Characterization of chronic pain, pain interference, and daily pain experiences in adult survivors of childhood cancer: a report from the Childhood Cancer Survivor Study.

ABSTRACT: Although survivors of childhood cancer are at an increased risk, little is known about the prevalence of chronic pain, associated interference, and daily pain experiences. Survivors (N = 233; mean age = 40.8 years, range 22-64 years; mean time since diagnosis = 32.7 years) from the Childhood Cancer Survivor Study completed pain and psychosocial measures. Survivors with chronic pain completed 2-week, daily measures assessing pain and psychological symptoms using mHealth-based ecological momentary assessment. Multivariable-modified Poisson and linear regression models estimated prevalence ratio estimates (PR) and mean effects with 95% confidence intervals (CI) for associations of key risk factors with chronic pain and pain interference, respectively. Multilevel mixed models examined outcomes of daily pain and pain interference with prior day symptoms. Ninety-six survivors (41%) reported chronic pain, of whom 23 (24%) had severe interference. Chronic pain was associated with previous intravenous methotrexate treatment (PR = 1.6, 95% CI 1.1-2.3), respiratory (PR = 1.8, 95% CI 1.2-2.5), gastrointestinal (PR = 1.6, 95% CI 11.0-2.3), and neurological (PR = 1.5, 95% CI 1.0-2.1) chronic health conditions, unemployment (PR = 1.4, 95% CI 1.0-1.9) and clinically significant depression and anxiety (PR = 2.9, 95% CI 2.0-4.2), as well as a diagnosis of childhood Ewing sarcoma or osteosarcoma (PR = 1.9, 95% CI 1.0-3.5). Higher pain interference was associated with cardiovascular and neurological conditions, unemployment and clinical levels of depression and/or anxiety, and fear of cancer recurrence. For male, but not female survivors, low sleep quality, elevated anxiety, and elevated depression predicted high pain intensity and interference the next day. A substantial proportion of childhood cancer survivors experience chronic pain and significant associated interference. Chronic pain should be routinely evaluated, and interventions are needed.

Single-nucleus RNA/ATAC-seq in early-stage HCM models predicts SWI/SNF-activation in mutant-myocytes, and allele-specific differences in fibroblasts.

Hypertrophic cardiomyopathy (HCM) is associated with phenotypic variability. To gain insights into transcriptional regulation of cardiac phenotype, single-nucleus linked RNA-/ATAC-seq was performed in 5-week-old control mouse-hearts (WT) and two HCM-models (R92W-TnT, R403Q-MyHC) that exhibit differences in heart size/function and fibrosis; mutant data was compared to WT. Analysis of 23,304 nuclei from mutant hearts, and 17,669 nuclei from WT, revealed similar dysregulation of gene expression, activation of AP-1 TFs (FOS, JUN) and the SWI/SNF complex in both mutant ventricular-myocytes. In contrast, marked differences were observed between mutants, for gene expression/TF enrichment, in fibroblasts, macrophages, endothelial cells. Cellchat predicted activation of pro-hypertrophic IGF-signaling in both mutant ventricular-myocytes, and profibrotic TGFß-signaling only in mutant-TnT fibroblasts. In summary, our bioinformatics analyses suggest that activation of IGF-signaling, AP-1 TFs and the SWI/SNF chromatin remodeler complex promotes myocyte hypertrophy in early-stage HCM. Selective activation of TGFß-signaling in mutant-TnT fibroblasts contributes to genotype-specific differences in cardiac fibrosis.

Continuous Atrial Fibrillation Monitoring From Photoplethysmography: Comparison Between Supervised Deep Learning and Heuristic Signal Processing.

