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Abstract Chronic obstructive pulmonary disease (COPD) is a common, costly, and morbid condition. Pulmonary rehabilitation, close monitoring, and early intervention during acute exacerbations of symptoms represent a comprehensive approach to improve outcomes, but the optimal means of delivering these services is uncertain. Logistical, financial, and social barriers to providing healthcare through face-to-face encounters, paired with recent developments in technology, have stimulated interest in exploring alternative models of care. The Healthy at Home study seeks to determine the feasibility of a multimodal, digitally enhanced intervention provided to participants with COPD longitudinally over 6 months. This paper details the recruitment, methods, and analysis plan for the study, which is recruiting 100 participants in its pilot phase. Participants were provided with several integrated services including a smartwatch to track physiological data, a study app to track symptoms and study instruments, access to a mobile integrated health program for acute clinical needs, and a virtual comprehensive pulmonary support service. Participants shared physiologic, demographic, and symptom reports, electronic health records, and claims data with the study team, facilitating a better understanding of their symptoms and potential care needs longitudinally. The Healthy at Home study seeks to develop a comprehensive digital phenotype of COPD by tracking and responding to multiple indices of disease behavior and facilitating early and nuanced responses to changes in participants’ health status. This study is registered at Clinicaltrials.gov (NCT06000696).

BackgroundStroke continues to be a leading cause of death and disability worldwide despite improvements in prevention and treatment. Traditional stroke risk calculators are biased and imprecise. Novel stroke predictors need to be identified. Recently, deep neural networks (DNNs) have been used to determine age from ECGs, otherwise known as the electrocardiographic-age (ECG-age), which predicts clinical outcomes. However, the relationship between ECG-age and stroke has not been well studied. We hypothesized that ECG-age is associated with incident stroke.MethodsIn this study, UK Biobank participants with available ECGs (from 2014 or later). ECG-age was estimated using a deep neural network (DNN) applied to raw ECG waveforms. We calculated the Δage (ECG-age minus chronological age) and classified individuals as having normal, accelerated, or decelerated aging if Δage was within, higher, or lower than the mean absolute error of the model, respectively. Multivariable Cox proportional hazards regression models adjusted for age, sex, and clinical factors were used to assess the association between Δage and incident stroke.ResultsThe study population included 67,757 UK Biobank participants (mean age 65 ± 8 years; 48.3% male). Every 10-year increase in Δage was associated with a 22% increase in incident stroke [HR, 1.22 (95% CI, 1.00–1.49)] in the multivariable-adjusted model. Accelerated aging was associated with a 42% increase in incident stroke [HR, 1.42 (95% CI, 1.12–1.80)] compared to normal aging. In addition, Δage was associated with prevalent stroke [OR, 1.28 (95% CI, 1.11–1.49)].ConclusionsDNN-estimated ECG-age was associated with incident and prevalent stroke in the UK Biobank. Further investigation is required to determine if ECG-age can be used as a reliable biomarker of stroke risk.

AbstractBackground Early detection of AF is critical for stroke prevention. Several commercially available smartwatches are FDA cleared for AF detection. However, little is known about how patient-physician relationships affect patients’ anxiety, activation, and health-related quality of life when prescribed smartwatch for AF detection.Methods Data were used from the Pulsewatch study (NCT03761394), which randomized adults (>50 years) with no contraindication to anticoagulation and a CHA2DS2-VASc risk score ≥2 to receive a smartwatch-smartphone app dyad for AF monitoring vs. conventional monitoring with an ECG patch (Cardea SoloTM) and monitored participants for up to 45 days. The Perceived Efficacy in Patient-Physician Interactions survey was used to assess patient confidence in physician interaction at baseline with scores ≥45 indicating high perceived efficacy in patient-provider interactions. Generalized Anxiety Disorder-7 Scale, Consumer Health Activation Index, and Short-Form Health Survey were utilized to examine anxiety, patient activation, and physical and mental health status, at baseline, 14, and 44 days, respectively. We used mixed-effects repeated measures linear regression models to assess changes in psychosocial outcomes among smartwatch users in relation to self-reported efficacy in physician interaction over the study period.Results A total of 93 participants (average age 64.1 ± 8.9 years; 43.0% female; 88.2% non-Hispanic white) were included in this analysis. At baseline, fifty-six (60%) participants reported high perceived efficacy in patient-physician interaction. In the fully adjusted models, high perceived efficacy (vs. low) at baseline was associated with greater patient activation and perceived mental health (β 12.0, p-value

Abstract Background Screening for atrial fibrillation (AF) is appealing because AF is common, when undiagnosed may increase stroke risk, and stroke is preventable with anticoagulants. This study assessed patient and primary care practitioner (PCP) acceptability of screening for AF using a 30-s single-lead electrocardiogram (SL-ECG) during outpatient visits. Methods Secondary analyses of a cluster randomized trial. All patients ≥ 65 years old without prevalent AF seen during a 1-year period and their PCPs. Screening using a SL-ECG was performed by medical assistants during check-in at 8 intervention sites among verbally consenting patients. PCPs were notified of “possible AF” results; management was left to their discretion. Control practices continued with usual care. Following the trial, PCPs were surveyed about AF screening. Outcomes included screening uptake and results, and PCP preferences for screening. Results Fifteen thousand three hundred ninety three patients were seen in intervention practices (mean age 73.9 years old, 59.7% female). Screening occurred at 78% of 38,502 individual encounters, and 91% of patients completed ≥ 1 screening. The positive predictive value of a “Possible AF” result (4.7% of SL-ECG tracings) at an encounter prior to a new AF diagnosis was 9.5%. Same-day 12-lead ECGs were slightly more frequent among intervention (7.0%) than control (6.2%) encounters (p = 0.07). Among the 208 PCPs completing a survey (73.6%; 78.9% intervention, 67.7% control), most favored screening for AF (87.2% vs. 83.6%, respectively), though SL-ECG screening was favored by intervention PCPs (86%) while control PCPs favored pulse palpation (65%). Both groups were less certain if AF screening should be done outside of office visits with patch monitors (47% unsure) or consumer devices (54% unsure). Conclusions Though the benefits and harms of screening for AF remain uncertain, most older patients underwent screening and PCPs were able to manage SL-ECG results, supporting the feasibility of routine primary care screening. PCPs exposed to a SL-ECG device preferred it over pulse palpation. PCPs were largely uncertain about AF screening done outside of practice visits. Trial registration ClinicalTrials.gov NCT03515057. Registered May 3, 2018.

ObjectivesTo examine the associations between catheter ablation treatment (CA) vs. medical management and cognitive impairment among older adults with atrial fibrillation (AF).MethodsAmbulatory patients who had AF, were ≥65-years-old, and were eligible to receive oral anticoagulation could be enrolled into the SAGE (Systematic Assessment of Geriatric Elements)-AF study from internal medicine and cardiology clinics in Massachusetts and Georgia between 2016 and 2018. Cognitive function was assessed using the Montreal Cognitive Assessment (MoCA) tool at baseline, 1-, and 2 years. Cognitive impairment was defined as a MoCA score ≤ 23. Multivariate-adjusted logistic regression of longitudinal repeated measures was used to examine associations between treatment with CA vs. medical management and cognitive impairment.Results887 participants were included in this analysis. On average, participants were 75.2 ± 6.7 years old, 48.6% women, and 87.4% white non-Hispanic. 193 (21.8%) participants received a CA before enrollment. Participants who had previously undergone CA were significantly less likely to be cognitively impaired during the 2-year study period (aOR 0.70, 95% CI 0.50–0.97) than those medically managed (i.e., rate and/or rhythm control), even after adjusting with propensity score for CA. At the 2-year follow-up a significantly greater number of individuals in the non-CA group were cognitively impaired (MoCA ≤ 23) compared to the CA-group (311 [44.8%] vs. 58 [30.1%], p = 0.0002).ConclusionIn this 2-year longitudinal prospective cohort study participants who underwent CA for AF before enrollment were less likely to have cognitive impairment than those who had not undergone CA.

