Remote-Use Applications of the Amyotrophic Lateral Sclerosis Functional Rating Scale-Revised Clinical Outcome Assessment Tool: A Scoping Review.

OBJECTIVES: In 2021, the US Congress passed the Accelerating Access to Critical Therapies for Amyotrophic Lateral Sclerosis Act. The law encourages development of "tools, methods, and processes" to improve clinical trial efficiency for neurodegenerative diseases. The Amyotrophic Lateral Sclerosis Functional Rating Scale-Revised (ALSFRS-R) is an outcome measure administered during in-person clinic visits and used to support investigational studies for persons living with amyotrophic lateral sclerosis. Availability of a standardized, remote-use version of the ALSFRS-R may promote more inclusive, decentralized clinical trials. A scoping literature review was conducted to identify existing remote-use ALSFRS-R tools, synthesize feasibility and comparability of administration modes, and summarize barriers and facilitators to inform development of a standardized remote-use ALSFRS-R tool. METHODS: Included studies reported comparisons between remote and in-person, clinician-reported, ALSFRS-R administration and were published in English (2002-2022). References were identified by searching peer-reviewed and gray literature. Twelve studies met the inclusion criteria and were analyzed to compare findings within and across modes of administration. RESULTS: Remote modes of ALSFRS-R administration were categorized into 4 nonmutually exclusive categories: telephone (n = 6), videoconferencing (n = 3), computer or online platforms (n = 3), mobile applications and wearables (n = 2), and 1 unspecified telemedicine modality (n = 1). Studies comparing in-person to telephone or videoconferencing administration reported high ALSFRS-R rating correlations and nonsignificant between-mode differences. CONCLUSIONS: There is insufficient information in the ALSFRS-R literature to support remote clinician administration for collecting high quality data. Future research should engage persons living with amyotrophic lateral sclerosis, care partners, and providers to develop a standardized remote-use ALSFRS-R version.

Framework for Digital Health Phenotypes in Heart Failure: From Wearable Devices to New Sensor Technologies.

Consider these 2 scenarios: Two individuals with heart failure (HF) have recently established with your clinic and followed for medical management and risk stratification. One is a 62-year-old man with nonischemic cardiomyopathy due to viral myocarditis, an ejection fraction (EF) of 40%, occasional rate-limiting dyspnea, and comorbidities of atrial fibrillation and hypertension. The other is a 75-year-old woman with ischemic cardiomyopathy, an EF of 35%, a prior hospitalization 6 months ago, and persistent symptoms of edema and orthopnea. Both have expressed interest in remote patient monitoring (RPM) with wearable and digital health devices that are commercially available such as a smartwatch-ECG, weight scales, and blood pressure monitoring technologies. While there is enthusiasm from both patients and their clinical teams to engage in a technology-driven approach to care, important questions arise such as "What are the patient requirements for participation in digital health programs?", "Can we anticipate improvements in HF status and lower the risk of future HF events including hospitalizations?", "Do the same type of devices in different patients provide accurate information on physiologic changes toward individualized risk assessments?", and "What are the systematic approaches to integrate digital health workflows and datasets from RPM into clinical HF programs?". Given the importance of such questions, embracing new technologies, as a core competency of a modern health care system requires a deeper understanding of how effective digital health programs can be designed to meet the needs of patients and their clinical teams. In this review, we propose a new framework of "Digital Phenotypes in HF" for how new devices and sensors and their respective datasets can be used to guide treatment and to predict disease trajectories within the heterogeneity of HF. Our objectives are to generate a systematic approach to evaluate digital health devices as they relate to the next phase of RPM in HF, to critically analyze the literature, and to apply the lessons learned from digital devices through present-day, real-world evidence examples.

Machine learning for holistic visualization of STEMI registry data.