BACKGROUND: Continuous monitoring for atrial fibrillation (AF) using photoplethysmography (PPG) from smartwatches or other wearables is challenging due to periods of poor signal quality during motion or suboptimal wearing. As a result, many consumer wearables sample infrequently and only analyze when the user is at rest, which limits the ability to perform continuous monitoring or to quantify AF. OBJECTIVES: This study aimed to compare 2 methods of continuous monitoring for AF in free-living patients: a well-validated signal processing (SP) heuristic and a convolutional deep neural network (DNN) trained on raw signal. METHODS: We collected 4 weeks of continuous PPG and electrocardiography signals in 204 free-living patients. Both SP and DNN models were developed and validated both on holdout patients and an external validation set. RESULTS: The results show that the SP model demonstrated receiver-operating characteristic area under the curve (AUC) of 0.972 (sensitivity 99.6%, specificity: 94.4%), which was similar to the DNN receiver-operating characteristic AUC of 0.973 (sensitivity 92.2, specificity: 95.5%); however, the DNN classified significantly more data (95% vs 62%), revealing its superior tolerance of tracings prone to motion artifact. Explainability analysis revealed that the DNN automatically suppresses motion artifacts, evaluates irregularity, and learns natural AF interbeat variability. The DNN performed better and analyzed more signal in the external validation cohort using a different population and PPG sensor (AUC, 0.994; 97% analyzed vs AUC, 0.989; 88% analyzed). CONCLUSIONS: DNNs perform at least as well as SP models, classify more data, and thus may be better for continuous PPG monitoring.

Factors associated with anxiety during the first two years of the COVID-19 pandemic in the United States: An analysis of the COVID-19 Citizen Science study.

COVID-19 increased the prevalence of clinically significant anxiety in the United States. To investigate contributing factors we analyzed anxiety, reported online via monthly Generalized Anxiety Disorders-7 (GAD-7) surveys between April 2020 and May 2022, in association with self-reported worry about the health effects of COVID-19, economic difficulty, personal COVID-19 experience, and subjective social status. 333,292 anxiety surveys from 50,172 participants (82% non-Hispanic white; 73% female; median age 55, IQR 42-66) showed high levels of anxiety, especially early in the pandemic. Anxiety scores showed strong independent associations with worry about the health effects of COVID-19 for oneself or family members (GAD-7 score +3.28 for highest vs. lowest category; 95% confidence interval: 3.24, 3.33; p<0.0001 for trend) and with difficulty paying for basic living expenses (+2.06; 1.97, 2.15, p<0.0001) in multivariable regression models after adjusting for demographic characteristics, COVID-19 case rates and death rates, and personal COVID-19 experience. High levels of COVID-19 health worry and economic stress were each more common among participants reporting lower subjective social status, and median anxiety scores for those experiencing both were in the range considered indicative of moderate to severe clinical anxiety disorders. In summary, health worry and economic difficulty both contributed to high rates of anxiety during the first two years of the COVID-19 pandemic in the US, especially in disadvantaged socioeconomic groups. Programs to address both health concerns and economic insecurity in vulnerable populations could help mitigate pandemic impacts on anxiety and mental health.

Acute alcohol exposure and electrocardiographic changes: Finding from the HOLIDAY trial.

BACKGROUND: Alcohol consumption is associated with a higher increased risk of atrial fibrillation (AF), but the acute effects on cardiac electrophysiology in humans remain poorly understood. The HOw ALcohol InDuces Atrial TachYarrhythmias (HOLIDAY) Trial revealed that alcohol shortened pulmonary vein atrial effective refractory periods, but more global electrophysiologic changes gleaned from the surface ECG have not yet been reported. METHODS: This was a secondary analysis of the HOLIDAY Trial. During AF ablation procedures, 100 adults were randomized to intravenous alcohol titrated to 0.08% blood alcohol concentration versus a volume and osmolarity-matched, masked, placebo. Intervals measured from 12­lead ECGs were compared between pre infusion and at infusion steady state (20 min). RESULTS: The average age was 60 years and 11% were female. No significant differences in the P-wave duration, PR, QRS or QT intervals, were present between alcohol and placebo arms. However, infusion of alcohol was associated with a statistically significant relative shortening of the JT interval (r: -14.73, p = 0.048) after multivariable adjustment. CONCLUSION: Acute exposure to alcohol was associated with a relative reduction in the JT interval, reflecting shortening of ventricular repolarization. These acute changes may reflect a more global shortening of refractoriness, suggesting immediate proarrhythmic effects pertinent to the atria and ventricles.

