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.

ImportanceUnderstanding left ventricular ejection fraction (LVEF) during coronary angiography can assist in disease management.ObjectiveTo develop an automated approach to predict LVEF from left coronary angiograms.Design, Setting, and ParticipantsThis was a cross-sectional study with external validation using patient data from December 12, 2012, to December 31, 2019, from the University of California, San Francisco (UCSF). Data were randomly split into training, development, and test data sets. External validation data were obtained from the University of Ottawa Heart Institute. Included in the analysis were all patients 18 years or older who received a coronary angiogram and transthoracic echocardiogram (TTE) within 3 months before or 1 month after the angiogram.ExposureA video-based deep neural network (DNN) called CathEF was used to discriminate (binary) reduced LVEF (≤40%) and to predict (continuous) LVEF percentage from standard angiogram videos of the left coronary artery. Guided class-discriminative gradient class activation mapping (GradCAM) was applied to visualize pixels in angiograms that contributed most to DNN LVEF prediction.ResultsA total of 4042 adult angiograms with corresponding TTE LVEF from 3679 UCSF patients were included in the analysis. Mean (SD) patient age was 64.3 (13.3) years, and 2212 patients were male (65%). In the UCSF test data set (n = 813), the video-based DNN discriminated (binary) reduced LVEF (≤40%) with an area under the receiver operating characteristic curve (AUROC) of 0.911 (95% CI, 0.887-0.934); diagnostic odds ratio for reduced LVEF was 22.7 (95% CI, 14.0-37.0). DNN-predicted continuous LVEF had a mean absolute error (MAE) of 8.5% (95% CI, 8.1%-9.0%) compared with TTE LVEF. Although DNN-predicted continuous LVEF differed 5% or less compared with TTE LVEF in 38.0% (309 of 813) of test data set studies, differences greater than 15% were observed in 15.2% (124 of 813). In external validation (n = 776), video-based DNN discriminated (binary) reduced LVEF (≤40%) with an AUROC of 0.906 (95% CI, 0.881-0.931), and DNN-predicted continuous LVEF had an MAE of 7.0% (95% CI, 6.6%-7.4%). Video-based DNN tended to overestimate low LVEFs and underestimate high LVEFs. Video-based DNN performance was consistent across sex, body mass index, low estimated glomerular filtration rate (≤45), presence of acute coronary syndromes, obstructive coronary artery disease, and left ventricular hypertrophy.Conclusion and relevanceThis cross-sectional study represents an early demonstration of estimating LVEF from standard angiogram videos of the left coronary artery using video-based DNNs. Further research can improve accuracy and reduce the variability of DNNs to maximize their clinical utility.

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.

BackgroundOral nirmatrelvir/ritonavir is a treatment for COVID-19, but whether treatment during the acute phase reduces the risk of developing Long COVID is unknown.MethodsUsing the Covid Citizen Science (CCS) online cohort, we surveyed individuals who reported their first SARS-CoV-2 positive test between March and August 2022 regarding Long COVID symptoms. We excluded those who were pregnant, unvaccinated, hospitalized for COVID-19, or received other antiviral therapy. The primary exposure was oral nirmatrelvir/ritonavir. The primary outcome was the presence of any Long COVID symptoms reported on cross-sectional surveys in November and December 2022. We used propensity-score models and inverse probability of treatment weighting to adjust for differences in treatment propensity. Our secondary question was whether symptom or test positivity rebound were associated with Long COVID.Results4684 individuals met the eligibility criteria, of whom 988 (21.1%) were treated and 3696 (78.9%) were untreated; 353/988 (35.7%) treated and 1258/3696 (34.0%) untreated responded to the survey. Median age was 55 years and 66% were female. We did not identify an association between nirmatrelvir/ritonavir treatment and Long COVID symptoms (OR 1.15; 95%CI 0.80-1.64). Among n=666 treated with nirmatrelvir/ritonavir who responded who responded to questions about rebound, rebound symptoms or test positivity were not associated with Long COVID symptoms (OR 1.34; 95%CI 0.74-2.41; p=0.33).ConclusionsWithin this cohort, treatment with nirmatrelvir/ritonavir among vaccinated, non-hospitalized individuals was not associated with lower prevalence of Long COVID symptoms or severity of Long COVID. Experiencing rebound symptoms or test positivity is not strongly associated with developing Long COVID.

