cellPACK assembles computational models of the biological mesoscale, an intermediate scale (10−7–10−8m) between molecular and cellular biology. cellPACK’s modular architecture unites existing and novel packing algorithms to generate, visualize and analyze comprehensive 3D models of complex biological environments that integrate data from multiple experimental systems biology and structural biology sources. cellPACK is currently available as open source code, with tools for validation of models and with recipes and models for five biological systems: blood plasma, cytoplasm, synaptic vesicles, HIV and a mycoplasma cell. We have applied cellPACK to model distributions of HIV envelope protein to test several hypotheses for consistency with experimental observations. Biologists, educators, and outreach specialists can interact with cellPACK models, develop new recipes and perform packing experiments through scripting and graphical user interfaces at http://cellPACK.org.

Background: Artificial intelligence (AI)-enabled analysis of 12-lead electrocardiograms (ECGs) may facilitate efficient estimation of incident atrial fibrillation (AF) risk. However, it remains unclear whether AI provides meaningful and generalizable improvement in predictive accuracy beyond clinical risk factors for AF. Methods: We trained a convolutional neural network ("ECG-AI") to infer 5-year incident AF risk using 12-lead ECGs in patients receiving longitudinal primary care at Massachusetts General Hospital (MGH). We then fit three Cox proportional hazards models, each composed of: a) ECG-AI 5-year AF probability, b) the Cohorts for Heart and Aging in Genomic Epidemiology AF (CHARGE-AF) clinical risk score, and c) terms for both ECG-AI and CHARGE-AF ("CH-AI"). We assessed model performance by calculating discrimination (area under the receiver operating characteristic curve, AUROC) and calibration in an internal test set and two external test sets (Brigham and Women's Hospital and UK Biobank). Models were recalibrated to estimate 2-year AF risk in the UK Biobank given limited available follow-up. We used saliency mapping to identify ECG features most influential on ECG-AI risk predictions and assessed correlation between ECG-AI and CHARGE-AF linear predictors. Results: The training set comprised 45,770 individuals (age 55±17 years, 53% women, 2,171 AF events), and the test sets comprised 83,162 individuals (age 59±13 years, 56% women, 2,424 AF events). AUROC was comparable using CHARGE-AF (MGH 0.802, 95% CI 0.767-0.836; BWH 0.752, 95% CI 0.741-0.763; UK Biobank 0.732, 95% CI 0.704-0.759) and ECG-AI (MGH 0.823, 95% CI 0.790-0.856; BWH 0.747, 95% CI 0.736-0.759; UK Biobank 0.705, 95% CI 0.673-0.737). AUROC was highest using CH-AI: MGH 0.838, 95% CI 0.807-0.869; BWH 0.777, 95% CI 0.766-0.788; UK Biobank 0.746, 95% CI 0.716-0.776). Calibration error was low using ECG-AI (MGH 0.0212; BWH 0.0129; UK Biobank 0.0035) and CH-AI (MGH 0.012; BWH 0.0108; UK Biobank 0.0001). In saliency analyses, the ECG P-wave had the greatest influence on AI model predictions. ECG-AI and CHARGE-AF linear predictors were correlated (Pearson r MGH 0.61, BWH 0.66, UK Biobank 0.41). Conclusions: AI-based analysis of 12-lead ECGs has similar predictive utility to a clinical risk factor model for incident AF and both approaches are complementary. ECG-AI may enable efficient quantification of future AF risk.