Association of nirmatrelvir for acute SARS-CoV-2 infection with subsequent Long COVID symptoms in an observational cohort study.

Oral nirmatrelvir/ritonavir is approved as treatment for acute COVID-19, but the effect of treatment during acute infection on risk of Long COVID is unknown. We hypothesized that nirmatrelvir treatment during acute SARS-CoV-2 infection reduces risk of developing Long COVID and rebound after treatment is associated with Long COVID. We conducted an observational cohort study within the Covid Citizen Science (CCS) study, an online cohort study with over 100 000 participants. We included vaccinated, nonhospitalized, nonpregnant individuals who reported their first SARS-CoV-2 positive test March-August 2022. Oral nirmatrelvir/ritonavir treatment was ascertained during acute SARS-CoV-2 infection. Patient-reported Long COVID symptoms, symptom rebound and test-positivity rebound were asked on subsequent surveys at least 3 months after SARS-CoV-2 infection. A total of 4684 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 Long COVID survey (n = 1611). Among 1611 participants, median age was 55 years and 66% were female. At 5.4 ± 1.3 months after infection, nirmatrelvir treatment was not associated with subsequent Long COVID symptoms (odds ratio [OR]: 1.15; 95% confidence interval [CI]: 0.80-1.64; p = 0.45). Among 666 treated who answered rebound questions, rebound symptoms or test positivity were not associated with Long COVID symptoms (OR: 1.34; 95% CI: 0.74-2.41; p = 0.33). Within this cohort of vaccinated, nonhospitalized individuals, oral nirmatrelvir treatment during acute SARS-CoV-2 infection and rebound after nirmatrelvir treatment were not associated with Long COVID symptoms more than 90 days after infection.

Cardiac contraction and relaxation are regulated by distinct subcellular cAMP pools.

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 the regulation of a specific protein kinase A (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.

Frequent Premature Atrial Contractions Lead to Adverse Atrial Remodeling and Atrial Fibrillation in a Swine Model.

BACKGROUND: Frequent premature atrial complexes (PACs) are associated with future incident atrial fibrillation (AF), but whether PACs contribute to development of AF through adverse atrial remodeling has not been studied. This study aimed to explore the effect of frequent PACs from different sites on atrial remodeling in a swine model. METHODS: Forty swine underwent baseline electrophysiologic studies and echocardiography followed by pacemaker implantations and paced PACs (50% burden) at 250-ms coupling intervals for 16 weeks in 4 groups: (1) lateral left atrium (LA) PACs by the coronary sinus (Lat-PAC; n=10), (2) interatrial septal PACs (Sep-PAC; n=10), (3) regular LA pacing at 130 beats/min (Reg-130; n=10), and (4) controls without PACs (n=10). At the final study, repeat studies were performed, followed by tissue histology and molecular analyses focusing on fibrotic pathways. RESULTS: Lat-PACs were associated with a longer P-wave duration (93.0±9.0 versus 74.2±8.2 and 58.8±7.6 ms; P<0.001) and greater echocardiographic mechanical dyssynchrony (57.5±11.6 versus 35.7±13.0 and 24.4±11.1 ms; P<0.001) compared with Sep-PACs and controls, respectively. After 16 weeks, Lat-PACs led to slower LA conduction velocity (1.1±0.2 versus 1.3±0.2 [Sep-PAC] versus 1.3±0.1 [Reg-130] versus 1.5±0.2 [controls] m/s; P<0.001) without significant change in atrial ERP. The Lat-PAC group had a significantly increased percentage of LA fibrosis and upregulated levels of extracellular matrix proteins (lysyl oxidase and collagen 1 and 8), as well as TGF-ß1 (transforming growth factor-ß1) signaling proteins (latent and monomer TGF-ß1 and phosphorylation/total ratio of SMAD2/3; P<0.05). The Lat-PAC group had the longest inducible AF duration (terminal to baseline: 131 [interquartile range 30, 192] seconds versus 16 [6, 26] seconds [Sep-PAC] versus 22 [11, 64] seconds [Reg-130] versus -1 [-16, 7] seconds [controls]; P<0.001). CONCLUSIONS: In this swine model, frequent PACs resulted in adverse atrial structural remodeling with a heightened propensity to AF. PACs originating from the lateral LA produced greater atrial remodeling and longer induced AF duration than the septal-origin PACs. These data provide evidence that frequent PACs can cause adverse atrial remodeling as well as AF, and that the location of ectopic PACs may be clinically meaningful.

