Search

The emergence of a plethora of new tobacco products marketed as being less harmful than smoking, such as electronic cigarettes and heated tobacco products, and the increased popularity of recreational marijuana have raised concerns about the potential cardiovascular risk associated with their use.The purpose of this study was to investigate whether the use of novel tobacco products or marijuana can cause the development of proarrhythmic substrate and eventually lead to arrhythmias.Rats were exposed to smoke from tobacco, marijuana, or cannabinoid-depleted marijuana, to aerosol from electronic cigarettes or heated tobacco products, or to clean air once per day for 8 weeks, following by assays for blood pressure, cardiac function, ex vivo electrophysiology, and histochemistry.The rats exposed to tobacco or marijuana products exhibited progressively increased systolic blood pressure, decreased cardiac systolic function with chamber dilation, and reduced overall heart rate variability, relative to the clean air negative control group. Atrial fibrillation and ventricular tachycardia testing by ex vivo optical mapping revealed a significantly higher susceptibility to each, with a shortened effective refractory period and prolonged calcium transient duration. Histological analysis indicated that in all exposure conditions except for air, exposure to smoke or aerosol from tobacco or marijuana products caused severe fibrosis with decreased microvessel density and higher level of sympathetic nerve innervation.These pathophysiological results indicate that tobacco and marijuana products can induce arrhythmogenic substrates involved in cardiac electrical, structural, and neural remodeling, facilitating the development of arrhythmias.

Valvular heart disease is associated with a high global burden of disease. Even mild aortic stenosis confers increased morbidity and mortality, prompting interest in understanding normal variation in valvular function at scale.We developed a deep learning model to study velocity-encoded magnetic resonance imaging in 47,223 UK Biobank participants. We calculated eight traits, including peak velocity, mean gradient, aortic valve area, forward stroke volume, mitral and aortic regurgitant volume, greatest average velocity, and ascending aortic diameter. We then computed sex-stratified reference ranges for these phenotypes in up to 31,909 healthy individuals. In healthy individuals, we found an annual decrement of 0.03cm2in the aortic valve area. Participants with mitral valve prolapse had a 1 standard deviation [SD] higher mitral regurgitant volume (P=9.6 × 10−12), and those with aortic stenosis had a 4.5 SD-higher mean gradient (P=1.5 × 10−431), validating the derived phenotypes’ associations with clinical disease. Greater levels of ApoB, triglycerides, and Lp(a) assayed nearly 10 years prior to imaging were associated with higher gradients across the aortic valve. Metabolomic profiles revealed that increased glycoprotein acetyls were also associated with an increased aortic valve mean gradient (0.92 SD, P=2.1 x 10−22). Finally, velocity-derived phenotypes were risk markers for aortic and mitral valve surgery even at thresholds below what is considered relevant disease currently.Using machine learning to quantify the rich phenotypic data of the UK Biobank, we report the largest assessment of valvular function and cardiovascular disease in the general population.

AimsThe behavior of pacemaker cardiomyocytes (PCs) in the sinoatrial node (SAN) is modulated by neurohormonal and paracrine factors, many of which signal through G-protein coupled receptors (GPCRs). The aims of the present study are to catalog GPCRs that are differentially expressed in the mammalian SAN and to define the acute physiological consequences of activating the cholecystokinin-A signaling system in the SAN and in isolated PCs.Methods and ResultsUsing bulk and single cell RNA sequencing datasets, we identify a set of GPCRs that are differentially expressed between SAN and right atrial tissue, including several whose roles in PCs and in the SAN have not been thoroughly characterized. Focusing on one such GPCR, Cholecystokinin-A receptor (CCKAR), we demonstrate expression ofCckarmRNA specifically in mammalian PCs, and further demonstrate that subsets of SAN fibroblasts and neurons within the cardiac intrinsic nervous system express cholecystokinin, the ligand for CCKAR. Using mouse models, we find that while baseline SAN function is not dramatically affected by loss of CCKAR, the firing rate of individual PCs is slowed by exposure to sulfated cholecystokinin-8 (sCCK-8), the high affinity ligand for CCKAR. The effect of sCCK-8 on firing rate is mediated by reduction in the rate of spontaneous phase 4 depolarization of PCs and is mitigated by activation of beta-adrenergic signaling. Finally, while perfusion of sCCK-8 onto the SANex-vivoreduces the beating rate modestly, exposure to sCCK-8 unexpectedly shortened the sinus node recovery time, a finding that has potential implications for treatment of SAN dysfunction.Conclusions(1) PCs express many GPCRs whose specific roles in SAN function have not been characterized, (2) the cholecystokinin-A pathway constitutes a novel signaling system that regulates SAN function and PC electrophysiology.Translational PerspectiveThe time required for the sinoatrial node (SAN) to resume function after overdrive suppression is called the sinus node recovery time (SNRT). In the tachy-brady syndrome, pathological prolongation of the SNRT results in symptomatic sinus pauses that follow the termination of a tachyarrhythmia, leading to loss of consciousness. Tachy-brady syndrome is the most common presentation of sinus node dysfunction and can only be treated with a permanent pacemaker. In this study, we find that activation of the cholecystokinin-A signaling system can shorten the SNRT, raising the possibility of developing new pharmacological methods to treat tachy-brady syndrome.

Background: Remote activity monitoring has the potential to evaluate real-world, motor function, and disability at home. The relationships of daily physical activity with spinal cord white matter and gray matter (GM) areas, multiple sclerosis (MS) disability and leg function, are unknown. Objective: Evaluate the association of structural central nervous system pathology with ambulatory disability. Methods: Fifty adults with progressive or relapsing MS with motor disability who could walk >2 minutes were assessed using clinician-evaluated, patient-reported outcomes, and quantitative brain and spinal cord magnetic resonance imaging (MRI) measures. Fitbit Flex2, worn on the non-dominant wrist, remotely assessed activity over 30 days. Univariate and multivariate analyses were performed to assess correlations between physical activity and other disability metrics. Results: Mean age was 53.3 years and median Expanded Disability Status Scale (EDSS) was 4.0. Average daily step counts (STEPS) were highly correlated with EDSS and walking measures. Greater STEPS were significantly correlated with greater C2-C3 spinal cord GM areas (ρ = 0.39, p = 0.04), total cord area (TCA; ρ = 0.35, p = 0.04), and cortical GM volume (ρ = 0.32, p = 0.04). Conclusion These results provide preliminary evidence that spinal cord GM area is a neuroanatomical substrate associated with STEPS. STEPS could serve as a proxy to alert clinicians and researchers to possible changes in structural nervous system pathology.