BACKGROUND: Widespread adoption of evidence-based guidelines and treatment pathways in ST-Elevation Myocardial Infarction (STEMI) patients has considerably improved cardiac survival and decreased the risk of recurrent myocardial infarction. However, survival outcomes appear to have plateaued over the last decade. The hope underpinning the current study is to engage data visualization to develop a more holistic understanding of the patient space, supported by principles and techniques borrowed from traditionally disparate disciplines, like cartography and machine learning. METHODS AND RESULTS: The Minnesota Heart Institute Foundation (MHIF) STEMI database is a large prospective regional STEMI registry consisting of 180 variables of heterogeneous data types on more than 5000 patients spanning 15 years. Initial assessment and preprocessing of the registry database was undertaken, followed by a first proof-of-concept implementation of an analytical workflow that involved machine learning, dimensionality reduction, and data visualization. 38 pre-admission variables were analyzed in an all-encompassing representation of pre-index STEMI event data. We aim to generate a holistic visual representation - a map of the multivariate patient space - by training a high-resolution self-organizing neural network consisting of several thousand neurons. The resulting 2-D lattice arrangement of n-dimensional neuron vectors allowed patients to be represented as point locations in a 2-D display space. Patient attributes were then visually examined and contextualized in the same display space, from demographics to pre-existing conditions, event-specific procedures, and STEMI outcomes. Data visualizations implemented in this study include a small-multiple display of neural component planes, composite visualization of the multivariate patient space, and overlay visualization of non-training attributes. CONCLUSION: Our study represents the first known marriage of cartography and machine learning techniques to obtain visualizations of the multivariate space of a regional STEMI registry. Combining cartographic mapping techniques and artificial neural networks permitted the transformation of the STEMI database into novel, two-dimensional visualizations of patient characteristics and outcomes. Notably, these visualizations also drive the discovery of anomalies in the data set, informing corrections applied to detected outliers, thereby further refining the registry for integrity and accuracy. Building on these advances, future efforts will focus on supporting further understanding of risk factors and predictors of outcomes in STEMI patients. More broadly, the thorough visual exploration of display spaces generated through a conjunction of dimensionality reduction with the mature technology base of geographic information systems appears a promising direction for biomedical research.

Coronary risk equivalence of diabetes assessed by SPECT-MPI.

BACKGROUND: Several publications and guidelines designate diabetes mellitus (DM) as a coronary artery disease (CAD) risk equivalent. The aim of this investigation was to examine DM cardiac risk equivalence from the perspective of stress SPECT myocardial perfusion imaging (MPI). METHODS AND RESULTS: We examined cardiovascular outcomes (cardiac death or nonfatal MI) of 17,499 patients referred for stress SPECT-MPI. Patients were stratified into four categories: non-DM without CAD, non-DM with CAD, DM without CAD, and DM with CAD, and normal or abnormal perfusion. Cardiac events occurred in 872 (5%), with event-free survival best among non-DM without CAD, worst in DM with CAD, and intermediate in DM without CAD, and non-DM with CAD. After multivariate adjustment, risk remained comparable between DM without CAD and non-DM with CAD [AHR 1.0 (95% CI 0.84-1.28), P =0.74]. Annualized event rates for normal subjects were 1.4% and 1.6% for non-DM with CAD and DM without CAD, respectively (P = 0.48) and 3.5% (P = 0.95) for both abnormal groups. After multivariate adjustment, outcomes were comparable within normal [AHR 1.4 (95% CI 0.98-1.96) P = 0.06] and abnormal [AHR 1.1 (95% CI 0.83-1.50) P = 0.49] MPI. CONCLUSIONS: Diabetic patients without CAD have comparable risk of cardiovascular events as non-diabetic patients with CAD after stratification by MPI results. These findings support diabetes as a CAD equivalent and suggest that MPI provides additional prognostic information in such patients.

Mobile technology and the digitization of healthcare.

