Body pressure prediction for pressure ulcer prevention in a bed head elevation operation

Wearable Prophylaxis Tool for AI-Driven Identification of Early Warning Patterns of Pressure Ulcers

As today's society ages, age-related diseases become more frequent. One very common but yet preventable disease is the development of pressure ulcers (PUs). PUs can occur if tissue is exposed to a long-lasting pressure load, e.g., lying on tissue without turning. The cure of PUs requires intensive care, especially for the elderly or people with preexisting conditions whose tissue needs longer healing times. The consequences are heavy suffering for the patient and extreme costs for the health care system. To avoid these consequences, our objective is to develop a pressure ulcer prophylaxis device. For that, we built a new sensor system able to monitor the pressure load and tissue vital signs in immediate local proximity at patient's predilection sites. In the clinical study, we found several indicators showing correlations between tissue perfusion and the risk of PU development, including strongly reduced SpO2 levels in body tissue prior to a diagnosed PU. Finally, we propose a prophylaxis system that allows for the prediction of PU developments in early stages before they become visible. This work is the first step in generating an effective system to warn patients or caregivers about developing PUs and taking appropriate preventative measures. Widespread application could reduce patient suffering and lead to substantial cost savings.

Effect of changing the lying posture angle by changing the bed posture on the average pressure, maximum pressure, and pressure area in the hip region

BACKGROUND: An electric bed can easily change posture from a lying position and was effective in preventing pressure ulcer. OBJECTIVE: This study aimed to identify the optimal posture for the prevention of pressure ulcers by analyzing pressure changes applied to the pelvic region. METHODS: Pressure changes resulting from lateral rotations of the body using an electronic adjustable bed and changes in the posture and angles of the trunk and knees were assessed. Twelve conditions with varying angles of the trunk and knees (15–35∘ in 5∘ increments) and varying lateral angles (20–35∘ in 5∘ increments) were tested. The pressure (maximum and average) and contact area in the pelvic region of 20 individuals without disabilities were calculated. RESULTS: The conditions in which the average and maximum pressures did not increase according to the increase in angle were 25∘ for the upper body and knee angles and 35∘ for the side. CONCLUSIONS: The body pressure changed according to the posture rather than according to physical characteristics. Lateral rotation combined with changes in the angles of the trunk and knees effectively prevented pressure ulcers. Changes in the posture at various angles prevented an increased pressure on the body.

Literature Review of Machine-Learning Algorithms for Pressure Ulcer Prevention: Challenges and Opportunities

Pressure ulcers are associated with significant morbidity, resulting in a decreased quality of life for the patient, and contributing to healthcare professional burnout, as well as an increase of health service costs. Their prompt diagnosis and treatment are important, and several studies have proposed solutions to help healthcare professionals in this process. This work analyzes studies that use machine-learning algorithms for risk assessment and management of preventive treatments for pressure ulcers. More specifically, it focuses on the use of machine-learning algorithms that combine information from intrinsic and extrinsic pressure-ulcer predisposing factors to produce recommendations/alerts to healthcare professionals. The review includes articles published from January 2010 to June 2021. From 60 records screened, seven articles were analyzed in full-text form. The results show that most of the proposed algorithms do not use information related to both intrinsic and extrinsic predisposing factors and that many of the approaches separately address one of the following three components: data acquisition; data analysis, and production of complementary support to well-informed clinical decision-making. Additionally, only a few studies describe in detail the outputs of the algorithm, such as alerts and recommendations, without assessing their impacts on healthcare professionals’ activities.