Surveillance and Alert-based Multiparameter Monitoring to Reduce Worsening Heart Failure Events: Results From SCALE-HF 1.

BACKGROUND: There is a need for better noninvasive remote monitoring solutions that prevent hospitalizations through the early prediction and management of heart failure (HF). SurveillanCe and Alert-Based Multiparameter Monitoring to ReducE Worsening Heart Failure Events (SCALE-HF 1) evaluated the performance of a novel congestion index that alerts to fluid accumulation preceding HF events. METHODS AND RESULTS: SCALE-HF 1 was a multicenter, prospective, observational study investigating HF event prediction using data from the cardiac scale. Participants with HF took measurements at home by standing barefoot on the scale for approximately 20 seconds each day. The congestion index was applied retrospectively, and an alert was generated when the index exceeded a fixed threshold established in prior studies. HF events were defined as unplanned administration of IV diuretics or admissions with a primary diagnosis of HF. Sensitivity was defined as the ratio of correctly identified HF events to the total number of HF events. We enrolled 329 participants (mean age 64 ± 14 years; 43% women; 32% Black; 56% with reduced ejection fraction) across 8 sites with 238 participant-years of follow-up and 69 usable HF events. The congestion index predicted 48 of the 69 HF events (70%) at 2.58 alerts per participant-year. In contrast, the standard weight rule (weight gain of >3 lb in 1 day or >5 lb in 7 days) predicted only 24 of the 69 HF events (35%) at 4.18 alerts per participant-year. The congestion index alerts had a significantly higher sensitivity (P < .01) at a lower alert rate than the standard weight rule. CONCLUSIONS: The congestion index alerts demonstrated sensitive prediction of HF events at a low alert rate, significantly exceeding the performance of weight-based monitoring. GOV IDENTIFIER: NCT04882449.

The Coronavirus Disease 2019 Rebound Study: A Prospective Cohort Study to Evaluate Viral and Symptom Rebound Differences in Participants Treated With Nirmatrelvir Plus Ritonavir Versus Untreated Controls.

BACKGROUND: The uptake of nirmatrelvir plus ritonavir (NPR) in patients with coronavirus disease 2019 (COVID-19) has been limited by concerns around the rebound phenomenon despite the scarcity of evidence around its epidemiology. The purpose of this study was to prospectively compare the epidemiology of rebound in NPR-treated and untreated participants with acute COVID-19 infection. METHODS: We designed a prospective, observational study in which participants who tested positive for COVID-19 and were clinically eligible for NPR were recruited to be evaluated for either viral or symptom clearance and rebound. Participants were assigned to the treatment or control group based on their decision to take NPR. Following initial diagnosis, both groups were provided 12 rapid antigen tests and asked to test on a regular schedule for 16 days and answer symptom surveys. Viral rebound based on test results and COVID-19 symptom rebound based on patient-reported symptoms were evaluated. RESULTS: Viral rebound incidence was 14.2% in the NPR treatment group (n = 127) and 9.3% in the control group (n = 43). Symptom rebound incidence was higher in the treatment group (18.9%) compared to controls (7.0%). There were no notable differences in viral rebound by age, gender, preexisting conditions, or major symptom groups during the acute phase or at the 1-month interval. CONCLUSIONS: This preliminary report suggests that rebound after clearance of test positivity or symptom resolution is higher than previously reported. However, notably we observed a similar rate of rebound in both the NPR treatment and control groups. Large studies with diverse participants and extended follow-up are needed to better understand the rebound phenomena.

The hospital at home in the USA: current status and future prospects.

The annual cost of hospital care services in the US has risen to over $1 trillion despite relatively worse health outcomes compared to similar nations. These trends accentuate a growing need for innovative care delivery models that reduce costs and improve outcomes. HaH-a program that provides patients acute-level hospital care at home-has made significant progress over the past two decades. Technological advancements in remote patient monitoring, wearable sensors, health information technology infrastructure, and multimodal health data processing have contributed to its rise across hospitals. More recently, the COVID-19 pandemic brought HaH into the mainstream, especially in the US, with reimbursement waivers that made the model financially acceptable for hospitals and payors. However, HaH continues to face serious challenges to gain widespread adoption. In this review, we evaluate the peer-reviewed evidence and discuss the promises, challenges, and what it would take to tap into the future potential of HaH.