Background Consumer wearable devices with health and wellness features are increasingly common and may enhance disease detection and management. Yet studies informing relationships between wearable device use, attitudes toward device data, and comprehensive clinical profiles are lacking. Methods and Results WATCH‐IT (Wearable Activity Tracking for Comprehensive Healthcare‐Integrated Technology) studied adults receiving longitudinal primary or ambulatory cardiovascular care in the Mass General Brigham health care system from January 2010 to July 2021. Participants completed a 20‐question electronic survey about perceptions and use of consumer wearable devices, with responses linked to electronic health records. Multivariable logistic regression was used to identify factors associated with device use. Among 214 992 individuals receiving longitudinal primary or cardiovascular care with an active electronic portal, 11 121 responded (5.2%). Most respondents (55.8%) currently used a wearable device, and most nonusers (95.3%) would use a wearable if provided at no cost. Although most users (70.2%) had not shared device data with their doctor previously, most believed it would be very (20.4%) or moderately (34.4%) important to share device‐related health information with providers. In multivariable models, older age (odds ratio [OR], 0.80 per 10‐year increase [95% CI, 0.77–0.82]), male sex (OR, 0.87 [95% CI, 0.80–0.95]), and heart failure (OR, 0.75 [95% CI, 0.63–0.89]) were associated with lower odds of wearable device use, whereas higher median income (OR, 1.08 per 1‐quartile increase [95% CI, 1.04–1.12]) and care in a cardiovascular medicine clinic (OR, 1.17 [95% CI, 1.05–1.30]) were associated with greater odds of device use. Conclusions Among patients in primary and cardiovascular medicine clinics, consumer wearable device use is common, and most users perceive value in wearable health data.

BackgroundIncreasing ownership of smartphones among Americans provides an opportunity to use these technologies to manage medical conditions. We examine the influence of baseline smartwatch ownership on changes in self-reported anxiety, patient engagement, and health-related quality of life when prescribed smartwatch for AF detection.MethodWe performed a post-hoc secondary analysis of the Pulsewatch study (NCT03761394), a clinical trial in which 120 participants were randomized to receive a smartwatch-smartphone app dyad and ECG patch monitor compared to an ECG patch monitor alone to establish the accuracy of the smartwatch-smartphone app dyad for detection of AF. At baseline, 14 days, and 44 days, participants completed the Generalized Anxiety Disorder-7 survey, the Health Survey SF-12, and the Consumer Health Activation Index. Mixed-effects linear regression models using repeated measures with anxiety, patient activation, physical and mental health status as outcomes were used to examine their association with smartwatch ownership at baseline.ResultsNinety-six participants, primarily White with high income and tertiary education, were randomized to receive a study smartwatch-smartphone dyad. Twenty-four (25%) participants previously owned a smartwatch. Compared to those who did not previously own a smartwatch, smartwatch owners reported significant greater increase in their self-reported physical health (β = 5.07, P

Abstract Long-term use of digital devices is critical for successful clinical or research use, but digital health studies are challenged by a rapid drop-off in participation. A nested e-cohort (eFHS) is embedded in the Framingham Heart Study and uses three system components: a new smartphone app, a digital blood pressure (BP) cuff, and a smartwatch. This study aims to identify factors associated with the use of individual eFHS system components over 1-year. Among 1948 eFHS enrollees, we examine participants who returned surveys within 90 days (n = 1918), and those who chose to use the smartwatch (n = 1243) and BP cuff (n = 1115). For each component, we investigate the same set of candidate predictors for usage and use generalized linear mixed models to select predictors (P

Artificial intelligence (AI) and machine learning (ML) have significantly impacted the field of cardiovascular medicine, especially cardiac electrophysiology (EP), on multiple fronts. The goal of this review is to familiarize readers with the field of AI and ML and their emerging role in EP. The current review is divided into 3 sections. In the first section, we discuss the definitions and basics of AI, ML, and big data. In the second section, we discuss their application to EP in the context of detection, prediction, and management of arrhythmias. Finally, we discuss the regulatory issues, challenges, and future directions of AI in EP.

Abstract The COVID-19 pandemic offers a unique context and opportunity to investigate changes in healthcare professional perceptions towards the adoption of novel medical technologies, such as point-of-care technologies (POCTs). POCTs are a nascent technology that has experienced rapid growth as a result of COVID-19 due to their ability to increase healthcare accessibility via near-patient delivery, including at-home. We surveyed healthcare professionals before and during COVID-19 to explore whether the pandemic altered their perceptions about the usefulness of POCTs. Our network analysis method provided a structure for understanding this changing phenomenon. We uncovered that POCTs are not only useful for diagnosing COVID-19, but healthcare professionals also perceive them as increasingly important for diagnosing other diseases, such as cardiovascular, endocrine, respiratory, and metabolic diseases. Healthcare professionals also viewed POCTs as facilitating the humanization of epidemiology by improving disease management/monitoring and strengthening the clinician-patient relationship. As the accuracy and integration of these technologies into mainstream healthcare delivery improves, hurdles to their adoption dissipate, thereby encouraging healthcare professionals to rely upon them more frequently to diagnose, manage, and monitor diseases. The technological advances made in POCTs during COVID-19, combined with shifting positive perceptions of their utility by healthcare professionals, may better prepare us for the next pandemic.

Background Hypertension is an important modifiable risk factor of serious maternal morbidity and mortality. Social determinants of health (SDoH) influence hypertension outcomes and may contribute to racial and ethnic differences in hypertension control. Our objective was to assess SDoH and blood pressure (BP) control by race and ethnicity in US women of childbearing age with hypertension. Methods and Results We studied women (aged 20–50 years) with hypertension (systolic BP ≥140 mm Hg or diastolic BP ≥90 mm Hg or use of antihypertensive medication) in the National Health and Nutrition Examination Surveys 2001 to 2018. SDoH and BP control (systolic BP

Background: Point-of-care testing (POCT) has applications across medical specialties and holds promise to improve patient care. While cardiovascular medicine has been attractive for POCT applications in recent years, little is known about how cardiovascular health professionals perceive them. Objective: The objective of our study was to examine differences in perceptions and attitudes towards POCTs between cardiovascular health professionals compared to other healthcare professionals. Methods: We surveyed healthcare professionals to assess perceptions of POCT usage and their benefits and concerns between October 2019 and March 2020. Questions regarding POCT perceptions were assessed on a 5-point Likert scale. Results: We received a total of 148 survey responses; of the responders, 52% were male, 59% were physicians, and 50% worked in a hospital setting. We found that cardiology professionals were less likely, compared to other specialties, to view POCTs as improving patient management or reducing errors. These cardiology professionals were not constrained by resources or a lack of investment opportunities to implement these technologies. Conclusion: This study provides a better understanding of perceptions about POCTs among healthcare specialists. To improve patient outcomes through the adoption and usage of POCTs, greater collaboration is advised among key industry and healthcare stakeholders.

