Usability of a digital health platform to support home hospitalization in heart failure patients: a multicentre feasibility study among healthcare professionals.

AIMS: Heart failure (HF) is a common cause of mortality and (re)hospitalizations. The NWE-Chance project explored the feasibility of providing hospitalizations at home (HH) supported by a newly developed digital health platform. The aim of this study was to explore the perceived usability by healthcare professionals (HCPs) of a digital platform in addition to HH for HF patients. METHODS AND RESULTS: A prospective, international, multicentre, single-arm interventional study was conducted. Sixty-three patients and 22 HCPs participated. The HH consisted of daily home visits by the nurse and use of the platform, consisting of a portable blood pressure device, weight scale, pulse oximeter, a wearable chest patch to measure vital signs (heart rate, respiratory rate, activity level, and posture), and an eCoach for the patient. Primary outcome was usability of the platform measured by the System Usability Scale halfway and at the end of the study. Overall usability was rated as sufficient (mean score 72.1 ± 8.9) and did not differ between the measurements moments (P = 0.690). The HCPs reported positive experiences (n = 7), negative experiences (n = 13), and recommendations (n = 6) for the future. Actual use of the platform was 79% of the HH days. CONCLUSION: A digital health platform to support HH was considered usable by HCPs, although actual use of the platform was limited. Therefore, several improvements in the integration of the digital platform into clinical workflows and in defining the precise role of the digital platform and its use are needed to add value before full implementation. REGISTRATION: clinicaltrials.gov NCT04084964.

Bringing the hospital to home: Patient-reported outcome measures of a digital health-supported home hospitalisation platform to support hospital care at home for heart failure patients.

Background: Hospitalisations for heart failure are frequent and costly, linked with a lower quality of life, and lead to higher morbidity and mortality. Home hospitalisation interventions could be a substitute for in-hospital stays to reduce the burden on patients. The current study aims to investigate patient-reported satisfaction and usability in combination with the safety of a digital health-supported home hospitalisation intervention for heart failure patients. Methods: We conducted an international, multicentre, single-arm, interventional study to investigate the feasibility and safety of a digital health-supported home hospitalisation platform. Patients with acute decompensation of known and well-assessed chronic heart failure with an indication for hospital admission were included. The primary outcome was patient satisfaction. Secondary outcomes were usability, adherence, and safety. Results: A total number of 66 patients were included, of which the data of 65 patients (98.5%) was analysed. A total of 86.1% of patients reported being very satisfied or totally satisfied. No patients reported to be not satisfied with the home hospitalisation intervention. The patients reported a sufficient usability score (mean score: 75.8% of 100%) for the digital health-supported home hospitalisation platform. The adherence to the daily measurements of blood pressure and weight was very high, whereas the adherence to the daily interaction with the eCoach was lower (69.3%). In 7 patients (10.8%), a conversion from home hospitalisation to regular hospitalisation was needed. Furthermore, 6 patients (9.2%) had rehospitalisation within 30 days after the end of the home hospitalisation intervention. Conclusion: A digitally supported home hospitalisation intervention is feasible. This study demonstrates high patient satisfaction and sufficiently high usability scores. The safety outcomes are comparable with traditional heart failure hospitalisations. This indicates that digitally supported home hospitalisation could be an alternative to in-hospital care for all age groups, yet further research is needed to prove the (cost-) effectiveness.

The Potential and Limitations of Mobile Health and Insertable Cardiac Monitors in the Detection of Atrial Fibrillation in Cryptogenic Stroke Patients: Preliminary Results From the REMOTE Trial.

