Continuous Monitoring Of Patients Research Articles

Integrated Neural Network Approach for Enhanced Vital Signal Analysis Using CW Radar

This study introduces a novel approach for analyzing vital signals using continuous-wave (CW) radar, employing an integrated neural network model to overcome the limitations associated with traditional step-by-step signal processing methods. Conventional methods for vital signal monitoring, such as electrocardiograms (ECGs) and sphygmomanometers, require direct contact and impose constraints on specific scenarios. Conversely, our study primarily focused on non-contact measurement techniques, particularly those using CW radar, which is known for its simplicity but faces challenges such as noise interference and complex signal processing. To address these issues, we propose a temporal convolutional network (TCN)-based framework that seamlessly integrates noise removal, demodulation, and fast Fourier transform (FFT) processes into a single neural network. This integration minimizes cumulative errors and processing time, which are common drawbacks of conventional methods. The TCN was trained using a dataset comprising preprocessed in-phase and quadrature (I/Q) signals from the CW radar and corresponding heart rates measured via ECG. The performance of the proposed method was evaluated based on the L1 loss and accuracy against the moving average of the estimated heart rates. The results indicate that the proposed approach has the potential for efficient and accurate non-contact vital signal analysis, opening new avenues in health monitoring and medical research. Additionally, the integration of CW radar and neural networks in our framework offers a robust and scalable solution, enhancing the practicality of non-contact health monitoring systems in diverse environments. This technology can be leveraged in healthcare robots to provide continuous and unobtrusive monitoring of patients’ vital signs, enabling timely interventions and improving overall patient care.

Personalized prediction of intradialytic hypotension in clinical practice: Development and evaluation of a novel AI dashboard incorporating risk factors from previous and current dialysis sessions

BackgroundIntradialytic hypotension (IDH) is one of the most common and critical complications of hemodialysis. Despite many proven factors associated with IDH, accurately predicting it before it occurs for individual patients during dialysis sessions remains a challenge. PurposeTo establish artificial intelligence (AI) predictive models for IDH, which consider risk factors from previous and ongoing dialysis to optimize model performance. We then implement a novel digital dashboard with the best model for continuous monitoring of patients’ status undergoing hemodialysis. The AI dashboard can display the real-time probability of IDH for each patient in the hemodialysis center providing an objective reference for care members for monitoring IDH and treating it in advance. MethodsEight machine learning (ML) algorithms, including Logistic Regression (LR), Random Forest (RF), Support Vector Machine (SVM), K Nearest Neighbor (KNN), Light Gradient Boosting Machine (LightGBM), Multilayer Perception (MLP), eXtreme Gradient Boosting (XGBoost), and NaiveBayes, were used to establish the predictive model of IDH to determine if the patient will acquire IDH within 60 min. In addition to real-time features, we incorporated several features sourced from previous dialysis sessions to improve the model’s performance. The electronic medical records of patients who had undergone hemodialysis at Chi Mei Medical Center between September 1, 2020 and December 31, 2020 were included in this research. Impact evaluation of AI assistance was conducted by IDH rate. ResultsThe results showed that the XGBoost model had the best performance (accuracy: 0.858, sensitivity: 0.858, specificity: 0.858, area under the curve: 0.936) and was chosen for AI dashboard implementation. The care members were delighted with the dashboard providing real-time scientific probabilities for IDH risk and historic predictive records in a graphic style. Other valuable functions were appended in the dashboard as well. Impact evaluation indicated a significant decrease in IDH rate after the application of AI assistance. ConclusionThis AI dashboard provides high-quality results in IDH risk prediction during hemodialysis. High-risk patients for IDH will be recognized 60 min earlier, promoting individualized preventive interventions as part of the treatment plan. Our approachis believed to promise an excellent way for IDH management.