Background: Cancer survivors face increased risk of heart disease, including atrial fibrillation (AF). Certain types of technology, such as consumer wearable devices, can be useful to monitor for AF, but little is known about wearables and AF monitoring in cancer survivor populations. Objective: The purpose of this study was to understand technology usage and preferences in cancer survivors with or at risk for AF, and to describe demographic factors associated with wearable device ownership in this population. Methods: Eligible patients completed a remote survey assessment regarding use of commercial wearable devices. The survey contained questions designed to assess commercial wearable device use, electronic health communications, and perceptions regarding the participant’s cardiac health. Results: A total of 424 cancer survivors (mean age 74.2 years; 53.1% female; 98.8% white) were studied. Although most participants owned a smartphone (85.9%), only 31.8% owned a wearable device. Over half (53.5%) of cancer survivors were worried about their heart health. Overall, patients believed arrhythmias (79.7%) were the most important heart condition for a wearable to detect. Survivors reported being most willing to share blood pressure (95.6%) and heart rate (95.3%) data with their providers and were least willing to share information about their diet, weight, and physical activity using these devices. Conclusion: Understanding factors such as device ownership, usage, and heart health concerns in cancer survivors can play an important role in improving cardiovascular monitoring and its accessibility. Long-term patient outcomes may be improved by incorporating wearable devices into routine care of cancer survivors.

Background: Heart-healthy diets are important in the prevention and treatment of hypertension (HTN), including among pregnant women. Yet, the barriers, facilitators, and beliefs/preferences regarding healthy eating are not well described in this population. Objective: To identify barriers and facilitators to healthy diet, examine the prevalence of food insecurity, and determine interest in specific healthy diet interventions. Methods: Pregnant women, aged 18–50 years (N = 38), diagnosed with HTN, hypertensive disorders in pregnancy (HDP), or risk factors for HDP, were recruited from a large academic medical center in central Massachusetts between June 2020 and June 2022. Participants completed an electronic survey using a 5-point Likert scale (strongly disagree to strongly agree). Results: The mean age of participants was 31.6 years (SD 5.5) and 35.1% identified as Hispanic. Finances and time were major barriers to a healthy diet, reported by 42.1% and 28.9% of participants, respectively. Participants reported that their partners and families were supportive of healthy eating and preparing meals at home, though 30.0% of those with children considered their children’s diet a barrier to preparing healthy meals. Additionally, 40.5% of the sample were considered food insecure. Everyone agreed that healthy diet was important for maternal and fetal health, and the most popular interventions were healthy ingredient grocery deliveries (89.4%) and meal deliveries (84.2%). Conclusion: Time and cost emerged as major challenges to healthy eating in these pregnant women. Such barriers, facilitators, and preferences can aid in intervention development and policy-level changes to mitigate obstacles to healthy eating in this vulnerable patient population.

Background: Telemedicine and commercial wearable devices capable of detecting atrial fibrillation (AF) have revolutionized arrhythmia care during coronavirus disease 2019. However, not much is known about virtual patient-provider interactions or device sharing behaviors. Objective: The purpose of this study was to characterize how participants with or at risk of AF are engaging with their providers in the context of telemedicine and using commercially wearable devices to manage their health. Methods: We developed a survey to describe participant behaviors around telemedicine encounters and commercial wearable device use. The survey was distributed to participants diagnosed with AF or those at risk of AF (as determined by being at least 65 years old and having a CHA2DS2-VASc stroke risk score of >2) in the University of Massachusetts Memorial Health Care system. Results: The survey was distributed to 23,530 patients, and there were 1222 (5.19%) participant responses. Among the participants, 327 (26.8%) had AF and 895 (73.2%) were at risk of AF. Neither device ownership nor device type use differed by AF status. After adjusting for covariates that may influence surveyed participant communication patterns, we found that participants with AF were more likely to share their wearable device-derived data with providers (adjusted odds ratio 1.87; 95% confidence interval 1.02–3.41). Rates of sharing physical activity or sleep data were low for both groups and did not differ by AF status. Conclusion: Compared with participants at risk of developing AF, those with AF were more likely to share heart rate and rhythm data from their commercial wearable devices with providers. However, both groups had similar rates of sharing physical activity and sleep data, telemedicine engagement, and technology use and ownership.

Abstract Background After hospital discharge, patients can experience symptoms prompting them to seek acute medical attention. Early evaluation of patients’ post-discharge symptoms by healthcare providers may improve appropriate healthcare utilization and patient safety. Post-discharge follow-up phone calls, which are used for routine transitional care in U.S. hospitals, serve as an important channel for provider-patient communication about symptoms. This study aimed to assess the facilitators and barriers to evaluating and triaging pain symptoms in cardiovascular patients through follow-up phone calls after their discharge from a large healthcare system in Central Massachusetts. We also discuss strategies that may help address the identified barriers. Methods Guided by the Practical, Robust, Implementation and Sustainability Model (PRISM), we completed semi-structured interviews with 7 nurses and 16 patients in 2020. Selected nurses conducted (or supervised) post-discharge follow-up calls on behalf of 5 clinical teams (2 primary care; 3 cardiology). We used thematic analysis to identify themes from interviews and mapped them to the domains of the PRISM model. Results Participants described common facilitators and barriers related to the four domains of PRISM: Intervention (I), Recipients (R), Implementation and Sustainability Infrastructure (ISI), and External Environment (EE). Facilitators include: (1) patients being willing to receive provider follow-up (R); (2) nurses experienced in symptom assessment (R); (3) good care coordination within individual clinical teams (R); (4) electronic health record system and call templates to support follow-up calls (ISI); and (5) national and institutional policies to support post-discharge follow-up (EE). Barriers include: (1) limitations of conducting symptom assessment by provider-initiated follow-up calls (I); (2) difficulty connecting patients and providers in a timely manner (R); (3) suboptimal coordination for transitional care among primary care and cardiology providers (R); and (4) lack of emphasis on post-discharge follow-up call reimbursement among cardiology clinics (EE). Specific barriers for pain assessment include: (1) concerns with pain medication misuse (R); and (2) no standardized pain assessment and triage protocol (ISI). Conclusions Strategies to empower patients, facilitate timely patient-provider communication, and support care coordination regarding pain evaluation and treatment may reduce the barriers and improve processes and outcomes of pain assessment and triage.

This collaborative statement from the Digital Health Committee of the Heart Rhythm Society provides everyday clinical scenarios in which wearables may be utilized by patients for cardiovascular health and arrhythmia management. We describe herein the spectrum of wearables that are commercially available for patients, and their benefits, shortcomings and areas for technological improvement. Although wearables for rhythm diagnosis and management have not been examined in large randomized clinical trials, undoubtedly the usage of wearables has quickly escalated in clinical practice. This document is the first of a planned series in which we will update information on wearables as they are revised and released to consumers.

Background: Atrial fibrillation (AF) is a common heart rhythm disorder that elevates stroke risk. Stroke survivors undergo routine heart rhythm monitoring for AF. Smartwatches are capable of AF detection and potentially can replace traditional cardiac monitoring in stroke patients. Objective: The goal of Pulsewatch is to assess the accuracy, usability, and adherence of a smartwatch-based AF detection system in stroke patients. Methods: The study will consist of two parts. Part I will have 6 focus groups with stroke patients, caretakers, and physicians, and a Hack-a-thon, to inform development of the Pulsewatch system. Part II is a randomized clinical trial with 2 phases designed to assess the accuracy and usability in the first phase (14 days) and adherence in the second phase (30 days). Participants will be randomized in a 3:1 ratio (intervention to control) for the first phase, and both arms will receive gold-standard electrocardiographic (ECG) monitoring. The intervention group additionally will receive a smartphone/smartwatch dyad with the Pulsewatch applications. Upon completion of 14 days, participants will be re-randomized in a 1:1 ratio. The intervention group will receive the Pulsewatch system and a handheld ECG device, while the control group will be passively monitored. Participants will complete questionnaires at enrollment and at 14- and 44-day follow-up visits to assess various psychosocial measures and health behaviors. Results: Part I was completed in August 2019. Enrollment for Part II began September 2019, with expected completion by the end of 2021. Conclusion: Pulsewatch aims to demonstrate that a smartwatch can be accurate for real-time AF detection, and that older stroke patients will find the system usable and will adhere to monitoring.

