Remote Monitoring Devices Research Articles

Device for remote and realtime monitoring of neonatal vital signs in neonatal intensive care unit using internet of things: proof-of-concept study.

Realtime and remote monitoring of neonatal vital signs is a crucial part of providing appropriate care in neonatal intensive care units (NICU) to reduce mortality and morbidity of newborns. In this study, a new approach, a device for remote and real-time monitoring of neonatal vital signs (DRRMNVS) in the neonatal intensive care unit using the internet of things (IoT), was proposed. The system integrates four vital signs: oxygen saturation, pulse rate, body temperature and respiration rate for continuous monitoring using the Blynk app and ThingSpeak IoT platforms. The Wemos D1 mini, a Wi-Fi microcontroller, was used to acquire the four biological biomarkers from sensors, process them and display the result on an OLED display for point of care monitoring and on the Blynk app and ThingSpeak for remote and continuous monitoring of vital signs. The Bland-Altman test was employed to test the agreement of DRRMNVS measurement with reference standards by taking measurements from ten healthy adults. The prototype of the proposed device was successfully developed and tested. Bias [limits of agreement] were: Oxygen saturation (SpO2): -0.1 [- 1.546 to + 1.346] %; pulse rate: -0.3 [- 2.159 to + 1.559] bpm; respiratory rate: -0.7 [- 0.247 to + 1.647] breaths/min; temperature: 0.21 [+ 0.015˚C to + 0.405˚C] ˚C. The proof-of-concept prototype was developed for $33.19. The developed DRRMNVS device was cheap and had acceptable measurement accuracy of vital signs in a controlled environment. The system has the potential to advance healthcare service delivery for neonates with further development from this proof-of-concept level.

Abstract 9265: Real-World Disparities in Remote Follow-Up of Cardiac Implantable Electronic Devices and Impact of the Covid-19 Pandemic: A Single Center Experience

Introduction: Remote monitoring (RM) of cardiac implantable electronic devices (CIEDs) has been shown to improve cardiovascular morbidity and mortality. To date, no studies have investigated disparities in utilization and delivery of RM. This study was performed to investigate if racial and socioeconomic disparities are present in CIED RM. Methods: Retrospective observational cohort study at a single tertiary care center in the United States. Patients who received a newly implanted CIED or device upgrade between 01/17 to 12/20 were included. Patients were classified as RM positive when they underwent at least ≥2 remote interrogations per year during follow-up. Results: Of all eligible patients, 2520 patients were included and 35% were female. The mean follow-up was 25 months. Mean age was 71±14 years. Pacemakers constituted 66% of implanted devices, while 26% were ICDs, and 8% CRT-Ds. Most patients (83%) were White non-Hispanic (WNH) patients. During follow-up, 66% of patients were classified as RM positive. In an adjusted regression model, these patients were younger (p=0.025) and more frequently had an ICD (HR=1.68; p<0.001). RM positive patients more frequently had an advanced college degree (HR=1.27; p=0.026) and used the online patient portal (HR=2.22; p<0.001). They lived in counties with higher median income (p=0.040) and were more frequently WNH patients (p=0.002). In a Cox regression analysis, RM status was associated with death (HR=0.487, p<0.001, Figure). During the years of the COVID-19 pandemic, RM positive patients increased and influence of income and race decreased. Conclusions: Despite being offered to all patients at implantation, significant disparities were present in CIED RM in this cohort. Disparities were partly reversed during COVID-19. Further studies are needed to examine health center and patient specific factors to overcome these barriers, and to facilitate equal opportunities to participate in RM.

Optimum systems integration architecture for monitoring to manage an electricity utility

Background: A Supervisory Control and Data Acquisition (SCADA) system is critical for remote monitoring and control of devices in various industries such as power utilities, oil and gas refineries, and manufacturing. Previous generations of SCADA systems have numerous limitations in today’s business environment. The latest technological advancements have brought forth new SCADA architecture variants that can be configured to ensure optimised operations. There is a need to assess the latest SCADA architectures that are posed to replace previous generations.Objectives: This research study aims to review various SCADA architectures and proposes an optimum SCADA system architecture for power utility. The proposed architecture is compared with the existing power utility SCADA system to highlight the impact and benefits of the proposed architecture.Methods: The research uses a qualitative approach and a comparative case study method to compare 10 SCADA architectures against a literature review-based criterion. A Multi-Criteria Decision-Making (MCDM) matrix is used to evaluate SCADA architectures and proposes an optimum Internet-of-Things (IoT)-SCADA system architecture for the power utility case study.Results: The research proposed an IoT-SCADA system architecture for optimum system functioning and compared the proposed architecture with the existing utility SCADA architecture. Moreover, the impact and benefits of the proposed architecture to the utility company are presented.Conclusion: The proposed IoT-SCADA system architecture has the potential to resolve many of the challenges encountered with previous generations of SCADA system architectures.