ImportanceProlonged symptoms following SARS-CoV-2 infection, or Long COVID, is common, but few prospective studies of Long COVID risk factors have been conducted.ObjectiveTo determine whether sociodemographic factors, lifestyle, or medical history preceding COVID-19 or characteristics of acute SARS-CoV-2 infection are associated with Long COVID.DesignCohort study with longitudinal assessment of symptoms before, during, and after SARS-CoV-2 infection, and cross-sectional assessment of Long COVID symptoms using data from the COVID-19 Citizen Science (CCS) study.SettingCCS is an online cohort study that began enrolling March 26, 2020. We included data collected between March 26, 2020, and May 18, 2022.ParticipantsAdult CCS participants who reported a positive SARS-CoV-2 test result (PCR, Antigen, or Antibody) more than 30 days prior to May 4, 2022, were surveyed.ExposuresAge, sex, race/ethnicity, education, employment, socioeconomic status/financial insecurity, self-reported medical history, vaccination status, time of infection (variant wave), number of acute symptoms, pre-COVID depression, anxiety, alcohol and drug use, sleep, exercise.Main OutcomePresence of at least 1 Long COVID symptom greater than 1 month after acute infection. Sensitivity analyses were performed considering only symptoms beyond 3 months and only severe symptoms.Results13,305 participants reported a SARS-CoV-2 positive test more than 30 days prior, 1480 (11.1% of eligible) responded to a survey about Long COVID symptoms, and 476 (32.2% of respondents) reported Long COVID symptoms (median 360 days after infection).Respondents’ mean age was 53 and 1017 (69%) were female. Common Long COVID symptoms included fatigue, reported by 230/476 (48.3%), shortness of breath (109, 22.9%), confusion/brain fog (108, 22.7%), headache (103, 21.6%), and altered taste or smell (98, 20.6%). In multivariable models, number of acute COVID-19 symptoms (OR 1.30 per symptom, 95%CI 1.20-1.40), lower socioeconomic status/financial insecurity (OR 1.62, 95%CI 1.02-2.63), pre-infection depression (OR 1.08, 95%CI 1.01-1.16), and earlier variants (OR 0.37 for Omicron compared to ancestral strain, 95%CI 0.15-0.90) were associated with Long COVID symptoms.Conclusions and RelevanceVariant wave, severity of acute infection, lower socioeconomic status and pre-existing depression are associated with Long COVID symptoms.Key PointsQuestionWhat are the patterns of symptoms and risk factors for Long COVID among SARS-CoV-2 infected individuals?FindingsPersistent symptoms were highly prevalent, especially fatigue, shortness of breath, headache, brain fog/confusion, and altered taste/smell, which persisted beyond 1 year among 56% of participants with symptoms; a minority of participants reported severe Long COVID symptoms. Number of acute symptoms during acute SARS-CoV-2 infection, financial insecurity, pre-existing depression, and infection with earlier variants are associated with prevalent Long COVID symptoms independent of vaccination, medical history, and other factors.MeaningSeverity of acute infection, SARS-CoV-2 variant, and financial insecurity and depression are associated with Long COVID symptoms.

BACKGROUND: The incidence of sudden cardiac death and sudden death caused by arrhythmia, as determined by autopsy, in persons with human immunodeficiency virus (HIV) infection has not been clearly established. METHODS: Between February 1, 2011, and September 16, 2016, we prospectively identified all new deaths due to out-of-hospital cardiac arrest among persons 18 to 90 years of age, with or without known HIV infection, for comprehensive autopsy and toxicologic and histologic testing. We compared the rates of sudden cardiac death and sudden death caused by arrhythmia between groups. RESULTS: Of 109 deaths from out-of-hospital cardiac arrest among 610 unexpected deaths in HIV-positive persons, 48 met World Health Organization criteria for presumed sudden cardiac death; of those, fewer than half (22) had an arrhythmic cause. A total of 505 presumed sudden cardiac deaths occurred between February 1, 2011, and March 1, 2014, in persons without known HIV infection. Observed incidence rates of presumed sudden cardiac death were 53.3 deaths per 100,000 person-years among persons with known HIV infection and 23.7 deaths per 100,000 person-years among persons without known HIV infection (incidence rate ratio, 2.25; 95% confidence interval [CI], 1.37 to 3.70). Observed incidence rates of sudden death caused by arrhythmia were 25.0 and 13.3 deaths per 100,000 person-years, respectively (incidence rate ratio, 1.87; 95% CI, 0.93 to 3.78). Among all presumed sudden cardiac deaths, death due to occult drug overdose was more common in persons with known HIV infection than in persons without known HIV infection (34% vs. 13%). Persons who were HIV-positive had higher histologic levels of interstitial myocardial fibrosis than persons without known HIV infection. CONCLUSIONS: In this postmortem study, the rates of presumed sudden cardiac death and myocardial fibrosis were higher among HIV-positive persons than among those without known HIV infection. One third of apparent sudden cardiac deaths in HIV-positive persons were due to occult drug overdose. (Supported by the National Heart, Lung, and Blood Institute.)

Background Tailored Antiplatelet Initiation to Lessen Outcomes Due to Decreased Clopidogrel Response after Percutaneous Coronary Intervention (TAILOR-PCI) is the largest cardiovascular genotype-based randomized pragmatic trial (NCT#01742117) to evaluate the role of genotype-guided selection of oral P2Y12 inhibitor therapy in improving ischemic outcomes after PCI. The trial has been extended from the original 12- to 24-month follow-up, using study coordinator-initiated telephone visits. TAILOR-PCI Digital Study tests the feasibility of extending the trial follow-up in a subset of patients for up to 24 months using state-of-the-art digital solutions. The rationale, design, and approach of extended digital study of patients recruited into a large, international, multi-center clinical trial has not been previously described. Methods A total of 930 patients from U.S. and Canadian sites previously enrolled in the 5,302 patient TAILOR-PCI trial within 23 months of randomization are invited by mail to the Digital Study website ( http://tailorpci.eurekaplatform.org ) and by up to 2 recruiting telephone calls. Eureka, a direct-to-participant digital research platform, is used to consent and collect prospective data on patients for the digital study. Patients are asked to answer health-related surveys at fixed intervals using the Eureka mobile app and or desktop platform. The likelihood of patients enrolled in a randomized clinical trial transitioning to a registry using digital technology, the reasons for nonparticipation and engagement rates are evaluated. To capture hospitalizations, patients may optionally enable geofencing, a process that allows background location tracking and triggering of surveys if a hospital visit greater than 4 hours is detected. In addition, patients answer digital hospitalization surveys every month. Hospitalization data received from the Digital Study will be compared to data collected from study coordinator telephone visits during the same time frame. Conclusions The TAILOR-PCI Digital Study evaluates the feasibility of transitioning a large multicenter randomized clinical trial to a digital registry. The study could provide evidence for the ability of digital technology to follow clinical trial patients and to ascertain trial-related events thus also building the foundation for conducting digital clinical trials. Such a digital approach may be especially pertinent in the era of COVID-19.

Objectives: This study sought to prospectively study the development and then regression of premature ventricular contraction (PVC)-induced cardiomyopathy, with the hypothesis that structural left ventricular (LV) changes that are of potential clinical significance may endure beyond the period of exposure to PVCs. Background: Recovery of LV function after eradication of PVCs in PVC-induced cardiomyopathy is incompletely defined. Methods: Fifteen swine were exposed to: 1) 50% paced PVCs from the LV lateral epicardium for 12 weeks (LV PVC, n = 5); 2) no pacing for 12 weeks (Control, n = 5); or 3) 50% paced LV PVCs for 12 weeks followed by pacing cessation for 4 weeks (Recovery, n = 5). LV function was quantified biweekly in sinus rhythm with echocardiography. Dyssynchrony was measured from pressure-volume loops at baseline and terminal studies. LV fibrosis was quantified after sacrifice. Results: LV ejection fraction during sinus rhythm fell between baseline and terminal studies in the LV PVC group (65.8 ± 3.0 to 39.3 ± 3.2; p < 0.05), whereas there was no significant change in the Control group (69.6 ± 3.0 to 72.2 ± 3.0; p = NS) or after Recovery (64.5 ± 3.4% to 61.4 ± 3.4%; p = NS) groups. There was a significant increase in LV dyssynchrony measured during sinus rhythm between baseline and terminal studies in the LV PVC group (4.0 ± 1.5% to 9.0 ± 1.5%; p < 0.05); there was a similar increase in dyssynchrony that persisted 4 weeks after PVC cessation in the Recovery group (4.4 ± 1.7% to 12.8 ± 1.7%; p < 0.05). After sacrifice, percent fibrosis was higher in the LV PVC group compared with Control (5.7 ± 0.3% vs. 3.0 ± 0.3%; p < 0.05) and remained elevated in Recovery (4.1 ± 0.3% vs. 3.0 ± 0.3%; p < 0.05) despite return to baseline LV ejection fraction. Conclusions: In a swine model of PVC-induced cardiomyopathy, cessation of PVCs for 4 weeks leads to normalization of LV systolic function but significant changes in myocardial fibrosis and LV dyssynchrony during sinus rhythm persist.