Preceding Night Sleep Quality and Atrial Fibrillation Episodes in the I-STOP-AFIB Randomized Trial.

BACKGROUND: Chronic sleep disruption is associated with incident atrial fibrillation (AF), but it is unclear whether poor sleep quality acutely triggers AF. OBJECTIVES: The aim of this study was to characterize the relationship between a given night's sleep quality and the risk of a discrete AF episode. METHODS: Patients with symptomatic paroxysmal AF in the I-STOP-AFIB (Individualized Studies of Triggers of Paroxysmal Atrial Fibrillation) trial reported sleep quality on a daily basis. Participants were also queried daily regarding AF episodes and were provided smartphone-based mobile electrocardiograms (ECGs) (KardiaMobile, AliveCor). RESULTS: Using 15,755 days of data from 419 patients, worse sleep quality on any given night was associated with a 15% greater odds of a self-reported AF episode the next day (OR: 1.15; 95% CI: 1.10-1.20; P < 0.0001) after adjustment for the day of the week. No statistically significant associations between worsening sleep quality and mobile ECG-confirmed AF events were observed (OR: 1.04; 95% CI: 0.95-1.13; P = 0.43), although substantially fewer of these mobile ECG-confirmed events may have limited statistical power. Poor sleep was also associated with longer self-reported AF episodes, with each progressive category of worsening sleep associated with 16 (95% CI: 12-21; P < 0.001) more minutes of AF the next day. CONCLUSIONS: Poor sleep was associated with an immediately heightened risk for self-reported AF episodes, and a dose-response relationship existed such that progressively worse sleep was associated with longer episodes of AF the next day. These data suggest that sleep quality may be a potentially modifiable trigger relevant to the near-term risk of a discrete AF episode.

Patient-Level Artificial Intelligence-Enhanced Electrocardiography in Hypertrophic Cardiomyopathy: Longitudinal Treatment and Clinical Biomarker Correlations.

BACKGROUND: Artificial intelligence (AI) applied to 12-lead electrocardiographs (ECGs) can detect hypertrophic cardiomyopathy (HCM). OBJECTIVES: The purpose of this study was to determine if AI-enhanced ECG (AI-ECG) can track longitudinal therapeutic response and changes in cardiac structure, function, or hemodynamics in obstructive HCM during mavacamten treatment. METHODS: We applied 2 independently developed AI-ECG algorithms (University of California-San Francisco and Mayo Clinic) to serial ECGs (n = 216) from the phase 2 PIONEER-OLE trial of mavacamten for symptomatic obstructive HCM (n = 13 patients, mean age 57.8 years, 69.2% male). Control ECGs from 2,600 age- and sex-matched individuals without HCM were obtained. AI-ECG output was correlated longitudinally to echocardiographic and laboratory metrics of mavacamten treatment response. RESULTS: In the validation cohorts, both algorithms exhibited similar performance for HCM diagnosis, and exhibited mean HCM score decreases during mavacamten treatment: patient-level score reduction ranged from approximately 0.80 to 0.45 for Mayo and 0.70 to 0.35 for USCF algorithms; 11 of 13 patients demonstrated absolute score reduction from start to end of follow-up for both algorithms. HCM scores were significantly associated with other HCM-relevant parameters, including left ventricular outflow tract gradient at rest, postexercise, and with Valsalva, and NT-proBNP level, independent of age and sex (all P < 0.01). For both algorithms, the strongest longitudinal correlation was between AI-ECG HCM score and left ventricular outflow tract gradient postexercise (slope estimate: University of California-San Francisco 0.70 [95% CI: 0.45-0.96], P < 0.0001; Mayo 0.40 [95% CI: 0.11-0.68], P = 0.007). CONCLUSIONS: AI-ECG analysis longitudinally correlated with changes in echocardiographic and laboratory markers during mavacamten treatment in obstructive HCM. These results provide early evidence for a potential paradigm for monitoring HCM therapeutic response.