Aim: This paper presents the preliminary results from the ongoing REMOTE trial. It aims to explore the opportunities and hurdles of using insertable cardiac monitors (ICMs) and photoplethysmography-based mobile health (PPG-based mHealth) using a smartphone or smartwatch to detect atrial fibrillation (AF) in cryptogenic stroke and transient ischemic attack (TIA) patients. Methods and Results: Cryptogenic stroke or TIA patients (n = 39) received an ICM to search for AF and were asked to use a blinded PPG-based mHealth application for 6 months simultaneously. They were randomized to smartphone or smartwatch monitoring. In total, 68,748 1-min recordings were performed using PPG-based mHealth. The number of mHealth recordings decreased significantly over time in both smartphone and smartwatch groups (p < 0.001 and p = 0.002, respectively). Insufficient signal quality was more frequently observed in smartwatch (43.3%) compared to smartphone recordings (17.8%, p < 0.001). However, when looking at the labeling of the mHealth recordings on a patient level, there was no significant difference in signal quality between both groups. Moreover, the use of a smartwatch resulted in significantly more 12-h periods (91.4%) that were clinically useful compared to smartphone users (84.8%) as they had at least one recording of sufficient signal quality. Simultaneously, continuous data was collected from the ICMs, resulting in approximately 6,660,000 min of data (i.e., almost a 100-fold increase compared to mHealth). The ICM algorithm detected AF and other cardiac arrhythmias in 10 and 19 patients, respectively. However, these were only confirmed after adjudication by the remote monitoring team in 1 (10%) and 5 (26.3%) patients, respectively. The confirmed AF was also detected by PPG-based mHealth. Conclusion: Based on the preliminary observations, our paper illustrates the potential as well as the limitations of PPG-based mHealth and ICMs to detect AF in cryptogenic stroke and TIA patients in four elements: (i) mHealth was able to detect AF in a patient in which AF was confirmed on the ICM; (ii) Even state-of-the-art ICMs yielded many false-positive AF registrations; (iii) Both mHealth and ICM still require physician revision; and (iv) Blinding of the mHealth results impairs compliance and motivation.

Will Smartphone Applications Replace the Insertable Cardiac Monitor in the Detection of Atrial Fibrillation? The First Comparison in a Case Report of a Cryptogenic Stroke Patient.

Background and Case: This case report exemplifies the clinical application of non-invasive photoplethysmography (PPG)-based rhythm monitoring in the awakening mobile health (mHealth) era to detect symptomatic and asymptomatic paroxysmal atrial fibrillation (AF) in a cryptogenic stroke patient. Despite extensive diagnostic workup, the etiology remains unknown in one out of three ischemic strokes (i.e., cryptogenic stroke). Prolonged cardiac monitoring can reveal asymptomatic atrial fibrillation in up to one-third of this population. This case report describes a cryptogenic stroke patient who received prolonged cardiac monitoring with an insertable cardiac monitor (ICM) as standard of care. In the context of a clinical study, the patient simultaneously monitored his heart rhythm with a PPG-based smartphone application. AF was detected simultaneously on both the ICM and smartphone application after three days of monitoring. Similar AF burden was detected during follow-up (five episodes, median duration of 28 and 34 h on ICM and mHealth, respectively, p = 0.5). The detection prompted the initiation of oral anticoagulation and AF catheter ablation procedure. Conclusion: This is the first report of the cryptogenic stroke patient in whom PPG-based mHealth was able to detect occurrence and burden of the symptomatic and asymptomatic paroxysmal AF episodes with similar precision as ICM. It accentuates the potential role of PPG-based mHealth in prolonged cardiac rhythm monitoring in cryptogenic stroke patients.

Vital Signs Prediction for COVID-19 Patients in ICU.

This study introduces machine learning predictive models to predict the future values of the monitored vital signs of COVID-19 ICU patients. The main vital sign predictors include heart rate, respiration rate, and oxygen saturation. We investigated the performances of the developed predictive models by considering different approaches. The first predictive model was developed by considering the following vital signs: heart rate, blood pressure (systolic, diastolic and mean arterial, pulse pressure), respiration rate, and oxygen saturation. Similar to the first approach, the second model was developed using the same vital signs, but it was trained and tested based on a leave-one-subject-out approach. The third predictive model was developed by considering three vital signs: heart rate (HR), respiration rate (RR), and oxygen saturation (SpO2). The fourth model was a leave-one-subject-out model for the three vital signs. Finally, the fifth predictive model was developed based on the same three vital signs, but with a five-minute observation rate, in contrast with the aforementioned four models, where the observation rate was hourly to bi-hourly. For the five models, the predicted measurements were those of the three upcoming observations (on average, three hours ahead). Based on the obtained results, we observed that by limiting the number of vital sign predictors (i.e., three vital signs), the prediction performance was still acceptable, with the average mean absolute percentage error (MAPE) being 12%,5%, and 21.4% for heart rate, oxygen saturation, and respiration rate, respectively. Moreover, increasing the observation rate could enhance the prediction performance to be, on average, 8%,4.8%, and 17.8% for heart rate, oxygen saturation, and respiration rate, respectively. It is envisioned that such models could be integrated with monitoring systems that could, using a limited number of vital signs, predict the health conditions of COVID-19 ICU patients in real-time.

