Antimicrobial Resistance Training for Hemodialysis Nurses

Introduction

Patients undergoing hemodialysis treatment are at an increased risk for infections because these patients require prolonged healthcare interventions, have weakened immune system, use antibiotics, receive treatment in a shred healthcare setting, and require vascular accesses such as catheters, fistulas and venous central lines. Infection is the second master cause of mortality in hemodialysis population, accounting for 8% of all deaths. to the general population, hemodialysis patients have higher rates of infection, particularly sepsis and its associated mortality.

Due to the high rate of infections among hemodialysis patients, there is a significant utilization of antimicrobial agents in this population. This extensive use has led to the emergence of multidrug-resistant organisms in hemodialysis units. Antimicrobial resistance (AMR) arises when microorganisms, including bacteria, fungi, viruses, and parasites, evolve mechanisms to resist the effects of antimicrobial agents. In recent years, there has been an increase in the prevalence of AMR in hemodialysis facilities worldwide, particularly among bacteria such as Staphylococcus aureus, Escherichia coli, and Klebsiella pneumonia. Staphylococcus aureus causes up to 70 % of vascular access infections in hemodialysis patients, and the problem worsens by the dissemination of methicillin-resistant S. aureus (MRSA) isolates. About 90 % of patients with MRSA infections require hospitalization, and 17 % die during hospital stay.

In Jordan, gram-positive bacterial infections and antibiotic resistance rates were high in Jordan. Methicillin-resistant S. aureus (MRSA) and methicillin-resistant coagulase-negative Staphylococci (MR-CoNS) infections were common and exhibited high rates of antibiotic resistance. Furthermore, streptococcus pneumonia showed increased resistance rates to most antimicrobials. Reports form Jordanian hospitals also showed a high prevalence of AMR, with particular note to methicillin-resistant Staphylococcus aureus (MRSA) that reached a rate of 45% (Ministry of Health National Action Plan, 2018).

Antimicrobial resistance is a complex and multifaceted global public health issue that threatens the effective prevention and treatment of infections. In 2019, the World Health Organization (WHO) announced AMR as one of the top 10 global public health threats facing humanity in 2019. Antimicrobial resistance renders standard treatments ineffective, complicating infection management, and elevating the risk of disease transmission, prolonged illness, and adverse outcomes. It also leads to increased morbidity, mortality, and hospital stays. The cost of AMR to the economy is significant as well. In addition to death and disability, prolonged illness results in longer hospital stays, leading to more expensive medications and increased financial burdens for those effected. AMR is predicted to cost the world economy between $300 billion and $1 trillion by 2050.

To mitigate the risk of antimicrobial resistance among patients undergoing hemodialysis, it is essential to implement stringent infection prevention and control measures. Additionally, raising awareness among healthcare providers, patients, and caregivers about the importance of responsible antibiotic use and infection prevention is crucial. Nurses play a crucial role in the management of AMR generally and in hemodialysis facilities specifically. They can promote the appropriate use of antimicrobial drugs and implement effective infection prevention and control measures. However, existing literature indicates that nurses often lack knowledge, attitudes, and skills related to various dimensions of antimicrobial resistance (AMR) in hemodialysis patients. This highlights the need for developing and implementing effective and innovative continuous educational programs to address these gaps.

Recently, advanced technology has made it possible to implement new types of behavioral change education. Mobile health (m-Health) is an example. The WHO defines m-Health as medical and public health practice supported by mobile devices, such as mobile phones, patient monitoring devices, personal digital assistants (PDAs), and other wireless devices. By making health information and existing health services more easily and remotely accessible, m-Health technologies are used to improve health and change health behaviors. Short Messaging Service (SMS) and Multimedia Messaging Service (MMS) as common examples of m-health are used to deliver cost-effective health promotion or behavior change interventions, regardless of geography. SMS and MMS require less effort, time, and human resources than phone calls or face-to-face meetings or appointments because they can be sent quickly and easily. Thus, given the ease of use of SMS or MMS, they could be used for potential health behavioral changes or risk reduction with little demand on staff or clinic resources.

Few studies have examined AMR training for nurses. Hendy et al. and Lutfiyati et al. both found that training on antimicrobial stewardship significantly improved nurses' knowledge, attitudes, and practices. Boban et al. observed significant improvement in the antimicrobial resistance knowledge and attitudes in higher education students, including nurses. Similarly, Kpokiri et al. noted that AMR training enhanced overall knowledge of antibiotic use and resistance among healthcare professionals.