Time in range prediction using the experimental mobile application in type 1 diabetes

BACKGROUND: Time in range (TIR) is a promising indicator of glycemic control used for evaluation of continuous glucose monitoring (CGM) for patients with diabetes mellitus (DM). The current problem is the assessment and prediction of TIR for patients who use self-monitoring of blood glucose (SМBG) corresponding low CGM availability for the majority of diabetic patients.AIM: To develop a predictive model of TIR for patients with T1DM based on data of the experimental mobile application.MATERIALS AND METHODS: An analysis of 1253 professional CGM profiles of patients with T1DM was performed. On the base of included records, TIR(CGM) was calculated and training models of 7-point SMBG profiles were generated. SMBG profiles’re loaded into the developed experimental mobile application that calculated standard glycemic control parameters. The dataset was divided into main and test samples (80 and 20%). For the main sample, the following methods’re used to develop predictive models: simple linear regression (SLR), multiple linear regression (MLR), artificial neural network (ANN). The effectiveness of the developed models was assessed on the test sample with the calculation of the mean absolute error (MAE), the root mean square error (RMSE).RESULTS: The 568 CGM profiles’re included in the study. TIR in the main group (n=454) — 45 [33; 65]%, in the test group (n=114) — 43 [33; 58]%. The most significant predictors of the regression models were the derived TIR (dTIR), p<0,001; derived time below range level 1 (dTBR1), p<0,001; standard deviation of blood glucose (SD), p=0,007. Determination coefficient for SLR (predictor: dTIR) — 0,844; for MLR (predictors: dTIR, dTBR1, SD) — 0,907. ANN multilayer perceptron models with two and one hidden layers’re developed, with the RMSE on the validation set 4,617 and 6,639%, respectively. The results of the forecast efficiency on the test sample were: dTIR: MAE — 6,82%, RMSE — 8,60%; SLR: MAE — 5,66%, RMSE — 7,34%; MLR: MAE — 4,18%, RMSE — 5,28%; ANN (2 layers): MAE — 4,14%, RMSE — 5,19%; ANN (1 layer): MAE — 4,44%, RMSE — 5,52%.CONCLUSION: ANN with two hidden layers and MLR demonstrated the best ability for TIR prediction. Further studies are required for clinical validation of developed prognostic models.

Navigating cancer care through digital dialogue: Analyzing key phrases shared by patients in real-time exchange messages using an electronic patient-reported outcome measure (ePROM).

e13661 Background: Cancer is a complex ailment, giving rise to a myriad of symptoms, side effects, and questions that may arise unexpectedly. There is a growing interest in adopting more efficient approaches for the continuous monitoring of patients, particularly through virtual platforms. This study focuses on analyzing the word frequency in the context of cancer assistance using an ePROM. Methods: We conducted an analysis of an ePROM embedded in a mobile app designed for monitoring patients undergoing cancer treatment. Developed collaboratively by medical oncologists and software engineers, this app empowers patients to document their symptoms, quality of life, emotions, emergency room visits, and hospitalizations. Upon app registration, patients willingly share their data with their medical team, along with essential demographic information. A standout feature is the online chat facilitating real-time communication with the medical team, consolidating all information securely in one place. This patient-centric app is provided free of charge. Our study involved a retrospective and descriptive analysis focusing solely on data related to exchanged messages in the online chat between cancer patients and the medical team. These conversations spanned from February 2022 to January 2024 and included patients referred by physicians recommending our platform. The analysis centered on the frequency of non-technical words used in real-time conversations, clustered into semantic groups. Results: Our findings revealed a total exchange of 470,597 words across 684 conversations over a two-year period. In the healthcare context, the most frequent words were categorized as follows: 1) Seeking in-person assistance (568; 0.12%): hospital (424 mentions), emergency (82), urgent/urgency (51), and primary care (UBS, Portuguese acronym) (11); 2) Doubts regarding anything (979; 0.20%): doubt (694 mentions) and question (285); 3) Non-technical one-word symptoms (5213; 1.10%): pain (1821 mentions), nausea (961), tiredness (514), diarrhea (428), vomit (364), fever (304), cough (221), numbness (197), dizziness (162), insomnia (130), constipation (101), and wheezing (10). Conclusions: Our analysis showed two key insights: first, doubts persist throughout treatment, intertwined with the management of side effects; and second, the burden on patients is substantial, whether stemming from uncertainty about the next steps or the necessity for in-person assistance. Cancer's mental and physical toll underscores the importance of having a readily available team to provide guidance, even on simple instructions. In this light, virtual platforms may represent the future of care delivery. Continuous patient monitoring emerges as pivotal for enhancing healthcare assistance, treatment, and outcomes across various cancer types.

