Continuous Monitoring Of Patients Research Articles

Letter to the Editor – Update from Ukraine: Project Results in Oncology Telerehabilitation Approved at the National Cancer Institute and Showcased at the 4th National PM&R Congress

Telerehabilitation has emerged as a vital component of oncology care, particularly under challenging conditions where access to healthcare services may be restricted. This Letter to the Editor provides an update from Ukraine on a pioneering hybrid cloud-based platform designed for the patient-centered telerehabilitation of oncology patients. We summarize the project’s main outcomes, including the development of advanced AI-driven models, comprehensive methodological guidelines, and specialized software services. Notably, the platform underwent clinical approbation at the nonprofit organization “National Cancer Institute,” where its capacity for continuous patient monitoring, real-time data integration, and personalized rehabilitation interventions was demonstrated. Furthermore, the efficacy and innovation of this platform were showcased at the 4th National Congress of Physical and Rehabilitation Medicine in Ukraine, highlighting its potential to optimize resource allocation and improve patient outcomes. By merging mathematical and AI-based methods, the project underscores the importance of interdisciplinary collaboration in developing robust telerehabilitation ecosystems. We hope that this update contributes to the dialogue on expanding and refining telerehabilitation solutions globally, particularly in the realm of oncology care.

Impact of HELLP Syndrome on Maternal Mortality and the Role of Nursing in Peru

In 2022, hypertensive disorders of pregnancy continued to be a global concern, affecting 10% of pregnant women, while HELLP syndrome (HS) complicated between 0.1% and 0.9% of pregnancies. In Latin America, approximately 27.6% of women with preeclampsia developed HS, with a maternal mortality rate of 14%. In Peru, maternal mortality increased significantly compared to previous years, with hypertensive disorders accounting for 30% of cases.HELLP syndrome, characterized by hemolysis, elevated liver enzymes, and thrombocytopenia, caused severe complications such as postpartum hemorrhage, pulmonary edema, and death. In rural areas of Peru, the lack of resources and specialized personnel hindered the timely management of this condition, exacerbating the risks for patients.The strategies implemented included improving prenatal care and training healthcare professionals. Nurses played a crucial role in the early identification of risk factors, continuous monitoring, and interdisciplinary management of critical patients. Additionally, the nursing care process ensured comprehensive care based on scientific and humanistic principles.Despite these efforts, inequity in access to healthcare services persisted, especially in remote areas. The need for public policies to strengthen healthcare infrastructure and promote equity was evident, being key to reducing maternal complications and improving health indicators in the country

Continuous vital sign monitoring of patients recovering from surgery on general wards: a narrative review.

Most postoperative deaths occur on general wards, often linked to complications associated with untreated changes in vital signs. Monitoring in these units is typically intermittent checks each shift or maximally every 4-6 h, which misses prolonged periods of subtle changes in physiology that can herald a critical downstream event. Continuous monitoring of vital signs is therefore intuitively necessary for patient safety. The past five decades have seen monitoring systems evolve rapidly, and today entirely wireless, wearable, and portable continuous surveillance of vital signs is possible on general wards. Introduction of this technology has the potential to modify both the sensing (afferent) and response (efferent) limbs of monitoring, and will allow earlier detection of vital signs perturbations. But this comes with challenges, including but not limited to issues with connectivity, data handling, alarm fatigue, information overload, and lack of meaningful clinical interventions. Evidence from before and after studies and retrospective propensity-matched data suggests that continuous ward monitoring decreases the risk of intensive care unit (ICU) admissions, rapid response calls, and in some instances, mortality. This review summarises the history of general ward monitoring and describes future directions, including opportunities to implement these devices using artificial intelligence, pattern detection, and user-friendly interfaces. Pragmatic, well designed and appropriately powered trials, and real-world implementation data are necessary to make continuous monitoring standard practice at every hospital bed.

AI frontiers in emergency care: the next evolution of nursing interventions.