Optimizing COVID-19 testing resources use with wearable sensors.

The timely identification of infectious pre-symptomatic and asymptomatic cases is key towards preventing the spread of a viral illness like COVID-19. Early identification has been done through routine testing programs, which are indeed costly and potentially burdensome for individuals who should be tested with high frequency. A supplemental tool is represented by wearable technology, that can passively monitor and identify individuals at high risk, alerting them to take a test. We designed a Markov chain model and simulated a routine testing and a wearable testing strategy to estimate the number of tests required and the average number of days in which an individual is infectious and undetected. According to our model, with 2 test per month available, we have that the number of infectious and undetected days is 4.1 in the case of routine testing, while it decreases by 46% and 27% with a wearable testing strategy in the presence or absence of self-reported symptoms. The proposed parametric model can be used for different viral illnesses by tuning its parameters. It shows that wearable technology informing a testing strategy can significantly reduce the number of infectious days in which an individuals can spread the virus. With the same number of infectious days, by using wearables we can potentially reduce the number of required tests and the cost of the testing strategy.

Objectively measured peri-vaccination sleep does not predict COVID-19 breakthrough infection.

Prior studies have shown that sleep duration peri-vaccination influences an individual's antibody response. However, whether peri-vaccination sleep affects real-world vaccine effectiveness is unknown. Here, we tested whether objectively measured sleep around COVID-19 vaccination affected breakthrough infection rates. DETECT is a study of digitally recruited participants who report COVID-19-related information, including vaccination and illness data. Objective sleep data are also recorded through activity trackers. We compared the impact of sleep duration, sleep efficiency, and frequency of awakenings on reported breakthrough infection after the 2nd vaccination and 1st COVID-19 booster. Logistic regression models were created to examine if sleep metrics predicted COVID-19 breakthrough infection independent of age and gender. Self-reported breakthrough COVID-19 infection following 2nd COVID-19 vaccination and 1st booster. 256 out of 5265 individuals reported a breakthrough infection after the 2nd vaccine, and 581 out of 2583 individuals reported a breakthrough after the 1st booster. There was no difference in sleep duration between those with and without breakthrough infection. Increased awakening frequency was associated with breakthrough infection after the 1st booster with 3.01 ± 0.65 awakenings/hour in the breakthrough group compared to 2.82 ± 0.65 awakenings/hour in those without breakthrough (P < 0.001). Cox proportional hazards modeling showed that age < 60 years (hazard ratio 2.15, P < 0.001) and frequency of awakenings (hazard ratio 1.17, P = 0.019) were associated with breakthrough infection after the 1st booster. Sleep duration was not associated with breakthrough infection after COVID vaccination. While increased awakening frequency during sleep was associated with breakthrough infection beyond traditional risk factors, the clinical implications of this finding are unclear.

Long-term changes in wearable sensor data in people with and without Long Covid.

To better understand the impact of Long COVID on an individual, we explored changes in daily wearable data (step count, resting heart rate (RHR), and sleep quantity) for up to one year in individuals relative to their pre-infection baseline among 279 people with and 274 without long COVID. Participants with Long COVID, defined as symptoms lasting for 30 days or longer, following a SARS-CoV-2 infection had significantly different RHR and activity trajectories than those who did not report Long COVID and were also more likely to be women, younger, unvaccinated, and report more acute-phase (first 2 weeks) symptoms than those without Long COVID. Demographic, vaccine, and acute-phase sensor data differences could be used for early identification of individuals most likely to develop Long COVID complications and track objective evidence of the therapeutic efficacy of any interventions.Trial Registration: https://classic.clinicaltrials.gov/ct2/show/NCT04336020 .

Multi-Ethnic Study of Atherosclerosis Early Heart Failure Study: Rationale, Design, and Baseline Characteristics.