The convergence of science and technology in our dynamic digital era has resulted in the development of innovative digital health devices that allow easy and accurate characterization in health and disease. Technological advancements and the miniaturization of diagnostic instruments to modern smartphone-connected and mobile health (mHealth) devices such as the iECG, handheld ultrasound, and lab-on-a-chip technologies have led to increasing enthusiasm for patient care with promises to decrease healthcare costs and to improve outcomes. This 'hype' for mHealth has recently intersected with the 'real world' and is providing important insights into how patients and practitioners are utilizing digital health technologies. It is also raising important questions regarding the evidence supporting widespread device use. In this state-of-the-art review, we assess the current literature of mHealth and aim to provide a framework for the advances in mHealth by understanding the various device, patient, and clinical factors as they relate to digital health from device designs and patient engagement, to clinical workflow and device regulation. We also outline new strategies for generation and analysis of mHealth data at the individual and population-based levels.

The prognostic impact of pre-implantation hyponatremia on morbidity and mortality among patients with left ventricular dysfunction and implantable cardioverter-defibrillators.

AIMS: Hyponatremia is commonly observed among patients with left ventricular (LV) dysfunction and is a marker for adverse outcomes. We aimed to determine the prognostic significance of pre-implant hyponatremia on the outcomes of death, acute decompensated heart failure (ADHF) and appropriate implantable cardioverter-defibrillator (ICD) therapy for ventricular arrhythmias among patients with ICDs. METHODS AND RESULTS: The study population consisted of patients with an ejection fraction ≤40% undergoing ICD implantation (n = 911) for the primary or secondary prevention of sudden cardiac death from 1997 to 2007. The predictive value of the severity of pre-implantation hyponatremia stratified into mild hyponatremia (n = 268, sodium 134-136 mmol/L), moderate hyponatremia (n = 105, sodium 131-133 mmol/L), and severe hyponatremia (n = 31, sodium ≤130 mmol/L) on the risk of death, ADHF, and appropriate ICD therapy for ventricular arrhythmias as compared with patients a normal serum sodium (n = 507, sodium ≥ 137 mmol/L), was calculated using multivariable Cox proportional hazards analyses. During a mean follow-up of 775 ± 750 days as the severity of hyponatremia (from a normal sodium to severe hyponatremia) increased an incremental incidence of death (25% to 61%, P < 0.001) and ADHF (11% to 26%, P = 0.004) was observed with a reduced incidence of ICD therapy for ventricular tachycardia/ventricular fibrillation (37-29%, P = 0.037). Compared with the normal sodium cohort, patients with severe hyponatremia demonstrated an increased risk of death [adjusted hazard ratio (AHR) 2.69 (95% confidence interval, CI 1.57-4.59), P = 0.004] and ADHF [AHR 2.98 (95% CI 1.41-6.30), P = 0.004], with a lower probability of appropriate ICD therapy [AHR 0.68 (95% CI 0.27-0.88), P = 0.031]. CONCLUSION: Hyponatremia is commonly observed among ICD recipients with LV dysfunction. Patients with an increasing severity of hyponatremia are at increased risk of death and HF related morbidity with a reduced incidence of appropriate ICD therapy particularly among patients with severe hyponatremia.

The healthcare utilization and cost of treating patients experiencing inappropriate implantable cardioverter defibrillator shocks: a propensity score study.

