Quantifying lumbar mobility using a single tri-axial accelerometer.

Background: Lumbar mobility is regarded as important for assessing and managing low back pain (LBP). Inertial Measurement Units (IMUs) are currently the most feasible technology for quantifying lumbar mobility in clinical and research settings. However, their gyroscopes are susceptible to drift errors, limiting their use for long-term remote monitoring. Research question: Can a single tri-axial accelerometer provide an accurate and feasible alternative to a multi-sensor IMU for quantifying lumbar flexion mobility and velocity? Methods: In this cross-sectional study, 18 healthy adults performed nine repetitions of full spinal flexion movements. Lumbar flexion mobility and velocity were quantified using a multi-sensor IMU and just the tri-axial accelerometer within the IMU. Correlations between the two methods were assessed for each percentile of the lumbar flexion movement cycle, and differences in measurements were modelled using a Generalised Additive Model (GAM). Results: Very high correlations (r > 0.90) in flexion angles and velocities were found between the two methods for most of the movement cycle. However, the accelerometer overestimated lumbar flexion angle at the start (-4.7° [95 % CI -7.6° to -1.8°]) and end (-4.8° [95 % CI -7.7° to -1.9°]) of movement cycles, but underestimated angles (maximal difference of 4.3° [95 % CI 1.4° to 7.2°]) between 7 % and 92 % of the movement cycle. For flexion velocity, the accelerometer underestimated at the start (16.6°/s [95%CI 16.0 to 17.2°/s]) and overestimated (-12.3°/s [95%CI -12.9 to -11.7°/s]) at the end of the movement, compared to the IMU. Significance: Despite the observed differences, the study suggests that a single tri-axial accelerometer could be a feasible tool for continuous remote monitoring of lumbar mobility and velocity. This finding has potential implications for the management of LBP, enabling more accessible and cost-effective monitoring of lumbar mobility in both clinical and research settings.

Temperature and ST-segment morphology remote monitoring: new perspectives for implantable cardiac monitors in Brugada syndrome.

INTRODUCTION: Patients with Brugada syndrome (BrS) face an increased risk of ventricular arrhythmias and sudden cardiac death. Implantable cardiac monitors (ICMs) have emerged as effective tools for detecting arrhythmias in BrS. Technological advancements, including temperature sensors and improved subcutaneous electrocardiogram (subECG) signal quality, hold promise for further enhancing their utility in this population. METHODS AND RESULTS: We present a case of a 40-year-old man exhibiting a BrS type 2 pattern on 12-lead ECG, who underwent ICM insertion (BIOMONITOR IIIm, BIOTRONIK) due to drug-induced BrS type 1 pattern and a history of syncope, with a negative response to programmed ventricular stimulation. The device contains an integrated temperature sensor and can transmit daily vital data, such as mean heart rate and physical activity. Several months later, remote alerts indicated a temperature increase, along with transmitted subECGs suggesting a fever-induced BrS type 1 pattern. The patient was promptly advised to commence antipyretic therapy. Over the following days, remotely monitored parameters showed decreases in mean temperature, physical activity, and mean heart rate, without further recurrence of abnormal subECGs. CONCLUSION: ICMs offer valuable insights beyond arrhythmia detection in BrS. Early detection of fever using embedded temperature sensors may improve patient management, while continuous subECG morphological analysis has the potential to enhance risk stratification in BrS patients.

Monitoring landfill volatile organic compounds emissions by an uncrewed aerial vehicle platform with infrared and visible-light cameras, remote monitoring, and sampling systems.