BACKGROUND: Current prevalence estimates of heart failure (HF) are primarily based on self-report or HF hospitalizations. There is an unmet need to define the prevalence and pathogenesis of early symptomatic HF, which may be undiagnosed and precedes HF hospitalization. METHODS: The MESA (Multi-Ethnic Study of Atherosclerosis) Early HF study was conducted during MESA exam 6 to determine the prevalence of early HF and investigate the transition from risk factors to early HF in a diverse population-based cohort of older adults. Between 2016 and 2018, 3285 MESA participants from 6 field centers underwent comprehensive speckle-tracking echocardiography with passive leg raise maneuver, Kansas City Cardiomyopathy Questionnaire, 6-minute walk test, arterial stiffness assessment, and proteomics (including NT-proBNP [N-terminal pro-B-type natriuretic peptide]). RESULTS: Median age was 73 (25th-75th percentile 67-81) years, 53.2% were female, 25.6% were Black, 12.8% were Chinese, and 40.0% were White. The prevalence of HF risk factors was high: hypertension, 61.9%; former or current smoking, 53.7%; obesity 34.8%; diabetes; 24.7%; and chronic kidney disease; 22%. Overt cardiovascular disease, which ranged from 2.1% (HF) to 13.6% (atrial fibrillation), was less common. Of the 3285 participants, 96% underwent proteomics, 94% Kansas City Cardiomyopathy Questionnaire, 93% speckle-tracking echocardiography with passive leg raise, 82% arterial stiffness exam, and 77% 6-minute walk test. Feasibility of resting speckle-tracking echocardiography (87%-99% across cardiac chambers) and passive leg raise Doppler/speckle-tracking echocardiography (>84%) measurements was high. A total of 120 unique echocardiographic indices were measured. CONCLUSIONS: The MESA Early HF study is a key resource for cardiovascular researchers who are interested in improving the epidemiological and phenotypic characterization of early HF. REGISTRATION: URL: https://www.clinicaltrials.gov; Unique identifier: NCT00005487.

Feasibility of wearable sensor signals and self-reported symptoms to prompt at-home testing for acute respiratory viruses in the USA (DETECT-AHEAD): a decentralised, randomised controlled trial.

BACKGROUND: Early identification of an acute respiratory infection is important for reducing transmission and enabling earlier therapeutic intervention. We aimed to prospectively evaluate the feasibility of home-based diagnostic self-testing of viral pathogens in individuals prompted to do so on the basis of self-reported symptoms or individual changes in physiological parameters detected via a wearable sensor. METHODS: DETECT-AHEAD was a prospective, decentralised, randomised controlled trial carried out in a subpopulation of an existing cohort (DETECT) of individuals enrolled in a digital-only observational study in the USA. Participants aged 18 years or older were randomly assigned (1:1:1) with a block randomisation scheme stratified by under-represented in biomedical research status. All participants were offered a wearable sensor (Fitbit Sense smartwatch). Participants in groups 1 and 2 received an at-home self-test kit (Alveo be.well) for two acute respiratory viral pathogens: SARS-CoV-2 and respiratory syncytial virus. Participants in group 1 could be alerted through the DETECT study app to take the at-home test on the basis of changes in their physiological data (as detected by our algorithm) or due to self-reported symptoms; those in group 2 were prompted via the app to self-test only due to symptoms. Group 3 served as the control group, without alerts or home testing capability. The primary endpoints, assessed on an intention-to-treat basis, were the number of acute respiratory infections presented (self-reported) and diagnosed (electronic health record), and the number of participants using at-home testing in groups 1 and 2. This trial is registered with ClinicalTrials.gov, NCT04336020. FINDINGS: Between Sept 28 and Dec 30, 2021, 450 participants were recruited and randomly assigned to group 1 (n=149), group 2 (n=151), or group 3 (n=150). 179 (40%) participants were male, 264 (59%) were female, and seven (2%) identified as other. 232 (52%) were from populations historically under-represented in biomedical research. 118 (39%) of the 300 participants in groups 1 and 2 were prompted to self-test, with 61 (52%) successfully completing self-testing. Participants were prompted to home-test more frequently due to symptoms (41 [28%] in group 1 and 51 [34%] in group 2) than due to detected physiological changes (26 [17%] in group 1). Significantly more participants in group 1 received alerts to test than did those in group 2 (67 [45%] vs 51 [34%]; p=0·047). Of the 61 individuals who were prompted to test and successfully did so, 19 (31%) tested positive for a viral pathogen-all for SARS-CoV-2. The individuals diagnosed as positive for SARS-CoV-2 in the electronic health record were eight (5%) in group 1, four (3%) in group 2, and two (1%) in group 3, but it was difficult to confirm if they were tied to symptomatic episodes documented in the trial. There were no adverse events. INTERPRETATION: In this direct-to-participant trial, we showed early feasibility of a decentralised programme to prompt individuals to use a viral pathogen diagnostic test based on symptoms tracked in the study app or physiological changes detected using a wearable sensor. Barriers to adequate participation and performance were also identified, which would need to be addressed before large-scale implementation. FUNDING: Janssen Pharmaceuticals.