Email-Based Recruitment Into the Health eHeart Study: Cohort Analysis of Invited Eligible Patients.

BACKGROUND: Web- or app-based digital health studies allow for more efficient collection of health data for research. However, remote recruitment into digital health studies can enroll nonrepresentative study samples, hindering the robustness and generalizability of findings. Through the comprehensive evaluation of an email-based campaign on recruitment into the Health eHeart Study, we aim to uncover key sociodemographic and clinical factors that contribute to enrollment. OBJECTIVE: This study sought to understand the factors related to participation, specifically regarding enrollment, in the Health eHeart Study as a result of a large-scale remote email recruitment campaign. METHODS: We conducted a cohort analysis on all invited University of California, San Francisco (UCSF) patients to identify sociodemographic and clinical predictors of enrollment into the Health eHeart Study. The primary outcome was enrollment, defined by account registration and consent into the Health eHeart Study. The email recruitment campaign was carried out from August 2015 to February 2016, with electronic health record data extracted between September 2019 and December 2019. RESULTS: The email recruitment campaign delivered at least 1 email invitation to 93.5% (193,606/206,983) of all invited patients and yielded a 3.6% (7012/193,606) registration rate among contacted patients and an 84.1% (5899/7012) consent rate among registered patients. Adjusted multivariate logistic regression models analyzed independent sociodemographic and clinical predictors of (1) registration among contacted participants and (2) consent among registered participants. Odds of registration were higher among patients who are older, women, non-Hispanic White, active patients with commercial insurance or Medicare, with a higher comorbidity burden, with congestive heart failure, and randomized to receive up to 2 recruitment emails. The odds of registration were lower among those with medical conditions such as dementia, chronic pulmonary disease, moderate or severe liver disease, paraplegia or hemiplegia, renal disease, or cancer. Odds of subsequent consent after initial registration were different, with an inverse trend of being lower among patients who are older and women. The odds of consent were also lower among those with peripheral vascular disease. However, the odds of consent remained higher among patients who were non-Hispanic White and those with commercial insurance. CONCLUSIONS: This study provides important insights into the potential returns on participant enrollment when digital health study teams invest resources in using email for recruitment. The findings show that participant enrollment was driven more strongly by sociodemographic factors than clinical factors. Overall, email is an extremely efficient means of recruiting participants from a large list into the Health eHeart Study. Despite some improvements in representation, the formulation of truly diverse studies will require additional resources and strategies to overcome persistent participation barriers.

Digital Tool-Assisted Hospitalization Detection in the Tailored Antiplatelet Initiation to Lessen Outcomes due to Decreased Clopidogrel Response After Percutaneous Coronary Intervention Study Compared to Traditional Site-Coordinator Ascertainment: Intervention Study.