This scoping review explores the utilization of artificial intelligence in emergency nursing, assessing its impact, potential benefits, and the obstacles faced in its adoption. It covers the scope of AI from advanced triage protocols to continuous monitoring of patients, assistance in diagnosis, and providing support for clinical decisions. The review notes that AI in emergency healthcare can lead to more efficient care and timely, data-driven actions, but also highlights significant issues such as safeguarding patient data, the necessity for dependable infrastructure, and concerns over discriminatory algorithms. The promise of AI in improving emergency healthcare practices and patient care is clear, yet the identified challenges must be carefully navigated to promote safe and ethical use. Further empirical research is called for to confirm the effectiveness of AI applications in the dynamic environment of emergency care setups.

A Smart Motor Rehabilitation System Based on the Internet of Things and Humanoid Robotics

The Internet of Things (IoT) is gaining increasing attention in healthcare due to its potential to enable continuous monitoring of patients, both at home and in controlled medical environments. In this paper, we explore the integration of IoT with human-robotics in the context of motor rehabilitation for groups of patients performing moderate physical routines, focused on balance, stretching, and posture. Specifically, we propose the I-TROPHYTS framework, which introduces a step-change in motor rehabilitation by advancing towards more sustainable medical services and personalized diagnostics. Our framework leverages wearable sensors to monitor patients’ vital signs and edge computing to detect and estimate motor routines. In addition, it incorporates a humanoid robot that mimics the actions of a physiotherapist, adapting motor routines in real-time based on the patient’s condition. All data from physiotherapy sessions are modeled using an ontology, enabling automatic reasoning and planning of robot actions. In this paper, we present the architecture of the proposed framework, which spans four layers, and discuss its enabling components. Furthermore, we detail the current deployment of the IoT system for patient monitoring and automatic identification of motor routines via Machine Learning techniques. Our experimental results, collected from a group of volunteers performing balance and stretching exercises, demonstrate that we can achieve nearly 100% accuracy in distinguishing between shoulder abduction and shoulder flexion, using Inertial Measurement Unit data from wearable IoT devices placed on the wrist and elbow of the test subjects.

AI-Powered Wearables for Continuous Patient Monitoring: Improving Chronic Disease Management and Preventive Care

AI-powered wearables, including smartwatches and fitness trackers, are now revolutionizing patient care with the ability to monitor patients' vital signs continuously in real-time. Such wearables monitor all important parameters of a patient, including heart rate, blood pressure, glucose level, and physical activity, thereby providing health professionals with invaluable insights into the condition of a patient. This continuous data allows for early detection of abnormalities, personalized treatment, and adjustments, hence better management of disease in patients suffering from chronic diseases like diabetes, hypertension, and cardiovascular disorders. AI algorithms scan this data in pursuit of foreseeing possible health risks to avoid acute medical events such as heart attacks or diabetic crises. Thus, AI-driven wearables would empower patients in the active management of their health condition, reduce hospital readmissions, and generally improve patient outcomes. This article describes the applications, benefits, and challenges of AI-integrated wearable systems for healthcare, focusing on chronic disease management and prevention of health strategies. Keywords: Wearables using Artificial Intelligence, Continuous Patient Monitoring, Chronic Disease Management, Diabetes, Heart Disease, Preventive Care, Smart Watches, Fitness Trackers, Vital Signs, Analytics of Health Data, Real-time Monitoring, Machine Learning, Personalized Treatment, Predictive Analytics.

Revolutionizing Healthcare with Intelligent Remote Health Monitoring

Revolutionizing healthcare through intelligent remote health monitoring involves leveraging advanced technologies such as artificial intelligence, wearable devices, and IoT to provide continuous and real-time monitoring of patient's health conditions. This approach enables proactive healthcare management by collecting and analyzing vital health data remotely, facilitating Prompt identification of possible wellness issues, and reducing the need for frequent hospital visits. It promotes personalized healthcare, improves patient outcomes, and enhances the efficiency of healthcare systems by allowing healthcare providers to make data-driven decisions. This transformation in healthcare delivery ensures better access to care, particularly for individuals in remote or underserved areas, while also addressing the growing demand for healthcare services globally. Revolutionizing healthcare through intelligent remote health monitoring involves leveraging Cutting-edge innovations like synthetic intelligence, wearable devices, and the Internet of Things (IoT) to provide continuous and real-time monitoring of patient's health conditions. This approach enables proactive healthcare management by collecting and analyzing vital health data remotely, facilitating Prompt identification of emerging health issues, and reducing the need for frequent hospital visits. The integrationof machine learning algorithms allows for predictive analytics, which can anticipate health deteriorations and recommend timely interventions. It promotes personalized healthcare by tailoring treatments to individual needs, improves patient outcomes through constant monitoring and timely responses, and enhances healthcare systems by allowing healthcare providers to make data-driven decision.