Heart Rate Variability in Psychiatric Disorders: A Systematic Review.

Introduction: Heart rate variability (HRV) is a measure of the fluctuation in time interval between consecutive heart beats. Decreased heart rate variability has been shown to have associations with autonomic dysfunction in psychiatric conditions such as depression, substance abuse, anxiety, and schizophrenia, although its use as a prognostic tool remains highly debated. This study aims to review the current literature on heart rate variability as a diagnostic and prognostic tool in psychiatric populations. Methods: A literature search was conducted using the MEDLINE, EMBASE, Cochrane, and PsycINFO libraries to identify full-text studies involving adult psychiatric populations that reported HRV measurements. From 1647 originally identified, 31 studies were narrowed down through an abstract and full-text screen. Studies were excluded if they enrolled adolescents or children, used animal models, enrolled patients with another primary diagnosis other than psychiatric as outlined by the diagnostic and statistical manual of mental disorders (DSM) V, or if they assessed HRV in the context of treatment rather than diagnosis. Study quality assessment was conducted using a modified Downs and Blacks quality assessment tool for observational rather than interventional studies. Data were reported in four tables: 1) summarizing study characteristics, 2) methods of HRV detection, 3) key findings and statistics, and 4) quality assessment. Results: There is significant variability between studies in their methodology of recording as well as reporting HRV, which makes it difficult to meaningfully interpret data that is clinically applicable due to the presence of significant bias in existing studies. The presence of an association between HRV and the severity of various psychiatric disorders, however, remains promising. Conclusion: Future studies should be done to further explore how HRV parameters may be used to enhance the diagnosis and prognosis of several psychiatric disorders.

Rare fatal effect of combined use of sildenafil and alcohol leading to Cerebrovascular Accident.

Sildenafil is the first internationally approved drug for erectile dysfunction. Unsupervised and non-prescribed use of sildenafil among young Indian population has increased in last few years. Sildenafil helps in erection of penis by inhibiting the action of Phosphodiesterase-5 (PDE-5) enzyme, present in the vasculature of corpus cavernosum muscle and lengthens the duration of erection. Documented adverse effects of sildenafil are headache, flushing, nasal congestion, dyspepsia, and slight decrease in systolic and diastolic blood pressure. We present a rare case of sudden death due to cerebrovascular hemorrhage after sildenafil use and concomitant alcohol intake. The history is that a 41-year-old male with no significant past medical and surgical history was staying at a hotel room with a female friend; he had consumed 2 tablets of sildenafil (50 mg each) and alcohol at night. Next morning, he developed uneasiness following which he was taken to the Hospital where he was declared dead on arrival. The important autopsy findings include edematous brain with about 300 g of clotted blood in the right basal ganglia extending to bilateral ventricles, and in pons region. Other significant findings on microscopic examination were hypertrophic ventricular wall of heart, fatty changes in liver and acute tubular necrosis and hypertensive changes in the kidney. The findings are discussed in the light of the literature about the lethal complications of combined use of sildenafil and alcohol including cerebrovascular accidents. As a forensic pathologist it is the duty of the doctor to execute meticulous autopsy along with ancillary investigations including toxicological analysis and to correlate all these findings to determine the possible effects of drugs when present, so as to gather knowledge about potentially fatal drugs and further create public awareness regarding the same.

Synchronized wearables for the detection of haemodynamic states via electrocardiography and multispectral photoplethysmography.

