked to identify the presence or absence of PVA through 'yes' or 'no' questions. Furthermore, the respondents were asked to specify the type of PVA if present. 

Part IV: Attitudes of the critical care nurses regarding ventilator waveform monitoring:

Measurement of attitudes was done based on six questions where the subjects had to respond on a 1 to 5-point Likert scale ranging from strongly disagree to strongly agree.

Method

The study lasted for three months, from the start of April 2021 to the last of June 2021. Written approval was obtained from the faculty's ethical committee and the hospitals' administrative authority after providing an official letter and after declaring the significance of the study.  The researcher developed the study tool after reviewing the relevant literature. Content validity was done for the developed tool by five experts in the field of critical care nursing and based on their recommendations, the necessary modifications were done. The Cronbach alpha test (0.82) was used to evaluate the reliability of the study. A pilot study was carried out on 10% of the sample to test the study tool's feasibility and applicability, and the results were excluded from the study. 

Data Collection

The researchers held a one-hour online zoom meeting with critical care nurses to obtain informed consent to participate in the study. The study purpose, tool, and the duration of responding and submitting the tool were clarified to the participants. The researcher turned the study tool into an electronic form on Google form software. The link of the electronic tool was shared with the Emails or WhatsApp of critical care nurses who met the inclusion criteria and agreed to respond to the electronic questionnaire voluntarily. To submit their responses, respondents had to respond to all questions (27 questions) that were compulsory and be committed to the time limit of 30 minutes. The collected data were analyzed using the appropriate statistical tests to assess the knowledge and attitudes of critical care nurses in Egypt regarding the use of ventilator waveform monitoring to detect PVA. 

Ethical considerations:

Before the implementation of the study, ethical approval was obtained from the Faculty of Nursing Ethics Committee at Alexandria University. Consent to participate in the study was obtained from all nurses after illustrating the aim of the study via emails and WhatsApp. Submitting the online questionnaire was considered as approval to participate in the study. All nurses were informed that participation in this study was optional, and they could withdraw from the study without giving reasons. Data confidentiality was assured during the implementation of the study.

Statistical Analysis:

Statistical Package for the Social Sciences (version 26.0; IBM Corp., Armonk, New York) was chosen to analyze the data of this study. Cronbach's alpha determined the reliability of the tool. Frequency tables and cross-tabulations were used to illustrate the results of categorical data and tested by the Chi-Square Test or Fisher's Exact Test. Quantitative data were summarized by the arithmetic mean and standard deviation. Student t-test was used to compare between means and One-Way Analysis of Variance (ANOVA) was applied.

Results

Table 1 illustrates more than half (51.5%) of the sample were females, and the age of more than three quarters (77.2%) of nurses was in their twenties with a mean age of 26.73 ± 3.967 years. Moreover, more than three-quarters (81.2%) have a bachelor's degree in nursing, nearly two-thirds (58.4%) have an experience in ICU less than five years, and more than half of them (52.5%) work at night.

Table 2 demonstrates that more than one-third of nurses (40.6%) have attended a training program on mechanical ventilation. About half (48.8%) of these programs were conducted in the university, more than one-third (39%) of these programs were named mechanical ventilation, and two-thirds (65.9%) of these courses lasted for less than one year. Previous courses were as follows; 57.4% attended courses about ventilator synchrony, 43.6% attended courses about waveform analysis, and 55.4% of nurses made trials to analyze the waveform independently. More than two-thirds (62.4%) have a care protocol for mechanically ventilated patients in their units, and more than three-quarters (79.4%) of these protocols contain items about waveform monitoring. The most responsible staff for waveform analysis in the studied units was the attending physician (62%).

Table 3 depicts the nurses' distribution according to the levels and mean scores of their knowledge and attitude about ventilator waveform monitoring. Most nurses (88.1%) have poor knowledge about the waveform analysis with a mean score of 2.337 ± 1.845. In addition, most of them (93.1%) have a negative attitude towards waveform analysis.

Table 4 explains the relationship between the nurses' knowledge about ventilator waveform monitoring and their essential characteristics as follows: there is no relation between nurse knowledge about ventilator waveform monitoring and their essential characteristics as sex, age, level of education, years of experience, or working shifts.

Table 5 shows the relationship between the nurses' knowledge about ventilator waveform monitoring and their training experiences. Additionally, there is a significant relationship between nurse knowledge about ventilator waveform monitoring and the attendance of training programs on mechanical ventilation (P = 0.031).