Monitoring and diagnosis of cardiac anomalies in hospitalized patients using IoT and LSTM neural networks

Continuous monitoring of patients in hospital settings is essential for preventing cardiac disease, representing a crucial measure to ensure the safety of hospitalized patients. However, current monitoring methods have significant limitations, such as patients only being monitored for short periods, which complicates the identification of changes in health status. This article presents a proof of concept for successfully implementing a continuous vital signs monitoring system using the Internet of Things (IoT), coupled with a Long Short-Term Memory (LSTM) neural network for detecting cardiac anomaly events.The development of the neural network hinged on a meticulously labeled database. This database, composed of 12-lead electrocardiogram (ECG) signals from 45,152 patients, was a labor of expertise. Collected at a sampling rate of 500 Hz, these data were labeled by experts, ensuring their high quality and reliability. This resource is fundamental for training machine learning models with high diagnostic precision, enabling accurate identification of cardiac rhythm patterns that indicate potential adverse conditions or those that deviate from a normal or stable electrocardiogram pattern. To make practical use of this extensive data, a robust system architecture was necessary to handle and analyze the data effectively.Microcontrollers played a crucial role in our system, facilitating the transmission of data to a web server. This was achieved using the Message Queuing Telemetry Transport (MQTT) protocol, a highly efficient and fast method for message transmission. The MQTT server, acting as a central hub, received the information and distributed it to two destinations. First, it sent the data to the real-time monitoring site, ensuring immediate access to patient vital signs. Second, it forwarded the data to the system hosting the neural network model. In this environment, the model processed the information and promptly identified any irregularities detected in the data.The results demonstrate that the model achieved high precision in identifying these conditions, enabling the issuance of alerts and the implementation of preventive measures with a high efficacy rate. Such actions reduce patients' health risks, allowing real-time detection and remote communication of vital signs to healthcare professionals.

Deep Learning-Based Real Time Human Detection System Using LiDAR Data for Smart Healthcare Monitoring

Abstract Continuous patient monitoring is a critical component in healthcare systems to ensure patient safety and well-being. Traditionally, this monitoring requires significant oversight by healthcare professionals, making it resourceintensive. Common methods, such as cameras, often compromise patient privacy by capturing detailed visual information. LiDAR technology presents a non-intrusive alternative for continuous monitoring, offering effectiveness across various lighting conditions due to its use of laser light. It provides high-resolution depth information encoded as intensity, making it an ideal solution for maintaining patient privacy while delivering precise monitoring capabilities. LiDAR was selected as a reference method because of its precision and ability to capture detailed depth information, which are crucial for accurate monitoring. In this project, a solution that leverages LiDAR technology to enhance patient monitoring systems was introduced. By training a YOLOv5 deep learning model using transfer learning, a method for accurate human detection and tracking within rooms using data collected from a digital LiDAR sensor was used. The high accuracy of the LiDAR sensor enables precise human tracking, which is critical for timely interventions and ensuring patient safety. This approach accelerates response times, preserves privacy, ensures safety during disease outbreaks, addresses healthcare worker shortages, and facilitates efficient monitoring of multiple patients simultaneously without invasive sensors. The ultimate goal of this project is to integrate this solution into Ambient Assisted Living (AAL) systems for elderly individuals, providing them with a safer and more autonomous living environment. Our trained YOLOv5 model has demonstrated exceptional performance, achieving a Mean Average Precision (mAP) of 99.4% at an Intersection over Union (IoU) threshold of 0.5, with a Precision of 99.6% and a Recall of 99.4%.