Background: The electronic Framingham Heart Study (eFHS) is an ongoing nested study, which includes FHS study participants, examining associations between health data from mobile devices with cardiovascular risk factors and disease. Objective: To describe application (app) design, report user characteristics, and describe usability and survey response rates. Methods: Eligible FHS participants were consented and offered a smartwatch (Apple Watch), a digital blood pressure (BP) cuff, and the eFHS smartphone app for administering surveys remotely. We assessed usability of the new app using 2 domains (functionality, aesthetics) of the Mobile App Rating Scale (MARS) and assessed survey completion rates at baseline and 3 months. Results: A total of 196 participants were recruited using the enhanced eFHS app. Of these, 97 (49.5%) completed the MARS instrument. Average age of participants was 53 ± 9 years, 51.5% were women, and 93.8% were white. Eighty-six percent of participants completed at least 1 measure on the baseline survey, and 50% completed the 3-month assessment. Overall subjective score of the app was 4.2 ± 0.7 on a scale from 1 to 5 stars. Of those who shared their health data with others, 46% shared their BP and 7.7% shared their physical activity with a health care provider. Conclusion: Participants rated the new, enhanced eFHS app positively overall. Mobile app survey completion rates were high, consistent with positive in-app ratings from participants. These mobile data collection modalities offer clinicians new opportunities to engage in conversations about health behaviors.

Background: Atrial fibrillation (AF) is the world’s most common heart rhythm disorder and even several minutes of AF episodes can contribute to risk for complications, including stroke. However, AF often goes undiagnosed owing to the fact that it can be paroxysmal, brief, and asymptomatic. Objective: To facilitate better AF monitoring, we studied the feasibility of AF detection using a continuous electrocardiogram (ECG) signal recorded from a novel wearable armband device. Methods: In our 2-step algorithm, we first calculate the R-R interval variability–based features to capture randomness that can indicate a segment of data possibly containing AF, and subsequently discriminate normal sinus rhythm from the possible AF episodes. Next, we use density Poincaré plot-derived image domain features along with a support vector machine to separate premature atrial/ventricular contraction episodes from any AF episodes. We trained and validated our model using the ECG data obtained from a subset of the MIMIC-III (Medical Information Mart for Intensive Care III) database containing 30 subjects. Results: When we tested our model using the novel wearable armband ECG dataset containing 12 subjects, the proposed method achieved sensitivity, specificity, accuracy, and F1 score of 99.89%, 99.99%, 99.98%, and 0.9989, respectively. Moreover, when compared with several existing methods with the armband data, our proposed method outperformed the others, which shows its efficacy. Conclusion: Our study suggests that the novel wearable armband device and our algorithm can be used as a potential tool for continuous AF monitoring with high accuracy.

Abstract Background Holistic care models emphasize management of comorbid conditions to improve patient-reported outcomes in treatment of atrial fibrillation (AF). We investigated relations between multimorbidity, physical frailty, and self-rated health (SRH) among older adults with AF. Methods Patients (n = 1235) with AF aged 65 years and older were recruited from five medical centers in Massachusetts and Georgia between 2015 and 2018. Ten previously diagnosed cardiometabolic and 8 non-cardiometabolic conditions were assessed from medical records. Physical Frailty was assessed with the Cardiovascular Health Study frailty scale. SRH was categorized as either “excellent/very good”, “good”, and “fair/poor”. Separate multivariable ordinal logistic models were used to examine the associations between multimorbidity and SRH, physical frailty and SRH, and multimorbidity and physical frailty. Results Overall, 16% of participants rated their health as fair/poor and 14% were frail. Hypertension (90%), dyslipidemia (80%), and heart failure (37%) were the most prevalent cardiometabolic conditions. Arthritis (51%), anemia (31%), and cancer (30%), the most common non-cardiometabolic diseases. After multivariable adjustment, patients with higher multimorbidity were more likely to report poorer health status (Odds Ratio (OR): 2.15 [95% CI: 1.53–3.03], ≥ 8 vs 1–4; OR: 1.37 [95% CI: 1.02–1.83], 5–7 vs 1–4), as did those with more prevalent cardiometabolic and non-cardiometabolic conditions. Patients who were pre-frail (OR: 1.73 [95% CI: 1.30–2.30]) or frail (OR: 6.81 [95% CI: 4.34–10.68]) reported poorer health status. Higher multimorbidity was associated with worse frailty status. Conclusions Multimorbidity and physical frailty were common and related to SRH. Our findings suggest that holistic management approaches may influence SRH among older patients with AF.

Background: Many digital health technologies capable of atrial fibrillation (AF) detection are directly available to patients. However, adaptation into clinical practice by heart rhythm healthcare practitioners (HCPs) is unclear. Objective: To examine HCP perspectives on use of commercial technologies for AF detection and management. Methods: We created an electronic survey for HCPs assessing practice demographics and perspectives on digital devices for AF detection and management. The survey was distributed electronically to all members of 3 heart rhythm professional societies. Results: We received 1601 responses out of 73,563 e-mails sent, with 43.6% from cardiac electrophysiologists, 12.8% from fellows, and 11.6% from advanced practice practitioners. Most respondents (62.3%) reported having recommended patient use of a digital device for AF detection. Those who did not had concerns about their accuracy (29.6%), clinical utility of results (22.8%), and integration into electronic health records (19.8%). Results from a 30-second single-lead electrocardiogram were sufficient for 42.7% of HCPs to recommend oral anticoagulation for patients at high risk for stroke. Respondents wanted more data comparing the accuracy of digital devices to conventional devices for AF monitoring (64.9%). A quarter (27.3%) of HCPs had no reservations recommending digital devices for AF detection, and most (53.4%) wanted guidelines from their professional societies providing guidance on their optimal use. Conclusion: Many HCPs have already integrated digital devices into their clinical practice. However, HCPs reported facing challenges when using digital technologies for AF detection, and professional society recommendations on their use are needed.

Background Little research has evaluated patient bleeding risk perceptions in comparison with calculated bleeding risk among oral anticoagulant users with atrial fibrillation. Our objective was to investigate underestimation of bleeding risk and to describe the characteristics and patient‐reported outcomes associated with underestimation of bleeding risk. Methods and Results In the SAGE‐AF (Systematic Assessment of Geriatric Elements in Atrial Fibrillation) study, a prospective cohort study of patients ≥65 years with atrial fibrillation, a CHA2DS2‐VASc risk score ≥2 and who were on oral anticoagulant therapy, we compared patients’ self‐reported bleeding risk with their predicted bleeding risk from their HAS‐BLED score. Among the 754 participants (mean age 74.8 years, 48.3% women), 68.0% underestimated their bleeding risk. Participants who were Asian or Pacific Islander, Black, Native American or Alaskan Native, Mixed Race or Hispanic (non‐White) (adjusted OR [AOR], 0.45; 95% CI, 0.24–0.82) and women (AOR, 0.62; 95% CI, 0.40–0.95) had significantly lower odds of underestimating their bleeding risk than respective comparison groups. Participants with a history of bleeding (AOR, 3.07; 95% CI, 1.73–5.44) and prior hypertension (AOR, 4.33; 95% CI, 2.43–7.72), stroke (AOR, 5.18; 95% CI, 1.87–14.40), or renal disease (AOR, 5.05; 95% CI, 2.98–8.57) had significantly higher odds of underestimating their bleeding risk. Conclusions We found that more than two‐thirds of patients with atrial fibrillation on oral anticoagulant therapy underestimated their bleeding risk and that participants with a history of bleeding and several comorbid conditions were more likely to underestimate their bleeding risk whereas non‐Whites and women were less likely to underestimate their bleeding risk. Clinicians should ensure that patients prescribed oral anticoagulant therapy have a thorough understanding of bleeding risk.