Abstract 11264: The Impact of the COVID-19 Pandemic on Cardiac Implantable Device Monitoring in Urban and Suburban Settings

Introduction: Remote and in-person monitoring of cardiac implantable devices is an important component to the overall healthcare of cardiology patients. The impact of the pandemic on follow-up and missed appointment rates at cardiology device clinics after the onset of shutdowns is unclear. We analyzed the impact of the COVID-19 pandemic on in-person and remote appointment no-show rates across a large network of cardiology device clinics, including three urban hub clinics and thirteen suburban and rural spoke sites. Methods: 60,750 encounters at Emory Healthcare’s Cardiology Device Clinic between 1/1/2019-12/31/2019 (“pre-COVID”) and 8/1/2020-7/31/2021 (“post-COVID”) were included. Visits were stratified by “in-person” and “remote” appointment types and analyzed according to no-show or completed status. Subgroup analysis was performed of geographic location (urban vs suburban) clinic sites. A chi-square test was used to analyze statistical significance (p < 0.05). Results: The cohort consisted of 26,356 “pre-COVID” and 34,394 “post-COVID” scheduled device visits. The overall no-show rates were higher in the post-COVID time frame (16.5% in 2019 and 19% in 2021-2021, p < 0.001). The higher no-show rates were similar for in-person and remote visits (19.5% vs 21.3% p <0.01 in-person; 15.6% vs 18.5% p <0.001 remote). There were differences in types of no-show rates among urban versus sub-urban clinic sites. For urban sites, there was no impact on in-person no-show rates (20.8% vs 20.3%, p = 0.64) for in-person appointments, but a significant difference (13.6% vs 17.3%, p < 0.01) for remote appointments. In contrast, for suburban sites, pre and post-COVID missed appointment rates showed effects on in-person visits (15.7% vs 23.4%, p < 0.01), but not for remote appointments (48.2% vs 46.9%, p = .56). Conclusions: The COVID-19 pandemic impacted long-term in-person and remote device monitoring rates significantly. The effects were different depending on urban versus suburban site location. This analysis of over 60,000 device monitoring visits revealed a substantial impact on no-show rates for all visits and site-specific trends for the type of no-show affected, which may help in the design of higher-adherence follow up schedules in the post-COVID era.

Clinical utility of remote monitoring for patients with cardiac implantable electrical devices.

Remote monitoring of cardiac implantable electronic devices (CIEDs) offers practical and clinical benefits juxtaposed against burdens associated with high transmission volume. We identified patients receiving de novo pacemakers (PPMs) and implantable cardiac defibrillators (ICDs) at a single academic medical center (January 2016-December 2019) with at least 1year of follow-up device care. We collected patient- and device-specific data at time of implant and assessed all remote and in-person interrogation reports for clinically actionable findings based on pre-specified criteria. Among 963 patients (mean age of 71 (± 14) years, 37% female), 655 (68%) underwent PPM, and 308 (32%) underwent ICD implant. Median follow-up was 874 (627-1221) days, during which time patients underwent a mean of 13 (10-16) total interrogations; remote interrogations comprised 53% of all device evaluations; and of these, 96% were scheduled transmissions. Overall, 22% of all CIED interrogations yielded significant findings with a slightly higher rate in the PPM than in the ICD group (23% vs. 20%, p < 0.01). Only 8% of remote interrogations produced clinically meaningful results, compared with 38% of in-person ones. In adjusted models, routine, remote transmissions were least likely to be useful for both PPM and ICD patients (p < 0.001), whereas time from initial device implant was inversely associated with probability of obtaining a useful interrogation (p < 0.001). Routine remote interrogations constitute the majority of device evaluations performed, but uncommonly identify clinically actionable findings.

Continuous remote monitoring in post-bariatric surgery patients: development of an early warning protocol.