BACKGROUND: Inappropriate shocks (IASs) from implantable cardioverter defibrillators (ICDs) are associated with decreased quality of life, but whether they increase healthcare utilization and treatment costs is unknown. We sought to determine the impact of IASs on subsequent healthcare utilization and treatment costs. METHODS: We conducted a case-control analysis of ICD patients at a single institution from 1997 to 2010 and who had ≥12 months of post-ICD implant follow-up. Cases included all patients experiencing an IAS during the first 12 months after implantation. Eligible control patients did not receive a shock of any kind during the 12 months after implantation. Propensity scores based on 36 covariates (area under curve = 0.78) were used to match cases to controls. We compared the rate (occurrences/person year [PY]) of healthcare utilization immediately following IAS to the end of the 12-month follow-up period to the rate in the no-shock group over 12 months of follow-up. We also compared 12-month postimplant treatment (outpatient clinic, emergency room, and hospitalization) costs in both groups. RESULTS: A total of 76 patients experiencing ≥1 IAS during the first 12 months after implant (contributing 48 PYs) were matched to 76 no-shock patients (contributing 76 PYs). Cardiovascular (CV)-related clinic visit and hospitalization rates were increased following an IAS compared to those not receiving a shock (4.0 vs 3.3 and 0.7 vs 0.5, respectively, P = 0.02 for both). CV-related emergency room visitation (0.15 vs 0.08) rates were also numerically higher following an IAS, but did not reach statistical significance (P = 0.26). Patients experiencing an IAS accrued greater treatment costs during the 12 months postimplant compared to no-shock patients ($13,973 ± $46,345 vs $6,790 ± $19,091, P = 0.001). CONCLUSION: Recipients of IAS utilize the healthcare system more frequently following an IAS than patients not experiencing a shock. This increased utilization results in higher costs of treating IAS patients during the 12 months postimplant.

The gender-paradox among patients with implantable cardioverter-defibrillators: a propensity-matched study.

BACKGROUND: Several meta-analyses of the implantable cardioverter-defibrillator (ICD) clinical trials have demonstrated that while men derived a mortality reduction with prophylactic ICD implantation, women did not. These trials also observed that women receive less appropriate ICD shock therapy compared to men. We aimed to investigate this "gender-paradox" among a heterogeneous community cohort of patients receiving ICDs. METHODS: We identified 1,445 consecutive patients undergoing ICD implantation from 1997 to 2007. The study population consisted of 582 patients, of whom 291 were women who could be propensity matched to 291 men, based on age, ejection fraction, implantation indication (primary or secondary), etiology of cardiomyopathy (ischemic or nonischemic), and the presence of a cardiac resynchronization therapy-defibrillator (CRT-D) device. The impact of gender difference on the probability of death and appropriate ICD shocks for ventricular arrhythmias was calculated using multivariable Cox proportional hazards analyses. RESULTS: During a mean follow-up of 909 ± 901 days, compared to men, women demonstrated a similar risk of death (25% vs 25%, adjusted hazard ratio [AHR] 1.05 [95% confidence interval (CI) 0.81-1.35], P = 0.74). In contrast, women demonstrated a decreased probability of appropriate ICD-shock therapy (14% vs 19%, AHR 0.80 [95% CI 0.59-0.88], P = 0.03) compared to men, and among cohorts with a nonischemic cardiomyopathy (10% vs 20%, P < 0.001) and CRT-D devices (7% vs 19%, P = 0.01). CONCLUSION: Among a community cohort with ICDs, women have a similar mortality compared to men while experiencing less appropriate ICD therapy. These results support the findings of a lower arrhythmic mortality among women.

Evaluation of the Charlson comorbidity index to predict early mortality in implantable cardioverter defibrillator patients.

BACKGROUND: Current guidelines consider the implantation of an implantable cardioverter defibrillator (ICD) a class III indication in patients with a life expectancy of <1 year. An evaluation of concomitant noncardiac conditions may identify patients whom may not derive benefit with ICD therapy. We sought to evaluate the association of the Charlson comorbidity index (CCI) on the prediction of early mortality (EM), death <1 year after ICD implant. METHODS: The study population consisted of patients (n = 1062) undergoing ICD implantation for the primary or secondary prevention of sudden cardiac death from 1997 to 2007. The predictive value of the CCI on the risk of EM and appropriate shock therapy for ventricular arrhythmias as compared to patients without EM after ICD implant was calculated using multivariable Cox proportional hazards and receiver operator analyses. RESULTS: Patients experiencing EM (n = 110) demonstrated higher CCI scores (mean 2.8 ± 1.3 vs 1.5 ± 1.2, P < 0.001) as compared to individuals without EM (n = 963). Among patients with a CCI of 0, 1, 2, 3, 4, and ≥5, the incidence of EM increased from 5% to 78%. The CCI was an independent predictor of EM (AHR 1.4 [95% CI 1.2-1.6], P < 0.001, per single score increase). Patients who experienced EM demonstrated a decreased incidence of appropriate ICD therapy when compared to patients without EM (AHR 0.4 [95% CI 0.2-0.7], P = 0.001). CONCLUSION: Noncardiac conditions are commonly observed among patients undergoing ICD implantation. Guidelines must incorporate a comprehensive assessment of concomitant comorbidities to minimize the risk of EM and to maximize the survival benefit with ICD therapy.