Most prior research has focused on traditional training interventions, highlighting a gap in exploring the potential, advantages, and effectiveness of mobile technology for AMR training. Although mobile health applications (MHAs) have been utilized for AMR training in various healthcare settings, their application in hemodialysis units remains unexplored. This identified gap is particularly relevant given the increasing need for mobile technologies to enable continuous education and provide timely cost-effective assistance to health professionals. The current study aimed to address this gap by examining the effectiveness of a mobile health training program on antimicrobial resistance among the knowledge, attitudes, and practices of nurses employed in hemodialysis units.

The results of this study are clinically significant to improve patient care and safety by enhancing practices regarding AMR. Theoretically, it helps facilitate insight on the use of technology for effective health education and behavior change. This research improves the theoretical understanding of the effectiveness of mobile learning platforms by measuring their impact on specific learning outcomes-knowledge, attitudes, and practices-within a specialized health care context. It also adds to the growing body of evidence regarding the role of innovative educational methods in professional development.

Method and Materials Research Design The study employed a pre/posttest randomized controlled experimental design. The study participants were selected using a convenience sampling method and then distributed randomly into the study groups (1:1) by a cluster random assignment, sequence generated by Random Allocation Software, this precaution taken to reduce contamination. The study involved two groups including the experimental and control groups. The experimental group received education about the use of antibiotics and prevention of AMR in HD units using the WhatsApp application via Chatbot software. However, the control group received the same educational content using the leaflets. The details of interventions were explained under the intervention section below.

Sample and Setting

Participants were selected from four major hospitals representing the private, military, public sectors.

The sample size was calculated using the G-power software (Version 3.1.7). With an alpha level of 0.05, a statistical power of 0.9, an effect size of .19 (Alhawatmeh et al., 2022), and a repeated-measures ANOVA test (mixed model), the minimum required total sample size was calculated to be 60 participants. Considering a 15% attrition rate, 9 participants were added, resulting in a final required sample size of 69. Out of the 69 participants initially recruited, 64 completed the study.

Intervention

The educational program developed by the researcher is meticulously designed, drawing on extensive insights from the WHO curricula guide (nurses' education and training on antimicrobial resistance; 2019) and the literature review addressed the use of antibiotics and prevention of AMR in hemodialysis facilities. The curriculum, using simple English, provides a broad set of learning objectives tailored to the expected roles of nurses.

The education program includes four main modular outline:

1. Foundations that build awareness of antimicrobial resistance, which includes the subtopics of Systemic approach to the individual with an infection and Understanding antimicrobial agents and their management.
2. Appropriate use of antimicrobial agents, which include the subtopic of Use of antimicrobial agents in clinical management.
3. Infection prevention and control, which includes the subtopics of Health care-associated infection, antimicrobial resistance and infection prevention and control.
4. Diagnostic stewardship and surveillance, which includes the subtopics of Interaction between diagnostic stewardship and surveillance of antimicrobial resistance and overall antimicrobial stewardship.

For the experimental group, the educational material was sent by a study researcher (X) to the WhatsApp number for all members of the experimental group via Chatbot software, which was designed by software engineer. This software is a strategic move that holds immense potential for transforming the way nurses access information and support. In the dynamic and demanding field of healthcare, nurses often face time constraints and require immediate access to relevant resources. A WhatsApp Chatbot designed for nursing education can serve as a virtual companion, providing real-time assistance and information crucial to their roles. The way information is presented and shared in a Chatbot is one of the most important factors that directly affects the user experience. In contrast to conventional interfaces, a Chatbot method is usually more dynamic and user-friendly since information is frequently communicated through conversational engagements (Fitria et al, 2023). AMR training program was presented in Chatbot software as outlines. The educational materials were sent in the form of written materials, photos, and videos. The study researcher (X) who was an experienced nurse with a master's degree in nursing explained how to use program to each nurse in experimental group by the following instructions and directions:

The program was auto-reply with the educational dialogue that had been previously entered to this program. Also, the researcher advised the participant to read each part carefully. Nurses were able to return to the researchers in order to clarify the vague points and answer any questions via telephone numbers. The application allows the researcher to view the parts that have been accessed and read by participants in the educational program. During one week, nurses in experimental group completed the AMR training program, then the Chabot software was closed before evaluation phase started.