Continuous monitoring of vital parameters after bariatric surgery can detect postoperative bleeding or anastomotic leakage. This report describes the development of a continuous remote early warning score (CREWS). This is an EWS-based notification protocol for deterioration detection in bariatric patients. Catharina Hospital, the Netherlands. Several CREWS protocols were developed by combining thresholds indicative of tachycardia and tachypnea using literature insights and expert sessions. These protocols were tested retrospectively using continuously measured vital signs in a cohort of 185 patients who underwent primary bariatric surgery. A wearable remote monitoring device (Healthdot, Philips) was used in hospital and at home up to 14 days after surgery. The outcomes included were demographics, use of beta-blockers, and complications necessitating reintervention. Thresholds of 110 beats per minute (bpm) and 20 breaths per minute (rpm) for heart rate andrespiration rate, respectively, detected postoperative bleeding and anastomotic leakage with 75% (3/4 patients) sensitivity. The protocol was silent (no alarms/day) in 69.5% of patients and produced more than 1 alarm/day in 1.6% of patients. The average postoperative heart rate was unaffected by the use of beta-blockers. A description of the steps in the development of an EWS protocol in bariatric patients based on continuous vital sign monitoring is useful. The most sensitive and silent protocol measured heart rate and respiratory rate with thresholds of 110 bpm and 20 rpm and appeared to be feasible for clinical use. There seemed to be no clinically relevant impact of beta-blockers. This CREWS protocol could be a starting point for future studies.

Prognostic significance of remotely monitored nocturnal heart rate in heart failure patients with reduced ejection fraction

Elevated resting heart rate is a risk factor for cardiovascular events. The purpose of this study was to investigate the clinical significance of nocturnal heart rate (nHR) and 24-hour mean heart rate (24h-HR) obtained by continuous remote monitoring (RM) of implantable devices. We analyzed daily-sampled trends of nHR, 24h-HR, and physical activity in patients on β-blocker therapy for chronic heart failure and with implantable cardioverter-defibrillators or cardiac resynchronization therapy defibrillators (CRT-Ds). Patients were grouped by average nHR and 24h-HR quartile during follow-up to estimate the respective incidence of nonarrhythmic death and device-treated ventricular tachycardia/fibrillation (VT/VF). The study cohort included 1330 patients (median age 69 years [interquartile range 61-77 years]; 41% [n = 550] with CRT-D; median follow-up 25 months [interquartile range 13-42 months]). Compared with patients in the lowest nHR quartile (≤57 beats/min) group, patients in the highest quartile group (>65 beats/min) had an increased risk of nonarrhythmic death (adjusted hazard ratio [AHR] 2.25; 95% confidence interval [CI] 1.13-4.50; P= .021) and VT/VF (AHR 1.98; 95% CI 1.40-2.79; P < .001) and were characterized by the lowest level of physical activity (P≤.0004 vs every other nHR quartiles). The highest 24h-HR quartile group (>75 beats/min) showed an increased risk of VT/VF (AHR 2.13; 95% CI 1.52-2.99; P< .001) and a weaker though significant association with nonarrhythmic mortality (AHR 1.80; 95% CI 1.00-3.22; P= .05) as compared with the lowest 24h-HR quartile group (≤65 beats/min). In remotely monitored patients with implantable cardioverter-defibrillator/CRT-D on β-blocker therapy for heart failure, elevated heart rates (nHR >65 beats/min and 24h-HR >75 beats/min) were associated with increased mortality and VT/VF risk. nHR showed a stronger association than 24h-HR with worst prognosis and lowest physical activity.

IoT-Based CO2 Monitoring and Forecasting System to Prevent Transmission of COVID-19

This paper presents the development of low-cost CO2 remote monitoring devices based on NDIR sensors, and the design of a Nonlinear Autoregressive Neural Network (NAR) that forecasts the indoor CO2 concentration in the short and medium term to avoid risks of SARS-CoV-2 contagion due to the accumulation of poor quality air previously breathed by other people. Different configurations of the NAR were analyzed, varying the number of layers, the number of neurons per layer and the number of input delays. The best network configurations predicted changes in CO2 concentration in an academic office up to a four-hour horizon with an RMS error around 30 ppm.