BACKGROUND: Accurate, timely ascertainment of clinical end points, particularly hospitalizations, is crucial for clinical trials. The Tailored Antiplatelet Initiation to Lessen Outcomes Due to Decreased Clopidogrel Response after Percutaneous Coronary Intervention (TAILOR-PCI) Digital Study extended the main TAILOR-PCI trial's follow-up to 2 years, using a smartphone-based research app featuring geofencing-triggered surveys and routine monthly mobile phone surveys to detect cardiovascular (CV) hospitalizations. This pilot study compared these digital tools to conventional site-coordinator ascertainment of CV hospitalizations. OBJECTIVE: The objectives were to evaluate geofencing-triggered notifications and routine monthly mobile phone surveys' performance in detecting CV hospitalizations compared to telephone visits and health record reviews by study coordinators at each site. METHODS: US and Canadian participants from the TAILOR-PCI Digital Follow-Up Study were invited to download the Eureka Research Platform mobile app, opting in for location tracking using geofencing, triggering a smartphone-based survey if near a hospital for ≥4 hours. Participants were sent monthly notifications for CV hospitalization surveys. RESULTS: From 85 participants who consented to the Digital Study, downloaded the mobile app, and had not previously completed their final follow-up visit, 73 (85.8%) initially opted in and consented to geofencing. There were 9 CV hospitalizations ascertained by study coordinators among 5 patients, whereas 8 out of 9 (88.9%) were detected by routine monthly hospitalization surveys. One CV hospitalization went undetected by the survey as it occurred within two weeks of the previous event, and the survey only allowed reporting of a single hospitalization. Among these, 3 were also detected by the geofencing algorithm, but 6 out of 9 (66.7%) were missed by geofencing: 1 occurred in a participant who never consented to geofencing, while 5 hospitalizations occurred among participants who had subsequently turned off geofencing prior to their hospitalization. Geofencing-detected hospitalizations were ascertained within a median of 2 (IQR 1-3) days, monthly surveys within 11 (IQR 6.5-25) days, and site coordinator methods within 38 (IQR 9-105) days. The geofencing algorithm triggered 245 notifications among 39 participants, with 128 (52.2%) from true hospital presence and 117 (47.8%) from nonhospital health care facility visits. Additional geofencing iterative improvements to reduce hospital misidentification were made to the algorithm at months 7 and 12, elevating the rate of true alerts from 35.4% (55 true alerts/155 total alerts before month 7) to 78.7% (59 true alerts/75 total alerts in months 7-12) and ultimately to 93.3% (14 true alerts/5 total alerts in months 13-21), respectively. CONCLUSIONS: The monthly digital survey detected most CV hospitalizations, while the geofencing survey enabled earlier detection but did not offer incremental value beyond traditional tools. Digital tools could potentially reduce the burden on study coordinators in ascertaining CV hospitalizations. The advantages of timely reporting via geofencing should be weighed against the issue of false notifications, which can be mitigated through algorithmic refinements.

Associations between tobacco and cannabis use and anxiety and depression among adults in the United States: Findings from the COVID-19 citizen science study.

BACKGROUND: Little is known about whether people who use both tobacco and cannabis (co-use) are more or less likely to have mental health disorders than single substance users or non-users. We aimed to examine associations between use of tobacco and/or cannabis with anxiety and depression. METHODS: We analyzed data from the COVID-19 Citizen Science Study, a digital cohort study, collected via online surveys during 2020-2022 from a convenience sample of 53,843 US adults (≥ 18 years old) nationwide. Past 30-day use of tobacco and cannabis was self-reported at baseline and categorized into four exclusive patterns: tobacco-only use, cannabis-only use, co-use of both substances, and non-use. Anxiety and depression were repeatedly measured in monthly surveys. To account for multiple assessments of mental health outcomes within a participant, we used Generalized Estimating Equations to examine associations between the patterns of tobacco and cannabis use with each outcome. RESULTS: In the total sample (mean age 51.0 years old, 67.9% female), 4.9% reported tobacco-only use, 6.9% cannabis-only use, 1.6% co-use, and 86.6% non-use. Proportions of reporting anxiety and depression were highest for the co-use group (26.5% and 28.3%, respectively) and lowest for the non-use group (10.6% and 11.2%, respectively). Compared to non-use, the adjusted odds of mental health disorders were highest for co-use (Anxiety: OR = 1.89, 95%CI = 1.64-2.18; Depression: OR = 1.77, 95%CI = 1.46-2.16), followed by cannabis-only use, and tobacco-only use. Compared to tobacco-only use, co-use (OR = 1.35, 95%CI = 1.08-1.69) and cannabis-only use (OR = 1.17, 95%CI = 1.00-1.37) were associated with higher adjusted odds for anxiety, but not for depression. Daily use (vs. non-daily use) of cigarettes, e-cigarettes, and cannabis were associated with higher adjusted odds for anxiety and depression. CONCLUSIONS: Use of tobacco and/or cannabis, particularly co-use of both substances, were associated with poor mental health. Integrating mental health support with tobacco and cannabis cessation may address this co-morbidity.