The global burden of diabetes is rapidly increasing, from 451 million people in 2019 to 693 million by 20451. The insidious onset of type 2 diabetes delays diagnosis and increases morbidity2. Given the multifactorial vascular effects of diabetes, we hypothesized that smartphone-based photoplethysmography could provide a widely accessible digital biomarker for diabetes. Here we developed a deep neural network (DNN) to detect prevalent diabetes using smartphone-based photoplethysmography from an initial cohort of 53,870 individuals (the ‘primary cohort’), which we then validated in a separate cohort of 7,806 individuals (the ‘contemporary cohort’) and a cohort of 181 prospectively enrolled individuals from three clinics (the ‘clinic cohort’). The DNN achieved an area under the curve for prevalent diabetes of 0.766 in the primary cohort (95% confidence interval: 0.750–0.782; sensitivity 75%, specificity 65%) and 0.740 in the contemporary cohort (95% confidence interval: 0.723–0.758; sensitivity 81%, specificity 54%). When the output of the DNN, called the DNN score, was included in a regression analysis alongside age, gender, race/ethnicity and body mass index, the area under the curve was 0.830 and the DNN score remained independently predictive of diabetes. The performance of the DNN in the clinic cohort was similar to that in other validation datasets. There was a significant and positive association between the continuous DNN score and hemoglobin A1c (P ≤ 0.001) among those with hemoglobin A1c data. These findings demonstrate that smartphone-based photoplethysmography provides a readily attainable, non-invasive digital biomarker of prevalent diabetes. A deep neural network applied to smartphone-based vascular imaging can detect diabetes, opening new possibilities for non-invasive diagnosis.

The 2019 novel coronavirus disease (COVID-19) pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) continues to spread and worsen in many parts of the world. As the pandemic grows, it is especially important to understand how the virus and the pandemic are affecting pregnant women and infants. While early data suggested that being infected with the virus did not increase the risk of adverse pregnancy or infant outcomes, as more information has emerged, it has become clear that risks for some adverse pregnancy and infant outcomes are increased (e.g., preterm birth, cesarean section, respiratory distress, and hospitalization). The Healthy Outcomes of Pregnancy for Everyone in the time of novel coronavirus disease-19 (HOPE COVID-19) study is a multi-year, prospective investigation designed to better understand how the SARS-CoV-2 virus and COVID-19 impact adverse pregnancy and infant outcomes. The study also examines how the pandemic exacerbates existing hardships such as social isolation, economic destabilization, job loss, housing instability, and/or family member sickness or death among minoritized and marginalized communities. Specifically, the study examines how pandemic-related hardships impact clinical outcomes and characterizes the experiences of Black, Latinx and low-income groups compared to those in other race/ethnicity and socioeconomic stratum. The study includes two nested cohorts. The survey only cohort will enroll 7500 women over a two-year period. The survey+testing cohort will enroll 2500 women over this same time period. Participants in both cohorts complete short surveys daily using a mobile phone application about COVID-19-related symptoms (e.g., fever and cough) and complete longer surveys once during each trimester and at 6–8 weeks and 6, 12 and 18 months after delivery that focus on the health and well-being of mothers and, after birth, of infants. Participants in the survey+testing cohort also have testing for SARS-CoV-2 and related antibodies during pregnancy and after birth as well as testing that looks at inflammation and for the presence of other infections like Influenza and Rhinovirus. Study results are expected to be reported on a rolling basis and will include quarterly reporting for participants and public health partners as well as more traditional scientific reporting.

Background Atrial fibrillation (AF), a common cause of stroke, often is asymptomatic. Smartphones and smartwatches can detect AF using heart rate patterns inferred using photoplethysmography (PPG); however, enhanced accuracy is required to reduce false positives in screening populations. Objective The purpose of this study was to test the hypothesis that a deep learning algorithm given raw, smartwatch-derived PPG waveforms would discriminate AF from normal sinus rhythm better than algorithms using heart rate alone. Methods Patients presenting for cardioversion of AF (n = 51) were given wrist-worn fitness trackers containing PPG sensors (Jawbone Health). Standard 12-lead electrocardiograms over-read by board-certified cardiac electrophysiologists were used as the reference standard. The accuracy of PPG signals to discriminate AF from sinus rhythm was evaluated by conventional measures of heart rate variability, a long short-term memory (LSTM) neural network given heart rate data only, and a deep convolutional-recurrent neural net (DNN) given the raw PPG data. Results From among 51 patients with persistent AF (age 63.6 ± 11.3 years; 78% male; 88% white), we randomly assigned 40 to train and 11 to test the algorithms. Whereas logistic regression analysis of heart rate variability yielded an area under the receiver operating characteristic curve (AUC) of 0.717 (sensitivity 0.741; specificity 0.584), the LSTM model given heart rate data exhibited AUC of 0.954 (sensitivity 0.810; specificity 0.921), and the DNN model given raw PPG data yielded the highest AUC of 0.983 (sensitivity 0.985; specificity 0.880). Conclusion A deep learning model given the raw PPG-based signal resulted in AF detection with high accuracy, performing better than conventional analyses relying on heart rate series data alone.

Background Adults with cardiovascular disease risk factors (CVRFs) are also at increased risk of developing cognitive decline and dementia. However, it is often difficult to study the relationships between CVRFs and cognitive function because cognitive assessment typically requires time-consuming in-person neuropsychological evaluations that may not be feasible for real-world situations. Objective We conducted a proof-of-concept study to determine if the association between CVRFs and cognitive function could be detected using web-based, self-administered cognitive tasks and CVRF assessment. Methods We recruited 239 participants aged ≥50 years (mean age 62.7 years, SD 8.8; 42.7% [n=102] female, 88.7% [n=212] White) who were enrolled in the Health eHeart Study, a web-based platform focused on cardiac disease. The participants self-reported CVRFs (hypertension, high cholesterol, diabetes, and atrial fibrillation) using web-based health surveys between August 2016 and July 2018. After an average of 3 years of follow-up, we remotely evaluated episodic memory, working memory, and executive function via the web-based Posit Science platform, BrainHQ. Raw data were normalized and averaged into 3 domain scores. We used linear regression models to examine the association between CVRFs and cognitive function. Results CVRF prevalence was 62.8% (n=150) for high cholesterol, 45.2% (n=108) for hypertension, 10.9% (n=26) for atrial fibrillation, and 7.5% (n=18) for diabetes. In multivariable models, atrial fibrillation was associated with worse working memory (β=-.51, 95% CI -0.91 to -0.11) and worse episodic memory (β=-.31, 95% CI -0.59 to -0.04); hypertension was associated with worse episodic memory (β=-.27, 95% CI -0.44 to -0.11). Diabetes and high cholesterol were not associated with cognitive performance. Conclusions Self-administered web-based tools can be used to detect both CVRFs and cognitive health. We observed that atrial fibrillation and hypertension were associated with worse cognitive function even in those in their 60s and 70s. The potential of mobile assessments to detect risk factors for cognitive aging merits further investigation.