Towards a machine-learning assisted non-invasive classification of dengue severity using wearable PPG data: a prospective clinical study

Dengue epidemics impose considerable strain on healthcare resources. Real-time continuous and non-invasive monitoring of patients admitted to the hospital could lead to improved care and outcomes. We evaluated the performance of a commercially available wearable (SmartCare) utilising photoplethysmography (PPG) to stratify clinical risk for a cohort of hospitalised patients with dengue in Vietnam. We performed a prospective observational study for adult and paediatric patients with a clinical diagnosis of dengue at the Hospital for Tropical Disease, Ho Chi Minh City, Vietnam. Patients underwent PPG monitoring early during admission alongside standard clinical care. PPG waveforms were analysed using machine learning models. Adult patients were classified between 3 severity classes: i) uncomplicated (ward-based), ii) moderate-severe (emergency department-based), and iii) severe (ICU-based). Data from paediatric patients were split into 2 classes: i) severe (during ICU stay) and ii) follow-up (14-21 days after the illness onset). Model performances were evaluated using standard classification metrics and 5-fold stratified cross-validation. We included PPG and clinical data from 132 adults and 15 paediatric patients with a median age of 28 (IQR, 21-35) and 12 (IQR, 9-13) years respectively. 1781h of PPG data were available for analysis. The best performing convolutional neural network models (CNN) achieved a precision of 0.785 and recall of 0.771 in classifying adult patients according to severity class and a precision of 0.891 and recall of 0.891 in classifying between disease and post-disease state in paediatric patients. We demonstrate that the use of a low-cost wearable provided clinically actionable data to differentiate between patients with dengue of varying severity. Continuous monitoring and connectivity to early warning systems could significantly benefit clinical care in dengue, particularly within an endemic setting. Work is currently underway to implement these models for dynamic risk predictions and assist in individualised patient care. EPSRC Centre for Doctoral Training in High-Performance Embedded and Distributed Systems (HiPEDS) (Grant: EP/L016796/1) and the Wellcome Trust (Grants: 215010/Z/18/Z and 215688/Z/19/Z).

Improving the Efficacy of Continuous Monitoring of Patients with Parkinson's Disease Through the Use of an Intelligent Computing Architecture Within the Medical Internet of Things

Telemonitoring has grown in popularity these days, particularly to assist patients with serious illnesses such as Parkinson's disease (PD). This study relies on the Daphnet dataset, which was trained and utilized to track five patients' whereabouts for an extensive dataset. Fuzzy logic was used in conjunction with a linear and Mobius map as part of the fog computing freezing of gait (FoG) detection system to provide a multi-level output (MLFM-map) that takes advantage of various spatial resolutions in motion data processing. Two improved Salp Swarm Algorithms with Fuzzy Logic (Fuzzy-ISSA) and transient search optimization algorithm (TSO) have been employed for the methodical training of this tool at the level of fog computing. When compared to other machine learning techniques, this one trains the FOG detection system quite quickly. In order to maximize performance while lowering computational complexity and testing time, the model architecture and parameters have been carefully considered. On average, the suggested method detected over 90% of FoG occurrences with extremely low latency in the original (test) dataset, demonstrating good to exceptional classification performance. In addition, the algorithm demonstrated a specificity of over 90% when tested on the test set. Various algorithms are used to train this design.