Wearable devices for remote monitoring of hospitalized patients with COVID-19 in Vietnam

Patients with severe COVID-19 disease require monitoring with pulse oximetry as a minimal requirement. In many low- and middle- income countries, this has been challenging due to lack of staff and equipment. Wearable pulse oximeters potentially offer an attractive means to address this need, due to their low cost, battery operability and capacity for remote monitoring. Between July and October 2021, Ho Chi Minh City experienced its first major wave of SARS-CoV-2 infection, leading to an unprecedented demand for monitoring in hospitalized patients. We assess the feasibility of a continuous remote monitoring system for patients with COVID-19 under these circumstances as we implemented 2 different systems using wearable pulse oximeter devices in a stepwise manner across 4 departments.

Cardiac device remote monitoring in 2022: Are digital and remote monitoring synonymous with ease and improvement?

Cardiac device remote monitoring in 2022: Are digital and remote monitoring synonymous with ease and improvement?

Clinical phenotyping of implantable cardioverter defibrillator patients to identify the association of remote device monitoring on survival benefit: a cluster analysis

Abstract Background/Introduction Over the past decade, various studies have demonstrated an association between remote device monitoring (RM) in implantable cardioverter-defibrillator (ICD) patients and all-cause mortality. However, it is unclear which clinical phenotypes account for this survival benefit. Purpose We aimed to identify clinical phenotypes within an ICD population on RM and conventional follow-up (NRM), and evaluate differences in long-term survival per clinical phenotype. Methods This is a single-center, retrospective, observational study of de novo ICD implantations (single- and dual chamber, biventricular and subcutaneous) between 2010 and 2021 in patients ≥18 years old. Data was extracted from electronic health records. Unsupervised two-step cluster analysis (TSC) was performed to identify distinct clinical phenotypes in the RM and NRM cohorts. Multinomial logistic regression analysis was performed to identify cluster characteristics, differences among the different clusters were analysed by Chi-squared test. Variable importance was evaluated for each of the 18 included clinical variables. Survival analysis was performed using the Kaplan-Meier method, log-rank was used to compare survival between groups. Results A total of 1872 ICD patients were analysed using TCS, comprising 1265 RM and 607 NRM patients, respectively. TCS for a mean follow-up duration of 5.4±3.1 years partitioned the RM and NRM groups into six clusters (Table 1). Clusters RM1 (n=444) and NRM3 (n=220) represented a predominantly primary prevention and DCM phenotype. Clusters RM2 (n=300) and NRM2 (n=173) indicated young and secondary prevention phenotypes. Clusters RM3 (n=220) and NRM1 (n=214) comprised an older, male and ischaemic cardiomyopathy (ICM) phenotype. In survival analysis 5-year cumulative incidence of mortality for the subsequent clusters was 7.4%, 4.5%, 11.6%, 41.4%, 29.8%, and 25.1%, respectively (log rank p-value &amp;lt;0.001, Figure 1). Thus, younger and DCM phenotypes (often with genetic aetiologies) experienced superior survival and high rates on RM, whereas older, male and ICM phenotypes with low rates on RM were associated with worse survival. In addition, between-group cluster-comparison of patients on RM only demonstrated important differences in long-term survival, where RM3 (comprising older, male with predominantly ICM) had poorer survival compared to young patients with a high prevalence of DCM, primary arrhythmia syndrome and low proportions of comorbidities (i.e. atrial arrhythmias, cerebral vascular accidents, diabetes mellitus). Conclusion We identify novel clinical phenotypes (clusters) of ICD patients on RM and having conventional follow-up with large differences in long-term survival. Future studies should further explore if these differences reflect benefit from RM, or the underlying natural history of each phenotype. These results may accordingly guide monitoring strategies tailored to specific ICD patient profiles. Funding Acknowledgement Type of funding sources: Public hospital(s). Main funding source(s): Public funding

Artificial intelligence-guided, single-lead EKG may be a game-changer for symptom-to-balloon time reduction in ST-elevated myocardial infarction