Blood pressure monitoring using telemedicine technologies in patients with chronic heart failure

Continuous monitoring of cardiovascular patients is one of the most important tasks in modern cardiology. Monitoring chronic heart failure (CHF) patients is critical because of instability, high prevalence and life-threatening nature of this disease. Currently, active search and development of optimal devices and programs for medical telemonitoring continues. This review presents the results of Russian and foreign studies on the use of invasive and non-invasive technologies for monitoring blood pressure (BP) levels and demonstrates their clinical significance. Innovative cuffed and cuffless BP monitoring devices are evaluated. The effectiveness of hypertension and CHF management using remote monitoring is compared with standard practice. The relationship between the risk of hospitalization for CHF and the monitoring is demonstrated. The paper presents data on the cost-effectiveness of using telemedicine technologies such as BP monitoring for CHF management.

Hybrid Machine Learning Approach for Early Stroke Prediction in Elderly People

Stroke represents a significant global health challenge, often leading to severe disability and mortality. The timely prediction and intervention of stroke are paramount in enhancing patient outcomes and reducing healthcare burdens. This project proposes a machine learning-based approach to predict stroke risk using multi-modal biosignals, specifically electrocardiogram (ECG) data. By leveraging advanced algorithms, including Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks, the system aims to classify patient health data into critical risk categories such as Normal, Abnormal, Ischemic, and Hemorrhagic. The study utilizes a comprehensive dataset consisting of ECG signals and incorporates techniques for data preprocessing, class balancing, and feature extraction. The predictive model is trained and validated using robust evaluation metrics, including accuracy, precision, recall, and F1-score. The findings underscore the efficacy of the proposed system in providing real-time stroke risk assessments, offering a cost-effective alternative to traditional diagnostic methods. Furthermore, this research explores the integration of wearable technology with machine learning, highlighting its potential for continuous patient monitoring and early detection of stroke symptoms. By creating a user-friendly interface for healthcare professionals and patients, the system aims to facilitate prompt decision-making and intervention, ultimately improving the overall quality of care for individuals at risk of stroke

Fatigue trajectories by wearable remote monitoring of breast cancer patients during radiotherapy

The aim of this pilot study was to assess the compliance of breast cancer (BC) patients with fitness tracker (FT) monitoring program during radiotherapy (RT) and to characterize radiation-induced fatigue (RIF) status through objective evaluation using FT-collected parameters. Thirty-six BC patients were invited to wear FT during their RT course for continuous monitoring of heart rate (HR) and step counts (STP). RIF assessment was performed weekly, according to CTCAE v5.0 and dichotomized into G0 vs. any-grade. A novel concept based on patient Repeated Activity Window (RAW) was introduced to evaluate HR and STP variations during RT. Several Machine Learning (ML) methods were trained to characterize RIF on the basis of HR and STP collected data. RIF of any grade was reported by 17 out of 36 patients (47%) included in the study. None of patient clinical variables were significantly correlated with RIF. All patients accepted the FT monitoring program, and for 32 patients FT collection efficiency was greater than 60%. For each patient, a distinct distribution of RAWs was identifiable over RT and across the entire patient cohort, with a total of 7950 RAWs processed. Six features related to RAWs, HR and STP were identified as associated with RIF. The best-performing classifier was the Bagged Trees model, showing a cross-validated ROC-AUC of 89% (95% CI 88–90%). This study confirms the feasibility of continuous biomedical monitoring of BC patients by FT. We successfully identify objective indicators of RIF through HR and STP variation measures within each patient’s RAW, thus providing a novel and practical approach to assess and manage RIF. This can significantly aid medical staff in evaluating RIF trajectories, potentially leading to better individualized care strategies and improved patient outcomes.

A RELEVÂNCIA DOS FARMACÊUTICOS NO AVANÇO E NOS DESAFIOS DO DESENVOLVIMENTO DE ANTIDEPRESSIVOS: EFICÁCIA, SEGURANÇA E ADESÃO AO TRATAMENTO.