An uncrewed aerial vehicle (UAV) platform equipped with dual imaging cameras, a gas sampling system, and a remote synchronous monitoring system was developed to sample and analyze volatile organic compounds (VOCs) emitted from landfills. The remote synchronous monitoring system provided real-time video to administrators with specific permissions to assist in identifying sampling sites within extensive landfill areas. The sampling system included four kits capable of collecting samples from different locations during a single flight mission. Each kit comprised a 1 L Tedlar bag for measuring landfill VOC concentrations according to the TO-15 method prescribed by the US Environmental Protection Agency. The air sample was introduced into a Tedlar bag via pumping. A known volume of the sample was subsequently concentrated using a solid multisorbent concentrator. Following this, the sample underwent cold trap concentration and thermal desorption. The concentrated sample was then transferred to a chromatography-mass spectrometry system for separation and analysis. Since the anaerobic catabolism of organic waste is exothermic and emits VOCs, this study employed UAV thermal imaging to locate principal emission sources for sampling. Visible-light imaging helped identify newer or older landfill sections, aiding in the selection of appropriate sampling sites, particularly when surfaces were thermally disturbed by solar radiation. Field measurements were conducted under three meteorological conditions: sunny morning, cirrus morning, and thin cloud evening (2 h after sunset), identifying 119, 122, and 111 chemical species respectively. The sequence of total VOC concentrations measured correlated with the meteorological conditions as follows: cirrus morning > thin cloud evening > sunny morning. The results indicated that ambient temperature and global solar radiation significantly influenced daytime gas emissions from landfills. Evening thermal images, unaffected by solar heating, facilitated more accurate identification of major VOC emission points, resulting in higher VOC concentrations compared to those recorded in the sunny morning. VOCs from the landfill were categorized into nine organic groups: alkanes, alkenes, carbonyls, aromatics, alcohols, esters, ethers, organic oxides, and others. The classification was based on carbon-containing compounds (Cn, where the compound contains n carbon atoms). Alkanes were predominant in terms of Cn presence, followed by alcohols and carbonyls. Among the organic groups, organic oxides, particularly 2-heptyl-1,3-dioxolane, exhibited the highest concentrations, succeeded by alkenes. Sampling under cloudy conditions or in the evening is recommended to minimize the effects of surface temperature anomalies caused by solar radiation, which vary due to differences in land composition.

Too much ado about data: Continuous remote monitoring of water temperatures, circulation and throughput can assist in the reduction of hospital-associated waterborne infections.

BACKGROUND: National and international guidance provides advice on maintenance and management of water systems in healthcare buildings, however, healthcare-associated waterborne infections (HAWI) are increasing. This narrative review identifies parameters critical to water quality in healthcare buildings and assesses if remote sensor monitoring can deliver safe water systems thus reducing HAWI. METHOD: A narrative review was performed using the following search terms 1) consistent water temperature AND waterborne pathogen control OR nosocomial infection 2) water throughput AND waterborne pathogen control OR nosocomial infection 3) remote monitoring of in-premise water systems AND continuous surveillance for temperature OR throughput OR flow OR use. Databases employed were PubMed, CDSR (Clinical Study Data Request) and DARE (Database of Abstracts of Reviews of Effects) from Jan 2013 - Mar 2024. FINDINGS: Single ensuite-patient rooms, expansion of wash-hand basins, widespread glove use, alcohol gel and wipes have increased water system stagnancy resulting in amplification of waterborne pathogens and transmission risk of Legionella, Pseudomonas and Non-Tuberculous Mycobacteria. Manual monitoring does not represent temperatures across large complex water systems. This review deems that multiple point continuous remote sensor monitoring is effective at identifying redundant and low use outlets, hydraulic imbalance and inconsistent temperature delivery across in-premise water systems. CONCLUSION: As remote monitoring becomes more common there will be greater recognition of failures in temperature control, hydraulics and balancing in water systems and there remains much to learn as we adopt this developing technology within our hospitals.

Prediction of heart failure events based on physiologic sensor data in HINODE defibrillator patients.