A Home Hospitalisation Strategy for Patients with an Acute Episode of Heart Failure Using a Digital Health-Supported Platform: A Multicentre Feasibility Study - A Rationale and Study Design.

BACKGROUND: Heart failure (HF) is a common cause of hospitalisation and mortality in elderly. The frequent rehospitalisations put a serious burden on patients, health-care budgets, and health-care capacity. Frequent hospital admissions are also associated with a substantial additional hazard for serious complications and reduced quality of life. The NWE-Chance project will explore the feasibility and scalability of providing home hospitalisation supported by a newly developed digital health-supported platform and daily visits of specialised nurses. METHODS/DESIGN: Hundred patients with chronic HF will be recruited over a 1-year period. The digital health-supported home hospitalisation strategy will be tested in 3 hospitals with different experience in delivering home hospitalisation: Isala Zwolle, Maastricht UMC+, both in The Netherlands, and Jessa Hospital, Hasselt in Belgium. The home hospitalisation intervention will have a maximal duration of 14 days. Feasibility will be measured with acceptability, satisfaction, and usability questionnaires for patients, nurses, and physicians. Furthermore, safety and costs will be assessed for 30 days after the start of the home hospitalisation intervention. DISCUSSION: The NWE-Chance project will be one of the first studies to examine the feasibility of a digital health-supported home hospitalisation platform for HF patients. It has the potential to augment current standard HF care and quality of life of HF patients and to innovate the standard HF care to potentially lower the hospitalisation-related complications, the burden of HF on health-care systems, and to potentially implement more patient-centred care strategies.

Towards the Monitoring of Functional Status in a Free-Living Environment for People with Hip or Knee Osteoarthritis: Design and Evaluation of the JOLO Blended Care App.

(1) Background: Joint loading is an important parameter in patients with osteoarthritis (OA). However, calculating joint loading relies on the performance of an extensive biomechanical analysis, which is not possible to do in a free-living situation. We propose the concept and design of a novel blended-care app called JOLO (Joint Load) that combines free-living information on activity with lab-based measures of joint loading in order to estimate a subject's functional status. (2) Method: We used an iterative design process to evaluate the usability of the JOLO app through questionnaires. The user interfaces that resulted from the iterations are described and provide a concept for feedback on functional status. (3) Results: In total, 44 people (20 people with OA and 24 health-care providers) participated in the testing of the JOLO app. OA patients rated the latest version of the JOLO app as moderately useful. Therapists were predominantly positive; however, their intention to use JOLO was low due to technological issues. (4) Conclusion: We can conclude that JOLO is promising, but further technological improvements concerning activity recognition, the development of personalized joint loading predictions and a more comfortable means to carry the device are needed to facilitate its integration as a blended-care program.

Vital Signs Prediction and Early Warning Score Calculation Based on Continuous Monitoring of Hospitalised Patients Using Wearable Technology.

In this prospective, interventional, international study, we investigate continuous monitoring of hospitalised patients' vital signs using wearable technology as a basis for real-time early warning scores (EWS) estimation and vital signs time-series prediction. The collected continuous monitored vital signs are heart rate, blood pressure, respiration rate, and oxygen saturation of a heterogeneous patient population hospitalised in cardiology, postsurgical, and dialysis wards. Two aspects are elaborated in this study. The first is the high-rate (every minute) estimation of the statistical values (e.g., minimum and mean) of the vital signs components of the EWS for one-minute segments in contrast with the conventional routine of 2 to 3 times per day. The second aspect explores the use of a hybrid machine learning algorithm of kNN-LS-SVM for predicting future values of monitored vital signs. It is demonstrated that a real-time implementation of EWS in clinical practice is possible. Furthermore, we showed a promising prediction performance of vital signs compared to the most recent state of the art of a boosted approach of LSTM. The reported mean absolute percentage errors of predicting one-hour averaged heart rate are 4.1, 4.5, and 5% for the upcoming one, two, and three hours respectively for cardiology patients. The obtained results in this study show the potential of using wearable technology to continuously monitor the vital signs of hospitalised patients as the real-time estimation of EWS in addition to a reliable prediction of the future values of these vital signs is presented. Ultimately, both approaches of high-rate EWS computation and vital signs time-series prediction is promising to provide efficient cost-utility, ease of mobility and portability, streaming analytics, and early warning for vital signs deterioration.