Technoeconomic Analysis for Deployment of Gait-Oriented Wearable Medical Internet-of-Things Platform in Catalonia

The Internet of Medical Things (IoMT) extends the concept of eHealth and mHealth for patients with continuous monitoring requirements. This research concentrates on the use of wearable devices based on the use of inertial measurement units (IMUs) that account for a gait analysis for its use in three health cases, equilibrium evaluation, fall prevention and surgery recovery, that impact a large elderly population. We also analyze two different scenarios for data capture: supervised by clinicians and unsupervised during activities of daily life (ADLs). The continuous monitoring of patients produces large amounts of data that are analyzed in specific IoMT platforms that must be connected to the health system platforms containing the health records of the patients. The aim of this study is to evaluate the factors that impact the cost of the deployment of such an IoMT solution. We use population data from Catalonia together with an IoMT deployment model for costs from the current deployment of connected devices for monitoring diabetic patients. Our study reveals the critical dependencies of the proposed IoMT platforms: from the devices and cloud cost, the size of the population using these services and the savings from the current model under key parameters such as fall reduction or rehabilitation duration. Future research should investigate the benefit of continuous monitoring in improving the quality of life of patients.

Tips and Thought Processes to Overcome Difficult Situations in Third Space Endoscopy.

Third space endoscopy (TSE) offers diverse treatment options for a variety of conditions of the gastrointestinal tract and motility disorders. Accuracy and precise dissection have helped treat complex conventional surgeries with TSE. Despite over a decade of its presence, difficult situations are routinely encountered. The first challenging situation is decision-making (before the procedure), the second is tackling procedure-induced pulmonary / insufflation-related adverse events and intra-operative complications (during the procedure), and the third is post-operative complications and morbidity (post-procedure). The performance of TSE procedures is not without risk. Patients should receive enough time to weigh their options, considering that attitudes toward risk impact decision-making. Continuous basic patient monitoring and the micro-movements of endoscopic and electrosurgical equipment is crucial to avoid accidental injury. Anaesthetists should be mindful of anticipated complications and closely monitor, diagnose, and treat them. Demanding situations need careful consideration, problem-solving, or persistence to overcome challenges. This brief review provides inputs on preventing and tackling difficult situations in TSE.

Point-of-care testing: a critical analysis of the market and future trends

Point-of-care testing (POCT) involves conducting diagnostic tests outside the laboratory. These tests are utilized for their quick and reliable results in detecting chronic diseases and acute infections. We examined the historical market value trends of the POCT industry and projected its future growth. POCT devices offer several advantages, such as portability, no specific storage requirements, and simplicity of use. However, issues such as inaccurate results can reduce demand for POCT compared to traditional laboratory testing alternatives. While the benefits of POCT are clear, it’s important to acknowledge the challenges. The accuracy of POCT devices can be compromised due to factors such as user error, environmental conditions, and limitations in technology. Furthermore, the cost-effectiveness of these devices is often a concern, particularly in resource-limited settings. The regulatory landscape for POCT is also complex and varies by region, which can pose challenges for manufacturers and end-users. Improvements in POCT devices’ sensitivity, specificity, cost, and turnaround time for test results could enhance their utility. Enhancements to a POCT device should be considered if they are economically viable and lead to a significant increase in demand. Our financial analysis of the POCT market revealed a positive growth trend. We identified potential areas for growth that could help the industry progress and expand in line with its projected growth in the coming years. The industry could focus on integrating intelligent technologies into POCT devices and collaborating with the innovative technology sector to increase revenue. It could also develop more sensitive and accurate POCT devices for non-communicable diseases. Another potential area of growth is in diseases that require continuous patient monitoring but where conventional clinical testing is time-consuming. Enhancing the POCT devices used in these areas could revolutionize medical diagnosis and potentially save many lives, provided they meet clinical standards.