For the control group, they were given the same educational content using leaflets. Nurses in control group were provided with the educational material received by experimental group via Chatbot software after evaluation completed.

Pilot Testing and Content Validity

The tools and educational program were subjected to content validity assessment (CVI) by a panel of four experts in the field of medicine, clinical pharmacology, infection control and nursing. The tool items were overlaid in a matrix with tow option rating scale: 1) Importance which is divided to 3 rating scales (Not necessary, Useful but not necessary, Essential), and 2) relevance that is divided into 4 rating scales (Not relevant/not clear, Item needs some revision, Relevant/ clear but need minor revision, very relevant/clear).

Statistics were subsequently used to assess the degree of reviewers' agreement on the importance and relevance of the tool items. The Content Validity Index values for the Knowledge of Antimicrobial Use and Antimicrobial Resistance, the Attitudes Toward Antimicrobial Use and Antimicrobial Resistance, the practice of Antimicrobial Use and Antimicrobial Resistance were 0.978 0.927 and 0.977 respectively. These values indicated an acceptable level of validity index values.

A pilot study was conducted on 15 registered nurses working in HD units to assess the clarity, objectivity, and feasibility of the study self-developed instruments and the educational programs' content. Through the pilot study, the time required for data collection was estimated. The nurses in the pilot study were not included in the main study sample.

Data Collection Procedure

Institutional Review Board (IRB) approval was obtained prior to commencing the study, followed by securing administrative approvals from the respective study settings. A list of nurses working in the hemodialysis units was then acquired from the head nurse of each unit at the target hospitals. This list included the names and telephone numbers of the nurses. After obtaining the list of nurses, each nurse was contacted by phone. The call followed these steps: the researcher introduced themselves, explained the purpose of the call and the study, and sought verbal consent from the nurse to participate if they met the study criteria. The researcher then gathered information from each nurse to assess their eligibility for the study.

A list of nurses who met the study criteria was compiled, including their work shifts (A, B, and C) and the names of their respective hospitals. We ensured that the nurses in each shift remained in the same shift throughout the duration of the study. Each shift at each hospital was treated as a cluster, with 6 participants recruited from each cluster. This resulted in the identification of 12 clusters (4 hospitals × 3 shifts). These 12 clusters were then randomly assigned to either the experimental or control group using computer-generated randomization, resulting in 6 clusters in each group. This randomization strategy was carried out by an independent researcher who was not involved in the recruitment process and remained blinded to the participant list. The purpose of employing cluster randomization was to prevent contamination between the intervention and control groups.

The researchers scheduled a meeting and interviewed each eligible nurse individually to obtain written consent and implement the baseline measurement. Upon written consent obtained by the study researchers, a well-trained nurse - a research assistant- from each hospital took the nurses' names from the researchers without knowing which of the two groups the nurse was assigned to. A well-trained nurse - a research assistant- from each hospital took the nurses' names from the researcher without knowing which of the two groups the nurse was assigned to. All nurses received the self-administered questionnaire from the research assistants. On the same day, the questionnaires were collected once the nurses had finished. The average time to complete the questionnaire was 30 minutes. The data obtained during this phase served as the baseline (Time 1). The same questionnaire, excluding the demographic characteristics, was completed again by the two study groups immediately at the end of interventions (Time 2) and one month later (Time 3). Each participant completed the study questionnaire individually in a private, quiet room in the hospital without anyone else present, including the research assistant.

Ethical Consideration Ethical approval was obtained from the Institutional Review Board (IRB) (Number: 2023/581). Informed written consent was obtained from participants following a thorough explanation of the study. All data were securely stored in a locked cabinet at the principal investigator's office, and participants' names were replaced with codes to ensure confidentiality. Participants were informed of their right to withdraw from the study at any time.

Data Analysis The Statistical Package for Social Sciences (SPSS version 26) will be used for analyzing the study data. The internal consistency reliability of the study instruments was tested using Cronbach's alpha. Descriptive statistics such as mean, standard deviation, percentage and frequency will be used to describe the study variables. For each main study variable, repeated-measures ANOVA test (within groups) will examine the mean differences over time for each group separately. Also, repeated-measures ANOVA tests (mixed model) will examine the mean score differences between the study groups over time. P-values of 0.05 or less will be used to determine whether the results are statistically significant.