Abstract Background Over decades, efforts to shave off life-saving minutes from ST-Elevated Myocardial Infarction (STEMI) care centred on reducing door-to-needle and door-to-balloon times. We firmly believe that symptom-to-balloon time should prove a better focus to this end. Challenges come with this goal as it heavily relies on a patient's perception and initiative to seek care, which we deem intelligent and wearable Artificial Intelligence (AI)-driven Single Lead EKG technologies as an attractive solution in modern-day cardiology. Purpose To provide an accurate, accessible, and cost-effective AI-driven Single Lead STEMI detection algorithm that can be embedded into wearable devices and employed in a self-administered fashion. Methods Database: EKG records from Mexico, Colombia, Argentina, and Brazil from April 2014 to December 2019. Dataset: A total of 11,567 12-lead EKG records of 10[s] length with a sampling frequency of 500 Hz, including the following balanced classes: angiographically confirmed and unconfirmed STEMI, branch blocks, non-specific ST-T abnormalities, normal and abnormal (200+ CPT codes, excluding those mentioned above). Cardiologists manually checked the label of each record to ensure precision. Pre-processing: We discard the first and last 250 samples as they may contain a standardisation pulse. The study applied a digital low pass filter of order 5 with a frequency cut-off of 35 Hz. The mean was subtracted from each Lead. Classification: The determined classes were “STEMI” (Including STEMI in different locations of the myocardium – anterior, inferior, and lateral); and “Not-STEMI” (Combination of randomly sample, branch blocks, non-specific ST-T changes, and abnormal records – 25% of each). Training and Testing: A 1-D Convolutional Neural Network was trained and tested with a dataset proportion of 90/10, respectively. A different model was trained and tested for each Lead, using the central 4,500 samples of the records. The last dense layer outputs a probability for each report of being STEMI or Not-STEMI. Lead V2 showed the best overall results. The model was further tested through the same methodology using the best Lead with a subset of the previous data, excluding the unconfirmed STEMI EKG records (Total 7,230 12-lead EKG records for Confirmed Only STEMI dataset). Performance metrics were reported for each experiment and compared. Results Combined STEMI data: Accuracy: 91.2%; Sensitivity: 89.6%; Specificity: 92.9%. Confirmed STEMI Only dataset: Accuracy: 92.4%; Sensitivity: 93.4%; Specificity: 91.4% (Figure 1). Conclusion By assiduously improving the quality of the model's input, we continue to assess our algorithm's performance and reliability for future clinical validation as a potential remote monitoring and early STEMI detection device. Funding Acknowledgement Type of funding sources: None.

UTILIZATION OF CONTINUOUS REMOTE MONITORING FOR A HIGH-RISK PATIENT REQUIRING INTERVAL TUMOR DEBULKING PALLIATIVE THERAPY

UTILIZATION OF CONTINUOUS REMOTE MONITORING FOR A HIGH-RISK PATIENT REQUIRING INTERVAL TUMOR DEBULKING PALLIATIVE THERAPY

Respinos: A Portable Device for Remote Vital Signs Monitoring of COVID-19 Patients.

The rapidly increasing number of COVID-19 patients has posed a massive burden on many healthcare systems worldwide. Moreover, the limited availability of diagnostic and treatment equipment makes it difficult to treat patients in the hospital. To reduce the burden and maintain the quality of care, asymptomatic patients or patients with mild symptoms are advised to self-isolate at home. However, self-isolated patients need to be continuously monitored as their health can turn into critical condition within a short time. Therefore, a portable device that can remotely monitor the condition and progression of the health of these patients is urgently needed. Here we present a portable device, called Respinos, that can monitor multiparameter vital signs including respiratory rate, heart rate, body temperature, and SpO2. It can also operate as a spirometer that measures forced vital capacity (FVC), forced expiratory volume (FEV), FEV in the first second (FEV1), and peak expiratory flow Rate (PEFR) parameters which are useful for detecting pulmonary diseases. The spirometer is designed in the form of a tube that can be ergonomically inflated by the patient, and is equipped with an accurate and disposable turbine based air flow sensor to evaluate the patient's respiratory condition. Respinos uses rechargeable batteries and wirelessly connects to a mobile application whereby the patient's condition can be monitored in real-time and consulted with doctors via chat. Extensive comparison against medical-grade reference devices showed good performance of Respinos. Overall results demonstrate the potential of Respinos for remote patient monitoring during and post pandemic.

Wireless Communication System and Its Application in Big Data Remote Monitoring and Decision-Making

The development of computer technology has promoted the widespread application of unmanned technology. Remote monitoring of wireless devices is an application of unmanned technology. To improve the remote monitoring of wireless devices, this study establishes a remote monitoring and decision-making framework based on wireless communication systems. With the wireless communication system, signals that characterize the operating status of devices can be obtained in real-time. Based on the collected signals, the remote monitoring system can identify the current health status of wireless devices, thereby providing auxiliary decision-making for device operation. In the case study, the main engine of an unmanned surface vehicle is used as the study object. The results show that most of the relative errors corresponding to the state identification results of the established remote monitoring framework are within 5%. Moreover, the results present that the linear correlation coefficients between the predicted and real results are greater than 0.95. Therefore, the established remote monitoring framework based on the wireless communication system has good reliability in the state identification of wireless devices.

