Technology and Health Care最新文献

Objective: The impact of the intrathecal infusion port system on patients with advanced cancer pain and the nursing workload on their primary caregivers was investigated in this study.

Methods: Between January 2018 and December 2021, a total of 26 patients with advanced cancer pain who received intrathecal infusion in our hospital, were enrolled in this study along with their primary caregivers. Assessments of patients' pain levels, adverse reactions, and quality of life, as well as the nursing workload of primary caregivers were evaluated.

Results: The findings revealed a significant reduction in pain scores, decreasing from (7.42 ± 0.58) one week before treatment to (2.00 ± 0.56) one week after treatment, and maintaining a similar level at (2.07 ± 0.56) one month post-treatment, with a concomitant decrease in associated complications. Moreover, an improvement in overall quality of life was observed, as evident in the total physical health scores, which increased from (33.97 ± 7.14) one week prior to treatment to (47.44 ± 10.14) one week post-treatment and (48.56 ± 9.35) one month post-treatment. The total psychological health scores exhibited a positive trajectory, rising from (52.50 ± 23.55) one week before treatment to (65.00 ± 16.55) one week after treatment, ultimately reaching (69.42 ± 15.64) one month post-treatment. Furthermore, the primary caregivers' nursing workload experienced a consistent decline, decreasing from (51.54 ± 7.30) one week before treatment to (46.54 ± 5.57) one week after treatment and further diminishing to (45.32 ± 6.41) one month after treatment. Repeated measures of variance showed that there were significant differences in pain, quality of life, and primary caregivers' nursing workload at different time points (P < 0.05).

Conclusion: The intrathecal infusion port system can effectively reduce the pain level of patients with advanced cancer pain, improve their quality of life, and reduce the nursing workload of their primary caregivers.

Background: Deep neural networks (DNNs) have recently been significantly applied to automatic arrhythmia classification. However, their classification accuracy still has room for improvement.

Objectives: The aim of this study is to address the existing limitations in current models by developing a more effective approach for automatic arrhythmia classification. The specific objectives include enhancing the receptive field sizes to capture more detailed information across various temporal scales, and incorporating inter-channel correlations to improve the feature extraction process.

Methods: This study proposes a pyramidal dense connectivity layer and bidirectional long short-term memory network (PDC-BiLSTM) to effectively extract waveform features across various temporal scales, which can capture the intricate details and the broader global information in the signals through a wide range of sensory fields. The efficient channel attention (ECA) is additionally introduced to dynamically allocate weights to each feature channel, assisting the model inefficiently prioritizing essential characteristics during the training process.

Results: The experimental results on the MIT-BIH arrhythmia database showed that the overall classification accuracy of the proposed method under the intra-patient paradigm reached 99.82%, and the positive predictive value, sensitivity and F1 Score were 99.64%, 97.61% and 98.60% respectively; under the inter-patient paradigm, the overall accuracy was 96.30%.

Conclusion: Compared with the latest research results in this field, the proposed model is also better than the existing models in terms of accuracy, which has the potential value of being applied to devices that assist in diagnosing cardiovascular diseases.

Background: Percutaneous transforaminal endoscopic discectomy (PTED) is an effective minimally invasive technique for treating lumbar disc herniation (LDH). However, precise channel establishment remains challenging. A three-dimensional visualization virtual surgery system (3DVVSS) is increasingly used in specific surgeries, yet its value in PTED remains uncertain.

Objective: To investigate the application of a 3DVVSS combined with a self-made intervertebral foramen positioning puncture device (IFPPD) in PTED for the treatment of LDH.

Methods: This study enrolled 120 LDH patients who underwent PTED between January 2021 and February 2022. Patients were randomly assigned to 3DVVSS combined with the IFPPD group (V group), and the traditional freehand methods group (T group). Hospitalization days, number of puncture attempts, fluoroscopy time, operation time, visual analog scale (VAS), Oswestry disability index (ODI), and complications were analyzed.

Results: All patients completed follow-up without serious complications. Hospitalization days between the two groups were comparable (p > 0.05). However, the V group showed statistically significant advantages over the T group in puncture time, number of puncture attempts, fluoroscopy times, and operation time (p < 0.05). All patients exhibited significant improvements in VAS and ODI compared to those of preoperation (p < 0.05). Still, there was no significant difference in VAS and ODI between T and V groups (p > 0.05).

Conclusion: 3DVVSS combined with IFPPD can significantly improve the successful puncture rate, and reduce the operation time and the fluoroscopy times, indicating its great potential in future clinical applications.

Background: Knee osteoarthritis (KOA) is a degenerative joint disease characterized by the breakdown of cartilage and underlying bone, often leading to pain and stiffness. There is limited research on the correlation between magnetic resonance imaging (MRI) manifestations and changes in quantitative parameters of the proton density-weighted spectral attenuated inversion recovery (PDW-SPAIR) sequence in the fat pad of KOA patients.

Objective: To investigate the MRI manifestations and quantitative parameters of fat pad PDW-SPAIR in patients with KOA and their predictive value.