Background: Non-Whites are more likely to suffer from cognitive impairment and complications of atrial fibrillation (AF) than Whites, though Whites are more likely to be diagnosed with AF. We examined whether non-Whites with AF are biologically older than Whites with AF and whether accelerated biological aging is associated with cognitive functioning. Methods: We used baseline data from the ongoing Systematic Assessment of Geriatric Elements in Atrial Fibrillation prospective cohort study, collected 2016–2020 across ambulatory care practices in Massachusetts and Georgia. Of 1244 enrolled, 974 participants with full biological data were included in the present analysis. Accelerated aging (AccA) was calculated based on a combination of biomarkers associated with age and physiological “wear and tear.” Findings: The main outcome was score on Montreal Cognitive Assessment (MoCA). Non-Whites had 2.9 years more AccA than Whites and higher AccA was associated with a lower MoCA score among both Whites (-0.06, 95% CI: -0.10, -0.03) and non-Whites (-0.14, 95% CI: -0.27, 0.02). This association was significantly greater among non-whites (-0.11, 95% CI: -0.20, -0.01). Interpretation: Non-White AF patients are functionally “older” than their White counterparts and experience a stronger deleterious association between AccA and cognition. These findings underscore the importance of taking functional age into account when treating patients with AF, particularly non-White patients, to enhance treatment and improve AF outcomes. Funding: The SAGE-AF study is supported by Grant R01HL126911 from the National Heart, Lung, and Blood Institute (NHLBI).

Background Atrial fibrillation (AF) screening is endorsed by certain guidelines for individuals aged ≥65 years. Yet many AF screening strategies exist, including the use of wrist‐worn wearable devices, and their comparative effectiveness is not well‐understood. Methods and Results We developed a decision‐analytic model simulating 50 million individuals with an age, sex, and comorbidity profile matching the United States population aged ≥65 years (ie, with a guideline‐based AF screening indication). We modeled no screening, in addition to 45 distinct AF screening strategies (comprising different modalities and screening intervals), each initiated at a clinical encounter. The primary effectiveness measure was quality‐adjusted life‐years, with incident stroke and major bleeding as secondary measures. We defined continuous or nearly continuous modalities as those capable of monitoring beyond a single time‐point (eg, patch monitor), and discrete modalities as those capable of only instantaneous AF detection (eg, 12‐lead ECG). In total, 10 AF screening strategies were effective compared with no screening (300–1500 quality‐adjusted life‐years gained/100 000 individuals screened). Nine (90%) effective strategies involved use of a continuous or nearly continuous modality such as patch monitor or wrist‐worn wearable device, whereas 1 (10%) relied on discrete modalities alone. Effective strategies reduced stroke incidence (number needed to screen to prevent a stroke: 3087–4445) but increased major bleeding (number needed to screen to cause a major bleed: 1815–4049) and intracranial hemorrhage (number needed to screen to cause intracranial hemorrhage: 7693–16 950). The test specificity was a highly influential model parameter on screening effectiveness. Conclusions When modeled from a clinician‐directed perspective, the comparative effectiveness of population‐based AF screening varies substantially upon the specific strategy used. Future screening interventions and guidelines should consider the relative effectiveness of specific AF screening strategies.

Background Anticoagulation is the mainstay for stroke prevention in patients with atrial fibrillation, but concerns about bleeding inhibit its use in residents of long‐term care facilities. Risk‐profiling algorithms using comorbid disease information (eg, CHADS2 and ATRIA [Anticoagulation and Risk Factors in Atrial Fibrillation]) have been available for years. In the long‐term care setting, however, providers and residents may place more value on geriatric conditions such as mobility impairment, activities of daily living dependency, cognitive impairment, low body mass index, weight loss, and fall history. Methods and Results Using a retrospective cohort design, we measured the association between geriatric conditions and anticoagulation use and type. After merging nursing home assessments containing information about geriatric conditions (Minimum Data Set 2015) with Medicare Part A 2014 to 2015 claims and prescription claims (Medicare Part D) 2015 to 2016, we identified 228 741 residents with atrial fibrillation and elevated stroke risk (CHA2DS2‐VASc score ≥2) for our main analysis. Recent fall, activities of daily living dependency, moderate and severe cognitive impairment, low body mass index, and unintentional weight loss were all associated with lower anticoagulation use even after adjustment for multiple predictors of stroke and bleeding (odds ratios ranging from 0.51 to 0.91). Residents with recent fall, low body mass index, and unintentional weight loss were more likely to be using a direct oral anticoagulant, although the magnitude of this effect was smaller. Conclusions Geriatric conditions were associated with lower anticoagulation use. Preventing stroke in these residents with potential for further physical and cognitive impairment would appear to be of paramount significance, although the net benefit of anticoagulation in these individuals warrants further research.

Abstract Background Religious beliefs and practices influence coping mechanisms and quality of life in patients with various chronic illnesses. However, little is known about the influence of religious practices on changes in health-related quality of life (HRQOL) among hospital survivors of an acute coronary syndrome (ACS). The present study examined the association between several items assessing religiosity and clinically meaningful changes in HRQOL between 1 and 6 months after hospital discharge for an ACS. Methods We recruited patients hospitalized for an ACS at six medical centers in Central Massachusetts and Georgia (2011–2013). Participants reported making petition prayers for their health, awareness of intercessory prayers by others, and deriving strength/comfort from religion. Generic HRQOL was assessed with the SF-36®v2 physical and mental component summary scores. Disease-specific HRQOL was evaluated using the Seattle Angina Questionnaire Quality of Life subscale (SAQ-QOL). We separately examined the association between each measure of religiosity and the likelihood of experiencing clinically meaningful increase in disease-specific HRQOL (defined as increases by ≥10.0 points) and Generic HRQOL (defined as increases by ≥3.0 points) between 1- and 6-months post-hospital discharge. Results Participants (n = 1039) were, on average, 62 years old, 33% were women, and 86% were non-Hispanic White. Two-thirds reported praying for their health, 88% were aware of intercessions by others, and 85% derived strength/comfort from religion. Approximately 42, 40, and 26% of participants experienced clinically meaningful increases in their mental, physical, and disease-specific HRQOL respectively. After adjustment for sociodemographic, psychosocial, and clinical characteristics, petition (aOR:1.49; 95% CI: 1.09–2.04) and intercessory (aOR:1.72; 95% CI: 1.12–2.63) prayers for health were associated with clinically meaningful increases in disease-specific and physical HRQOL respectively. Conclusions Most ACS survivors in a contemporary, multiracial cohort acknowledged praying for their health, were aware of intercessory prayers made for their health and derived strength and comfort from religion. Patients who prayed for their health and those aware of intercessions made for their health experienced improvement in their generic physical and disease-specific HRQOL over time. Healthcare providers should recognize that patients may use prayer as a coping strategy for improving their well-being and recovery after a life-threatening illness.

We have developed a novel method to accurately detect QRS complex peaks using the variable frequency complex demodulation (VFCDM) method. The approach's novelty stems from reconstructing an ECG signal using only the frequency components associated with the QRS waveforms by VFCDM decomposition. After signal reconstruction, both top and bottom sides of the signal are used for peak detection, after which we compare the locations of the peaks detected from both sides to ensure that false peaks are minimized. Finally, we impose position-dependent adaptive thresholds to remove any remaining false peaks from the prior step. We applied the proposed method to the widely benchmarked MIT-BIH arrhythmia dataset and obtained among the best results compared with many of the recently published methods. Our approach resulted in 99.94% sensitivity, 99.95% positive predictive value, and a 0.11% detection error rate. Three other datasets-the MIMIC III database, University of Massachusetts atrial fibrillation data, and SCUBA diving in salt water ECG data-were used to further test the robustness of our proposed algorithm. For all these three datasets, our method retained consistently higher accuracy when compared with the BioSig Matlab toolbox, which is publicly available and known to be reliable for ECG peak detection.