Table 6 demonstrates the relationship between the nurses' attitude about ventilator waveform monitoring and their basic characteristics. There is a significant relationship between attitude about ventilator waveform monitoring and their level of education (P = 0.002).

Table 7 shows the relationship between the nurses' attitudes about ventilator waveform monitoring and their training experience. There is a significant relationship between nurse attitude about ventilator waveform monitoring and their attendance of previous courses containing waveform analysis (P = 0.020).

Table 8 depicts the relationship between nurses' attitude towards ventilator waveform monitoring and their level of knowledge about waveform monitoring. There is a highly significant relation (P = 0.000).

Table (1): Distribution of the studied nurses according to their demographic characteristics

Table (2): Distribution of the studied nurses according to their training experiences

Table (3): Distribution of the studied nurses according to the levels and mean scores of their knowledge and attitude about ventilator waveform monitoring:

Table (4): Relationship between the nurses' knowledge about ventilator waveform monitoring and their basic characteristics

Table (5): Relationship between the nurses' knowledge about ventilator waveform monitoring and their training experiences

X² Chi Square Test * statistically significant at p ≤ 0.05

Table (6): Relationship between the nurses' attitude towards ventilator waveform monitoring and their basic characteristics

X² Chi Square Test * statistically significant at p ≤ 0.05

Table (7): Relationship between the nurses' attitude towards ventilator waveform monitoring and their training experiences:

Table (8): Relationship between the nurses' attitude towards ventilator waveform monitoring and their level of knowledge:

Discussion

Patient-ventilator asynchrony is a prevalent problem in which patients need invasive mechanical ventilation (MV) in the critical care unit (CCU). It has an incidence rate ranging from 10–85%. Patient outcomes vary significantly between studies, but patient-ventilator asynchrony is related to poorer outcomes. Delayed detection and management of patient-ventilator asynchrony could lead to an extended duration of mechanical ventilation, extended ICU stay, and higher mortality. Consequently, this necessitates more significant attention to patient-ventilator asynchrony during managing patients undergoing invasive mechanical ventilation. The nurse who spends the most time at the bedside can play a vital role in detecting patient-ventilator asynchrony. Waveform monitoring is a non-invasive and reliable method for detecting patient-ventilator asynchrony, but it is also regarded as a difficult task even for skilled clinicians.

This study assessed the knowledge and attitudes of critical care nurses with varying educational levels, experiences, and training levels toward using ventilator waveform monitoring to identify PVA. The present study results are compatible with those of previous studies since the attending physician was the primarily responsible staff for waveform analysis in the studied units. This result agrees with the review conducted by Enricho et al.⁽¹⁾, who found that nurse-led ventilator waveform analysis is a practice rarely performed. Moreover, the current study results revealed that the most significant number of the studied nurses (88.1%) had poor knowledge and negative attitudes (93.1%) toward identifying patient-ventilator asynchrony using ventilator waveform analysis. These results agree with Ramirez et al. ⁽³⁰⁾, who assessed the ability of more than three hundred professionals that work in CCUs to identify patient-ventilator asynchrony through analyzing ventilator waveforms. Their results demonstrated that less than one-quarter of healthcare professionals could identify all kinds of PVA. Regarding the nurses' negative attitudes toward identifying PVA using ventilator waveform analysis in the current study, they are mostly related to other time-sensitive things that are going on, especially in a busy ICU environment, and the nursing work overloads are high in ICUs, particularly in Egypt because of a high shortage of nursing staff.

The current study revealed no relation between nurses' knowledge about ventilator waveform monitoring and their characteristics as sex, age, level of education, years of experience, or working shifts. These results agree with Colombo et al.⁽²⁶⁾, who found that even experienced physicians were not able to recognize different types of patient-ventilator asynchronies. In addition, Ramirez et al. ⁽³⁰⁾ performed a large worldwide study to evaluate the factors affecting the ability of all ICU staff to detect patient-ventilator asynchronies. Their results revealed that the professional role and the experience of the ICU team members did not influence the correctness in detecting the patient-ventilator asynchronies. Smith et al. ⁽¹⁷⁾ carried out a quasi-experimental study to evaluate the impact of education on patient-ventilator synchrony on the clinician's level of data and patient's mean duration of mechanical ventilation. They found neither experience nor the profession was a significant factor in identifying Asynchrony correctly through analyzing ventilator waveforms.