Polysomnography findings and respiratory muscle function in infants with early onset spinal muscular atrophy after gene replacement as monotherapy: A prospective study

BackgroundGene replacement therapy (onasemnogene abeparvovec) is associated with an improvement of the prognosis of children with spinal muscular atrophy, but information on long-term respiratory outcome is scarce. The aim of this study was to report the polysomnography findings and respiratory muscle function of infants with treatment-naive spinal muscular atrophy type 1 and 2 up to 24 months after onasemnogene abeparvovec monotherapy. MethodsA clinical and motor evaluation, respiratory muscle function testing, and polysomnography were performed repeatedly. ResultsFifteen spinal muscular atrophy patients (1 presymptomatic, 7 type 1b, 6 type 1c, and 1 type 2) were included at a median age of 8.6 months (range 3.8–12.6) and followed for 24 months. The thoracic over head circumference ratio was close to normal at baseline (median 1.00 (range 0.90–1.05)) and increased significantly over time. All polysomnography and nocturnal gas exchange parameters were within normal ranges at baseline (median apnea-hypopnea index 2.5 events/hour (range 0.4–5.3)) and follow-up. The inspiratory muscle strength was normal at baseline but tended to slightly decrease over time and the expiratory muscle strength was low at any time especially for patients with recurrent respiratory infections (median (range) at baseline in cmH2O: crying esophageal pressure 54 (30–110), crying transdiaphragmatic pressure 65 (35–107), gastric pressure during maximal cough 26 (10–130), esophageal pressure during maximal cough 61 (38–150)). Only 3 patients required noninvasive ventilation. ConclusionA continuous respiratory monitoring of spinal muscular atrophy patients during the first years of life following onasemnogene abeparvovec monotherapy seems recommended despite the normality of polysomnography parameters.

Wireless Patient Health Monitoring System

Wireless Patient Health Monitoring Systems (WPHMS) have emerged as transformative solutions in modern healthcare, facilitating continuous and remote monitoring of patients' vital signs in real-time. This paper presents a comprehensive exploration of a Wireless Patient Health Monitoring System leveraging the Blynk app, a versatile IoT platform. The system is designed to enable seamless data collection, transmission, and visualization of critical health parameters such as pulse rate, temperature, and blood pressure. By integrating cutting-edge hardware components with intuitive software interfaces, the proposed system offers unprecedented flexibility and accessibility in patient monitoring, transcending the limitations of traditional wired monitoring systems. The research begins with a thorough review of existing literature on wireless healthcare technologies, highlighting the evolution of patient monitoring systems and the role of IoT platforms in revolutionizing healthcare delivery. Building upon this foundation, the paper proceeds to outline the hardware architecture of the WPHMS, detailing the selection and integration of microcontroller boards, sensor modules.

Comparative Analysis of Artificial Intelligence (AI) Languages in Predicting Sequential Organ Failure Assessment (SOFA) Scores.

The Sequential Organ Failure Assessment (SOFA) score plays a crucial role in intensive care units (ICUs) by providing a reliable measure of a patient's organ function or extent of failure. However, the precise assessment is time-consuming, and daily assessment in clinical practice in the ICU can be challenging. Realistic scenarios in an ICU setting were created, and the data mining precision of ChatGPT 4.0 Plus, Bard, and Perplexity AI were assessed using Spearman's as well as the intraclass correlation coefficients regarding the accuracy in determining the SOFA score. The strongest correlation was observed between the actual SOFA score and the score calculated by ChatGPT 4.0 Plus (r correlation coefficient 0.92) (p<0.001). In contrast, the correlation between the actual SOFA and that calculated by Bard was moderate(r=0.59, p=0.070), while the correlation with Perplexity AI was substantial, at 0.89, with a p<0.001. The interclass correlation coefficient analysis of SOFA with those of ChatGPT 4.0 Plus, Bard, and Perplexity AI was ICC=0.94. Artificial intelligence (AI) tools, particularly ChatGPT 4.0 Plus, show significant promise in assisting with automated SOFA score calculations via AI data mining in ICU settings. They offer a pathway to reduce the manual workload and increase the efficiency of continuous patient monitoring and assessment. However, further development and validation are necessary to ensure accuracy and reliability in a critical care environment.

Emerging trends in management of long COVID with a focus on pulmonary rehabilitation: A review.