Detecting motor symptom fluctuations in Parkinson’s disease with generative adversarial networks

Parkinson’s disease is a neurodegenerative disorder characterized by several motor symptoms that develop gradually: tremor, bradykinesia, limb rigidity, and gait and balance problems. While there is no cure, levodopa therapy has been shown to mitigate symptoms. A patient on levodopa experiences cycles in the severity of their symptoms, characterized by an ON state—when the drug is active—and an OFF state—when symptoms worsen as the drug wears off. The longitudinal progression of the disease is monitored using episodic assessments performed by trained physicians in the clinic, such as the Unified Parkinson’s Disease Rating Scale (UPDRS). Lately, there has been an effort in the field to develop continuous, objective measures of motor symptoms based on wearable sensors and other remote monitoring devices. In this work, we present an effort towards such a solution that uses a single wearable inertial sensor to automatically assess the postural instability and gait disorder (PIGD) of a Parkinson’s disease patient. Sensor data was collected from two independent studies of subjects performing the UPDRS test and then used to train and validate a convolutional neural network model. Given the typical limited size of such studies we also employed the use of generative adversarial networks to improve the performance of deep-learning models that usually require larger amounts of data for training. We show that for a 2-min walk test, our method’s predicted PIGD scores can be used to identify a patient’s ON/OFF states better than a physician evaluated on the same criteria. This result paves the way for more reliable, continuous tracking of Parkinson’s disease symptoms.

EP News: Quality Improvement and Outcomes: Remote monitoring of pediatric cardiac implantable electronic devices

Remote monitoring of pediatric cardiac implantable electronic devices (CIEDs) has become an important tool in outpatient management. Recent guidelines (Shah et al, JACC Clin Electrophysiol 2021;7:1437, PMID 34794667) from the Pediatric and Congenital Electrophysiology Society (PACES) have recommended remote monitoring of CIEDs every 3–12 months for pacemakers and every 3–6 months for implantable cardioverter-defibrillators, with an increase in frequency when the device is approaching elective replacement indicators.

Abstract P302: Efficacy Of A Digital Hypertension Self-management And Lifestyle Coaching Program In Reducing Blood Pressure Across Sex, Language And Racial Groups

Introduction: Hypertension affects nearly half of adults in the U.S. and is more prevalent in non-Hispanic black and Hispanic people than other groups. HEDIS now includes blood pressure readings from remote monitoring devices that are digitally stored and transmitted to the provider. Digital interventions used to support self-management of hypertension are effective in reducing blood pressure. Healthcare policies that support adoption of innovative digital interventions can positively impact groups with greater health disparities. Hypothesis: We tested the hypothesis that systolic blood pressure (SBP) change observed after 6 months in a smartphone-based hypertension self-management program would demonstrate similar outcomes across subgroups and not be impacted by known health inequities based on sex, language or race. Methods: A cohort study of individuals who enrolled in a digital-only hypertension self-management program without live coaching was used to evaluate SBP change at 6 months and its association with age, sex, language (English/Spanish), and race. Because race information was only available in a subset of the cohort who shared electronic medical records, the largest sub-groups (432 white and 127 non-Hispanic black adults) were compared. Results: 15,361 (age 57 ± .8y, 37% female) people uploaded SBP data at both time points. A reduction in SBP was observed (t = 15.1, p&lt;.01), and the majority (62.3%) reduced SBP. 83.6% (4139 of 4947) of those with stage 2 hypertension (SBP ≥ 140 mm Hg) at program start reduced SBP, with an average reduction of 16.6 ± 0.18 mm Hg. No difference in SBP change was found based on age, race, or preferred language (all ps &gt; .05). Females had a greater reduction in SBP than males (1.5 ± 0.19 mm Hg, p&lt;.01). Conclusions: This study supports the use of a smartphone-based hypertension program incorporating AI driven, digital-only self-management and lifestyle coaching to reduce SBP similarly across age, race, and preferred language groups. Results indicate that digital self-management tools have the potential to bridge health inequity gaps. Future studies could assess whether digital behavioral change coaching leads to reduced SBP across disadvantaged groups based on Area of Deprivation Index (ADI).

Optimized Signal Quality Assessment for Photoplethysmogram Signals Using Feature Selection.

