Flexible Textile-Based Pressure Sensing System Applied in the Operating Room for Pressure Injury Monitoring of Cardiac Operation Patients

Nursing interventions to prevent pressure injury among open heart surgery patients: A systematic review

BACKGROUND

Nurses are vital in identifying and preventive pressure injuries (PIs) in hospitalized patients undergoing open heart surgery. Interventions to prevent PIs are crucial for every critical patient, and it's essential to recognize that preventing PIs involves a complex intervention.

AIM

To examine the nursing interventions for the prevention of PI in patients with open heart surgery.

METHOD

A systematic review study. Web of Science, Science Direct, PubMed, Scopus, MEDLINE Ultimate, CINAHL Ultimate, ULAKBIM, Cochrane Library, Google Scholar and university library databases were scanned. The initial search performed in the databases was updated on 4 February 2023, and on 7 April 2024, for potential publications included in that period. Data between February 2013 and April 2024 were scanned. The databases were searched with the keywords 'pressure injury', 'nursing interventions' and 'open heart surgery'. The systematic compilation process was carried out in accordance with the guidelines in the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guide.

RESULTS

Seventeen studies were examined using nursing interventions that applied to the selected study population. Care packages included an inflatable head pad, a pressure sensor mattress cover, multi-layer silicone foam, pressure-reducing coatings, endotracheal tube (ETT) repositioning and cuff pressure regulation. Interventions to reduce PI in open heart surgery patients are applied in the preadmission, perioperative and postoperative periods.

CONCLUSION

It was concluded that care packages, inflatable head pads, pressure sensor bedspreads, multi-layered silicone foam, pressure-reducing covers, ETT repositioning and cuff pressure regulation were effective in all nursing interventions. The strength of the available evidence was rated from strong to weak.

RELEVANCE TO CLINICAL PRACTICE

These findings reveal an efficient combination of multi-component nursing interventions for preventing PIs in planning patient care in the intensive care. The interventions that are used throughout the patient's entire care process are crucial for the prevention of PIs.

Can a prolonged healing pressure injury be benefited by using an AI mattress? A case study

BACKGROUND

Pressure injuries are a common and serious issue for bedridden residents in long-term-care facilities. Areas of bony prominences, such as the scapula, sacrum, and heels, are more likely to develop pressure injuries. The management of pressure injury wounds include dressing changes, repositioning, away from moisture, decreasing the occurrence of friction and shear, and more. Some supportive surfaces are also used for pressure injury cases such as gel pads, alternating pressure air mattresses, and air-fluidized beds. The aim of this case study was to determine whether the use of an artificial intelligent mattress can improve a nursing home resident with prolonged pressure injury.

CASE PRESENTATION

A retrospective study design was conducted for this case study. A 79-year-old male developed a pressure injury in the sacrum. His pressure injury was initially at stage 4, with a score of 12 by the Braden scale. The PUSH score was 16. During 5.5 months of routine care plus the use of the traditional alternative air mattress, in the nursing home, the wound stayed in stage 3 but the PUSH score increased up to 11. An artificial intelligence mattress utilizing 3D InterSoft was used to detect the bony prominences and redistribute the external pressure of the skin. It implements a color guided schematic of 26 colors to indicate the amount of pressure of the skin.

RESULTS

The wound size was decreased and all eczema on the resident's back diminished. The PUSH score was down to 6, as the artificial intelligent mattress was added into the routine care. The staff also reported that the resident's quality of sleep improved and moaning decreased. The hemiplegic side is at greater risk of developing pressure injury.

CONCLUSIONS

This novice device appeared to accelerate wound healing in this case. In the future, more cases should be tested, and different care models or mattress can be explored.