Nanosensor technologies and the digital transformation of healthcare.

Nanosensors are nanoscale devices that measure physical attributes and convert these signals into analyzable information. In preparation, for the impending reality of nanosensors in clinical practice, we confront important questions regarding the evidence supporting widespread device use. Our objectives are to demonstrate the value and implications for new nanosensors as they relate to the next phase of remote patient monitoring and to apply lessons learned from digital health devices through real-world examples.

A Novel Earbud Detects Aortic Stenosis Murmur Before and After Transcatheter Aortic Valve Replacement.

Noninvasive infrasonic hemodynography using the MindMics earbuds captures low-frequency acoustic vibrations throughout the cardiac cycle. In an n-of-1 analysis, we propose a new method of assessing severe aortic stenosis by using infrasonic hemodynography to detect its characteristic systolic ejection murmur before and after transcatheter aortic valve replacement.

Convolution Neural Network Algorithm for Shockable Arrhythmia Classification Within a Digitally Connected Automated External Defibrillator.

Background Diagnosis of shockable rhythms leading to defibrillation remains integral to improving out-of-hospital cardiac arrest outcomes. New machine learning techniques have emerged to diagnose arrhythmias on ECGs. In out-of-hospital cardiac arrest, an algorithm within an automated external defibrillator is the major determinant to deliver defibrillation. This study developed and validated the performance of a convolution neural network (CNN) to diagnose shockable arrhythmias within a novel, miniaturized automated external defibrillator. Methods and Results There were 26 464 single-lead ECGs that comprised the study data set. ECGs of 7-s duration were retrospectively adjudicated by 3 physician readers (N=18 total readers). After exclusions (N=1582), ECGs were divided into training (N=23 156), validation (N=721), and test data sets (N=1005). CNN performance to diagnose shockable and nonshockable rhythms was reported with area under the receiver operating characteristic curve analysis, F1, and sensitivity and specificity calculations. The duration for the CNN to output was reported with the algorithm running within the automated external defibrillator. Internal and external validation analyses included CNN performance among arrhythmias, often mistaken for shockable rhythms, and performance among ECGs modified with noise to mimic artifacts. The CNN algorithm achieved an area under the receiver operating characteristic curve of 0.995 (95% CI, 0.990-1.0), sensitivity of 98%, and specificity of 100% to diagnose shockable rhythms. The F1 scores were 0.990 and 0.995 for shockable and nonshockable rhythms, respectively. After input of a 7-s ECG, the CNN generated an output in 383±29 ms (total time of 7.383 s). The CNN outperformed adjudicators in classifying atrial arrhythmias as nonshockable (specificity of 99.3%-98.1%) and was robust against noise artifacts (area under the receiver operating characteristic curve range, 0.871-0.999). Conclusions We demonstrate high diagnostic performance of a CNN algorithm for shockable and nonshockable rhythm arrhythmia classifications within a digitally connected automated external defibrillator. Registration URL: https://clinicaltrials.gov/ct2/show/NCT03662802; Unique identifier: NCT03662802.

Bidirectional ventricular tachycardia due to coronary allograft vasculopathy a unique presentation.