Aims Physical activity may be an important modifiable risk factor for atrial fibrillation (AF), but associations have been variable and generally based on self-reported activity. Methods and results We analysed 93 669 participants of the UK Biobank prospective cohort study without prevalent AF who wore a wrist-based accelerometer for 1 week. We categorized whether measured activity met the standard recommendations of the European Society of Cardiology, American Heart Association, and World Health Organization [moderate-to-vigorous physical activity (MVPA) ≥150 min/week]. We tested associations between guideline-adherent activity and incident AF (primary) and stroke (secondary) using Cox proportional hazards models adjusted for age, sex, and each component of the Cohorts for Heart and Aging Research in Genomic Epidemiology AF (CHARGE-AF) risk score. We also assessed correlation between accelerometer-derived and self-reported activity. The mean age was 62 ± 8 years and 57% were women. Over a median of 5.2 years, 2338 incident AF events occurred. In multivariable adjusted models, guideline-adherent activity was associated with lower risks of AF [hazard ratio (HR) 0.82, 95% confidence interval (CI) 0.75–0.89; incidence 3.5/1000 person-years, 95% CI 3.3–3.8 vs. 6.5/1000 person-years, 95% CI 6.1–6.8] and stroke (HR 0.76, 95% CI 0.64–0.90; incidence 1.0/1000 person-years, 95% CI 0.9–1.1 vs. 1.8/1000 person-years, 95% CI 1.6–2.0). Correlation between accelerometer-derived and self-reported MVPA was weak (Spearman r = 0.16, 95% CI 0.16–0.17). Self-reported activity was not associated with incident AF or stroke. Conclusions Greater accelerometer-derived physical activity is associated with lower risks of AF and stroke. Future preventive efforts to reduce AF risk may be most effective when targeting adherence to objective activity thresholds.

Purpose of Review-This review describes the novel category of wearable ECG monitors and identifies where patients, healthcare providers, and device manufacturers should focus efforts to maximize the clinical benefit of these devices.Recent Findings-Notable wearable ECG monitors include the AliveCor Kardia devices, Apple Watch Series 4, and several others. The most common use case is monitoring for atrial fibrillation. The available evidence validates the ability of the Kardia devices and Apple Watch to distinguish atrial fibrillation from sinus rhythm. Key questions for manufacturers include how to calibrate each device's algorithms and streamline workflows for healthcare providers.Summary-Wearable ECG monitors are currently most useful to detect atrial fibrillation. Further study is needed to demonstrate whether wearable ECG monitors improve patient outcomes, and to expand their use into other indications. Device manufacturers and healthcare providers must work together to establish new workflows to process and act on wearable ECG data.

Electronic health record (EHR) datasets are statistically powerful but are subject to ascertainment bias and missingness. Using the Mass General Brigham multi-institutional EHR, we approximated a community-based cohort by sampling patients receiving longitudinal primary care between 2001-2018 (Community Care Cohort Project [C3PO], n = 520,868). We utilized natural language processing (NLP) to recover vital signs from unstructured notes. We assessed the validity of C3PO by deploying established risk models for myocardial infarction/stroke and atrial fibrillation. We then compared C3PO to Convenience Samples including all individuals from the same EHR with complete data, but without a longitudinal primary care requirement. NLP reduced the missingness of vital signs by 31%. NLP-recovered vital signs were highly correlated with values derived from structured fields (Pearson r range 0.95–0.99). Atrial fibrillation and myocardial infarction/stroke incidence were lower and risk models were better calibrated in C3PO as opposed to the Convenience Samples (calibration error range for myocardial infarction/stroke: 0.012–0.030 in C3PO vs. 0.028–0.046 in Convenience Samples; calibration error for atrial fibrillation 0.028 in C3PO vs. 0.036 in Convenience Samples). Sampling patients receiving regular primary care and using NLP to recover missing data may reduce bias and maximize generalizability of EHR research.

Within an academic health system, consensus guidelines do appear to influence the decision for surgery in patients with PHPT. However, the level of compliance is generally low, and similar to that observed in community practice.