AIMS: Hospitalizations are common in patients with heart failure and are associated with high mortality, readmission and economic burden. Detecting early signs of worsening heart failure may enable earlier intervention and reduce hospitalizations. The HeartLogic algorithm is designed to predict worsening heart failure using diagnostic data from multiple device sensors. The main objective of this analysis was to evaluate the sensitivity of the HeartLogic alert calculation in predicting worsening heart failure events (HFEs). We also evaluated the false positive alert rate (FPR) and compared the incidence of HFEs occurring in a HeartLogic alert state to those occurring out of an alert state. METHODS: The HINODE study enrolled 144 patients (81 ICD and 63 CRT-D) with device sensor data transmitted via a remote monitoring system. HeartLogic alerts were then retrospectively simulated using relevant sensor data. Clinicians and patients were blinded to calculated alerts. Reported adverse events with HF symptoms were adjudicated and classified by an independent HFE committee. Sensitivity was defined as the ratio of the number of detected usable HFEs (true positives) to the total number of usable HFEs. A false positive alert was defined as an alert with no usable HFE between the alert onset date and the alert recovery date plus 30 days. The patient follow-up period was categorized as in alert state or out of alert state. The event rate ratio was the HFE rate calculated in alert to out of alert. RESULTS: The patient cohort was 79% male and had an average age of 68 ± 12 years. This analysis yielded 244 years of follow-up data with 73 HFEs from 37 patients. A total of 311 HeartLogic alerts at the nominal threshold (16) occurred across 106 patients providing an alert rate of 1.27 alerts per patient-year. The HFE rate was 8.4 times greater while in alert compared with out of alert (1.09 vs. 0.13 events per patient-year; P < 0.001). At the nominal alert threshold, 80.8% of HFEs were detected by a HeartLogic alert [95% confidence interval (CI): 69.9%-89.1%]. The median time from first true positive alert to an adjudicated clinical HFE was 53 days. The FPR was 1.16 (95% CI: 0.98-1.38) alerts per patient-year. CONCLUSIONS: Results suggest that signs of worsening HF can be detected successfully with remote patient follow-up. The use of HeartLogic may predict periods of increased risk for HF or clinically significant events, allowing for early intervention and reduction of hospitalization in a vulnerable patient population.

Artificial intelligence-enhanced patient evaluation: bridging art and science.

The advent of digital health and artificial intelligence (AI) has promised to revolutionize clinical care, but real-world patient evaluation has yet to witness transformative changes. As history taking and physical examination continue to rely on long-established practices, a growing pipeline of AI-enhanced digital tools may soon augment the traditional clinical encounter into a data-driven process. This article presents an evidence-backed vision of how promising AI applications may enhance traditional practices, streamlining tedious tasks while elevating diverse data sources, including AI-enabled stethoscopes, cameras, and wearable sensors, to platforms for personalized medicine and efficient care delivery. Through the lens of traditional patient evaluation, we illustrate how digital technologies may soon be interwoven into routine clinical workflows, introducing a novel paradigm of longitudinal monitoring. Finally, we provide a skeptic's view on the practical, ethical, and regulatory challenges that limit the uptake of such technologies.

A biobehavioral observational study to understand the multilevel determinants of cardiovascular health in Black women: the BLOOM Study protocol.

BACKGROUND: The racial/ethnic and gender disparities in cardiovascular disease (CVD) morbidity and mortality in the United States are evident. Across nearly every metric, non-Hispanic Black women have poorer overall cardiovascular health. Emerging evidence shows a disproportionately high burden of increased CVD risk factors in Black women of childbearing age, which has a far-reaching impact on both maternal and child outcomes, resulting in premature onset of CVD and further widens the racial disparities in CVD. There is growing recognition that the fundamental driver of persistent racial/ethnic disparities in CVD, as well as disparities in behavioral risk factors such as physical activity and sleep, is structural racism. Further, the lived personal experience of racial discrimination not only has a negative impact on health behaviors, but also links to various physiological pathways to CVD risks, such as internalized stress resulting in a pro-inflammatory state. Limited research, however, has examined the interaction between daily experience and health behaviors, which are influenced by upstream social determinants of health, and the downstream effect on biological/physiological indicators of cardiovascular health in non-pregnant Black women of childbearing age. METHODS/DESIGN: The BLOOM Study is an observational study that combines real-time ambulatory assessments over a 10-day monitoring period with in-depth cross-sectional lab-based physiological and biological assessments. We will use a wrist-worn actigraphy device to capture 24-h movement behaviors and electronic ecological momentary assessment to capture perceived discrimination, microaggression, and stress. Blood pressure will be captured continuously through a wristband. Saliva samples will be self-collected to assess cortisol level as a biomarker of psychological stress. Lab assessments include a fasting venous blood sample, and assessment of various indices of peripheral and cerebral vascular function/health. Participants' address or primary residence will be used to obtain neighborhood-level built environmental and social environmental characteristics. We plan to enroll 80 healthy Black women who are between 18 and 49 years old for this study. DISCUSSION: Results from this study will inform the development of multilevel (i.e., individual, interpersonal, and social-environmental levels) lifestyle interventions tailored to Black women based on their lived experiences with the goal of reducing CVD risk. GOV IDENTIFIER: NCT06150989.