CathAI: fully automated coronary angiography interpretation and stenosis estimation.

Coronary angiography is the primary procedure for diagnosis and management decisions in coronary artery disease (CAD), but ad-hoc visual assessment of angiograms has high variability. Here we report a fully automated approach to interpret angiographic coronary artery stenosis from standard coronary angiograms. Using 13,843 angiographic studies from 11,972 adult patients at University of California, San Francisco (UCSF), between April 1, 2008 and December 31, 2019, we train neural networks to accomplish four sequential necessary tasks for automatic coronary artery stenosis localization and estimation. Algorithms are internally validated against criterion-standard labels for each task in hold-out test datasets. Algorithms are then externally validated in real-world angiograms from the University of Ottawa Heart Institute (UOHI) and also retrained using quantitative coronary angiography (QCA) data from the Montreal Heart Institute (MHI) core lab. The CathAI system achieves state-of-the-art performance across all tasks on unselected, real-world angiograms. Positive predictive value, sensitivity and F1 score are all ≥90% to identify projection angle and ≥93% for left/right coronary artery angiogram detection. To predict obstructive CAD stenosis (≥70%), CathAI exhibits an AUC of 0.862 (95% CI: 0.843-0.880). In UOHI external validation, CathAI achieves AUC 0.869 (95% CI: 0.830-0.907) to predict obstructive CAD. In the MHI QCA dataset, CathAI achieves an AUC of 0.775 (95%. CI: 0.594-0.955) after retraining. In conclusion, multiple purpose-built neural networks can function in sequence to accomplish automated analysis of real-world angiograms, which could increase standardization and reproducibility in angiographic coronary stenosis assessment.

A common allele of HLA is associated with asymptomatic SARS-CoV-2 infection.

Studies have demonstrated that at least 20% of individuals infected with SARS-CoV-2 remain asymptomatic1-4. Although most global efforts have focused on severe illness in COVID-19, examining asymptomatic infection provides a unique opportunity to consider early immunological features that promote rapid viral clearance. Here, postulating that variation in the human leukocyte antigen (HLA) loci may underly processes mediating asymptomatic infection, we enrolled 29,947 individuals, for whom high-resolution HLA genotyping data were available, in a smartphone-based study designed to track COVID-19 symptoms and outcomes. Our discovery cohort (n = 1,428) comprised unvaccinated individuals who reported a positive test result for SARS-CoV-2. We tested for association of five HLA loci with disease course and identified a strong association between HLA-B*15:01 and asymptomatic infection, observed 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 samples from individuals carrying HLA-B*15:01 were reactive to the immunodominant SARS-CoV-2 S-derived peptide NQKLIANQF. The majority of the reactive T cells displayed a memory phenotype, were highly polyfunctional and were cross-reactive to a peptide derived from seasonal coronaviruses. The crystal structure of HLA-B*15:01-peptide complexes demonstrates that the peptides NQKLIANQF and NQKLIANAF (from OC43-CoV and HKU1-CoV) share a similar ability to be stabilized and presented by HLA-B*15:01. Finally, we show that the structural similarity of the peptides underpins T cell cross-reactivity of high-affinity public T cell receptors, providing the molecular basis for HLA-B*15:01-mediated pre-existing immunity.

Interindividual Variability in Self-Monitoring of Blood Pressure Using Consumer-Purchased Wireless Devices.