With the increasing use of wearable healthcare devices for remote patient monitoring, reliable signal quality assessment (SQA) is required to ensure the high accuracy of interpretation and diagnosis on the recorded data from patients. Photoplethysmographic (PPG) signals non-invasively measured by wearable devices are extensively used to provide information about the cardiovascular system and its associated diseases. In this study, we propose an approach to optimize the quality assessment of the PPG signals. We used an ensemble-based feature selection scheme to enhance the prediction performance of the classification model to assess the quality of the PPG signals. Our approach for feature and subset size selection yielded the best-suited feature subset, which was optimized to differentiate between the clean and artifact corrupted PPG segments. A high discriminatory power was achieved between two classes on the test data by the proposed feature selection approach, which led to strong performance on all dependent and independent test datasets. We achieved accuracy, sensitivity, and specificity rates of higher than 0.93, 0.89, and 0.97, respectively, for dependent test datasets, independent of heartbeat type, i.e., atrial fibrillation (AF) or non-AF data including normal sinus rhythm (NSR), premature atrial contraction (PAC), and premature ventricular contraction (PVC). For independent test datasets, accuracy, sensitivity, and specificity rates were greater than 0.93, 0.89, and 0.97, respectively, on PPG data recorded from AF and non-AF subjects. These results were found to be more accurate than those of all of the contemporary methods cited in this work. As the results illustrate, the advantage of our proposed scheme is its robustness against dynamic variations in the PPG signal during long-term 14-day recordings accompanied with different types of physical activities and a diverse range of fluctuations and waveforms caused by different individual hemodynamic characteristics, and various types of recording devices. This robustness instills confidence in the application of the algorithm to various kinds of wearable devices as a reliable PPG signal quality assessment approach.

313.5: Meeting Packed Red Blood Cell Requirement for the Transmedics Organ Care System (OCS) Liver Transplantation Trial (PROTECT Trial) At Henry Ford Hospital

Introduction: In January 2016, HFHS Transplant Institute began the PROSPECT trial, an interventional trial studying normothermic preservation of the donated liver before transplant using the Liver Organ Care System (OCSTM). The Henry Ford Hospital Transfusion Service (TS) provided the required packed red blood cells (PRBC). Background: The TransMedics (OCSTM) liver trial was an international, randomized trial studying the intervention of a portable liver system to preserve donor livers for transplant. The system utilizes an oxygenated blood-based perfusate mixed with nutrient solution in normothermic perfusion for organ preservation. The TS provided PRBC needed for the study, final PRBC used dictated by the condition and size of the retrieved liver. TS study goal was to secure proper transfusion needs for liver perfusion while also adhering to blood inventory management. Methods: Organ procurement candidates for the PROTECT trial were selected based on pre-determined conditions and consented according to that protocol. Once the patient was consented and the liver transplantation was scheduled, the PROTECT trial team notified the TS,and 5 units of group O Positive, leukoreduced PRBC less than 10 days old were selected, and placed inside a temperature-controlled packaging container along with a remote temperature monitoring device. The container was then transported with the procurement team to the harvesting location. Once the insulated container was returned to the TS, the temperature-monitoring device information was uploaded to verify that the returned PRBC have been stored within their proper temperature limits. Results: The TS support for the PROTECT trial at HFHS occurred from January 2016 until September 2021. The table below shows the activity related to this trial. - Issued Utilized Returned Wasted PRBC units 240 132 104 4 In summary, 29/48 study events used 4 units; 3/48 study events used 5 units; on 15/48 events the organ harvesting was cancelled; all units returned had an acceptable temperature range and returned to the available inventory. For one study event, 4 PRBC were spiked in preparation for use in the perfusate prior to the event being cancelled. These PRBC were discarded and deemed wasted. Conclusions: During the PROTECT trial no PRBCs were wasted due to inappropriate storage conditions. The joint use of a temperature-controlled container and remote temperature monitoring device allowed for the appropriate return of unused PRBCs to the TS blood inventory, thus avoiding any wastage. Only 3/48 events used 5 units, the\us issuing 4 PRBC to meet the OCS™ support may suffice, further decreasing the risk for wastage of any PRBC units. After receiving FDA in September 2021, alternatives to maintain the required PRBC temperature should be addressed to minimize wastage at this time of limited blood availability for the support of the TransMedics Organ Care System (OCSTM) for liver organ perfusion.