Introduction: This article explores the crucial role of pharmacists in advancing and managing the challenges related to antidepressant development, focusing specifically on efficacy, safety, and treatment adherence. The collected data highlight how pharmacists contribute to the efficacy of antidepressants through appropriate medication selection, dose adjustment, and continuous patient monitoring. Additionally, it discusses the merit of pharmacists in ensuring patient safety, inferring the identification and management of side effects, as well as strategies to achieve better treatment adherence, which is often compromised by adverse effects and the complexity of therapeutic regimens. Methodology: Studies from 2020 to 2024 were included, employing a combination of quantitative and qualitative approaches, with data gathered from bibliographic references within the platforms: PubMed, Scielo, Lilacs, and DeCS from BVS, which provided a cohesive basis for the creation of this work. Results: The research revealed that pharmacists play a fundamental role in optimizing antidepressant efficacy and reducing associated safety risks, as well as in implementing strategies to improve treatment adherence, such as patient education and personalized support. Conclusion: Despite advancements in antidepressant development, challenges related to efficacy, safety, and adherence remain significant. Pharmacists are essential in overcoming these challenges and ensuring more effective and safe treatment for patients. Continued research is necessary to explore new management strategies and support the ongoing advancement of antidepressant pharmacotherapy.

DeepLPos: a comprehensive hybrid deep learning model for lying position recognition using a tactile sensor array system

Abstract Unpredictable limb movements or turning motions can significantly disrupt the accurate extraction of physiological signals, such as respiratory and heart rates. In clinical environments, reliable detection of lying positions is crucial for continuous patient monitoring, particularly during sleep. In this paper, a smart sleeping position recognition system is proposed, which employs a tactile pressure sensor array based on the unique structure of ‘the electrostatic double-layer capacitors’. The sensor array, comprising 64 rows and 32 columns (2048 nodes), captures four types of healthy lying positions using an 8-bit AD module. Despite challenges arising from limited experimental samples for accurate training, we propose DeepLPos, a hybrid deep learning approach combining generative adversarial networks and the you only look once network. To tackle the differentiation challenge between supine and prone positions, we introduce an SPD Conv attention module to enhance the resolution of detailed descriptions in pressure images. The model is further pruned to optimize both structure and parameters, enabling efficient real-time detection. Evaluated on the SLP dataset, the proposed system achieves an accuracy of 97.5% with a real-time processing speed of 0.069 s per frame, demonstrating its potential for practical, high-precision measurement and monitoring applications in healthcare.

Continuous QTc monitoring for patients intoxicated with QTc prolonging medication in the ED: a proof of principle.

Background: To determine when patients presenting with an overdose of QT-interval prolonging drugs can be discharged safely can be challenging, especially when the moment of intoxication and/or the substance(s) ingested are unknown. Objective: In a proof of principle study, we aimed to study if continuous QTc interval analysis can be used to establish optimal observation duration of patients with an intoxication with QTc prolonging medication. Methods: For patients presenting with an intoxication with QT-interval prolonging drugs in the emergency department (ED), ECG signals sampled at 500Hz were pre-processed, and the average heart rate corrected QT-interval (QTc) per 5 minutes was calculated and plotted against time. A third order polynomial was fitted to visualize when the QTc would be highest (the electrophysiological Tmax). This point in time was compared to the estimated Tmax based on pharmacokinetic properties of the ingested substance. Results: In a retrospective biobank-based study, a total of 22 ED visits (of 15 patients) were analyzed. An electrophysiological Tmax could be calculated for 17/22 visits. The remaining five patients presented either long after the electrophysiological Tmax (n=4) or were admitted to the ward before reaching the Tmax (n=1). The average (SD) difference between the estimated Tmax based on drug properties and the calculated electrophysiological Tmax was 18 (133) minutes (range -158-296 minutes). Despite the wide range, there was a significant correlation between recorded electrophysiological Tmax and estimated Tmax (r=0.67, p=0.012). Conclusion: Continuous electrophysiological monitoring can be used as an adjunct to determine the toxicokinetic Tmax for patients presenting with an intoxication, especially when the time of ingestion or the substance ingested are unknown.