Association between amount of biventricular pacing and heart failure status measured by a multisensor implantable defibrillator algorithm.

Background: Achieving a high biventricular pacing percentage (BiV%) is crucial for optimizing outcomes in cardiac resynchronization therapy (CRT). The HeartLogic index, a multiparametric heart failure (HF) risk score, incorporates implantable cardioverter-defibrillator (ICD)-measured variables and has demonstrated its predictive ability for impending HF decompensation. Objective: This study aimed to investigate the relationship between daily BiV% in CRT ICD patients and their HF status, assessed using the HeartLogic algorithm. Methods: The HeartLogic algorithm was activated in 306 patients across 26 centers, with a median follow-up of 26 months (25th-75th percentile: 15-37). Results: During the follow-up period, 619 HeartLogic alerts were recorded in 186 patients. Overall, daily values associated with the best clinical status (highest first heart sound, intrathoracic impedance, patient activity; lowest combined index, third heart sound, respiration rate, night heart rate) were associated with a BiV% exceeding 99%. We identified 455 instances of BiV% dropping below 98% after consistent pacing periods. Longer episodes of reduced BiV% (hazard ratio: 2.68; 95% CI: 1.02-9.72; P = .045) and lower BiV% (hazard ratio: 3.97; 95% CI: 1.74-9.06; P=.001) were linked to a higher risk of HeartLogic alerts. BiV% drops exceeding 7 days predicted alerts with 90% sensitivity (95% CI [74%-98%]) and 55% specificity (95% CI [51%-60%]), while BiV% ≤96% predicted alerts with 74% sensitivity (95% CI [55%-88%]) and 81% specificity (95% CI [77%-85%]). Conclusion: A clear correlation was observed between reduced daily BiV% and worsening clinical conditions, as indicated by the HeartLogic index. Importantly, even minor reductions in pacing percentage and duration were associated with an increased risk of HF alerts.

Determinants of global cardiac implantable electrical device remote monitoring utilization - Results from an international survey.

Background: Despite near-global availability of remote monitoring (RM) in patients with cardiac implantable electronic devices (CIED), there is a high geographical variability in the uptake and use of RM. The underlying reasons for this geographic disparity remain largely unknown. Objectives: To study the determinants of worldwide RM utilization and identify locoregional barriers of RM uptake. Methods: An international survey was administered to all CIED clinic personnel using the Heart Rhythm Society global network collecting demographic information, as well as information on the use of RM, the organization of the CIED clinic, and details on local reimbursement and clinic funding. The most complete response from each center was included in the current analysis. Stepwise forward multivariate linear regression was performed to identify determinants of the percentage of patients with a CIED on RM. Results: A total of 302 responses from 47 different countries were included, 61.3% by physicians and 62.3% from hospital-based CIED clinics. The median percentage of CIED patients on RM was 80% (interquartile range, 40-90). Predictors of RM use were gross national income per capita (0.76% per US$1000, 95% CI 0.72-1.00, P < .001), office-based clinics (7.48%, 95% CI 1.53-13.44, P = .014), and presence of clinic funding (per-patient payment model 7.90% [95% CI 0.63-15.17, P = .033); global budget 3.56% (95% CI -6.14 to 13.25, P = .471]). Conclusion: The high variability in RM utilization can partly be explained by economic and structural barriers that may warrant specific efforts by all stakeholders to increase RM utilization.

Silk fibroin wetting stability film induced by polyamide-amine-epichlorohydrin (PAE) for intelligent sensing system.