Bidirectional ventricular tachycardia (BVT) is an uncommon type of polymorphic ventricular tachycardia (PVT) with alternating polarity of the QRS complex most commonly described digitalis toxicity. Recent data has demonstrated the possible molecular basis of this electrocardiographic phenomenon. To our knowledge this is the first reported case of BVT in a patient with orthotopic cardiac transplantation and coronary allograft vasculopathy.

Artificial intelligence in healthcare: a primer for medical education in radiomics.

The application of artificial intelligence (AI) to healthcare has garnered significant enthusiasm in recent years. Despite the adoption of new analytic approaches, medical education on AI is lacking. We aim to create a usable AI primer for medical education. We discuss how to generate a clinical question involving AI, what data are suitable for AI research, how to prepare a dataset for training and how to determine if the output has clinical utility. To illustrate this process, we focused on an example of how medical imaging is employed in designing a machine learning model. Our proposed medical education curriculum addresses AI's potential and limitations for enhancing clinicians' skills in research, applied statistics and care delivery.

The application of artificial intelligence (AI) to healthcare has generated increasing interest in recent years; however, medical education on AI is lacking. With this primer, we provide an overview on how to understand AI, gain exposure to machine learning (ML) and how to develop research questions utilizing ML. Using an example of a ML application in imaging, we provide a practical approach to understanding and executing a ML analysis. Our proposed medical education curriculum provides a framework for healthcare education which we hope will propel healthcare institutions to implement ML laboratories and training environments and improve access to this transformative paradigm.

COVID-19 telehealth preparedness: a cross-sectional assessment of cardiology practices in the USA.

Aim: The COVID-19 pandemic forced medical practices to augment healthcare delivery to remote and virtual services. We describe the results of a nationwide survey of cardiovascular professionals regarding telehealth perspectives. Materials & methods: A 31-question survey was sent early in the pandemic to assess the impact of COVID-19 on telehealth adoption & reimbursement. Results: A total of 342 clinicians across 42 states participated. 77% were using telehealth, with the majority initiating usage 2 months after the COVID-19 shutdown. A variety of video-based systems were used. Telehealth integration requirements differed, with electronic medical record integration being mandated in more urban than rural practices (70 vs 59%; p < 0.005). Many implementation barriers surfaced, with over 75% of respondents emphasizing reimbursement uncertainty and concerns for telehealth generalizability given the complexity of cardiovascular diseases. Conclusion: Substantial variation exists in telehealth practices. Further studies and legislation are needed to improve access, reimbursement and the quality of telehealth-based cardiovascular care.

As the COVID-19 pandemic was just beginning, the American College of Cardiology administered a survey to cardiology professionals across the USA regarding their preparedness for telehealth and video-visits. The results demonstrated rapid adoption of video based telehealth services, however revealed uncertainty for how to best use these services in different practice settings. Many providers expressed concerns about how these visits will be compensated, but fortunately federal agencies have dramatically changed the way telehealth is reimbursed as the pandemic has progressed. Further studies are needed to explore the impact of telehealth on healthcare inequality, however we hope that rather it serves to increase healthcare access to all.

Identifying novel phenotypes of elevated left ventricular end diastolic pressure using hierarchical clustering of features derived from electromechanical waveform data.