Key Points Question What factors are associated with adverse effects after COVID-19 vaccination? Findings In an online cohort study including 19 586 adults who received a COVID-19 vaccination, the factors most strongly associated with adverse effects were full vaccination dose, brand of vaccine, younger age, female sex, and having had COVID-19 before vaccination. Allergic reaction or anaphylaxis was reported in 0.3% of participants after partial vaccination and 0.2% of participants after full vaccination. Meaning These findings suggest that some individuals experience more adverse effects after COVID-19 vaccination, but serious adverse effects are rare., This cohort study evaluates factors potentially associated with participant-reported adverse effects after COVID-19 vaccination., Importance Little is known about the factors associated with COVID-19 vaccine adverse effects in a real-world population. Objective To evaluate factors potentially associated with participant-reported adverse effects after COVID-19 vaccination. Design, Setting, and Participants The COVID-19 Citizen Science Study, an online cohort study, includes adults aged 18 years and older with a smartphone or internet access. Participants complete daily, weekly, and monthly surveys on health and COVID-19–related events. This analysis includes participants who provided consent between March 26, 2020, and May 19, 2021, and received at least 1 COVID-19 vaccine dose. Exposures Participant-reported COVID-19 vaccination. Main Outcomes and Measures Participant-reported adverse effects and adverse effect severity. Candidate factors in multivariable logistic regression models included age, sex, race, ethnicity, subjective social status, prior COVID-19 infection, medical conditions, substance use, vaccine dose, and vaccine brand. Results The 19 586 participants had a median (IQR) age of 54 (38-66) years, and 13 420 (68.8%) were women. Allergic reaction or anaphylaxis was reported in 26 of 8680 participants (0.3%) after 1 dose of the BNT162b2 (Pfizer/BioNTech) or mRNA-1273 (Moderna) vaccine, 27 of 11 141 (0.2%) after 2 doses of the BNT162b2 or mRNA-1273 vaccine or 1 dose of the JNJ-78436735 (Johnson & Johnson) vaccine. The strongest factors associated with adverse effects were vaccine dose (2 doses of BNT162b2 or mRNA-1273 or 1 dose of JNJ-78436735 vs 1 dose of BNT162b2 or mRNA-1273; odds ratio [OR], 3.10; 95% CI, 2.89-3.34; P

Introduction: Prior studies report higher cardiovascular mortality in persons experiencing homelessness (PEH), but presume cardiac cause of death (COD) from EMS records or death certificates. The effect of homelessness on autopsy-defined SCD is unknown. In this prospective postmortem study of all SCDs in San Francisco (SF) County, we hypothesized a greater incidence of presumed SCD in PEH, but more non-cardiac COD on autopsy. Methods: Between 2/2011 - 3/2014, all incident World Health Organization-defined (presumed) SCDs ages 18-90 were autopsied via active surveillance of out of hospital deaths in the POST SCD Study. A multidisciplinary committee adjudicated COD. Autopsy-defined SCDs had no extra-cardiac COD (e.g., overdose, pulmonary embolism, stroke). Homeless status was determined from medical records; rates of presumed and autopsy-defined SCD were compared to those for housed individuals. The countywide homeless population was determined from the 2013 SF Homeless Count and Survey. Results: We identified 525 presumed SCDs over 37 months: 36 (7%) were PEH and 489 (93%) were housed. Presumed SCDs in PEH were younger than housed subjects (56.5 ± 9.9 v. 63.2 ± 14.6 years), more often male (97% v. 67%), less likely to be Asian (0% v. 22%) or have dyslipidemia (3% v. 32%), but more likely to have alcohol (58% v. 21%) and substance use disorder (44% v. 13%, p Conclusion: In this 3-year comprehensive postmortem study of all incident SCDs in a diverse urban setting, homelessness was associated with >3-fold higher risk of autopsy-confirmed SCD and 8-fold higher risk of sudden mortality overall. Only one-fourth of presumed SCDs in PEH were autopsy-confirmed SCDs, while one-third of non-cardiac causes were due to occult drug overdose.

Background Heart failure (HF) is a leading cause of cardiac morbidity among women, whose risk factors differ from those in men. We used machine-learning approaches to develop risk- prediction models for incident HF in a cohort of postmenopausal women from the Women’s Health Initiative (WHI). Methods We used 2 machine-learning methods—Least Absolute Shrinkage and Selection Operator (LASSO) and Classification and Regression Trees (CART)—to perform variable selection on 1227 baseline WHI variables for the primary outcome of incident HF. These variables were then used to construct separate Cox proportional hazard models, and we compared these results, using receiver-operating characteristic (ROC) curve analysis, against a comparator model built using variables from the Atherosclerosis Risk in Communities (ARIC) HF prediction model. We analyzed 43,709 women who had 2222 incident HF events; median follow-up was 14.3 years. Results LASSO selected 10 predictors, and CART selected 11 predictors. The highest correlation between selected variables was 0.46. In addition to selecting well-established predictors such as age, myocardial infarction, and smoking, novel predictors included physical function, number of pregnancies, number of previous live births and age at menopause. In ROC analysis, the CART-derived model had the highest C-statistic of 0.83 (95% confidence interval [CI], 0.81-0.85), followed by LASSO 0.82 (95% CI, 0.81-0.84) and ARIC 0.73 (95% CI, 0.70-0.76). Conclusions Machine-learning approaches can be used to develop HF risk-prediction models that can have better discrimination compared with an established HF risk model and may provide a basis for investigating novel HF predictors.