Protein materials gain new functions and applicability through redesigns in protein structure and engineering confer. However, the application and development of proteins for use in flexible devices that fit in flexible devices that fit the surface of human skin is hindered by their poor wet stability. Here, we described the design of wet-stable materials based on the reconstruction of silk fibroin (SF). The combination of polyamide-amine-epichlorohydrin (PAE) was used as a traction rope to bring SF molecular chains closer to each other, to facilitate the self-assembly of SF through branching and lengthening of molecular chains, and change its crystalline structure. SF/PAE composite films that exhibited huge improvement in ductility and wet stability were combined with flexible SF substrates via patterning and ion sputtering to prepare flexible sensors. In addition, the SF/PAE sensing system equipped with a microprocessor and Bluetooth module enabled the real-time remote acquisition of human health signals such as vocal cords, joints, pulse and meridians. This reconfiguration of the SF structure will advance the systematic exploration of protein structures and the development of protein materials for intelligent device applications.

Behavior of the OptiVol2 fluid index and intrathoracic impedance on remote monitoring As a detector of subclinical device infection early after implantation.

We report the behavior of OptiVol2 fluid index (OVFI2) and intrathoracic impedance on remote monitoring before the appearance of signs of infection. A sustained rise in OVFI2 early after implantation reflects peri-device fluid retention.

Optimizing remote heart failure management: First experiences with the HeartInsight score for implanted defibrillators.

We explored the results of two tests of the novel HeartInsight algorithm for heart failure (HF) prediction, reconstructing trends from historical cases. Results suggest potential extension of HeartInsight to implantable cardioverter defibrillators patients without history of HF and illustrate the importance of the baseline clinical profile in enhancing algorithm specificity.

Artificial intelligence to detect noise events in remote monitoring data.

Background: Remote monitoring (RM) of cardiac implantable electrical devices (CIEDs) can detect various events early. However, the diagnostic ability of CIEDs has not been sufficient, especially for lead failure. The first notification of lead failure was almost noise events, which were detected as arrhythmia by the CIED. A human must analyze the intracardiac electrogram to accurately detect lead failure. However, the number of arrhythmic events is too large for human analysis. Artificial intelligence (AI) seems to be helpful in the early and accurate detection of lead failure before human analysis. Objective: To test whether a neural network can be trained to precisely identify noise events in the intracardiac electrogram of RM data. Methods: We analyzed 21 918 RM data consisting of 12 925 and 1884 Medtronic and Boston Scientific data, respectively. Among these, 153 and 52 Medtronic and Boston Scientific data, respectively, were diagnosed as noise events by human analysis. In Medtronic, 306 events, including 153 noise events and randomly selected 153 out of 12 692 nonnoise events, were analyzed in a five-fold cross-validation with a convolutional neural network. The Boston Scientific data were analyzed similarly. Results: The precision rate, recall rate, F1 score, accuracy rate, and the area under the curve were 85.8 ± 4.0%, 91.6 ± 6.7%, 88.4 ± 2.0%, 88.0 ± 2.0%, and 0.958 ± 0.021 in Medtronic and 88.4 ± 12.8%, 81.0 ± 9.3%, 84.1 ± 8.3%, 84.2 ± 8.3% and 0.928 ± 0.041 in Boston Scientific. Five-fold cross-validation with a weighted loss function could increase the recall rate. Conclusions: AI can accurately detect noise events. AI analysis may be helpful for detecting lead failure events early and accurately.

Remote monitoring of cardiac implantable electronic devices using smart device interface versus radiofrequency-based interface: A systematic review.

