Pressure ulcer prevention knowledge, practices, and their associated factors among nurses in Gurage Zone Hospitals, South Ethiopia, 2021

Pressure ulcer prevention and treatment interventions in Sub-Saharan Africa: A systematic review

BACKGROUND

The high burden of pressure ulcers (PUs) in Sub-Saharan Africa (SSA), coupled with the limited resources, underscores the need for preventive and context-specific treatment strategies.

PURPOSE

Therefore, the purpose of this systematic review was to establish and elucidate PU prevention and treatment interventions tested in SSA.

METHODS

This systematic review of the literature used, PRISMA to guide the search.

FINDINGS

The review identified nine studies on PU prevention (three) and treatment (six). Low-cost interventions assembled from locally available materials and multifaceted policies significantly prevented and treated PUs. The interventions included wound dressing agents, simple negative pressure suction devices that significantly treated PUs, and water-based bed surfaces.

DISCUSSION

There were gaps in the interventions that have been proven successful in other global settings.

CONCLUSION

In SSA, there is a need for nurses to tailor, test, and disseminate findings from evidence-based projects for PU prevention that have been successful in similar settings.

Nurses' barriers to the pressure ulcer risk assessment scales implementation: A phenomenological study

AIM

The aim of the study was to explore nurses' barriers to the pressure ulcer risk assessment scales implementation.

DESIGN

A qualitative descriptive phenomenological study.

METHODS

The research participants comprised of 10 nurses at various medical and surgical departments in a teaching hospital northeast of Namibia. Data were collected between August and September 2022 through in-depth, semi-structured face-to-face individual interviews and analysed using Colaizzi's 7-step method.

RESULTS

The interviews with nurses, led to two major themes being discovered: (1) factors hindering the effective prevention of pressure ulcers; (2) suggestions for improvements in the utilization of risk assessment scales. The participants noted that they had inadequate knowledge of the formal risk assessment scales; there were inadequate resources and insufficient staff; there were no policies or guidelines regarding the management of pressure ulcers, all of which influenced their utilization of pressure risk assessment scales. Education and training, the provision of equipment, the hiring of new staff and the formulation of policies/guidelines would thus improve the nurses' utilization of the scales.

CONCLUSION

The findings of this study have uncovered three primary factors that have a detrimental impact on the utilization of risk assessment scales by nurses, that is their lack knowledge on pressure ulcer risk assessment scales; a shortage of staff and equipment; and an absence of policies/guidelines. The findings from this study provide valuable implications for guiding quality improvement initiatives aimed at enhancing the standard of care in Namibia and other resource-limited settings.

Machine Learning Techniques, Applications, and Potential Future Opportunities in Pressure Injuries (Bedsores) Management: A Systematic Review

Pressure Injuries (PI) are one of the most common health conditions in the United States. Most acute or long-term care patients are at risk of developing PI. Machine Learning (ML) has been utilized to manage patients with PI, in which one systematic review describes how ML is used in PI management in 32 studies. This research, different from the previous systematic review, summarizes the previous contributions of ML in PI from January 2007 to July 2022, categorizes the studies according to medical specialties, analyzes gaps, and identifies opportunities for future research directions. PRISMA guidelines were adopted using the four most common databases (PubMed, Web of Science, Scopus, and Science Direct) and other resources, which result in 90 eligible studies. The reviewed articles are divided into three categories based on PI time of occurrence: before occurrence (48%); at time of occurrence (16%); and after occurrence (36%). Each category is further broken down into sub-fields based on medical specialties, which result in sixteen specialties. Each specialty is analyzed in terms of methods, inputs, and outputs. The most relevant and potentially useful applications and methods in PI management are outlined and discussed. This includes deep learning techniques and hybrid models, integration of existing risk assessment tools with ML that leads to a partnership between provider assessment and patients' Electronic Health Records (EHR).

Nurses' Knowledge Concerning Prevention and Treatment of Pressure Ulcers

Pressure ulcers have high prevalence in patients and can be prevented with proper nursing interventions. The aim of this study was to evaluate nurses' knowledge about prevention and treatment of pressure ulcers. The present study was conducted with 111 nurses working in a General hospital in Greece. In this study, nurses had adequate knowledge about prevention and treatment of pressure ulcers. Respondents answered correctly in questions about staging of pressure ulcers (98.2%, 98.2%, 99.1%, 97.3%, 93.7%), the role of changing position in prevention of pressure ulcer (97.3%), the use of foam mattress (85.6%) and stretched sheets (92.8%), the use of antiseptic solution (95.5%, 85.6%), and interventions to reduce pressure on patients' heels (88.3%). A number of participants had adequate level of knowledge in the questions about the role of diet in healing of pressure ulcers (71.2%), products or devices for prevention or treatment of pressure ulcers (61.3%, 36.9%), healing of pressure ulcers (58.6%, 46.8%), prevention of bone pressure ulcers (31.5%), cleaning of a pressure ulcer (52.3%), and the role of low blood pressure as a risk factor for pressure ulcer (55.9%). However, the development of educational programs can help nurses to improve their knowledge about prevention and treatment of pressure ulcers.

A Hybrid System of Braden Scale and Machine Learning to Predict Hospital-Acquired Pressure Injuries (Bedsores): A Retrospective Observational Cohort Study

Background: The Braden Scale is commonly used to determine Hospital-Acquired Pressure Injuries (HAPI). However, the volume of patients who are identified as being at risk stretches already limited resources, and caregivers are limited by the number of factors that can reasonably assess during patient care. In the last decade, machine learning techniques have been used to predict HAPI by utilizing related risk factors. Nevertheless, none of these studies consider the change in patient status from admission until discharge. Objectives: To develop an integrated system of Braden and machine learning to predict HAPI and assist with resource allocation for early interventions. The proposed approach captures the change in patients' risk by assessing factors three times across hospitalization. Design: Retrospective observational cohort study. Setting(s): This research was conducted at ChristianaCare hospital in Delaware, United States. Participants: Patients discharged between May 2020 and February 2022. Patients with HAPI were identified from Nursing documents (N = 15,889). Methods: Support Vector Machine (SVM) was adopted to predict patients' risk for developing HAPI using multiple risk factors in addition to Braden. Multiple performance metrics were used to compare the results of the integrated system versus Braden alone. Results: The HAPI rate is 3%. The integrated system achieved better sensitivity (74.29 ± 1.23) and detection prevalence (24.27 ± 0.16) than the Braden scale alone (sensitivity (66.90 ± 4.66) and detection prevalence (41.96 ± 1.35)). The most important risk factors to predict HAPI were Braden sub-factors, overall Braden, visiting ICU during hospitalization, and Glasgow coma score. Conclusions: The integrated system which combines SVM with Braden offers better performance than Braden and reduces the number of patients identified as at-risk. Furthermore, it allows for better allocation of resources to high-risk patients. It will result in cost savings and better utilization of resources. Relevance to clinical practice: The developed model provides an automated system to predict HAPI patients in real time and allows for ongoing intervention for patients identified as at-risk. Moreover, the integrated system is used to determine the number of nurses needed for early interventions. Reporting Method: EQUATOR guidelines (TRIPOD) were adopted in this research to develop the prediction model. Patient or Public Contribution: This research was based on a secondary analysis of patients' Electronic Health Records. The dataset was de-identified and patient identifiers were removed before processing and modeling.