Long-term electrocardiogram (ECG) signals recorded in an intensive care unit (ICU) are often corrupted by severe motion and noise artifacts (MNA), which may lead to many false alarms, including inaccurate detection of atrial fibrillation (AF). We developed an automated method to detect MNA from ECG recordings in the medical information mart for intensive care (MIMIC) III database. Since AF detection is often based on identification of irregular RR intervals derived from the QRS complexes, the main design focus of our MNA detection algorithm was to identify the corrupted QRS complexes of the ECG signals. The MNA in the MIMIC III database contains not only motion-induced noise but also a plethora of non-ECG waveforms, which must also be automatically identified. Our algorithm is designed to first discriminate between the ECG and non-ECG waveforms using both time and spectral-domain properties. For the segments of data containing ECG waveforms, a time-frequency spectrum and its subband decomposition approach were used to identify MNA and high-frequency noise ECG segments, respectively. The algorithm was tested on data from 35 subjects in normal sinus rhythm and 25 AF subjects. The proposed method is shown to accurately discriminate between segments that contained real ECG waveforms and those that did not, even though the latter were numerous in some subjects. In addition, we found a significant reduction (>94%) in false positive detection of AF in normal subjects when our MNA detection algorithm was used. Without using it, we inaccurately detected AF due to the MNA.

We developed a novel method for QRS complex and P wave detection in the electrocardiogram (ECG) signal. The approach reconstructs two different signals for the purpose of QRS and P wave detection from the modes obtained by the complete ensemble empirical mode decomposition with adaptive noise, taking only those modes that best represent the signal dynamics. This approach eliminates the need for conventional filtering. We first detect QRS complex locations, followed by removal of QRS complexes from the reconstructed signal to enable P wave detection. We introduce a novel method of P wave detection from both the positive and negative amplitudes of the ECG signal and an adaptive P wave search approach to find the true P wave. Our detection method automatically identifies P waves without prior information. The proposed method was validated on two well-known annotated databases—the MIT BIH Arrythmia database (MITDB) and The QT database (QTDB). The QRS detection algorithm resulted in 99.96% sensitivity, 99.9% positive predictive value, and an error of 0.13% on all validation databases. The P wave detection method had better performance when compared to other well-known methods. The performance of our P wave detection on the QTDB showed a sensitivity of 99.96%, a positive predictive value of 99.47%, and the mean error in P peak detection was less than or equal to one sample (4 ms) on average.

Background: Geriatric conditions, such as frailty and cognitive impairment, are prevalent in older patients with atrial fibrillation (AF). We examined the prognostic value of geriatric conditions for predicting 1-year mortality and bleeding events in these patients. Methods: SAGE (Systematic Assessment of Geriatric Elements)-AF study is a multicenter cohort study which enrolled individuals (mean age 75 years, 48% women, 86% taking oral anticoagulation) 65 years and older with AF and CHA2DS2 -VASc score of 2 or higher from clinics in Massachusetts and Georgia, USA between 2016 and 2018. A six-component geriatric assessment included validated measures of frailty, cognitive function, social support, depressive symptoms, vision, and hearing was performed at baseline. Study endpoints included all-cause mortality and clinically relevant bleeding. Results: At 1 year, 1,097 (96.5%) individuals attended the follow up visit, 44 (3.9%) had died, and 56 (5.1%) had clinically relevant bleeding. After adjustment for demographic and clinical factors, social isolation (odds ratio [OR] 1.69, 95% confidence interval [CI]: 1.01–2.84), depression (OR 1.94, 95% CI: 1.28–2.95) and frailty (OR 2.55, 95% CI: 1.55–4.19) were significantly associated with the composite endpoint of death or clinically relevant bleeding. After multivariable adjustment, depression (OR 1.79, 95% CI 1.09–2.93) and frailty (OR 2.83, 95% CI 1.55–5.17) were significantly associated with clinically relevant bleeding. Conclusions: Social isolation, depression, and frailty were prognostic of dying or experiencing clinically relevant bleeding during the coming year in older men and women with AF. Assessing geriatric impairments merits consideration in the care of these patients.

The commercialization of medical devices and biotechnology products is characterized by high failure rates and long development lead times particularly among start-up enterprises. To increase the success rate of these high-risk ventures, the University of Massachusetts Lowell (UML) and University of Massachusetts Medical School (UMMS) partnered to create key academic support centers with programs to accelerate entrepreneurship and innovation in this industry. In 2008, UML and UMMS founded the Massachusetts Medical Device Development Center (M2D2), which is a business and technology incubator that provides business planning, product prototyping, laboratory services, access to clinical testing, and ecosystem networking to medical device and biotech start-up firms. M2D2 has three physical locations that encompass approximately 40,000 square feet. Recently, M2D2 leveraged these resources to expand into new areas such as health security, point of care technologies for heart, lung, blood, and sleep disorders, and rapid diagnostics to detect SARS-CoV-2. Since its inception, M2D2 has vetted approximately 260 medical device and biotech start-up companies for inclusion in its programs and provided active support to more than 80 firms. This manuscript describes how two UMass campuses leveraged institutional, state, and Federal resources to create a thriving entrepreneurial environment for medical device and biotech companies.

Background Frailty is associated bidirectionally with cardiovascular disease. However, the relations between frailty and atrial fibrillation (AF) have not been fully elucidated. Methods and Results Using the FHS (Framingham Heart Study) Offspring cohort, we sought to examine both the association between frailty (2005–2008) and incident AF through 2016 and the association between prevalent AF and frailty status (2011–2014). Frailty was defined using the Fried phenotype. Models adjusted for age, sex, and smoking. Cox proportional hazards models, adjusted for competing risk of death, assessed the association between prevalent frailty and incident AF. Logistic regression models assessed the association between prevalent AF and new‐onset frailty. For the incident AF analysis, we included 2053 participants (56% women; mean age, 69.7±6.9 years). By Fried criteria, 1018 (50%) were robust, 903 (44%) were prefrail, and 132 (6%) were frail. In total, 306 incident cases of AF occurred during an average 9.2 (SD, 3.1) follow‐up years. After adjustment, there was no statistically significant association between prevalent frailty status and incident AF (prefrail versus robust: hazard ratio [HR], 1.22 [95% CI, 0.95–1.55]; frail versus robust: HR, 0.92 [95% CI, 0.57–1.47]). At follow‐up, there were 111 new cases of frailty. After adjustment, there was no statistically significant association between prevalent AF and new‐onset frailty (odds ratio, 0.48 [95% CI, 0.17–1.36]). Conclusions Although a bidirectional association between frailty and cardiovascular disease has been suggested, we did not find evidence of an association between frailty and AF. Our findings may be limited by sample size and should be further explored in other populations.

Background Among older patients with atrial fibrillation, there are limited data examining clinically meaningful changes in quality of life (QoL). We examined the extent of, and factors associated with, clinically meaningful change in QoL over 1‐year among older adults with atrial fibrillation. Methods and Results Patients from cardiology, electrophysiology, and primary care clinics in Massachusetts and Georgia were enrolled in a cohort study (2015–2018). The Atrial Fibrillation Effect on Quality‐of‐Life questionnaire was used to assess overall QoL and across 3 subscales: symptoms, daily activities, and treatment concern. Clinically meaningful change in QoL (ie, difference between 1‐year and baseline QoL score) was categorized as either a decline (≤−5.0 points), no clinically meaningful change (−5.0 to +5.0 points), or an increase (≥+5.0 points). Ordinal logistic models were used to examine factors associated with QoL changes. Participants (n=1097) were on average 75 years old, 48% were women, and 87% White. Approximately 40% experienced a clinically meaningful increase in QoL and 1 in every 5 patients experienced a decline in QoL. After multivariable adjustment, women, non‐Whites, those who reported depressive and anxiety symptoms, fair/poor self‐rated health, low social support, heart failure, or diabetes mellitus experienced clinically meaningful declines in QoL. Conclusions These findings provide insights to the magnitude of, and factors associated with, clinically meaningful change in QoL among older patients with atrial fibrillation. Assessment of comorbidities and psychosocial factors may help identify patients at high risk for declining QoL and those who require additional surveillance to maximize important clinical and patient‐centered outcomes.