Background: Guidelines recommended remote monitoring (RM) in managing patients with Cardiac Implantable Electronic Devices. In recent years, smart device (phone or tablet) monitoring-based RM (SM-RM) was introduced. This study aims to systematically review SM-RM versus bedside monitor RM (BM-RM) using radiofrequency in terms of compliance, connectivity, and episode transmission time. Methods: We conducted a systematic review, searching three international databases from inception until July 2023 for studies comparing SM-RM (intervention group) versus BM-RM (control group). Results: Two matched studies (21 978 patients) were retrieved (SM-RM arm: 9642 patients, BM-RM arm: 12 336 patients). There is significantly higher compliance among SM-RM patients compared with BM-RM patients in both pacemaker and defibrillator patients. Manyam et al. found that more SM-RM patients than BM-RM patients transmitted at least once (98.1% vs. 94.3%, p < .001), and Tarakji et al. showed that SM-RM patients have higher success rates of scheduled transmissions than traditional BM-RM methods (SM-RM: 94.6%, pacemaker manual: 56.3%, pacemaker wireless: 77.0%, defibrillator wireless: 87.1%). There were higher enrolment rates, completed scheduled and patient-initiated transmissions, shorter episode transmission time, and higher connectivity among SM-RM patients compared to BM-RM patients. Younger patients (aged <75) had more patient-initiated transmissions, and a higher proportion had ≥10 transmissions compared with older patients (aged ≥75) in both SM-RM and BM-RM groups. Conclusion: SM-RM is a step in the right direction, with good compliance, connectivity, and shorter episode transmission time, empowering patients to be in control of their health. Further research on cost-effectiveness and long-term clinical outcomes can be carried out.

Remote Vital Sign Monitoring in Admission Avoidance Hospital at Home: A Systematic Review.

OBJECTIVES: To examine randomized controlled trials (RCTs) of "hospital at home" (HAH) for admission avoidance in adults presenting with acute physical illness to identify the use of vital sign monitoring approaches and evidence for their effectiveness. DESIGN: Systematic review. SETTING AND PARTICIPANTS: This review compared strategies for vital sign monitoring in admission avoidance HAH for adults presenting with acute physical illness. Vital sign monitoring can support HAH acute multidisciplinary care by contributing to safety, determining requirement of further assessment, and guiding clinical decisions. There are a wide range of systems currently available, including reliable and automated continuous remote monitoring using wearable devices. METHODS: Eligible studies were identified through updated database and trial registries searches (March 2, 2016, to February 15, 2023), and existing systematic reviews. Risk of bias was assessed using the Cochrane risk of bias 2 tool. Random effects meta-analyses were performed, and narrative summaries provided stratified by vital sign monitoring approach. RESULTS: Twenty-one eligible RCTs (3459 participants) were identified. Two approaches to vital sign monitoring were characterized: manual and automated. Reporting was insufficient in the majority of studies for classification. For HAH compared to hospital care, 6-monthly mortality risk ratio (RR) was 0.94 (95% CI 0.78-1.12), 3-monthly readmission to hospital RR 1.02 (0.77-1.35), and length of stay mean difference 1.91 days (0.71-3.12). Readmission to hospital was reduced in the automated monitoring subgroup (RR 0.30 95% CI 0.11-0.86). CONCLUSIONS AND IMPLICATIONS: This review highlights gaps in the reporting and evidence base informing remote vital sign monitoring in alternatives to admission for acute illness, despite expanding implementation in clinical practice. Although continuous vital sign monitoring using wearable devices may offer added benefit, its use in existing RCTs is limited. Recommendations for the implementation and evaluation of remote monitoring in future clinical trials are proposed.

Mechanical Method for Rapid Determination of Step Count Sensor Settings.

With the increased push for personalized medicine, researchers and clinicians have begun exploring the use of wearable sensors to track patient activity. These sensors typically prioritize device life over robust onboard analysis, which results in lower accuracies in step count, particularly at lower cadences. To optimize the accuracy of activity-monitoring devices, particularly at slower walking speeds, proven methods must be established to identify suitable settings in a controlled and repeatable manner prior to human validation trials. Currently, there are no methods for optimizing these low-power wearable sensor settings prior to human validation, which requires manual counting for in-laboratory participants and is limited by time and the cadences that can be tested. This article proposes a novel method for determining sensor step counting accuracy prior to human validation trials by using a mechanical camshaft actuator that produces continuous steps. Sensor error was identified across a representative subspace of possible sensor setting combinations at cadences ranging from 30 steps/min to 110 steps/min. These true errors were then used to train a multivariate polynomial regression to model errors across all possible setting combinations and cadences. The resulting model predicted errors with an R2 of 0.8 and root-mean-square error (RMSE) of 0.044 across all setting combinations. An optimization algorithm was then used to determine the combinations of settings that produced the lowest RMSE and median error for three ranges of cadence that represent disabled low-mobility ambulators, disabled high-mobility ambulators, and healthy ambulators (30-60, 20-90, and 30-110 steps/min, respectively). The model identified six setting combinations for each range of interest that achieved a ±10% error in cadence prior to human validation. The anticipated range of errors from the optimized settings at lower walking speeds are lower than the reported errors of wearable sensors (±30%), suggesting that pre-human-validation optimization of sensors may decrease errors at lower cadences. This method provides a novel and efficient approach to optimizing the accuracy of wearable activity monitors prior to human validation trials.