Long COVID, or post-acute sequelae of COVID-19 (PASC), represents a complex condition with persistent symptoms following SARS-Cov-2 infection. The symptoms include fatigue, dyspnoea, cognitive impairment, decreased quality of life in variable levels of severity. Potential mechanisms behind long COVID include vascular damage, immune dysregulation and viral persistence. Diagnosing long COVID involves medical evaluation by multidisciplinary team and assessment of persistent symptoms with scoring systems in development. Treatment strategies are symptom-focused, encompassing multidisciplinary care, rehabilitation and tailored exercise programmes. Pulmonary rehabilitation, an effective and critical component of long COVID management, has shown promise, particularly for patients with respiratory symptoms such as dyspnoea. These programmes, which combine exercise, breathing techniques, education and psychological support, improve symptoms, quality of life and overall recovery. Innovative technologies, such as telemedicine, wearable devices, telerehabilitation, are transforming long COVID management. Telemedicine facilitates consultations and interventions, eliminating healthcare access barriers. Wearable devices enable remote and continuous monitoring of patients during their rehabilitation activities. Telerehabilitation has proven to be safe and feasible and to have high potential for COVID-19 recovery. This review provides a concise overview of long COVID, encompassing its definition, prevalence, mechanisms, clinical manifestations, diagnosis and management approaches. It emphasizes the significance of multidisciplinary approach in diagnosis and treatment of long COVID, with focus on pulmonary rehabilitation and innovative technology advances to effectively address the management of long COVID.

IoT Based Health Monitoring System

Abstract: This paper presents a comprehensive system designed for continuous monitoring of patients' physiological parameters using Internet of Things (IoT) technology. In recent times, patient monitoring systems have gained significant traction among researchers and caregivers alike. The proposed system is adept at monitoring key physiological indicators such as pulse rate, body temperature, and heart rate from the patient's body. Data collected by the system is transmitted to an IoT cloud platform via a Wi-Fi module, where the patient's health status is securely stored. This setup allows medical professionals or authorized individuals to remotely monitor the patient's condition in real-time through the cloud server. The primary aim of this research is to provide efficient and effective healthcare facilities to patients, thereby enhancing overall patient care.

Wireless Patient Monitoring System: IoT in Healthcare

Abstract: In the conventional medical field, diagnosis often requires numerous sensors and significant human effort, posing challenges for large-scale implementation due to the scarcity of medical professionals and infrastructure. Addressing this issue, this research proposes an IoT-based healthcare application aimed at continuous wireless monitoring of patients, utilizing both web and mobile platforms. The system's primary objective is to create a cost-effective solution for transmitting vital patient signs during emergencies. Sensors are employed to measure these signs, with data transmitted via a wireless network to the cloud for storage, facilitated by a Wi-Fi module connected to a controller. Cloud-based processing enables analysis of the data, with subsequent feedback provided remotely to medical professionals for further assessment. This remote viewing alleviates the burden on doctors while offering accurate patient health status updates. Additionally, in urgent situations, the system automatically alerts doctors, ensuring prompt attention to critical cases. These innovations promise to streamline healthcare delivery, enhancing patient care and reducing the strain on medical resources

Characterizing Diffusion from Microdialysis Catheters in the Human Brain: A Magnetic Resonance Imaging Study With Gadobutrol.