Establishment of a model for predicting the outcome of induced labor in full-term pregnancy based on machine learning algorithm

To evaluate and establish a prediction model of the outcome of induced labor based on machine learning algorithm. This was a cross-sectional design. The subjects were divided into primipara and multipara, and the risk factors for the outcomes of induced labor were assessed by multifactor logistic regression analysis. The outcome model of labor induced with oxytocin (OT) was constructed based on the four machine learning algorithms, including AdaBoost, logistic regression, naive Bayes classifier, and support vector machine. Factors, such as accuracy, recall, precision, F1 value, and receiver operating characteristic curve, were used to evaluate the prediction performance of the model, and the clinical application of the model was verified. A total of 907 participants were included in this study. Logistic regression algorithm obtained better results in both primipara and multipara groups compared to the other three models. The accuracy of the model for the prediction of "successful induction of labor" was 94.24% and 96.55%, and that of "failed induction of labor" was 65.00% and 66.67% in the primipara and the multipara groups, respectively. This study established a prediction model of OT-induced labor based on the Logistic regression algorithm, with rapid response, high accuracy, and strong extrapolation, which was critical for obstetric clinical nursing.

Evaluation of pressure ulcer risk and development in operating rooms

AIM

This study aimed to determine the risk and development of pressure ulcers in operating rooms.

MATERIALS AND METHODS

The sample of the study included a total of 250 patients. In the study, the risk of pressure ulcers was assessed before the operation, and the development of pressure ulcers was evaluated within 24 h after the operation.

RESULTS

The risk of pressure ulcers was low before the operation, and Stage I pressure ulcer developed in 12.8% of the patients within 24 h after the operation. The patients had pressure ulcers mostly in their sacrum. Their mean 3S Intraoperative Risk Assessment Scale of Pressure Sore score was 15.68 ± 4.84, suggesting that they were not at risk of developing pressure ulcers. Having a chronic disease (OR = 8.986; 95% CI = 3.697-21.845), undergoing general anesthesia (OR = 3.084; 95% CI = 1.323-7.194), and orthopedic surgery (OR = 10.172; 95% CI = 3.121-33.155) were statistically significant risk factors for pressure ulcers (p < 0.001). Additionally, moderately edematous skin (OR = 3.838; 95% CI = 1.024-14.386), overweight/underweight (OR = 16.333; 95% CI = 3.779-70.602), intraoperative bleeding greater than 800 ml (OR = 13.000; 95% CI = 3.451-48.969), operation time longer than 5 h (OR = 21.667; 95% CI = 2.122-221.223), moderate intraoperative stress (OR = 4.917; 95% CI = 0.425-56.916), body temperature higher than 38.3 °C or lower than 36.1 °C (OR = 5.462; 95% CI = 2.161-13.805), and intraoperative prone position (OR = 3.354; 95% CI = 1.386-8.115) were statistically significant risk factors for the development of pressure ulcers.

CONCLUSION

According to our preoperative pressure ulcer risk assessment, it is very important to take additional protective measures both during and after surgical operations to prevent pressure ulcers.

A fabric-based multifunctional sensor for the early detection of skin decubitus ulcers

Monitoring biosignals at the skin interface is necessary to suppress the potential for decubitus ulcers in immobile patients confined to bed. We develop conformally contacted, disposable, and breathable fabric-based electronic devices to detect skin impedance, applied pressure, and temperature, simultaneously. Based on the experimental evaluation of the multifunctional sensors, a combination of robust AgNW electrodes, soft ionogel capacitive pressure sensor, and resistive temperature sensor on fabric provides alarmed the initiation of early-stage decubitus ulcers without signal distortion under the external stimulus. For clinical verification, an animal model is established with a pair of magnets to mimic a human decubitus ulcers model in murine in vivo. The evidence of pressure-induced ischemic injury is confirmed with the naked eye and histological and molecular biomarker analyses. Our multifunctional integrated sensor detects the critical time for early-stage decubitus ulcer, establishing a robust correlation with the biophysical parameters of skin ischemia and integrity, including temperature and impedance.

Ultrasonic Intelligent Diagnosis of Papillary Thyroid Carcinoma Based on Machine Learning