Efficacy of the Cardiac Implantable Electronic Device Multisensory Triage-HF Algorithm in Heart Failure Care: A Real-World Clinical Experience.

Heart failure (HF) admissions are burdensome, and the mainstay of prevention is the timely detection of impending fluid retention, creating a window for medical treatment intensification. This study evaluated the accuracy and performance of a Triage-HF-guided carepath in real-world ambulatory HF patients in daily clinical practice. In this prospective, observational study, 92 adult HF patients (71 males (78%), with a median age of 69 [IQR 59-75] years) with the Triage-HF algorithm activated in their cardiac implantable electronic devices (CIEDs), were monitored. Following high-risk alerts, an HF nurse contacted patients to identify signs and symptoms of fluid retention. The sensitivity and specificity were 83% and 97%, respectively. The positive predictive value was 89%, and negative predictive value was 94%. The unexplained alert rate was 0.05 alerts/patient year, and the false negative rate was 0.11 alerts/patient year. Ambulatory diuretics were initiated or escalated in 77% of high-risk alert episodes. In 23% (n = 6), admission was ultimately required. The median alert handling time was 2 days. Fifty-eight percent (n = 18) of high-risk alerts were classified as true positives in the first week, followed by 29% in the second-third weeks (n = 9), and 13% (n = 4) in the fourth-sixth weeks. Common sensory triggers included an elevated night ventricular rate (84%), OptiVol (71%), and reduced patient activity (71%). The CIED-based Triage-HF algorithm-driven carepath enables the timely detection of impending fluid retention in a contemporary ambulatory setting, providing an opportunity for clinical action.

Remotely monitored physical activity from older people with cardiac devices associates with physical functioning.

INTRODUCTION: Accelerometer-derived physical activity (PA) from cardiac devices are available via remote monitoring platforms yet rarely reviewed in clinical practice. We aimed to investigate the association between PA and clinical measures of frailty and physical functioning. METHODS: The PATTErn study (A study of Physical Activity paTTerns and major health Events in older people with implantable cardiac devices) enrolled participants aged 60 + undergoing remote cardiac monitoring. Frailty was measured using the Fried criteria and gait speed (m/s), and physical functioning by NYHA class and SF-36 physical functioning score. Activity was reported as mean time active/day across 30-days prior to enrolment (30-day PA). Multivariable regression methods were utilised to estimate associations between PA and frailty/functioning (OR = odds ratio, ß = beta coefficient, CI = confidence intervals). RESULTS: Data were available for 140 participants (median age 73, 70.7% male). Median 30-day PA across the analysis cohort was 134.9 min/day (IQR 60.8-195.9). PA was not significantly associated with Fried frailty status on multivariate analysis, however was associated with gait speed (ß = 0.04, 95% CI 0.01-0.07, p = 0.01) and measures of physical functioning (NYHA class: OR 0.73, 95% CI 0.57-0.92, p = 0.01, SF-36 physical functioning: ß = 4.60, 95% CI 1.38-7.83, p = 0.005). CONCLUSIONS: PA from cardiac devices was associated with physical functioning and gait speed. This highlights the importance of reviewing remote monitoring PA data to identify patients who could benefit from existing interventions. Further research should investigate how to embed this into clinical pathways.