Towards personalized fluid monitoring in haemodialysis patients: thoracic bioimpedance signal shows strong correlation with fluid changes, a cohort study.

BACKGROUND: Haemodialysis (HD) patients are burdened by frequent fluid shifts which amplify their comorbidities. Bioimpedance (bioZ) is a promising technique to monitor changes in fluid status. The aim of this study is to investigate if the thoracic bioZ signal can track fluid changes during a HD session. METHODS: Prevalent patients from a single centre HD unit were monitored during one to six consecutive HD sessions using a wearable multi-frequency thoracic bioZ device. Ultrafiltration volume (UFV) was determined based on the interdialytic weight gain and target dry weight set by clinicians. The correlation between the bioZ signal and UFV was analysed on population level. Additionally regression models were built and validated per dialysis session. RESULTS: 66 patients were included, resulting in a total of 133 HD sessions. Spearman correlation between the thoracic bioZ and UFV showed a significant strong correlation of 0.755 (p < 0.01) on population level. Regression analysis per session revealed a strong relation between the bioZ value and the UFV (R2 = 0.982). The fluid extraction prediction error of the leave-one-out cross validation was very small (56.2 ml [- 121.1-194.1 ml]) across all sessions at all frequencies. CONCLUSIONS: This study demonstrated that thoracic bioZ is strongly correlated with fluid shifts during HD over a large range of UFVs. Furthermore, leave-one-out cross validation is a step towards personalized fluid monitoring during HD and could contribute to the creation of autonomous dialysis.

The Added Value of In-Hospital Tracking of the Efficacy of Decongestion Therapy and Prognostic Value of a Wearable Thoracic Impedance Sensor in Acutely Decompensated Heart Failure With Volume Overload: Prospective Cohort Study.

BACKGROUND: Incomplete relief of congestion in acute decompensated heart failure (HF) is related to poor outcomes. However, congestion can be difficult to evaluate, stressing the urgent need for new objective approaches. Due to its inverse correlation with tissue hydration, continuous bioimpedance monitoring might be an effective method for serial fluid status assessments. OBJECTIVE: This study aimed to determine whether in-hospital bioimpedance monitoring can be used to track fluid changes (ie, the efficacy of decongestion therapy) and the relationships between bioimpedance changes and HF hospitalization and all-cause mortality. METHODS: A wearable bioimpedance monitoring device was used for thoracic impedance measurements. Thirty-six patients with signs of acute decompensated HF and volume overload were included. Changes in the resistance at 80 kHz (R80kHz) were analyzed, with fluid balance (fluid in/out) used as a reference. Patients were divided into two groups depending on the change in R80kHz during hospitalization: increase in R80kHz or decrease in R80kHz. Clinical outcomes in terms of HF rehospitalization and all-cause mortality were studied at 30 days and 1 year of follow-up. RESULTS: During hospitalization, R80kHz increased for 24 patients, and decreased for 12 patients. For the total study sample, a moderate negative correlation was found between changes in fluid balance (in/out) and relative changes in R80kHz during hospitalization (rs=-0.51, P<.001). Clinical outcomes at both 30 days and 1 year of follow-up were significantly better for patients with an increase in R80kHz. At 1 year of follow-up, 88% (21/24) of patients with an increase in R80kHz were free from all-cause mortality, compared with 50% (6/12) of patients with a decrease in R80kHz (P=.01); 75% (18/24) and 25% (3/12) were free from all-cause mortality and HF hospitalization, respectively (P=.01). A decrease in R80kHz resulted in a significant hazard ratio of 4.96 (95% CI 1.82-14.37, P=.003) on the composite endpoint. CONCLUSIONS: The wearable bioimpedance device was able to track changes in fluid status during hospitalization and is a convenient method to assess the efficacy of decongestion therapy during hospitalization. Patients who do not show an improvement in thoracic impedance tend to have worse clinical outcomes, indicating the potential use of thoracic impedance as a prognostic parameter.