Hypertrophic cardiomyopathy (HCM) patients have a high incidence of atrial fibrillation (AF) and increased stroke risk, even with low CHAHCM patients with documented paroxysmal/persistent/permanent AF (n = 191) were considered AF cases, and the remaining patients in sinus rhythm (n = 640) were tagged as No-AF. We evaluated 93 clinical variables; the most informative variables useful for distinguishing AF from No-AF cases were selected based on the 2-sampleWe identified 18 highly informative variables that are positively (n = 11) and negatively (n = 7) correlated with AF in HCM. Next, patient records were represented via these 18 variables. Data imbalance resulting from the relatively low number of AF cases was addressed via a combination of oversampling and undersampling strategies. We trained and tested multiple classifiers under this sampling approach, showing effective classification. Specifically, an ensemble of logistic regression and naïve Bayes classifiers, trained based on the 18 variables and corrected for data imbalance, proved most effective for separating AF from No-AF cases (sensitivity = 0.74, specificity = 0.70, C-index = 0.80).Our model (HCM-AF-Risk Model) is the first machine learning-based method for identification of AF cases in HCM. This model demonstrates good performance, addresses data imbalance, and suggests that AF is associated with a more severe cardiac HCM phenotype.Les patients atteints d’une cardiomyopathie hypertrophique (CMH) présentent une forte incidence de fibrillation auriculaire (FA) et un risque accru d’accident vasculaire cérébral (AVC), malgré des scores CHANous avons considéré les patients atteints d’une CMH qui ont une FA paroxystique/persistante/permanente documentée (n = 191) comme des cas de FA, et avons étiqueté les autres patients en rythme sinusal (n = 640) comme des cas sans FA. Nous avons évalué 93 variables cliniques; nous avons sélectionné les variables les plus informatives qui sont utiles pour distinguer les cas de FA des cas sans FA en fonction du test t pour deux échantillons et du critère de gain d’information.Nous avons relevé 18 variables hautement informatives qui ont une corrélation positive (n = 11) et une corrélation négative (n = 7) avec la FA en présence d’une CMH. Ensuite, nous avons représenté les dossiers des patients au moyen de ces 18 variables. Nous avons remédié au déséquilibre des données, qui résulte du nombre relativement faible de cas de FA, grâce à une combinaison de stratégies de suréchantillonnage et de sous-échantillonnage. Nous avons formé et testé de nombreux classificateurs selon cette approche d’échantillonnage, qui montre une classification efficace. Particulièrement, un ensemble de régression logistique et de classificateurs bayésiens naïfs formés en fonction des 18 variables et corrigés en fonction du déséquilibre des données s’est révélé le plus efficace pour séparer les cas de FA des cas sans FA (sensibilité = 0,74, spécificité = 0,70, indice C = 0,80).Notre modèle (modèle de risque de CMH-FA) est la première méthode d’apprentissage automatique qui sert à déterminer les cas de FA en présence de CMH. Ce modèle permet de démontrer une bonne performance, de remédier au déséquilibre des données, et de croire que la FA est associée à un phénotype grave de CMH.

Background Falling is common in people with multiple sclerosis (MS) but tends to be under-ascertained and under-treated. Objective To evaluate fall risk in people with MS. Methods Ninety-four people with MS, able to walk > 2 min with or without an assistive device (Expanded Disability Status Scale (EDSS ≤ 6.5) were recruited. Clinic-based measures were recorded at baseline and 1 year. Patient-reported outcomes (PROs), including a fall survey and the MS Walking Scale (MSWS-12), were completed at baseline, 1.5, 3, 6, 9, and 12 months. Average daily step counts (STEPS) were recorded using a wrist-worn accelerometer. Results 50/94 participants (53.2%) reported falling at least once. Only 56% of participants who reported a fall on research questionnaires had medical-record documented falls. Fallers had greater disability [median EDSS 5.5 (IQR 4.0–6.0) versus 2.5 (IQR 1.5–4.0), p p = 0.003), and took fewer STEPS (mean difference − 1,979, p = 0.007) than Non-Fallers. Stepwise regression revealed MSWS-12 as a major predictor of future falls. Conclusions Falling is common in people with MS, under-reported, and under-ascertained by neurologists in clinic. Multimodal fall screening in clinic and remotely may help improve patient care by identifying those at greatest risk, allowing for timely intervention and referral to specialized physical rehabilitation.

IMPORTANCE: Millions of clinicians rely daily on automated preliminary electrocardiogram (ECG) interpretation. Critical comparisons of machine learning–based automated analysis against clinically accepted standards of care are lacking. OBJECTIVE: To use readily available 12-lead ECG data to train and apply an explainability technique to a convolutional neural network (CNN) that achieves high performance against clinical standards of care. DESIGN, SETTING, AND PARTICIPANTS: This cross-sectional study was conducted using data from January 1, 2003, to December 31, 2018. Data were obtained in a commonly available 12-lead ECG format from a single-center tertiary care institution. All patients aged 18 years or older who received ECGs at the University of California, San Francisco, were included, yielding a total of 365 009 patients. Data were analyzed from January 1, 2019, to March 2, 2021. EXPOSURES: A CNN was trained to predict the presence of 38 diagnostic classes in 5 categories from 12-lead ECG data. A CNN explainability technique called LIME (Linear Interpretable Model-Agnostic Explanations) was used to visualize ECG segments contributing to CNN diagnoses. MAIN OUTCOMES AND MEASURES: Area under the receiver operating characteristic curve (AUC), sensitivity, and specificity were calculated for the CNN in the holdout test data set against cardiologist clinical diagnoses. For a second validation, 3 electrophysiologists provided consensus committee diagnoses against which the CNN, cardiologist clinical diagnosis, and MUSE (GE Healthcare) automated analysis performance was compared using the F1 score; AUC, sensitivity, and specificity were also calculated for the CNN against the consensus committee. RESULTS: A total of 992 748 ECGs from 365 009 adult patients (mean [SD] age, 56.2 [17.6] years; 183 600 women [50.3%]; and 175 277 White patients [48.0%]) were included in the analysis. In 91 440 test data set ECGs, the CNN demonstrated an AUC of at least 0.960 for 32 of 38 classes (84.2%). Against the consensus committee diagnoses, the CNN had higher frequency-weighted mean F1 scores than both cardiologists and MUSE in all 5 categories (CNN frequency-weighted F1 score for rhythm, 0.812; conduction, 0.729; chamber diagnosis, 0.598; infarct, 0.674; and other diagnosis, 0.875). For 32 of 38 classes (84.2%), the CNN had AUCs of at least 0.910 and demonstrated comparable F1 scores and higher sensitivity than cardiologists, except for atrial fibrillation (CNN F1 score, 0.847 vs cardiologist F1 score, 0.881), junctional rhythm (0.526 vs 0.727), premature ventricular complex (0.786 vs 0.800), and Wolff-Parkinson-White (0.800 vs 0.842). Compared with MUSE, the CNN had higher F1 scores for all classes except supraventricular tachycardia (CNN F1 score, 0.696 vs MUSE F1 score, 0.714). The LIME technique highlighted physiologically relevant ECG segments. CONCLUSIONS AND RELEVANCE: The results of this cross-sectional study suggest that readily available ECG data can be used to train a CNN algorithm to achieve comparable performance to clinical cardiologists and exceed the performance of MUSE automated analysis for most diagnoses, with some exceptions. The LIME explainability technique applied to CNNs highlights physiologically relevant ECG segments that contribute to the CNN’s diagnoses.

Background The COVID-19 pandemic has catalyzed a global public response and innovation in clinical study methods. Objective The COVID-19 Citizen Science study was designed to generate knowledge about participant-reported COVID-19 symptoms, behaviors, and disease occurrence. Methods COVID-19 Citizen Science is a longitudinal cohort study launched on March 26, 2020, on the Eureka Research Platform. This study illustrates important advances in digital clinical studies, including entirely digital study participation, targeted recruitment strategies, electronic consent, recurrent and time-updated assessments, integration with smartphone-based measurements, analytics for recruitment and engagement, connection with partner studies, novel engagement strategies such as participant-proposed questions, and feedback in the form of real-time results to participants. Results As of February 2021, the study has enrolled over 50,000 participants. Study enrollment and participation are ongoing. Over the lifetime of the study, an average of 59% of participants have completed at least one survey in the past 4 weeks. Conclusions Insights about COVID-19 symptoms, behaviors, and disease occurrence can be drawn through digital clinical studies. Continued innovation in digital clinical study methods represents the future of clinical research. International Registered Report Identifier (IRRID) DERR1-10.2196/28169