Cerebral microdialysis (CMD) catheters allow continuous monitoring of patients' cerebral metabolism in severe traumatic brain injury (TBI). The catheters consist of a terminal semi-permeable membrane that is inserted into the brain's interstitium to allow perfusion fluid to equalize with the surrounding cerebral extracellular environment before being recovered through a central non-porous channel. However, it is unclear how far recovered fluid and suspended metabolites have diffused from within the brain, and therefore what volume or region of brain tissue the analyses of metabolism represent. We assessed diffusion of the small magnetic resonance (MR)-detectible molecule gadobutrol from microdialysis catheters in six subjects (complete data five subjects, incomplete data one subject) who had sustained a severe TBI. Diffusion pattern and distance in cerebral white matter were assessed using T1 (time for MR spin-lattice relaxation) maps at 1 mm isotropic resolution in a 3 Tesla MR scanner. Gadobutrol at 10 mmol/L diffused from cerebral microdialysis catheters in a uniform spheroidal (ellipsoid of revolution) pattern around the catheters' semipermeable membranes, and across gray matter-white matter boundaries. Evidence of gadobutrol diffusion was found up to a mean of 13.4 ± 0.5 mm (mean ± standard deviation [SD]) from catheters, but with a steep concentration drop off so that ≤50% of maximum concentration was achieved at ∼4 mm, and ≤10% of maximum was found beyond ∼7 mm from the catheters. There was little variation between subjects. The relaxivity of gadobutrol in human cerebral white matter was estimated to be 1.61 ± 0.38 L.mmol-1sec-1 (mean ± SD); assuming gadobutrol remained extracellular thereby occupying 20% of total tissue volume (interstitium), and concentration equilibrium with perfusion fluid was achieved immediately adjacent to catheters after 24 h of perfusion. No statistically significant change was found in the concentration of the extracellular metabolites glucose, lactate, pyruvate, nor the lactate/pyruvate ratio during gadobutrol perfusion when compared with period of baseline microdialysis perfusion. Cerebral microdialysis allows continuous monitoring of regional cerebral metabolism-the volume of which is now clearer from this study. It also has the potential to deliver small molecule therapies to focal pathologies of the human brain. This study provides a platform for future development of new catheters optimally designed to treat such conditions.

Brave new world: expanding home care in stem cell transplantation and advanced therapies with new technologies.

Hematopoietic stem cell transplantation and cell therapies like CAR-T are costly, complex therapeutic procedures. Outpatient models, including at-home transplantation, have been developed, resulting in similar survival results, reduced costs, and increased patient satisfaction. The complexity and safety of the process can be addressed with various emerging technologies (artificial intelligence, wearable sensors, point-of-care analytical devices, drones, virtual assistants) that allow continuous patient monitoring and improved decision-making processes. Patients, caregivers, and staff can also benefit from improved training with simulation or virtual reality. However, many technical, operational, and above all, ethical concerns need to be addressed. Finally, outpatient or at-home hematopoietic transplantation or CAR-T therapy creates a different, integrated operative system that must be planned, designed, and carefully adapted to the patient's characteristics and distance from the hospital. Patients, clinicians, and their clinical environments can benefit from technically improved at-home transplantation.

Feasibility of a nurse-led, mHealth-assisted, and team-based collaborative care model for heart failure care in India: Findings from a multi-stakeholder qualitative study

Background Heart failure (HF) management is often challenging due to poor adherence to GDMT and self-care. Continuous monitoring of patients by a dedicated care manager may enhance adherence to self-care and treatment and prevent hospitalisations. For the adoption and acceptance of a collaborative care model (CCM) for HF management in Indian settings, understanding the perspectives of all stakeholders regarding its various components and feasibility is needed. Therefore, we aimed to obtain perceptions of potential challenges to care and suggestions on multiple components of the proposed CCM in managing HF and its feasibility. Methods In-depth interviews were done among HF patients, caregivers, nurses, and cardiologists from private, co-operative, and public sector tertiary care hospital settings that cater to HF patients in Kerala, India. An in-depth interview guide was used to elicit the data. Data were analysed using Python QualCoder version 2.2. We used a framework method for the analysis of data. Results A total of 22 in-depth interviews were conducted. We found that the existing care for HF in many settings was inadequate for continuous engagement with the patients. Non-adherence to treatment and other self-care measures, was noted as a major challenge to HF care. Healthcare providers and patients felt nurses were better at leading collaborative care. However, various barriers, including technical and technological, and the apprehensions of nurses in leading the CCM were identified. The stakeholders also identified the mHealth-assisted CCM as a potential tool to save money. The stakeholders also appreciated the role of nurses in creating confidence in patients. Conclusions A nurse-led, mHealth-assisted, and team-based collaborative care was recognised as an excellent step to improve patient adherence. Effective implementation of it could reduce hospitalisations and improve patients' ability to manage their HF symptoms.