Continuous monitoring of heart failure biomarker using a sensitive graphene-based electrical sensor

Abstract Heart Failure (HF) is a complex condition that imposes significant burdens on both patients and healthcare systems. The economic impact of HF therapy and management is substantial, underscoring the urgency for proactive measures to mitigate its development. Regular assessments play a pivotal role in identifying individuals at risk and initiating timely interventions to prevent or delay HF progression [1, 2]. NT-proBNP, an amine-terminated form of brain natriuretic peptide, emerges as a promising biomarker in the realm of chronic HF diagnosis, prognosis, and risk assessment [3]. Its utility lies in providing valuable insights into disease progression and prognosis. In recent years, biosensors based on Field Effect Transistors with Graphene (Gr) gate (GFET) have garnered attention for their rapid response, sensitivity, and on-chip integration [4]. Here, we present a sensitive miniaturized GFET sensor for continuous NT-proBNP measurement. The sensor is an array of GFET devices with sensing gates of a monolayer Gr decorated with aptamers as catch probes. Averaging over similar devices in an array approach ensures measurement consistency throughout the experiment. Moreover, the significant challenge posed by the Debye length in FET biosensors is effectively addressed through the functionalization with short folding aptamers [4]. The sensor is built based on a chip comprising an array of 28 individual micro GFETs (Fig. 1a). For the functionalization of Gr gates, we utilized 1-pyrene butyric acid N-hydroxysuccinimide ester as a linker between the aptamer probes and the Gr surface. Subsequently, the Gr surface was incubated with an amine-terminated aptamer solution (Fig. 1c). A schematic diagram of the sensory chip having a droplet of the sample is shown in Fig. 1b. To quantify NT-proBNP levels an 8µL droplet of the buffer solution containing a specific NT-proBNP concentration was deposited onto the sensor. The drain current (IDS) served as the sensor signal for each target concentration (Figs. 2a and 2b). The sensor signal decreased with increasing target concentration, while negligible response was observed when injecting blank buffers. This phenomenon can be attributed to the folding of aptamers upon binding to their target. Also, continuous measurement was conducted at a fixed gate voltage of 0.9V, recording the drain current while the target concentration gradually increased in the sample droplet. This process revealed a decrease in sensor current as we expected (Fig. 2c). The sensor exhibits remarkably low detection limits of 50 pg/mL and a linear range from 100 pg/mL to 10 ng/mL, which are highly conducive for early HF diagnosis and aligning well with reported NT-proBNP levels in patient biofluid [5]. The proposed sensor holds promise as a wearable device for continuous monitoring of high-risk patients. Additionally, this technology shows potential as a detection tool for acute heart failure in hospitalized patients.Sensor viewSensor responses

Breast relapse of myeloid sarcoma and SARS-CoV-2 infection following hematopoietic stem cell transplantation in mixed-phenotype acute leukemia: a case report

Abstract Background Mixed-phenotype acute leukemia is a rare, high-risk subtype of acute leukemia. Myeloid sarcoma is a localized tumor formed by the proliferation and infiltration of primitive or immature myeloid cells outside the bone marrow. It is often an extramedullary manifestation of acute myeloid leukemia, and imaging lacks specificity, leading to potential misdiagnosis. Case Presentation A female patient diagnosed with mixed-phenotype acute leukemia achieved complete remission after chemotherapy and allogeneic hematopoietic stem cell transplantation. However, a relapse occurred in the breast after transplantation. The patient achieved local control through mastectomy and removal of the axillary lymph nodes on the affected side. Unfortunately, during the subsequent course of chemotherapy, the patient died of a severe pulmonary infection caused by coronavirus disease 2019. Conclusion Continuous treatment and monitoring of mixed-phenotype acute leukemia patients are essential, especially for those with extramedullary manifestations (such as breast involvement).

Optimization model for mass casualty management system using qos-aware routing protocol and casualty triage prediction