Introduction: Elevated left ventricular end diastolic pressure (LVEDP) is a consequence of compromised left ventricular compliance and an important measure of myocardial dysfunction. An algorithm was developed to predict elevated LVEDP utilizing electro-mechanical (EM) waveform features. We examined the hierarchical clustering of selected features developed from these EM waveforms in order to identify important patient subgroups and assess their possible prognostic significance. Materials and methods: Patients presenting with cardiovascular symptoms (N = 396) underwent EM data collection and direct LVEDP measurement by left heart catheterization. LVEDP was classified as non-elevated ( ≤ 12 mmHg) or elevated (≥25 mmHg). The 30 most contributive features to the algorithm output were extracted from EM data and input to an unsupervised hierarchical clustering algorithm. The resultant dendrogram was divided into five clusters, and patient metadata overlaid. Results: The cluster with highest LVEDP (cluster 1) was most dissimilar from the lowest LVEDP cluster (cluster 5) in both clustering and with respect to clinical characteristics. In contrast to the cluster demonstrating the highest percentage of elevated LVEDP patients, the lowest was predominantly non-elevated LVEDP, younger, lower BMI, and males with a higher rate of significant coronary artery disease (CAD). The next adjacent cluster (cluster 2) to that of the highest LVEDP (cluster 1) had the second lowest LVEDP of all clusters. Cluster 2 differed from Cluster 1 primarily based on features extracted from the electrical data, and those that quantified predictability and variability of the signal. There was a low predictability and high variability in the highest LVEDP cluster 1, and the opposite in adjacent cluster 2. Conclusion: This analysis identified subgroups of patients with varying degrees of LVEDP elevation based on waveform features. An approach to stratify movement between clusters and possible progression of myocardial dysfunction may include changes in features that differentiate clusters; specifically, reductions in electrical signal predictability and increases in variability. Identification of phenotypes of myocardial dysfunction evidenced by elevated LVEDP and knowledge of factors promoting transition to clusters with higher levels of left ventricular filling pressures could permit early risk stratification and improve patient selection for novel therapeutic interventions.

In-ear infrasonic hemodynography with a digital health device for cardiovascular monitoring using the human audiome.

Human bodily mechanisms and functions produce low-frequency vibrations. Our ability to perceive these vibrations is limited by our range of hearing. However, in-ear infrasonic hemodynography (IH) can measure low-frequency vibrations (<20 Hz) created by vital organs as an acoustic waveform. This is captured using a technology that can be embedded into wearable devices such as in-ear headphones. IH can acquire sound signals that travel within arteries, fluids, bones, and muscles in proximity to the ear canal, allowing for measurements of an individual's unique audiome. We describe the heart rate and heart rhythm results obtained in time-series analysis of the in-ear IH data taken simultaneously with ECG recordings in two dedicated clinical studies. We demonstrate a high correlation (r = 0.99) between IH and ECG acquired interbeat interval and heart rate measurements and show that IH can continuously monitor physiological changes in heart rate induced by various breathing exercises. We also show that IH can differentiate between atrial fibrillation and sinus rhythm with performance similar to ECG. The results represent a demonstration of IH capabilities to deliver accurate heart rate and heart rhythm measurements comparable to ECG, in a wearable form factor. The development of IH shows promise for monitoring acoustic imprints of the human body that will enable new real-time applications in cardiovascular health that are continuous and noninvasive.

Multicenter validation of a machine learning phase space electro-mechanical pulse wave analysis to predict elevated left ventricular end diastolic pressure at the point-of-care.