An Evaluation of the NightOwl Home Sleep Apnea Testing System.

STUDY OBJECTIVES: The objective of this study was to evaluate the performance of a miniaturized home sleep apnea test, called NightOwl. The system consists of a sensor placed on the fingertip and a cloud-based analytics software. The sensor acquires accelerometer and photoplethysmographic data. The software derives actigraphy from the former, and blood oxygen saturation and peripheral arterial tone, among other features, from the latter. METHODS: Data of 101 participants who underwent an in-laboratory polysomnography (PSG), while wearing the NightOwl sensor, were collected. In order to establish an external benchmark, all PSG tests were edited by a somnologist of Younes Medical Technologies Ltd. (YMT) after analysis by the Michele Sleep Scoring System (MSSS). The respiratory event index (REI) derived by NightOwl (NightOwl-REI), the apnea-hypopnea index (AHI) derived by Ziekenhuis Oost-Limburg (ZOL-AHI), and the AHI derived by YMT (MSSS-AHI) were compared. RESULTS: The NightOwl-REI had a high correlation with the MSSS-AHI (ρ = .87, P < .001), which was close to the correlation between the ZOL-AHI and MSSS-AHI (ρ = .84, P < .001). The NightOwl-REI and ZOL-AHI had a correlation of .77 (P < .001). After categorization of the AHI, the agreement between the NightOwl-REI and the MSSS-AHI was .812 and the agreement between the ZOL-AHI and MSSS-AHI was .743, after double-labeling near-boundary participants. CONCLUSIONS: The NightOwl-REI achieved a close correlation and REI-categorization with the MSSS-AHI, especially in light of the significant inter-scorer variability of the analysis of the PSG.

A Novel Intelligent Two-Way Communication System for Remote Heart Failure Medication Uptitration (the CardioCoach Study): Randomized Controlled Feasibility Trial.

BACKGROUND: European Society of Cardiology guidelines for the treatment of heart failure (HF) prescribe uptitration of angiotensin-converting enzyme inhibitors (ACE-I) and ß-blockers to the maximum-tolerated, evidence-based dose. Although HF prognosis can drastically improve when correctly implementing these guidelines, studies have shown that they are insufficiently implemented in clinical practice. OBJECTIVE: The aim of this study was to verify whether supplementing the usual care with the CardioCoach follow-up tool is feasible and safe, and whether the tool is more efficient in implementing the guideline recommendations for ß-blocker and ACE-I. METHODS: A total of 25 HF patients were randomly assigned to either the usual care control group (n=10) or CardioCoach intervention group (n=15), and observed for 6 months. The CardioCoach follow-up tool is a two-way communication platform with decision support algorithms for semiautomatic remote medication uptitration. Remote monitoring sensors automatically transmit patient's blood pressure, heart rate, and weight on a daily basis. RESULTS: Patients' satisfaction and adherence for medication intake (10,018/10,825, 92.55%) and vital sign measurements (4504/4758, 94.66%) were excellent. However, the number of technical issues that arose was large, with 831 phone contacts (median 41, IQR 32-65) in total. The semiautomatic remote uptitration was safe, as there were no adverse events and no false positive uptitration proposals. Although no significant differences were found between both groups, a higher number of patients were on guideline-recommended medication dose in both groups compared with previous reports. CONCLUSIONS: The CardioCoach follow-up tool for remote uptitration is feasible and safe and was found to be efficient in facilitating information exchange between care providers, with high patient satisfaction and adherence. TRIAL REGISTRATION: ClinicalTrials.gov NCT03294811; https://clinicaltrials.gov/ct2/show/NCT03294811 (Archived by WebCite at http://www.webcitation.org/6xLiWVsgM).