Background Direct‐acting oral anticoagulant (DOAC) dosing guidelines for atrial fibrillation recommend dose alteration based on age, renal function, body weight, and drug‐drug interactions. There is paucity of data describing the frequency and factors associated with prescription of potentially inappropriate doses. Methods and Results In the ongoing SAGE‐AF (Systematic Assessment of Geriatric Elements in Atrial Fibrillation) study, we performed geriatric assessments (frailty, cognitive impairment, sensory impairments, social isolation, and depression) for participants with atrial fibrillation (age ≥65 years, CHA2DS2VASc ≥2, no anticoagulant contraindications). We developed an algorithm to analyze DOAC dose appropriateness accounting for drug‐drug interactions, age, renal function, and body weight. We also examined whether geriatric impairments were related to inappropriate dosing. Of 1064 patients prescribed anticoagulants, 460 received a DOAC. Participants were aged 74±7 years, 49% were women, and 82% were white. A quarter (23%; n=105) of participants received inappropriate DOAC dose, of whom 82 (78%) were underdosed and 23 (22%) were overdosed. Among participants receiving an inappropriate dose, 12 (11%) were identified using the drug‐drug interactions criteria and would have otherwise been misclassified. In multivariable regression analyses, older age, higher CHA2DS2VASc score, and history of renal failure were associated with inappropriate DOAC dosing (P

Point-of-care testing (POCT) is an emerging technology that provides crucial assistance in delivering healthcare. The COVID-19 pandemic led to the accelerated importance of POCT technology due to its in-home accessibility. While POCT use and implementation has increased, little research has been published about how healthcare professionals perceive these technologies. The objective of our study was to examine the current perspectives of healthcare professionals towards POCT. We surveyed healthcare professionals to quantify perceptions of POCT usage, adoption, benefits, and concerns between October 2020 and November 2020. Questions regarding POCT perception were assessed on a 5-point Likert Scale. We received a total of 287 survey responses. Of the respondents, 53.7% were male, 66.6% were white, and 30.7% have been in practice for over 20 years. We found that the most supported benefit was POCTs ability to improve patient management (92%) and that the most supported concern was that POCTs lead to over-testing (30%). This study provides a better understanding of healthcare workers’ perspectives on POCT. To improve patient outcomes through the usage of POCT, greater research is needed to assess the needs and concerns of industry and healthcare stakeholders.

Objective: We have developed a peak detection algorithm for accurate determination of heart rate, using photoplethysmographic (PPG) signals from a smartwatch, even in the presence of various cardiac rhythms, including normal sinus rhythm (NSR), premature atrial contraction (PAC), premature ventricle contraction (PVC), and atrial fibrillation (AF). Given the clinical need for accurate heart rate estimation in patients with AF, we developed a novel approach that reduces heart rate estimation errors when compared to peak detection algorithms designed for NSR. Methods: Our peak detection method is composed of a sequential series of algorithms that are combined to discriminate the various arrhythmias described above. Moreover, a novel Poincaré plot scheme is used to discriminate between basal heart rate AF and rapid ventricular response (RVR) AF, and to differentiate PAC/PVC from NSR and AF. Training of the algorithm was performed only with Samsung Simband smartwatch data, whereas independent testing data which had more samples than did the training data were obtained from Samsung’s Gear S3 and Galaxy Watch 3. Results: The new PPG peak detection algorithm provides significantly lower average heart rate and interbeat interval beat-to-beat estimation errors—30% and 66% lower—and mean heart rate and mean interbeat interval estimation errors—60% and 77% lower—when compared to the best of the seven other traditional peak detection algorithms that are known to be accurate for NSR. Our new PPG peak detection algorithm was the overall best performers for other arrhythmias. Conclusion: The proposed method for PPG peak detection automatically detects and discriminates between various arrhythmias among different waveforms of PPG data, delivers significantly lower heart rate estimation errors for participants with AF, and reduces the number of false negative peaks. Significance: By enabling accurate determination of heart rate despite the presence of AF with rapid ventricular response or PAC/PVCs, we enable clinicians to make more accurate recommendations for heart rate control from PPG data.

Photoplethysmography (PPG) is a technique to detect blood volume changes in an optical way. Representative PPG applications are the measurements of oxygen saturation, heart rate, and respiratory rate. However, the PPG signals are sensitive to motion and noise artifacts (MNAs), especially when they are obtained from smartphone cameras. Moreover, the PPG signals are different among users and each individual's PPG signal has a unique characteristic. Hence, an effective MNA detection and reduction method for smartphone PPG signals, which adapts itself to each user in a personalized way, is highly demanded. In this paper, a concept of the probabilistic neural network is introduced to be used with the proposed extracted parameters. The signal amplitude, standard deviation of peak to peak time intervals and amplitudes, along with the mean of moving standard deviation, signal slope changes, and the optimal autoregressive model order are proposed for effective MNA detection. Accordingly, the performance of the proposed personalized algorithm is compared with conventional MNA detection algorithms. As for the performance metrics, we considered accuracy, sensitivity, and specificity. The results show that the overall performance of the personalized MNA detection is enhanced compared to the generalized algorithm. The average values of the accuracy, sensitivity, and specificity of the personalized one are 98.07%, 92.6%, and 99.78%, respectively, while these are 89.92%, 84.21%, and 93.63% for the general one.

Sepsis is defined by life-threatening organ dysfunction during infection and is the leading cause of death in hospitals. During sepsis, there is a high risk that new onset of atrial fibrillation (AF) can occur, which is associated with significant morbidity and mortality. Consequently, early prediction of AF during sepsis would allow testing of interventions in the intensive care unit (ICU) to prevent AF and its severe complications. In this paper, we present a novel automated AF prediction algorithm for critically ill sepsis patients using electrocardiogram (ECG) signals. From the heart rate signal collected from 5-min ECG, feature extraction is performed using the traditional time, frequency, and nonlinear domain methods. Moreover, variable frequency complex demodulation and tunable Q-factor wavelet-transform-based time–frequency methods are applied to extract novel features from the heart rate signal. Using a selected feature subset, several machine learning classifiers, including support vector machine (SVM) and random forest (RF), were trained using only the 2001 Computers in Cardiology data set. For testing the proposed method, 50 critically ill ICU subjects from the Medical Information Mart for Intensive Care (MIMIC) III database were used in this study. Using distinct and independent testing data from MIMIC III, the SVM achieved 80% sensitivity, 100% specificity, 90% accuracy, 100% positive predictive value, and 83.33% negative predictive value for predicting AF immediately prior to the onset of AF, while the RF achieved 88% AF prediction accuracy. When we analyzed how much in advance we can predict AF events in critically ill sepsis patients, the algorithm achieved 80% accuracy for predicting AF events 10 min early. Our algorithm outperformed a state-of-the-art method for predicting AF in ICU patients, further demonstrating the efficacy of our proposed method. The annotations of patients’ AF transition information will be made publicly available for other investigators. Our algorithm to predict AF onset is applicable for any ECG modality including patch electrodes and wearables, including Holter, loop recorder, and implantable devices.