BACKGROUND: Phase space is a mechanical systems approach and large-scale data representation of an object in 3-dimensional space. Whether such techniques can be applied to predict left ventricular pressures non-invasively and at the point-of-care is unknown. OBJECTIVE: This study prospectively validated a phase space machine-learned approach based on a novel electro-mechanical pulse wave method of data collection through orthogonal voltage gradient (OVG) and photoplethysmography (PPG) for the prediction of elevated left ventricular end diastolic pressure (LVEDP). METHODS: Consecutive outpatients across 15 US-based healthcare centers with symptoms suggestive of coronary artery disease were enrolled at the time of elective cardiac catheterization and underwent OVG and PPG data acquisition immediately prior to angiography with signals paired with LVEDP (IDENTIFY; NCT #03864081). The primary objective was to validate a ML algorithm for prediction of elevated LVEDP using a definition of ≥25 mmHg (study cohort) and normal LVEDP ≤ 12 mmHg (control cohort), using AUC as the measure of diagnostic accuracy. Secondary objectives included performance of the ML predictor in a propensity matched cohort (age and gender) and performance for an elevated LVEDP across a spectrum of comparative LVEDP (<12 through 24 at 1 mmHg increments). Features were extracted from the OVG and PPG datasets and were analyzed using machine-learning approaches. RESULTS: The study cohort consisted of 684 subjects stratified into three LVEDP categories, ≤12 mmHg (N = 258), LVEDP 13-24 mmHg (N = 347), and LVEDP ≥25 mmHg (N = 79). Testing of the ML predictor demonstrated an AUC of 0.81 (95% CI 0.76-0.86) for the prediction of an elevated LVEDP with a sensitivity of 82% and specificity of 68%, respectively. Among a propensity matched cohort (N = 79) the ML predictor demonstrated a similar result AUC 0.79 (95% CI: 0.72-0.8). Using a constant definition of elevated LVEDP and varying the lower threshold across LVEDP the ML predictor demonstrated and AUC ranging from 0.79-0.82. CONCLUSION: The phase space ML analysis provides a robust prediction for an elevated LVEDP at the point-of-care. These data suggest a potential role for an OVG and PPG derived electro-mechanical pulse wave strategy to determine if LVEDP is elevated in patients with symptoms suggestive of cardiac disease.

Development of a culturally and linguistically sensitive virtual reality educational platform to improve vaccine acceptance within a refugee population: the SHIFA community engagement-public health innovation programme.

OBJECTIVES: To combat misinformation, engender trust and increase health literacy, we developed a culturally and linguistically appropriate virtual reality (VR) vaccination education platform using community-engaged approaches within a Somali refugee community. DESIGN: Community-based participatory research (CBPR) methods including focus group discussions, interviews, and surveys were conducted with Somali community members and expert advisors to design the educational content. Co-design approaches with community input were employed in a phased approach to develop the VR storyline. PARTICIPANTS: 60 adult Somali refugees and seven expert advisors who specialise in healthcare, autism research, technology development and community engagement. SETTING: Somali refugees participated at the offices of a community-based organisation, Somali Family Service, in San Diego, California and online. Expert advisors responded to surveys virtually. RESULTS: We find that a CBPR approach can be effectively used for the co-design of a VR educational programme. Additionally, cultural and linguistic sensitivities can be incorporated within a VR educational programme and are essential factors for effective community engagement. Finally, effective VR utilisation requires flexibility so that it can be used among community members with varying levels of health and technology literacy. CONCLUSION: We describe using community co-design to create a culturally and linguistically sensitive VR experience promoting vaccination within a refugee community. Our approach to VR development incorporated community members at each step of the process. Our methodology is potentially applicable to other populations where cultural sensitivities and language are common health education barriers.

Digital Health: Opportunities and Challenges to Develop the Next-Generation Technology-Enabled Models of Cardiovascular Care.

The wide gap between the development of new healthcare technologies and their integration into clinical practice argues for a deeper understanding of how effective quality improvement can be designed to meet the needs of patients and their clinical teams. The COVID-19 pandemic has forced us to address this gap and create long-term strategies to bridge it. On the one hand, it has enabled the rapid implementation of telehealth. On the other hand, it has raised important questions about our preparedness to adopt and employ new digital tools as part of a new process of care. While healthcare organizations are seeking to improve the quality of care by integrating innovations in digital health, they must also address key issues such as patient experience, develop clinical decision support systems that analyze digital health data trends, and create efficient clinical workflows. Given the breadth of such requirements, embracing new technologies as a core competency of a modern healthcare system introduces a host of questions, such as "How best do patients participate in digital health programs that promote behavioral changes and mitigate risk?" and "What type of data analytics are required that enable a deeper understanding of disease phenotypes and corresponding treatment decisions?" This review presents the challenges in implementing digital health technology and discusses how patient-centered digital health programs are designed within real-world models of remote monitoring. It also provides a framework for developing new devices and wearables for the next generation of data-driven, technology-enabled cardiovascular care.