Thyroid diseases are divided into papillary carcinoma and nodular diseases, which are very harmful to the human body. Ultrasound is a common diagnostic method for thyroid diseases. In the process of diagnosis, doctors need to observe the characteristics of ultrasound images, combined with professional knowledge and clinical experience, to give the disease situation of patients. However, different doctors have different clinical experience and professional backgrounds, and the diagnosis results lack objectivity and consistency, so an intelligent diagnosis technology for thyroid diseases based on the ultrasound image is needed in clinic, which can give objective and reliable diagnosis opinions on thyroid diseases by extracting the texture, shape, and other information of the image and assist doctors in clinical diagnosis. This paper mainly studies the intelligent ultrasonic diagnosis of papillary thyroid cancer based on machine learning, compares the ultrasonic characteristics of PTMC diagnosed by using the new ultrasound technology (CEUS and UE), and summarizes the differential diagnosis effect and clinical application value of the two technology methods for PTMC. In this paper, machine learning, diffuse thyroid image features, and RBM learning methods are used to study the ultrasonic intelligent diagnosis of papillary thyroid cancer based on machine learning. At the same time, the new contrast-enhanced ultrasound (CEUS) technology and ultrasound elastography (UE) technology are used to obtain the experimental phenomena in the experiment of ultrasonic intelligent diagnosis of papillary thyroid cancer. The results showed that 90% of the cases were diagnosed by contrast-enhanced ultrasound and confirmed by postoperative pathology. CEUS and UE have reliable practical value in the diagnosis of PTMC, and the combined application of CEUS and UE can improve the sensitivity and accuracy of PTMC diagnosis.

Evaluation System of Smart Logistics Comprehensive Management Based on Hospital Data Fusion Technology

With the acceleration of the informatization process, but because of the late start of the informatization construction of logistics management, the current digital system construction of logistics management has not been popularized, and the intelligent logistics integrated management evaluation system is also extremely lacking. In order to solve the lack of existing intelligent logistics comprehensive management evaluation system, this paper introduces the research of intelligent logistics comprehensive management evaluation system based on hospital data fusion technology. This paper analyzes and utilizes the Kalman filter and adaptive weighted data fusion technology in data fusion technology and then analyzes the evaluation index and system design principles of the intelligent logistics comprehensive management evaluation system and then designs the application layer from the application layer. Design the application layer from the application layer. Then design the framework of the intelligent logistics comprehensive management evaluation system at the network layer and the data layer. The system is finally tested, and the test results show that the evaluation accuracy of the system reaches 80%.

Prevention Effects of Chain Management on Pressure Ulcers of Hospitalized Patients

The study focused on the preventive effects of the chain management model on pressure ulcers in the operating room. Sqoop big data collection module is used to collect patient information from various hospital information systems in a distributed manner. The data were from the clinical data center of the Zhongshan Hospital Xiamen University General Hospital, and 268 patients were selected as the research subjects. A chain management model is constructed, concerning the preventive measures, the management of each link, the perioperative pressure ulcer management, and the reporting of pressure ulcers. Then, the two groups were compared for the SAS and SDS scores before and after nursing, the pressure ulcer sites, pressure ulcer reporting rate, pressure ulcer staging, and nursing satisfaction. The results show that it is not that more collection modules will lead to better cluster performance and that the execution delay is caused by MapReduce requiring the JAVA virtual machine, and after reaching a certain point, the increase in the number of tasks will slow down the process, and as data size increases, DataNote has an expanded capability to analyze data. After nursing treatment, the SAS and SDS scores of the two groups of patients were significantly lower than before treatment (P < 0.05). The pressure ulcers were mainly distributed in the forehead, mandible, cheeks, front chest, and knees in the two groups, and the difference between the two groups was statistically significant (P < 0.05). The total satisfaction of the observation group was 93.28%, and the total satisfaction of the control group was 92.54%. The patients' satisfaction with the chain management model was higher than that of conventional nursing.

Battery-free, wireless soft sensors for continuous multi-site measurements of pressure and temperature from patients at risk for pressure injuries

Capabilities for continuous monitoring of pressures and temperatures at critical skin interfaces can help to guide care strategies that minimize the potential for pressure injuries in hospitalized patients or in individuals confined to the bed. This paper introduces a soft, skin-mountable class of sensor system for this purpose. The design includes a pressure-responsive element based on membrane deflection and a battery-free, wireless mode of operation capable of multi-site measurements at strategic locations across the body. Such devices yield continuous, simultaneous readings of pressure and temperature in a sequential readout scheme from a pair of primary antennas mounted under the bedding and connected to a wireless reader and a multiplexer located at the bedside. Experimental evaluation of the sensor and the complete system includes benchtop measurements and numerical simulations of the key features. Clinical trials involving two hemiplegic patients and a tetraplegic patient demonstrate the feasibility, functionality and long-term stability of this technology in operating hospital settings.