BACKGROUND: Engagement with self-monitoring of blood pressure (BP) declines, on average, over time but may vary substantially by individual. OBJECTIVES: We aimed to describe different 1-year patterns (groups) of self-monitoring of BP behaviors, identify predictors of those groups, and examine the association of self-monitoring of BP groups with BP levels over time. METHODS: We analyzed device-recorded BP measurements collected by the Health eHeart Study-an ongoing prospective eCohort study-from participants with a wireless consumer-purchased device that transmitted date- and time-stamped BP data to the study through a full 12 months of observation starting from the first day they used the device. Participants received no instruction on device use. We applied clustering analysis to identify 1-year self-monitoring, of BP patterns. RESULTS: Participants had a mean age of 52 years and were male and White. Using clustering algorithms, we found that a model with three groups fit the data well: persistent daily use (9.1% of participants), persistent weekly use (21.2%), and sporadic use only (69.7%). Persistent daily use was more common among older participants who had higher Week 1 self-monitoring of BP frequency and was associated with lower BP levels than the persistent weekly use or sporadic use groups throughout the year. CONCLUSION: We identified three distinct self-monitoring of BP groups, with nearly 10% sustaining a daily use pattern associated with lower BP levels.

Mechanical Dispersion Discriminates between Arrhythmic and Non-Arrhythmic Sudden Death: From the POST SCD Study.

Background: Global longitudinal strain (GLS) and mechanical dispersion (MD) by speckle-tracking echocardiography can predict sudden cardiac death (SCD) beyond left ventricular ejection fraction (LVEF) alone. However, prior studies have presumed cardiac cause from EMS records or death certificates rather than gold-standard autopsies. Objectives: We sought to investigate whether abnormal GLS and MD, reflective of underlying myocardial fibrosis, are associated with autopsy-defined sudden arrhythmic death (SAD) in a comprehensive postmortem study. Methods: We identified and autopsied all World Health Organization-defined (presumed) SCDs ages 18-90 via active surveillance of out of hospital deaths in the ongoing San Francisco POstmortem Systematic InvesTigation of Sudden Cardiac Death (POST SCD) Study to refine presumed SCDs to true cardiac causes. We retrieved all available pre-mortem echocardiograms and assessed LVEF, LV-GLS, and MD. The extent of LV myocardial fibrosis was assessed and quantified histologically. Results: Of 652 autopsied subjects, 65 (10%) had echocardiograms available for primary review, obtained at a mean 1.5 years before SCD. Of these, 37 (56%) were SADs and 29 (44%) were non-SADs; fibrosis was quantified in 38 (58%). SADs were predominantly male, but had similar age, race, baseline comorbidities, and LVEF compared to non-SADs (all p>0.05). SADs had significantly reduced LV-GLS (median: -11.4% versus -18.5%, p=0.008) and increased MD (median: 14.8 ms versus 9.4 ms, p=0.006) compared to non-SADs. MD was associated with total LV fibrosis by linear regression in SADs (r=0.58, p=0.002). Conclusion: In this countywide postmortem study of all sudden deaths, autopsy-confirmed arrhythmic deaths had significantly lower LV-GLS and increased MD than non-arrhythmic sudden deaths. Increased MD correlated with higher histologic levels of LV fibrosis in SADs. These findings suggest that increased MD, which is a surrogate for the extent of myocardial fibrosis, may improve risk stratification and specification for SAD beyond LVEF. PERSPECTIVES: Competency in medical knowledge: Mechanical dispersion derived from speckle tracking echocardiography provides better discrimination between autopsy-defined arrhythmic vs non-arrhythmic sudden death than LVEF or LV-GLS. Histological ventricular fibrosis correlates with increased mechanical dispersion in SAD.Translational outlook: Speckle tracking echocardiography parameters, in particular mechanical dispersion, may be considered as a non-invasive surrogate marker for myocardial fibrosis and risk stratification in SCD.

Assessment of valvular function in over 47,000 people using deep learning-based flow measurements.

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 0.03cm 2 in 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.

Automated Assessment of Cardiac Systolic Function From Coronary Angiograms With Video-Based Artificial Intelligence Algorithms.

Importance: Understanding left ventricular ejection fraction (LVEF) during coronary angiography can assist in disease management. Objective: To develop an automated approach to predict LVEF from left coronary angiograms. Design, Setting, and Participants: This 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. Exposure: A 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. Results: A 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 relevance: This 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.