Protocol-driven remote monitoring of cardiac resynchronization therapy as part of a heart failure disease management strategy.

BACKGROUND: Cardiac resynchronisation therapy (CRT) is an established treatment for heart failure (HF) with reduced ejection fraction. CRT devices are equipped with remote monitoring functions, which are pivotal in the detection of device problems, but may also facilitate disease management. The aim of this study was to provide a comprehensive overview of the clinical interventions taken based on remote monitoring. METHODS: This is a single centre observational study of consecutive CRT patients (n = 192) participating in protocol-driven remote follow-up. Incoming technical- and disease-related alerts were analysed together with subsequently triggered interventions. RESULTS: During 34 ± 13 months of follow-up, 1372 alert-containing notifications were received (2.53 per patient-year of follow-up), comprising 1696 unique alerts (3.12 per patient-year of follow-up). In 60%, notifications resulted in a phone contact. Technical alerts constituted 8% of incoming alerts (0.23 per patient-year of follow-up). Rhythm (1.43 per patient-year of follow-up) and bioimpedance alerts (0.98 per patient-year of follow-up) were the most frequent disease-related alerts. Notifications included a rhythm alert in 39%, which triggered referral to the emergency room (4%), outpatient cardiology clinic (36%) or general practitioner (7%), or resulted in medication changes (13%). Sole bioimpedance notifications resulted in a telephone contact in 91%, which triggered outpatient evaluation in 8% versus medication changes in 10%. Clinical outcome was excellent with 97% 1-year survival. CONCLUSIONS: Remote CRT follow-up resulted in 0.23 technical- versus 2.64 disease-related alerts annually. Rhythm and bioimpedance notifications constituted the majority of incoming notifications which triggered an actual intervention in 22% and 15% of cases, respectively.

Bioimpedance Alerts from Cardiovascular Implantable Electronic Devices: Observational Study of Diagnostic Relevance and Clinical Outcomes.

BACKGROUND: The use of implantable cardioverter-defibrillators (ICDs) and cardiac resynchronization therapy (CRT) devices is expanding in the treatment of heart failure. Most of the current devices are equipped with remote monitoring functions, including bioimpedance for fluid status monitoring. The question remains whether bioimpedance measurements positively impact clinical outcome. OBJECTIVE: The aim of this study was to provide a comprehensive overview of the clinical interventions taken based on remote bioimpedance monitoring alerts and their impact on clinical outcome. METHODS: This is a single-center observational study of consecutive ICD and CRT patients (n=282) participating in protocol-driven remote follow-up. Bioimpedance alerts were analyzed with subsequently triggered interventions. RESULTS: A total of 55.0% (155/282) of patients had an ICD or CRT device equipped with a remote bioimpedance algorithm. During 34 (SD 12) months of follow-up, 1751 remote monitoring alarm notifications were received (2.2 per patient-year of follow-up), comprising 2096 unique alerts (2.6 per patient-year of follow-up). Since 591 (28.2%) of all incoming alerts were bioimpedance-related, patients with an ICD or CRT including a bioimpedance algorithm had significantly more alerts (3.4 versus 1.8 alerts per patient-year of follow-up, P<.001). Bioimpedance-only alerts resulted in a phone contact in 91.0% (498/547) of cases, which triggered an actual intervention in 15.9% (87/547) of cases, since in 75.1% (411/547) of cases reenforcing heart failure education sufficed. Overall survival was lower in patients with a cardiovascular implantable electronic device with a bioimpedance algorithm; however, this difference was driven by differences in baseline characteristics (adjusted hazard ratio of 2.118, 95% CI 0.845-5.791). No significant differences between both groups were observed in terms of the number of follow-up visits in the outpatient heart failure clinic, the number of hospital admissions with a primary diagnosis of heart failure, or mean length of hospital stay. CONCLUSIONS: Bioimpedance-only alerts constituted a substantial amount of incoming alerts when turned on during remote follow-up and triggered an additional intervention in only 16% of cases since in 75% of cases, providing general heart failure education sufficed. The high frequency of heart failure education that was provided could have contributed to fewer heart failure-related hospitalizations despite significant differences in baseline characteristics.