The objectives of this longitudinal study were to examine differences between whites and blacks, and across two geographical regions, in the socio-demographic, clinical, and psychosocial characteristics, hospital treatment practices, and post-discharge mortality for hospital survivors of an acute coronary syndrome (ACS). In this prospective cohort study, we performed in-person interviews and medical record abstractions for patients discharged from the hospital after an ACS at participating sites in Central Massachusetts and Central Georgia during 2011–2013. Among the 1143 whites in Central Massachusetts, 514 whites in Central Georgia, and 277 blacks in Central Georgia, we observed a gradient of socioeconomic position with whites in Central Massachusetts being the most privileged, followed by whites and then blacks from Central Georgia; similar gradients pertained to psychosocial vulnerability (e.g., 10.7%, 25.1%, and 49.1% had cognitive impairment, respectively) and to the hospital receipt of all 4 evidence-based cardiac medications (35.5%, 18.1%, and 14.4%, respectively) used in the acute management of patients hospitalized with an ACS. Multivariable adjusted odds ratios (95% confidence intervals) for the receipt of a percutaneous coronary intervention for whites and blacks in Georgia vs. whites in Massachusetts were 0.57 (0.46–0.71) and 0.40(0.30–0.52), respectively. Thirty-day and one-year mortality risks exhibited a similar gradient. The results of this contemporary clinical/epidemiologic study in a diverse patient cohort suggest that racial and geographic disparities continue to exist for patients hospitalized with an ACS.

Background Low gait speed has been linked with impaired mood, cognition, and quality of life (QOL) in older adults. We examined whether low gait speed was associated with impaired mood, cognition, and QOL among older adults with atrial fibrillation (AF). Methods and Results Participants (n=1185) had a diagnosis of AF, aged ≥65 years, CHA2DS2VASc ≥2 and had no contraindications to anticoagulation. Participants completed a 15‐foot walk test, and low gait speed was categorized using cutoffs from the Fried Frailty Index. Participants self‐reported measures of depressive symptoms (Patient Health Questionnaire 9 ≥10), anxiety symptoms (Generalized Anxiety Disorder 7 ≥10), cognitive impairment (Montreal Cognitive Assessment ≤23), and potentially impaired Atrial Fibrillation Effect Quality‐of‐Life Questionnaire

Background: Geriatric conditions are common among patients with atrial fibrillation (AF) and relate to complications of oral anticoagulation (OAC).Objective: To examine the prevalence of geriatric conditions among older patients with AF on OAC and relate type of OAC to geriatric conditions.Methods: Participants had a diagnosis of AF, were aged ≥65 years, CHA2DS2VASC ≥ 2, and had no OAC contraindications. Participants completed a 6-component geriatric assessment that included validated measures of frailty (CHS Frailty Scale), cognitive function (MoCA), social support (MOS), depressive symptoms (PHQ9), vision, and hearing. Type of OAC prescribed was documented in medical records.Results: 86% of participants were prescribed an OAC. These participants were on average aged 75.7 (SD: 7.1) years, 49% were women, two thirds were frail or pre-frail, and 44% received a DOAC. DOAC users were younger, had lower CHA2DS2VASC and HAS-BLED scores, and were less likely to be frail. In Massachusetts, pre-frailty was associated with a significantly lower odds of DOAC vs. VKA use (OR = 0.64, 95%CI 0.45, 0.91). Pre-frailty (OR = 0.33, 95%CI 0.18–0.59) and social isolation (OR = 0.38, 95%CI 0.14–0.99) were associated with lower odds of DOAC receipt in patients aged 75 years or older. Social isolation was associated with higher odds of DOAC use (OR = 2.13, 95%CI 1.05–4.29) in patients aged 65–74 years.Conclusions: Geriatric conditions were common and related to type of OAC prescribed, differentially by age group. Research is needed to evaluate whether a geriatric examination can be used clinically to better inform OAC decision-making in older patients with AF.

Background Little is known regarding use of cardiac therapies and clinical outcomes among older myocardial infarction (MI) patients with cognitive impairment. Methods and Results Patients ≥65 years old with MI in the NCDR (National Cardiovascular Data Registry) Chest Pain–MI Registry between January 2015 and December 2016 were categorized by presence and degree of chart‐documented cognitive impairment. We evaluated whether cognitive impairment was associated with all‐cause in‐hospital mortality after adjusting for known prognosticators. Among 43 812 ST‐segment–elevation myocardial infarction (STEMI) patients, 3.9% had mild and 2.0% had moderate/severe cognitive impairment; among 90 904 non–ST‐segment–elevation myocardial infarction (NSTEMI patients, 5.7% had mild and 2.6% had moderate/severe cognitive impairment. A statistically significant but numerically small difference in the use of primary percutaneous coronary intervention was observed between patients with STEMI with and without cognitive impairment (none, 92.1% versus mild, 92.8% versus moderate/severe, 90.4%; P=0.03); use of fibrinolysis was lower among patients with cognitive impairment (none, 40.9% versus mild, 27.4% versus moderate/severe, 24.2%; P

Background Direct acting oral anticoagulants (DOACs) theoretically could contribute to addressing underuse of anticoagulation in non‐valvular atrial fibrillation (NVAF). Few studies have examined this prospect, however. The potential of DOACs to address underuse of anticoagulation in NVAF could be magnified within a healthcare system that sharply limits patients’ exposure to out‐of‐pocket copayments, such as the Veterans Health Administration (VA). Methods and Results We used a clinical data set of all patients with NVAF treated within VA from 2007 to 2016 (n=987 373). We examined how the proportion of patients receiving any anticoagulation, and which agent was prescribed, changed over time. When first approved for VA use in 2011, DOACs constituted a tiny proportion of all prescriptions for anticoagulants (2%); by 2016, this proportion had increased to 45% of all prescriptions and 67% of new prescriptions. Patient characteristics associated with receiving a DOAC, rather than warfarin, included white race, better kidney function, fewer comorbid conditions overall, and no history of stroke or bleeding. In 2007, before the introduction of DOACs, 56% of VA patients with NVAF were receiving anticoagulation; this dipped to 44% in 2012 just after the introduction of DOACs and had risen back to 51% by 2016. Conclusions These results do not suggest that the availability of DOACs has led to an increased proportion of patients with NVAF receiving anticoagulation, even in the context of a healthcare system that sharply limits patients’ exposure to out‐of‐pocket copayments.

Background Studies show suboptimal provision of smoking cessation assistance (counseling or pharmacotherapy) for current smokers attempting to quit. We aimed to identify smoking cessation assistance patterns in US cardiology practices. Methods and Results Among 328 749 current smokers seen between January 1, 2013, and March 31, 2016, in 348 NCDR (National Cardiovascular Data Registry) PINNACLE (Practice Innovation and Clinical Excellence)‐affiliated cardiology practices, we measured the rates of cessation assistance. We used multivariable hierarchical logistic regression models to determine provider‐, practice‐, and patient‐level predictors of cessation assistance. We measured provider variation in cessation assistance using median rate ratio (the likelihood that the same patient would receive the same assistance at by any given provider; >1.2 suggests significant variation). Smoking cessation assistance was documented in only 34% of encounters. Despite adjustment of provider, practice, and patient characteristics, there was large provider‐level variation in cessation assistance (median rate ratio, 6 [95% CI, 5.76–6.32]). Practice location in the South region (odds ratio [OR], 0.48 [0.37–0.63] versus West region) and rural or suburban location (OR, 0.92 [0.88–0.95] for rural; OR, 0.94 [0.91–0.97] for suburban versus urban) were associated with lower rates of cessation assistance. Similarly, older age (OR, 0.88 [0.88–0.89] per 10‐year increase), diabetes mellitus (OR, 0.84 [0.82–0.87]), and atrial fibrillation (OR, 0.93 [0.91–0.96]) were associated with lower odds of receiving cessation assistance. Conclusions In a large contemporary US registry, only 1 in 3 smokers presenting for a cardiology visit received smoking cessation assistance. Our findings suggest the presence of a large deficit and largely idiosyncratic provider‐level variation in the provision of smoking cessation assistance.