Methods: 82 patients with KOA admitted to the hospital from January 2023 to January 2024 were selected as the study subjects. All of them received an MRI examination before the operation. Their MRI manifestations and quantitative parameters of fat pad PDW-SPAIR were analyzed to evaluate their application value in patients with KOA.

Results: Among the 82 patients, there were 25 cases of bony defects, 43 cases of cartilage defects, and 14 cases of hydrops articuli. There were 63 cases of bone marrow edema, 54 cases of soft tissue swelling, 68 cases of synovitis, and 42 cases of bone erosion. Different quantitative parameters of fat pad PDW-SPAIR showed that patients with cartilage defects were 50, 53, 32, and 32. Cartilage defects were 130, 131, 53, and 49. There were statistically significant differences in signal intensity ratios of non-cartilage and cartilage defects displayed by different quantitative parameters of fat pad PDW-SPAIR (p < 0.05). Spearman correlation analysis showed that PD-FSE-SAG-SPAIR, PD-FSE-COR-SPAIR, T2-FSE-SAG, and T1-FSE-COR positively correlated with KOA (p < 0.05).

Conclusion: MRI manifestations and quantitative parameters of fat pad PDW-SPAIR may be helpful for the clinical prediction of KOA.

Background: The Department of Thoracic Surgery of the First Clinical Medical College of Guangdong Medical University took the lead in introducing the flipped classroom teaching mode combining scientific research expansion into the undergraduate teaching of clinical medicine in 2018. This teaching mode has achieved good teaching effects.

Objective: Through the flipped classroom based on role exchange, it is observed whether students can achieve a comprehensive understanding of lung cancer, whether they have basic scientific research quality, innovative application ability and stimulate students' enthusiasm for learning.

Methods: During the research process, teachers prepare their lessons meticulously. Students interpret the study materials line by line as required, identify difficulties, and engage in discussions to resolve them. If unable to resolve, they can ask questions through various channels such as QQ, WeChat, or email. This allows students to choose their study time independently, achieving personalized learning. Teachers adjust in real-time and effectively formulate teaching plans, objectives, and progress.

Results: For this teaching method reform, most students believed that this teaching improved their teamwork skills, PPT creation skills, and active problem-solving abilities, improved their understanding of scientific research, stimulated their interest in scientific research, deepened their understanding and understanding of lung cancer diagnosis and treatment, and enhanced their enthusiasm for learning.

Conclusion: This not only stimulated students' enthusiasm for learning thoracic surgery but also improved their scientific research and clinical thinking abilities, resulting in good teaching outcomes.

Background: Poorly regulated and insufficiently maintained medical devices (MDs) carry high risk on safety and performance parameters impacting the clinical effectiveness and efficiency of patient diagnosis and treatment. After the MD directive (MDD) had been in force for 25 years, in 2017 the new MD Regulation (MDR) was introduced. One of the more stringent requirement is a need for better control of MD safety and performance post-market surveillance mechanisms.

Objective: To address this, we have developed an automated system for management of MDs, based on their safety and performance measurement parameters, that use machine learning algorithm as a core of its functioning.

Methods: In total, 1997 samples were collected during the inspection process of defibrillator inspections performed by an ISO 17020 accredited laboratory at various healthcare institutions in Bosnia and Herzegovina. This paper presents solution developed for defibrillators, but proposed system is scalable to any other type of MDs, both diagnostic and therapeutic.

Results: Various machine learning algorithms were considered, including Decision Tree (DT), Random Forest (RF), Naïve Bayes (NB) and Logistic Regression (LR). In addition, random forest regressor and XG Boost algorithms were tested for their predictive capabilities in the field of defibrillator output error prediction. These algorithms were selected because of their ability to handle large datasets and their potential for achieving high prediction accuracy. The highest accuracy achieved on this dataset was 94.8% using the Naive Bayes algorithm. The XGBoost Regressor with its r² of 0.99 emerged as a powerful tool, showcasing exceptional predictive accuracy and the ability to capture a substantial portion of the dataset's variability.

Conclusion: The results of this study demonstrate that clinical engineering (CE) and health technology management (HTM) departments in healthcare institutions can benefit from proposed automatization of defibrillator maintenance scheduling in terms of increased safety and treatment of patients, on one side, and cost optimization in MD management departments, on the other side.

The COVID-19 pandemic underscores the necessity for proactive measures against emerging diseases, epitomized by WHO's "Disease X." Among the myriad of indicators tracking COVID-19 progression, the count of hospitalized patients assumes a pivotal role. This metric facilitates timely responses from government agencies, enabling proactive allocation and management of medical resources. In this study, we introduce a novel hybrid intelligent approach, the EMD&LSTM-ARIMA model. This model integrates three techniques: Empirical Mode Decomposition (EMD) to decompose the data into intrinsic mode functions, Long Short-Term Memory (LSTM) neural network for capturing long-term dependencies and nonlinear relationships, and the Auto-Regressive Integrated Moving Average (ARIMA) model for handling linear trends and time series forecasting. We verify its high predictive power and utility through training and forecasting COVID-19 hospitalizations in the UK, Canada, Italy, and Japan. Our analysis reveals that all forecasted error rates remain below 10%, with Mean Absolute Percentage Error (MAPE) values obtained for these four countries as 2.30%, 3.33%, 1.63%, and 2.89%, respectively.