Cardiovascular health is typically monitored by measuring blood pressure. Here we describe a wireless on-skin system consisting of synchronized sensors for chest electrocardiography and peripheral multispectral photoplethysmography for the continuous monitoring of metrics related to vascular resistance, cardiac output and blood-pressure regulation. We used data from the sensors to train a support-vector-machine model for the classification of haemodynamic states (resulting from exposure to heat or cold, physical exercise, breath holding, performing the Valsalva manoeuvre or from vasopressor administration during post-operative hypotension) that independently affect blood pressure, cardiac output and vascular resistance. The model classified the haemodynamic states on the basis of an unseen subset of sensor data for 10 healthy individuals, 20 patients with hypertension undergoing haemodynamic stimuli and 15 patients recovering from cardiac surgery, with an average precision of 0.878 and an overall area under the receiver operating characteristic curve of 0.958. The multinodal sensor system may provide clinically actionable insights into haemodynamic states for use in the management of cardiovascular disease.

Improving participant representation in the era of digital clinical studies.

Traditional clinical research relies on conventional strategies to invite and enroll research participants. However, these strategies often fail to reach potential participants from marginalized communities or that reflect the diversity of the nation, such as race, ethnicity, or geography. As we discuss here, the digital clinical study model sets the stage for improved and equitable participation in biomedical research.

Smartphone apps in the COVID-19 pandemic.

At the beginning of the COVID-19 pandemic, analog tools such as nasopharyngeal swabs for PCR tests were center stage and the major prevention tactics of masking and physical distancing were a throwback to the 1918 influenza pandemic. Overall, there has been scant regard for digital tools, particularly those based on smartphone apps, which is surprising given the ubiquity of smartphones across the globe. Smartphone apps, given accessibility in the time of physical distancing, were widely used for tracking, tracing and educating the public about COVID-19. Despite limitations, such as concerns around data privacy, data security, digital health illiteracy and structural inequities, there is ample evidence that apps are beneficial for understanding outbreak epidemiology, individual screening and contact tracing. While there were successes and failures in each category, outbreak epidemiology and individual screening were substantially enhanced by the reach of smartphone apps and accessory wearables. Continued use of apps within the digital infrastructure promises to provide an important tool for rigorous investigation of outcomes both in the ongoing outbreak and in future epidemics.

The Power of Patient Engagement With Electronic Health Records as Research Participants.

Electronic health record (EHR) technology has become a central digital health tool throughout health care. EHR systems are responsible for a growing number of vital functions for hospitals and providers. More recently, patient-facing EHR tools are allowing patients to interact with their EHR and connect external sources of health data, such as wearable fitness trackers, personal genomics, and outside health services, to it. As patients become more engaged with their EHR, the volume and variety of digital health information will serve an increasingly useful role in health care and health research. Particularly due to the COVID-19 pandemic, the ability for the biomedical research community to pivot to fully remote research, driven largely by EHR data capture and other digital health tools, is an exciting development that can significantly reduce burden on study participants, improve diversity in clinical research, and equip researchers with more robust clinical data. In this viewpoint, we describe how patient engagement with EHR technology is poised to advance the digital clinical trial space, an innovative research model that is uniquely accessible and inclusive for study participants.

Sensor-based surveillance for digitising real-time COVID-19 tracking in the USA (DETECT): a multivariable, population-based, modelling study.