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 Patients' self-reports suggest that acute alcohol consumption may trigger a discrete atrial fibrillation (AF) event. OBJECTIVE To objectively ascertain whether alcohol consumption heightens risk for an AF episode. DESIGN A prospective, case-crossover analysis. SETTING Ambulatory persons in their natural environments. PARTICIPANTS Consenting patients with paroxysmal AF. MEASUREMENTS Participants were fitted with a continuous electrocardiogram (ECG) monitor and an ankle-worn transdermal ethanol sensor for 4 weeks. Real-time documentation of each alcoholic drink consumed was self-recorded using a button on the ECG recording device. Fingerstick blood tests for phosphatidylethanol (PEth) were used to corroborate ascertainments of drinking events. RESULTS Of 100 participants (mean age, 64 years [SD, 15]; 79% male; 85% White), 56 had at least 1 episode of AF. Results of PEth testing correlated with the number of real-time recorded drinks and with events detected by the transdermal alcohol sensor. An AF episode was associated with 2-fold higher odds of 1 alcoholic drink (odds ratio [OR], 2.02 [95% CI, 1.38 to 3.17]) and greater than 3-fold higher odds of at least 2 drinks (OR, 3.58 [CI, 1.63 to 7.89]) in the preceding 4 hours. Episodes of AF were also associated with higher odds of peak blood alcohol concentration (OR, 1.38 [CI, 1.04 to 1.83] per 0.1% increase in blood alcohol concentration) and the total area under the curve of alcohol exposure (OR, 1.14 [CI, 1.06 to 1.22] per 4.7% increase in alcohol exposure) inferred from the transdermal ethanol sensor in the preceding 12 hours. LIMITATION Confounding by other time-varying exposures that may accompany alcohol consumption cannot be excluded, and the findings from the current study of patients with AF consuming alcohol may not apply to the general population. CONCLUSION Individual AF episodes were associated with higher odds of recent alcohol consumption, providing objective evidence that a modifiable behavior may influence the probability that a discrete AF event will occur. PRIMARY FUNDING SOURCE National Institute on Alcohol Abuse and Alcoholism.

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.

Cells interpret a variety of signals through G protein-coupled receptors (GPCRs) and stimulate the generation of second messengers such as cyclic adenosine monophosphate (cAMP). A long-standing puzzle is deciphering how GPCRs elicit different physiological responses despite generating similar levels of cAMP. We previously showed that some GPCRs generate cAMP from both the plasma membrane and the Golgi apparatus. Here, we demonstrate that cardiomyocytes distinguish between subcellular cAMP inputs to elicit different physiological outputs. We show that generating cAMP from the Golgi leads to regulation of a specific PKA target that increases the rate of cardiomyocyte relaxation. In contrast, cAMP generation from the plasma membrane activates a different PKA target that increases contractile force. We further validated the physiological consequences of these observations in intact zebrafish and mice. Thus, we demonstrate that the same GPCR acting through the same second messenger regulates cardiac contraction and relaxation dependent on its subcellular location.

Early detection and accurate burden estimation of atrial fibrillation (AFib) can provide the foundation for effective physician treatment. New approaches to accomplish this have attracted tremendous attention in recent years. In this paper, we develop a novel passive smartwatch-based system to detect AFib episodes and estimate the AFib burden in an ambulatory free-living environment without user engagement. Our system leverages a built-in PPG sensor to collect heart rhythm without user engagement. Then, a data preprocessor module includes time-frequency (TF) analysis to augment features in both the time and frequency domain. Finally, a lightweight multi-view convolutional neural network consisting of 19 layers achieves the AFib detection. To validate our system, we carry out a research study that enrolls 53 participants across three months, where we collect and annotate more than 27,622 hours of data. Our system achieves an average of 91.6% accuracy, 93.0% specificity, and 90.8% sensitivity without dropping any data. Moreover, our system takes 0.51 million parameters and costs 5.18 ms per inference. These results reveal that our proposed system can provide a clinical assessment of AFib in daily living.

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.