Objectives: Firefighting is one of the tasks that is physically difficult to perform and carries a high risk of injury and death. A better understanding of the underlying factors that influence the causes of fire scene injuries can improve firefighters' safety. For this reason, the study aimed to determine the importance of Smart Personal Protective Equipment and wearable technology in protecting the health and safety of firefighters by using them instead of traditional equipment and systems. According to expert opinions and literature reviews, the dangers faced by firefighters have been determined to be thermal, physical, biological, environmental, and chemical.

Methods: Analytical Hierarchy Process (AHP) and Preference Ranking Organization Method for Enrichment Evaluations (PROMETHEE) methods were used in the study. The AHP method was preferred because it is a systematic decision-making method that includes both ranking and comparison techniques. The PROMETHEE method was preferred because it provides the opportunity to make effective decisions in a very short time by basing the decision-making process on a scientific basis. In addition to the graphical representation of the ranking of alternatives, it offers decision-makers the opportunity to make various statistical analyses.

Results: The weights of the hazards were calculated using the AHP method. Physical hazards accounted for the highest weight. PROMETHEE was used in the ranking of wearable smart technological products to protect the health and safety of firefighters.

Conclusions: Products are listed as Personal Protection System, PROeTEX PPE, Wearable IoT Device, Flame Resistant Shirt, Fall Detection Systems, Smart Wearable Underwear, and WASP. With the study, it was concluded that the risk of firefighters being trapped would play an essential role in the prevention of death and injury. Improvements in wearable technological products used in the fire department will yield better results and increase safety.

Background: This study was prompted by the crucial impact of abnormal plantar pressure on diabetic foot ulcer development and the notable lack of its monitoring in daily life. Our research introduces a cloud-based, user-friendly plantar pressure monitoring system designed for seamless integration into daily routines.

Objective: This innovative system aims to enable early ulcer prediction and proactive prevention, thereby substantially improving diabetic foot care through enhanced self-care and timely intervention.

Methods: A novel, user-centric plantar pressure monitoring system was developed, integrating a wearable device, mobile application, and cloud computing for instantaneous diabetic foot care. This configuration facilitates comprehensive monitoring at 64 underfoot points. It encourages user engagement in health management. The system wirelessly transmits data to the cloud, where insights are processed and made available on the app, fostering proactive self-care through immediate feedback. Tailored for daily use, our system streamlines home monitoring, enhancing early ulcer detection and preventative measures.

Results: A feasibility study validated our system's accuracy, demonstrating a relative error of approximately 4% compared to a commercial pressure sensing walkway. This precision affirms the system's efficacy for home-based monitoring and its potential in diabetic foot ulcer prevention, positioning it as a viable instrument for self-managed care.

Conclusions: The system dynamically captures and analyzes plantar pressure distribution and gait cycle details, highlighting its utility in early diabetic foot ulcer detection and management. Offering real-time, actionable data, it stands as a critical tool for individuals to actively participate in their foot health care, epitomizing the essence of self-managed healthcare practices.

Background: The left ventricular assist device (LVAD) has been proven to be an effective therapy for providing temporary circulatory support. However, the use of this device can cause myocardial injury due to multiple insertions of various catheters.

Objective: Therefore, this study aimed to evaluate the hemodynamic performance of a newly developed double-lumen catheter (DLC) for LVAD.

Methods: Two different LVAD DLC prototypes (a semi-circular and a concentric catheter) were designed based on the structure of venous DLC. Computational fluid dynamics (CFD) simulations were performed using the finite element method. The CFD results were confirmed through the testing of the 31 Fr prototype. The aorta is a large vessel with shear rates up to >300 s^-1 and we used a reasonable approximation to model blood as a Newtonian fluid.

Results: At a flow rate of 5 L/min, the semi-circular prototype achieved an infusion pressure of 74.68 mmHg, while the concentric prototype achieved an infusion pressure of 46.11 mmHg. The CFD results matched the experimental results with a mean percentage error of less than 7%. The peak wall shear stress in the semi-circular prototype (717.5 Pa) was higher than the hemolysis threshold (400 Pa), which could cause blood damage, and it also had a higher hemolysis index compared to concentric prototype. Moreover, both prototypes exhibited areas of blood stagnation and recirculation, suggesting a possible risk of thrombosis.

Conclusion: Both prototypes of the LVAD DLC demonstrated similar blood flow rates. The semi-circular prototype showed superior infusion pressure compared to the concentric prototype, but had poorer hemolysis performance. However, the potential risk of thrombosis for both still exists. Therefore, further in vivo experiments are necessary to verify the safety and effectiveness of the LVAD DLC.