BACKGROUND: Traditional viral illness surveillance relies on in-person clinical or laboratory data, paper-based data collection, and outdated technology for data transfer and aggregation. We aimed to assess whether continuous sensor data can provide an early warning signal for COVID-19 activity as individual physiological and behavioural changes might precede symptom onset, care seeking, and diagnostic testing. METHODS: This multivariable, population-based, modelling study recruited adult (aged ≥18 years) participants living in the USA who had a smartwatch or fitness tracker on any device that connected to Apple HealthKit or Google Fit and had joined the DETECT study by downloading the MyDataHelps app. In the model development cohort, we included people who had participated in DETECT between April 1, 2020, and Jan 14, 2022. In the validation cohort, we included individuals who had participated between Jan 15 and Feb 15, 2022. When a participant joins DETECT, they fill out an intake survey of demographic information, including their ZIP code (postal code), and surveys on symptoms, symptom onset, and viral illness test dates and results, if they become unwell. When a participant connects their device, historical sensor data are collected, if available. Sensor data continue to be collected unless a participant withdraws from the study. Using sensor data, we collected each participant's daily resting heart rate and step count during the entire study period and identified anomalous sensor days, in which resting heart rate was higher than, and step count was lower than, a specified threshold calculated for each individual by use of their baseline data. The proportion of users with anomalous data each day was used to create a 7-day moving average. For the main cohort, a negative binomial model predicting 7-day moving averages for COVID-19 case counts, as reported by the Centers for Disease Control and Prevention (CDC), in real time, 6 days in the future, and 12 days in the future in the USA and California was fitted with CDC-reported data from 3 days before alone (H0) or in combination with anomalous sensor data (H1). We compared the predictions with Pearson correlation. We then validated the model in the validation cohort. FINDINGS: Between April 1, 2020, and Jan 14, 2022, 35 842 participants enrolled in DETECT, of whom 4006 in California and 28 527 in the USA were included in our main cohort. The H1 model significantly outperformed the H0 model in predicting the 7-day moving average COVID-19 case counts in California and the USA. For example, Pearson correlation coefficients for predictions 12 days in the future increased by 32·9% in California (from 0·70 [95% CI 0·65-0·73] to 0·93 [0·92-0·94]) and by 12·2% (from 0·82 [0·79-0·84] to 0·92 [0·91-0·93]) in the USA from the H0 model to the H1 model. Our validation model also showed significant correlations for predictions in real time, 6 days in the future, and 12 days in the future. INTERPRETATION: Our study showed that passively collected sensor data from consenting participants can provide real-time disease tracking and forecasting. With a growing population of wearable technology users, these sensor data could be integrated into viral surveillance programmes. FUNDING: The National Center for Advancing Translational Sciences of the US National Institutes of Health, The Rockefeller Foundation, and Amazon Web Services.

Left Atrial Strain and the Risk of Atrial Arrhythmias From Extended Ambulatory Cardiac Monitoring: MESA.

Background Abnormalities in left atrial (LA) function often occur before LA structural changes and clinically identified atrial fibrillation (AF). Little is known about the relationship between LA strain and the risk of subclinical atrial arrhythmias detected from extended ambulatory cardiac monitoring. Methods and Results A total of 1441 participants of MESA (Multi-Ethnic Study of Atherosclerosis) completed speckle-tracking echocardiography and cardiac monitoring during 2016 to 2018 (mean age, 73 years); participants in AF during echocardiography or during the entire cardiac monitoring period were excluded. Absolute values of LA reservoir, booster pump, and conduit strains were measured. We evaluated associations of LA strain with monitor-detected AF, premature atrial contractions, and supraventricular tachycardia. Primary analyses adjusted for demographic variables, blood pressure, diabetes, smoking, and clinical cardiovascular disease. Cardiac monitoring (median, 14 days) detected AF in 3%. Each SD (4.0%) lower (worse) LA booster pump strain was associated with 84% higher risk of monitor-detected AF (95% CI, 30%-162%), 39% higher premature atrial contraction frequency (95% CI, 27%-53%), and 19% higher supraventricular tachycardia frequency (95% CI, 10%-29%). Additional adjustment for NT-proBNP (N-terminal pro-B-type natriuretic peptide), LA volume index, tissue Doppler a' peak velocity, left ventricular ejection fraction, and global longitudinal strain had little impact on associations. Findings were similar for LA reservoir strain and null for LA conduit strain. Conclusions In a multiethnic community-based cohort, impaired LA strain was an important correlate of subclinical atrial arrhythmias, even after adjustment for conventional measures of LA structure and function.

An update on ACE2 amplification and its therapeutic potential.

The renin angiotensin system (RAS) plays an important role in the pathogenesis of variety of diseases. Targeting the formation and action of angiotensin II (Ang II), the main RAS peptide, has been the key therapeutic target for last three decades. ACE-related carboxypeptidase (ACE2), a monocarboxypeptidase that had been discovered 20 years ago, is one of the catalytically most potent enzymes known to degrade Ang II to Ang-(1-7), a peptide that is increasingly accepted to have organ-protective properties that oppose and counterbalance those of Ang II. In addition to its role as a RAS enzyme ACE2 is the main receptor for SARS-CoV-2. In this review, we discuss various strategies that have been used to achieve amplification of ACE2 activity including the potential therapeutic potential of soluble recombinant ACE2 protein and novel shorter ACE2 variants.