Effectiveness of a hybrid approach in integrating GLP-1 agonists and lifestyle guidance for obesity and pre-diabetes management: RWE retrospective study

AimEmerging anti-obesity pharmacotherapy provides an option to correct maladaptive physiological and hormonal changes associated with obesity. One of the widely used medications in this context is glucagon-like peptide 1 (GLP-1) agonists. However, the misuse of these medications without any guidance and monitoring of lifestyle modifications can lead to unfavorable outcomes. The study aims to evaluate the effectiveness of a hybrid care model, incorporating GLP-1 and GLP-1/GIP agonist therapies, in managing obese patients with/without pre-diabetes. This study showcases the midway results of a 6-month program, which includes a multidisciplinary care team and digital technology for continuous engagement and monitoring of patients, both in-clinic and remotely. MethodsIn a retrospective observational study, 115 participants were treated with GLP-1s (semaglutide, tirzepatide, and liraglutide). Physicians, dietitians, and coaches worked together to support behavioral changes using a dedicated app provided to patients. At the care team end, an integrated portal enabled continuous data flow allowing for the care team to provide personalized care via chat at regular intervals. Data collected included food logs, continuous glucose monitoring (CGM), and digital biomarkers such as sleep and activity. ResultsAt the midpoint of the program, participants exhibited statistically significant improvements in various metabolic parameters. Mean weight reduction was 8 %, with significant reductions in BMI, fat mass, and cholesterol levels. 24 (20.9 %) of patients lost ≥5 % of body weight, 55 (47.8 %) patients lost ≥10 % weight, and 36 (31.3 %) patients lost ≥15 % weight. Sub-analysis of pre-diabetic patients (n=36) demonstrated substantial improvements, including control of pre-diabetes in 80.6 % of cases and reduced HbA1c levels back to normoglycemia (5.39 ± 0.27). ConclusionThe Zone.Health's program, which combines pharmacotherapy with continuous engagement and monitoring to enable sustainable lifestyle modifications, demonstrated significant improvements in weight, body composition, and metabolic markers. Pre-diabetes was also effectively addressed. It is necessary to conduct further research to assess the long-term sustainability and optimal adoption of such care models into clinical practice.

Pro-Con Debate: Universal Versus Selective Continuous Monitoring of Postoperative Patients.

In this Pro-Con commentary article, we discuss use of continuous physiologic monitoring for clinical deterioration, specifically respiratory depression in the postoperative population. The Pro position advocates for 24/7 continuous surveillance monitoring of all patients starting in the postanesthesia care unit until discharge from the hospital. The strongest arguments for universal monitoring relate to inadequate assessment and algorithms for patient risk. We argue that the need for hospitalization in and of itself is a sufficient predictor of an individual's risk for unexpected respiratory deterioration. In addition, general care units carry the added risk that even the most severe respiratory events will not be recognized in a timely fashion, largely due to higher patient to nurse staffing ratios and limited intermittent vital signs assessments (e.g., every 4 hours). Continuous monitoring configured properly using a "surveillance model" can adequately detect patients' respiratory deterioration while minimizing alarm fatigue and the costs of the surveillance systems. The Con position advocates for a mixed approach of time-limited continuous pulse oximetry monitoring for all patients receiving opioids, with additional remote pulse oximetry monitoring for patients identified as having a high risk of respiratory depression. Alarm fatigue, clinical resource limitations, and cost are the strongest arguments for selective monitoring, which is a more targeted approach. The proponents of the con position acknowledge that postoperative respiratory monitoring is certainly indicated for all patients, but not all patients need the same level of monitoring. The analysis and discussion of each point of view describes who, when, where, and how continuous monitoring should be implemented. Consideration of various system-level factors are addressed, including clinical resource availability, alarm design, system costs, patient and staff acceptance, risk-assessment algorithms, and respiratory event detection. Literature is reviewed, findings are described, and recommendations for design of monitoring systems and implementation of monitoring are described for the pro and con positions.