OBJECTIVES This study hypothesized that paroxysmal atrial fibrillation (PAF) reflects the presence of a more severe cardiac hypertrophic cardiomyopathy (HCM) phenotype.BACKGROUND HCM is characterized by myocyte hypertrophy, fibrosis, and a high prevalence of PAF. It is currently unresolved whether atrial fibrillation (AF) is a marker or a mediator of adverse outcomes in HCM.METHODS This study retrospectively examined 45 HCM patients who underwent cardiovascular magnetic resonance (CMR) imaging in sinus rhythm. The function of all 4 cardiac chambers was assessed, as well as late gadolinium enhancement (LGE) in the left atrium (LA) and left ventricle (LV), as indicators of fibrosis. A fat-saturated, 3-dimensional inversion recovery-prepared, fast-spoiled, gradient-recalled echo sequence, and the image intensity ratio method were used to measure LA-LGE; LGE in the LV was quantified using a semi-automated threshold technique.RESULTS HCM patients (n = 45) were divided into 2 groups (PAF, no AF) based on history of PAF. All HCM patients had LGE in the LA posterior wall. The PAF group (n = 18) had higher LA volume, a lower LA ejection fraction, a lower global peak longitudinal LA strain (PLAS), and a higher amount of LA-LGE compared with the no AF group (n = 27). A modest inverse association was noted between the LA ejection fraction, PLAS, and LA-LGE; a positive association was present between LV-LGE and LA-LGE. The PAF group had lower ejection fractions in the LV, right atrium, and right ventricle compared with those in the no AF group.CONCLUSIONS PAF is associated with a greater degree of structural LA remodeling and global myopathy, which suggests a more severe cardiac HCM phenotype. (c) 2019 The Authors. Published by Elsevier on behalf of the American College of Cardiology Foundation. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Background Maximal oxygen consumption (VO2max) is one of the most predictive biometrics for cardiovascular health and overall mortality. However, VO2max is rarely measured in large-scale research studies or routine clinical care because of the high cost, participant burden, and requirement for specialized equipment and staff. Objective To overcome the limitations of clinical VO2max measurement, we aim to develop a digital VO2max estimation protocol that can be self-administered remotely using only the sensors within a smartphone. We also aim to validate this measure within a broadly representative population across a spectrum of smartphone devices. Methods Two smartphone-based VO2max estimation protocols were developed: a 12-minute run test (12-MRT) based on distance measured by GPS and a 3-minute step test (3-MST) based on heart rate recovery measured by a camera. In a 101-person cohort, balanced across age deciles and sex, participants completed a gold standard treadmill-based VO2max measurement, two silver standard clinical protocols, and the smartphone-based 12-MRT and 3-MST protocols in the clinic and at home. In a separate 120-participant cohort, the video-based heart rate measurement underlying the 3-MST was measured for accuracy in individuals across the spectrum skin tones while using 8 different smartphones ranging in cost from US $99 to US $999. Results When compared with gold standard VO2max testing, Lin concordance was pc=0.66 for 12-MRT and pc=0.61 for 3-MST. However, in remote settings, the 12-MRT was significantly less concordant with the gold standard (pc=0.25) compared with the 3-MST (pc=0.61), although both had high test-retest reliability (12-MRT intraclass correlation coefficient=0.88; 3-MST intraclass correlation coefficient=0.86). On the basis of the finding that 3-MST concordance was generalizable to remote settings whereas 12-MRT was not, the video-based heart rate measure within the 3-MST was selected for further investigation. Heart rate measurements in any of the combinations of the six Fitzpatrick skin tones and 8 smartphones resulted in a concordance of pc≥0.81. Performance did not correlate with device cost, with all phones selling under US $200 performing better than pc>0.92. Conclusions These findings demonstrate the importance of validating mobile health measures in the real world across a diverse cohort and spectrum of hardware. The 3-MST protocol, termed as heart snapshot, measured VO2max with similar accuracy to supervised in-clinic tests such as the Tecumseh (pc=0.94) protocol, while also generalizing to remote and unsupervised measurements. Heart snapshot measurements demonstrated fidelity across demographic variation in age and sex, across diverse skin pigmentation, and between various iOS and Android phone configurations. This software is freely available for all validation data and analysis code.

Despite some inconsistent reporting of symptoms, studies have demonstrated that at least 20% of individuals infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) will remain asymptomatic. Although most global efforts have focused on understanding factors underlying severe illness in COVID-19 (coronavirus disease of 2019), the examination of asymptomatic infection provides a unique opportunity to consider early disease and immunologic features promoting rapid viral clearance. Owing to its critical role in the immune response, we postulated that variation in the human leukocyte antigen (HLA) loci may underly processes mediating asymptomatic infection. We enrolled 29,947 individuals registered in the National Marrow Donor Program for whom high-resolution HLA genotyping data were available in the UCSF Citizen Science smartphone-based study designed to track COVID-19 symptoms and outcomes. Our discovery cohort (n=1428) was comprised of unvaccinated, self-identified subjects who reported a positive test result for SARS-CoV-2. We tested for association of five HLA loci (HLA-A, -B, -C, -DRB1, -DQB1) with disease course and identified a strong association of HLA-B*15:01 with asymptomatic infection, and reproduced this association in two independent cohorts. Suggesting that this genetic association is due to pre-existing T-cell immunity, we show that T cells from pre-pandemic individuals carrying HLA-B*15:01 were reactive to the immunodominant SARS-CoV-2 S-derived peptide NQKLIANQF, and 100% of the reactive cells displayed memory phenotype. Finally, we characterize the protein structure of HLA-B*15:01-peptide complexes, demonstrating that the NQKLIANQF peptide from SARS-CoV-2, and the highly homologous NQKLIANAF from seasonal coronaviruses OC43-CoV and HKU1-CoV, share similar ability to be stabilized and presented by HLA-B*15:01, providing the molecular basis for T-cell cross-reactivity and HLA-B*15:01-mediated pre-existing immunity.

Key Points Question How often do persons with new febrile illness access coronavirus testing and receive a test result within 7 days of illness onset? Findings In this cohort study, generally low rates of coronavirus testing were observed in 2679 participants reporting new onset of febrile illness. Although testing rates improved somewhat during the study period, timely coronavirus test results were sought and received by only 25.9% of newly febrile persons at the end of the study analysis period in late October 2020. Meaning Our results suggest systematic underuse of coronavirus testing in patients with febrile illness that may contribute to community transmission., This cohort study examines rates of testing for SARS-CoV-2 and receipt of test results within 7 days of febrile illness onset using data from smartphone-based weekly surveys administered as part of the COVID-19 Citizen Science Study., Importance Active SARS-CoV-2 (coronavirus) transmission continues in the US. It is unclear whether better access to coronavirus testing and more consistent use of testing could substantially reduce transmission. Objective To describe coronavirus testing in persons with new onset of febrile illness and analyze whether there are changes over time and differences by race and ethnicity. Design, Setting, and Participants This cohort study used data from the COVID-19 Citizen Science Study, launched in March 2020, which recruited participants via press release, word-of-mouth, and partner organizations. Participants completed daily surveys about COVID-19 symptoms and weekly surveys about coronavirus testing. All adults (aged at least 18 years) with a smartphone were eligible to join. For this analysis, US participants with new onset of febrile illness from April 2020 to October 2020 were included. Data analysis was performed from November 2020 to March 2021. Main Outcomes and Measures Receipt of a coronavirus test result within 7 days of febrile illness onset. Results Of the 2679 participants included in this analysis, the mean (SD) age was 46.3 (13.4) years, 1983 were female (74%), 2017 were college educated (75%), and a total of 3865 distinct new febrile illness episodes were reported (300 episodes [7.8%] from Hispanic participants, 71 episodes [1.8%] from Black participants, and 3494 episodes [90.4%] from not Black, not Hispanic participants) between April 2 and October 23, 2020. In weekly surveys delivered during the 14 days after fever onset, 12% overall (753 participants) indicated receipt of a test result. Using serial survey responses and parametric time-to-event modeling, it was estimated that by 7 days after onset of febrile illness, a total of 20.5% (95% CI, 19.1%-22.0%) had received a test result. This proportion increased from 9.8% (95% CI, 7.5%-12.0%) early in the epidemic to 24.1% (95% CI, 21.5%-26.7%) at the end of July, but testing rates did not substantially improve since then, increasing to 25.9% (95% CI; 21.6%-30.3%) in late October at the start of the winter surge. Black participants reported receiving a test result about half as often as others (7% [7 of 103] of survey responses vs 12% [53 of 461] for Hispanic vs 13% [693 of 5516] for not Black, not Hispanic; P = .03). This association was not statistically significant in adjusted time-to-event models (hazard ratio = 0.59 vs not Black, not Hispanic participants; 95% CI, 0.26-1.34). Conclusions and Relevance Systematic underuse of coronavirus testing was observed in this cohort study through late October 2020, at the beginning of the winter COVID-19 surge, which may have contributed to preventable coronavirus transmission.