The present study's results revealed a significant relationship between nurse knowledge about identifying different types of PVAs using ventilator waveform monitoring and the previous attendance of training programs on mechanical ventilation (P = 0.031). This finding is consistent with those of Chacón et al. ⁽³¹⁾, who found that critical care nurses who were exposed to their training program on ventilator waveform analysis, were able to identify the studied type of asynchrony equally to well-trained physicians. Moreover, Fusi et al. ⁽³³⁾ demonstrated nurses' ability to detect patient-ventilator asynchronies through ventilator waveform monitoring by comparing nurses’ knowledge before and after receiving the specific educational course on ventilator waveform analysis. The difference in the nurses’ knowledge was significant at the end of the training (p < 0.001), as well as after six months (p < 0.001).

Furthermore, the current study results are compatible with those of Ramirez et al. ⁽³⁰⁾, which revealed that only professional staff who received previous training in ventilator waveform analysis were significantly able to identify patient-ventilator asynchronies. The current study's findings agree with Smith et al. ⁽¹⁷⁾, who found that the knowledge level of either nurses or respiratory therapists was significantly affected by receiving education on the patient-ventilator synchrony and asynchrony. Recently, Ramírez et al. ⁽³²⁾ found that a specific training program in mechanical ventilation was among the main factors associated with proper recognition of PVA.

To our knowledge, no prior research has been carried out to evaluate the critical care nurses' attitude toward ventilator waveform monitoring before. The current study findings revealed a highly significant relation between nurses' attitude toward ventilator waveform monitoring and their level of knowledge about different types of PVAs (P = 0.000). Moreover, the results of the current study found a significant relationship between nurse attitude about ventilator waveform monitoring and their attendance of previous courses containing waveform analysis (P = 0.020). These findings emphasize the importance of receiving training on patient-ventilator asynchrony and ventilator waveform monitoring, which agreed with the previous studies' findings.

Conclusion

According to the present study's findings, the majority of the critical care nurses have poor knowledge levels and negative attitudes regarding using ventilator waveform monitoring to detect PVA. Only previous training on mechanical ventilation and attendance of previous courses containing ventilator waveform analysis were found to have a significant relationship with nurses' level of knowledge and attitude regarding ventilator waveform monitoring. Based on this study’s findings, it is highly recommended to incorporate patient ventilator-synchrony and ventilator waveform analysis in the educational courses of critical care nursing and to emphasize conducting continuous training of critical care nurses on ventilator waveform analysis, identifying different types of patient-ventilator asynchronies, and their management. Further research is recommended to study the effect of nurse-led patient-ventilator asynchronous management protocols on patient outcomes.

Abbreviations

Declarations

Acknowledgements 

Not applicable. 

Authors’ contributions 

Farida Khalil Mohamed: conceptualization, methodology, writing—original draft, Writing—review & editing, visualization, supervision, project administration. Mohammed Adel Ghoneam: methodology, investigation, writing—original draft, Writing—review & editing. Mohamed Ezzelregal Abdelgawad: conceptualization, methodology, investigation, writing—original draft, Writing—review & editing.

Funding 

Open access funding provided by The Science, Technology & Innovation Funding Authority (STDF) in cooperation with The Egyptian Knowledge Bank (EKB). This research did not receive any specific grants from funding agencies in the public, commercial, or not-for-proft sectors.

Availability of data and materials 

The datasets generated and/or analysed during the current study are available from the corresponding author on reasonable request.

Ethics approval and consent to participate 

All methods were performed in accordance with the relevant guidelines and regulations, including the Declaration of Helsinki. Research Ethics Committee of the Faculty of Nursing, Alexandria University, Egypt, approved the current study. The first page of the questionnaire provided a full explanation of the study's purpose and assured confidentiality, anonymity, and the right to refuse to participate in the survey for all potential participants. The first page also stated that the questionnaires' completion and return would be regarded as consent to participate in the study. None of the researchers had any professional or private relation to any of the study participants. 

Consent for publication 

The article does not contain any individual details, and therefore, consent for publication is not applicable. Study participants were informed that the collected data are for research purposes and will be published.

Competing interests 

The authors declare that they have no competing interests. 

Author details 

Farida Khalil Ibrahim Mohamed: Critical Care and Emergency Nursing Department, Faculty of Nursing, Alexandria University, Egypt. 

Mohammed Adel Ghoneam: Medical-Surgical Nursing Department, Faculty of Nursing, Beni-Suef University, Egypt.

Mohamed Ezzelregal Abdelgawad: Critical Care and Emergency Nursing Department, Faculty of Nursing, Alexandria University, Egypt. ([email protected]) 

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