Background and Purpose— Classification of stroke as cardioembolic in etiology can be challenging, particularly since the predominant cause, atrial fibrillation (AF), may not be present at the time of stroke. Efficient tools that discriminate cardioembolic from noncardioembolic strokes may improve care as anticoagulation is frequently indicated after cardioembolism. We sought to assess and quantify the discriminative power of AF risk as a classifier for cardioembolism in a real-world population of patients with acute ischemic stroke. Methods— We performed a cross-sectional analysis of a multi-institutional sample of patients with acute ischemic stroke. We systematically adjudicated stroke subtype and examined associations between AF risk using CHA 2 DS 2 -VASc, Cohorts for Heart and Aging Research in Genomic Epidemiology-AF score, and the recently developed Electronic Health Record–Based AF score, and cardioembolic stroke using logistic regression. We compared the ability of AF risk to discriminate cardioembolism by calculating C statistics and sensitivity/specificity cutoffs for cardioembolic stroke. Results— Of 1431 individuals with ischemic stroke (age, 65±15; 40% women), 323 (22.6%) had cardioembolism. AF risk was significantly associated with cardioembolism (CHA 2 DS 2 -VASc: odds ratio [OR] per SD, 1.69 [95% CI, 1.49–1.93]; Cohorts for Heart and Aging Research in Genomic Epidemiology-AF score: OR, 2.22 [95% CI, 1.90–2.60]; electronic Health Record–Based AF: OR, 2.55 [95% CI, 2.16–3.04]). Discrimination was greater for Cohorts for Heart and Aging Research in Genomic Epidemiology-AF score (C index, 0.695 [95% CI, 0.663–0.726]) and Electronic Health Record–Based AF score (0.713 [95% CI, 0.681–0.744]) versus CHA 2 DS 2 -VASc (C index, 0.651 [95% CI, 0.619–0.683]). Examination of AF scores across a range of thresholds indicated that AF risk may facilitate identification of individuals at low likelihood of cardioembolism (eg, negative likelihood ratios for Electronic Health Record–Based AF score ranged 0.31–0.10 at sensitivity thresholds 0.90–0.99). Conclusions— AF risk scores associate with cardioembolic stroke and exhibit moderate discrimination. Utilization of AF risk scores at the time of stroke may be most useful for identifying individuals at low probability of cardioembolism. Future analyses are warranted to assess whether stroke subtype classification can be enhanced to improve outcomes in undifferentiated stroke.

ImportanceGuidelines recommend 150 minutes or more of moderate to vigorous physical activity (MVPA) per week for overall health benefit, but the relative effects of concentrated vs more evenly distributed activity are unclear.ObjectiveTo examine associations between an accelerometer-derived “weekend warrior” pattern (ie, most MVPA achieved over 1-2 days) vs MVPA spread more evenly with risk of incident cardiovascular events.Design, Setting, and ParticipantsRetrospective analysis of UK Biobank cohort study participants providing a full week of accelerometer-based physical activity data between June 8, 2013, and December 30, 2015.ExposuresThree MVPA patterns were compared: active weekend warrior (active WW, ≥150 minutes with ≥50% of total MVPA achieved in 1-2 days), active regular (≥150 minutes and not meeting active WW status), and inactive (<150 minutes). The same patterns were assessed using the sample median threshold of 230.4 minutes or more of MVPA per week.Main Outcomes and MeasuresAssociations between activity pattern and incident atrial fibrillation, myocardial infarction, heart failure, and stroke were assessed using Cox proportional hazards regression, adjusted for age, sex, racial and ethnic background, tobacco use, alcohol intake, Townsend Deprivation Index, employment status, self-reported health, and diet quality.ResultsA total of 89 573 individuals (mean [SD] age, 62 [7.8] years; 56% women) who underwent accelerometry were included. When stratified at the threshold of 150 minutes or more of MVPA per week, a total of 37 872 were in the active WW group (42.2%), 21 473 were in the active regular group (24.0%), and 30 228 were in the inactive group (33.7%). In multivariable-adjusted models, both activity patterns were associated with similarly lower risks of incident atrial fibrillation (active WW: hazard ratio [HR], 0.78 [95% CI, 0.74-0.83]; active regular: 0.81 [95% CI, 0.74-0.88; inactive: HR, 1.00 [95% CI, 0.94-1.07]), myocardial infarction (active WW: 0.73 [95% CI, 0.67-0.80]; active regular: 0.65 [95% CI, 0.57-0.74]; and inactive: 1.00 [95% CI, 0.91-1.10]), heart failure (active WW: 0.62 [95% CI, 0.56-0.68]; active regular: 0.64 [95% CI, 0.56-0.73]; and inactive: 1.00 [95% CI, 0.92-1.09]), and stroke (active WW: 0.79 [95% CI, 0.71-0.88]; active regular: 0.83 [95% CI, 0.72-0.97]; and inactive: 1.00 [95% CI, 0.90-1.11]). Findings were consistent at the median threshold of 230.4 minutes or more of MVPA per week, although associations with stroke were no longer significant (active WW: 0.89 [95% CI, 0.79-1.02]; active regular: 0.87 [95% CI, 0.74-1.02]; and inactive: 1.00 [95% CI, 0.90-1.11]).Conclusions and RelevancePhysical activity concentrated within 1 to 2 days was associated with similarly lower risk of cardiovascular outcomes to more evenly distributed activity.