Background: Sudden cardiac death (SCD) studies report higher incidence in men and Black people but presume cardiac cause. We sought to identify sex and racial differences in rates and causes of presumed SCDs in a prospective postmortem study in San Francisco County. Methods: All incident presumed SCDs meeting the World Health Organization definition ages 18 to 90 were autopsied via active surveillance of consecutive out-of-hospital deaths in the POST SCD study (Postmortem Systematic Investigation of Sudden Cardiac Death; February 1, 2011, to March 1, 2014). Autopsy-defined sudden arrhythmic deaths had no extracardiac cause or acute heart failure. Results: Among 541 presumed SCDs, 525 (97%) were autopsied; 362 (69%) were men, 110 Asian (21%), 81 Black (15%), 40 Hispanic (8%), 279 White (53%), and 15 other race (3%). Adjusted for age and race, women had more noncardiac causes of presumed SCD, including pulmonary emboli (8% versus 2%) and neurological causes (10% versus 3%, both P P =0.02) and nonischemic causes (53% versus 39%; P P P P P , all Conclusions: In this countywide postmortem study of presumed SCDs, women had more nonischemic and noncardiac causes. Black women had higher rates of autopsy-defined sudden arrhythmic death than White women while Asian and Hispanic men had lower rates than White men. These findings have implications for risk stratification and prevention of sudden mortality in women and minority populations.

Excess alcohol use is an important determinant of death and disability. Machine learning (ML)-driven interventions leveraging smart-breathalyzer data may help reduce these harms. We developed a digital phenotype of long-term smart-breathalyzer behavior to predict individuals’ breath alcohol concentration (BrAC) levels trained on data from a smart breathalyzer. We analyzed roughly one million datapoints from 33,452 users of a commercial smart-breathalyzer device, collected between 2013 and 2017. For validation, we analyzed the associations between state-level observed smart-breathalyzer BrAC levels and impaired-driving motor vehicle death rates. Behavioral, geolocation-based, and time-series-derived features were fed to an ML algorithm using training (70% of the cohort), development (10% of the cohort), and test (20% of the cohort) sets to predict the likelihood of a BrAC exceeding the legal driving limit (0.08 g/dL). States with higher average BrAC levels had significantly higher alcohol-related driving death rates, adjusted for the number of users per state B (SE) = 91.38 (15.16), p

The National Institutes of Health (NIH) launched the Rapid Acceleration of Diagnostics (RADxSM) Tech initiative to support the development and commercialization of novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) point-of-care test devices. The primary objective of the Clinical Studies Core (CSC) was to perform SARS-CoV-2 device studies involving diverse populations and settings. Within a few months, the infrastructure for clinical studies was developed, including a master protocol, digital study platform, data management system, single IRB, and multi-site partnerships. Data from some studies are being used to support Emergency Use Authorization of novel SARS-CoV-2 test devices. The CSC reduced the typical time and cost of developing medical devices and highlighted the impactful role of academic and NIH partnership in addressing public health needs at a rapid pace during a global pandemic. The structure, deployment, and lessons learned from this experience are widely applicable to future in vitro diagnostic device clinical studies.

Background Consumer devices with broad reach may be useful in screening for atrial fibrillation (AF) in appropriate populations. However, currently no consumer devices are capable of continuous monitoring for AF. Objective The purpose of this study was to estimate the sensitivity and specificity of a smartwatch algorithm for continuous detection of AF from sinus rhythm in a free-living setting. Methods We studied a commercially available smartwatch with photoplethysmography (W-PPG) and electrocardiogram (W-ECG) capabilities. We validated a novel W-PPG algorithm combined with a W-ECG algorithm in a free-living setting, and compared the results to those of a 28-day continuous ECG patch (P-ECG). Results A total of 204 participants completed the free-living study, recording 81,944 hours with both P-ECG and smartwatch measurements. We found sensitivity of 87.8% (95% confidence interval [CI] 83.6%–91.0%) and specificity of 97.4% (95% CI 97.1%–97.7%) for the W-PPG algorithm (every 5-minute classification); sensitivity of 98.9% (95% CI 98.1%–99.4%) and specificity of 99.3% (95% CI 99.1%–99.5%) for the W-ECG algorithm; and sensitivity of 96.9% (95% CI 93.7%–98.5%) and specificity of 99.3% (95% CI 98.4%–99.7%) for W-PPG triggered W-ECG with a single W-ECG required for confirmation of AF. We found a very strong correlation of W-PPG in quantifying AF burden compared to P-ECG (r = 0.98). Conclusion Our findings demonstrate that a novel algorithm using a commercially available smartwatch can continuously detect AF with excellent performance and that confirmation with W-ECG further enhances specificity. In addition, our W-PPG algorithm can estimate AF burden. Further research is needed to determine whether this algorithm is useful in screening for AF in select at-risk patients.

The goal of this study was to investigate the characteristics of mitral valve prolapse (MVP) in a post-mortem study of consecutive sudden cardiac deaths (SCDs) in subjects up to 90 years of age.Up to 2.3% of subjects with MVPs experience SCD, but by convention SCD is rarely confirmed by autopsy. In a post-mortem study of persons 40 years of age, 7% of SCDs were caused by MVP; bileaflet involvement, mitral annular disjunction (MAD), and replacement fibrosis were common.In the San Francisco POST SCD (Postmortem Systematic Investigation of Sudden Cardiac Death) study, autopsies have been performed on1,000 consecutive World Health Organization-defined (presumed) cases of SCD in subjects aged 18 to 90 years since 2011; a total of 603 were adjudicated. Autopsy-defined sudden arrhythmic death (SAD) required absence of nonarrhythmic cause; MVP diagnosis required leaflet billowing. One hundred antemortem echocardiograms were revised to identify additional MVPs missed on autopsy.Among the 603 presumed SCDs, 339 (56%) were autopsy-defined SADs, with MVP identified in 7 (1%). Six additional MVPs were identified by review of echocardiograms, for a prevalence of at least 2% among 603 presumed SCDs and 4% among 339 SADs (vs. 264 non-SADs; p = 0.02). All 6 additional MVPs had monoleaflet rather than bileaflet involvement and mild mitral regurgitation, ruling out hemodynamic cause. Less than one-half had MAD with replacement fibrosis, but all had multisite interstitial fibrosis.In a countywide post-mortem study of all adult cases of SCD, MVP prevalence was at least 4% of SADs, but one-half were missed on autopsy. Monoleaflet MVP was often underdiagnosed post-mortem. Compared with young cases of SCD, lethal MVP in older cases of SCD did not consistently have bileaflet anatomy, replacement fibrosis, or MAD.