Abstract Purpose Unexpected events, whether man-made or natural, cause significant distress and disorder. The number and magnitude of disasters and catastrophes have been rising globally in recent decades, according to historical data. Continued real-time monitoring of mass casualty along with the arrangement of needed medical resources to handle the mass casualty is required to reduce the mortality and morbidity rates. Methods Electronic tag-based casualty monitoring and machine learning-based Decision Support Systems are emerging as a more effective and proactive solution that provides continuous remote monitoring of patients. A novel framework based on Body-to-Body Network, Prediction model, and Genetic Algorithm-based medical resource optimization is proposed for the continuous monitoring of the mass casualty and medical resource allocation at the incident. The aim of this work is to give priority to the handling of critical casualties. Firstly, a Quality of Service and load-sensitive routing protocol for transmitting mass casualties' physiological parameters across a wireless network is proposed, with the critical casualty being emphasized. Secondly, the clinical seriousness degree of the mass casualty is predicted using Backpropagation Artificial Neural Network. Finally, an optimization model using a Genetic algorithm and queuing theory is proposed to find the required optimal number of medical resources to handle critical and non-critical casualties separately. Also, the proposed optimization model considers the predicted clinical level transition rates of the mass casualty. Results The performance and accuracy of the proposed framework are evaluated using the MIMIC-II dataset. The outcome demonstrates that the framework emphasizes critical casualty management. Furthermore, the framework allocates an adequate number of servers by incorporating the proposed routing protocol in comparison to the AODV protocol. Conclusion The inclusion of a prediction model in the framework aids in allocating an adequate number of servers by considering the predicted clinical deteriorating transition rates of casualties at a mass casualty incident. In terms of the estimated length of the casualty at the incident, the results suggest that incorporating a medical resource optimization model outperforms the non-optimal option.

ANN modeling for predicting muscle-implanted antenna performance for skin and fat thickness variations at 2.45 GHz

Abstract Implantable antenna is embedded in implantable medical devices to communicate with external world for continuous patient monitoring. The implantable antenna performance may vary with patients due to their different skin and fat thickness owing to their environment, location and concentration of collagen in skin, estrogen and testosterone concentration, age, diseases like obesity, Gottron syndrome etc. Estimation of IMA performance requires a proper description of its dependence on variable skin and fat thickness which is not studied extensively in previous related works. In this article, we have designed a muscle-implanted antenna at 2.45 GHz to examine its performance dependence on variable skin and fat thickness values. In this study, we have designed an artificial neural network (ANN) for prediction of antenna performance parameters at 2.45 GHz for wide range of skin and fat thickness variations. The designed ANN model can predict antenna performance with ∼0.99 % error. Here, accurate dependence analysis of radiation performance of implantable antenna in terms of S 11, realized gain, bandwidth and operating frequency on skin and fat thickness variations is performed which has not been studied yet and use of ANN in this field for accurate prediction is also a novel approach.

Clinical investigation into risk factors for delirium post-cardiac surgery and its implications for nursing intervention guided by behavior change theory

BackgroundThis study explores the factors contributing to the occurrence of delirium following cardiac surgery and devises nursing strategies rooted in behavior change theory.MethodsA cohort of 320 cardiac surgery patients was selected, categorized into two groups: 93 cases where postoperative delirium (POD) was anticipated, and 227 cases where it was not. Preoperative, intraoperative, and postoperative factors of POD were scrutinized using single-factor analysis, while binary logistic regression analysis was employed to pinpoint risk factors.ResultsAmong the 320 patients, 93 displayed POD symptoms post-surgery, yielding an incidence of 29.06%. Preoperative univariate analysis disclosed significant differences in gender, age, smoking, hypertension, and diabetes (P < 0.05). Intraoperatively, significant differences were noted in the American Society of Anesthesiologists (ASA) anesthesia grade (II, III, and IV), surgery time, cardiopulmonary bypass duration, and aortic occlusion duration (P < 0.05). Post-surgery, significant differences were observed in the duration of Intensive Care Unit (ICU) stay, mechanical ventilation time, and visual analogue scale (VAS) scores (P < 0.05). Multivariate Logistic regression identified surgery time (OR = 2.334, P < 0.001), ICU admission duration (OR = 1.457, P < 0.001), mechanical ventilation time (OR = 1.235, P = 0.004), and VAS scores (OR = 2.986, P < 0.001) as independent risk factors for POD. ROC curve analysis indicated higher sensitivity and specificity in predicting POD with surgery time, ICU stay duration, mechanical ventilation time, and VAS scores.ConclusionIrrespective of the surgical intervention type, surgery time, ICU stay duration, mechanical ventilation time, and VAS scores are recognized as risk factors for POD in cardiac surgery patients. Hence, continuous patient monitoring and early intervention tailored to specific risk factors are essential in clinical practice to mitigate POD incidence.