BACKGROUND The COVID-19 pandemic has catalyzed a global public response and innovation in clinical study methods. OBJECTIVE The COVID-19 Citizen Science study was designed to generate knowledge about participant-reported COVID-19 symptoms, behaviors, and disease occurrence. METHODS COVID-19 Citizen Science is a longitudinal cohort study launched on March 26, 2020, on the Eureka Research Platform. This study illustrates important advances in digital clinical studies, including entirely digital study participation, targeted recruitment strategies, electronic consent, recurrent and time-updated assessments, integration with smartphone-based measurements, analytics for recruitment and engagement, connection with partner studies, novel engagement strategies such as participant-proposed questions, and feedback in the form of real-time results to participants. RESULTS As of February 2021, the study has enrolled over 50,000 participants. Study enrollment and participation are ongoing. Over the lifetime of the study, an average of 59% of participants have completed at least one survey in the past 4 weeks. CONCLUSIONS Insights about COVID-19 symptoms, behaviors, and disease occurrence can be drawn through digital clinical studies. Continued innovation in digital clinical study methods represents the future of clinical research. INTERNATIONAL REGISTERED REPORT DERR1-10.2196/28169

Clinical risk stratification for sudden cardiac death (SCD) in hypertrophic cardiomyopathy (HC) employs rules derived from American College of Cardiology Foundation/American Heart Association (ACCF/AHA) guidelines or the HCM Risk-SCD model (C-index ∼0.69), which utilize a few clinical variables. We assessed whether data-driven machine learning methods that consider a wider range of variables can effectively identify HC patients with ventricular arrhythmias (VAr) that lead to SCD. We scanned the electronic health records of 711 HC patients for sustained ventricular tachycardia or ventricular fibrillation. Patients with ventricular tachycardia or ventricular fibrillation (n = 61) were tagged as VAr cases and the remaining (n = 650) as non-VAr. The 2-sample ttest and information gain criterion were used to identify the most informative clinical variables that distinguish VAr from non-VAr; patient records were reduced to include only these variables. Data imbalance stemming from low number of VAr cases was addressed by applying a combination of over- and undersampling strategies. We trained and tested multiple classifiers under this sampling approach, showing effective classification. We evaluated 93 clinical variables, of which 22 proved predictive of VAr. The ensemble of logistic regression and naive Bayes classifiers, trained based on these 22 variables and corrected for data imbalance, was most effective in separating VAr from non-VAr cases (sensitivity = 0.73, specificity = 0.76, C-index = 0.83). Our method (HCM-VAr-Risk Model) identified 12 new predictors of VAr, in addition to 10 established SCD predictors. In conclusion, this is the first application of machine learning for identifying HC patients with VAr, using clinical attributes. Our model demonstrates good performance (C-index) compared with currently employed SCD prediction algorithms, while addressing imbalance inherent in clinical data.

Sidney Aung,1 Eric Vittinghoff,2 Gregory Nah,1 Noah D Peyser,1 Mark J Pletcher,2 Jeffrey E Olgin,1 Gregory M Marcus1 1Division of Cardiology, University of California, San Francisco, San Francisco, CA, USA; 2Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, CA, USACorrespondence: Gregory M MarcusUniversity of California, San Francisco, 505 Parnassus Ave, M1180B, San Francisco, CA, 94143, USATel +1 415-476-3450Fax +1 415-476-6260Email greg.marcus@ucsf.eduIntroduction: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which causes coronavirus disease 2019 (Covid-19), has been a serious threat to global health. Previous work has focused primarily on hospitalized patients or on identifying risk factors for disease severity and mortality once the infection has taken place. We sought to leverage the ubiquity of smartphones and mobile applications to study risk factors for Covid-19 infection in a large, geographically heterogenous cohort.Methods: We analyzed data obtained from the Covid-19 Citizen Science (CCS) Study, a worldwide, mobile application-based cohort. After employing forward selection to identify variables with p values < 0.1, multivariable logistic regression models were utilized to identify independent risk factors associated with prevalent SARS-CoV-2 infection.Results: Among 36,041 participants in 113 countries and all 50 states in the US, 484 participants had prevalent SARS-CoV-2 infection. After multivariable adjustment, being a healthcare worker, living with at least one school-aged child, having pets at home, and having immunodeficiency were each associated with an increased odds of SARS-CoV-2. The association between pets and prevalent SARS-CoV-2 was driven by dog ownership. After adjustment for the same covariates, Asian or Pacific Islander race, receiving a flu shot within the past year, increased level of education, and smoking or vaping marijuana within the last 30 days were each associated with a lower odds of SARS-CoV-2.Conclusion: We identified various characteristics and behaviors, many of which are potentially modifiable, associated with prevalent SARS-CoV-2 infection in a world-wide mobile application-based cohort.Keywords: COVID-19, epidemiology, cohort study, digital health, mobile applications

BackgroundIn the absence of universal testing, effective therapies, or vaccines, identifying risk factors for viral infection, particularly readily modifiable exposures and behaviors, is required to identify effective strategies against viral infection and transmission.MethodsWe conducted a world-wide mobile application-based prospective cohort study available to English speaking adults with a smartphone. We collected self-reported characteristics, exposures, and behaviors, as well as smartphone-based geolocation data. Our main outcome was incident symptoms of viral infection, defined as fevers and chills plus one other symptom previously shown to occur with SARS-CoV-2 infection, determined by daily surveys.FindingsAmong 14, 335 participants residing in all 50 US states and 93 different countries followed for a median 21 days (IQR 10-26 days), 424 (3%) developed incident viral symptoms. In pooled multivariable logistic regression models, female biological sex (odds ration [OR] 1.75, 95% CI 1.39-2.20, pInterpretationWhile several immutable characteristics were associated with the risk of developing viral symptoms, multiple immediately modifiable exposures and habits that influence risk were also observed, potentially identifying readily accessible strategies to mitigate risk in the Covid-19 era.FundingThis study was funded by IU2CEB021881-01 and 3U2CEB021881-05S1 from the NIH/ NIBIB to Drs. Marcus, Olgin, and Pletcher.Research in contextEvidence before this studyPredictors of incident viral infection have been determined largely from cross-sectional studies prone to recall bias among individuals representing geographically constrained regions, and most were conducted before the era of the current Covid-19 pandemic.Added value of this studyWe conducted a world-wide, mobile application-based, longitudinal cohort study utilizing time-updated predictors and outcomes, providing novel and current information regarding risk-factors for incident viral symptoms based on real-time information in the era of Covid-19.Implications of all the available evidenceThese data suggest that certain immutable characteristics influence the risk for incident viral symptoms, while smoking cessation, physical distancing to avoid contact with individuals outside the household, regular exercise, and sanitizing one’s phone may each help mitigate the risk of viral infection.