Steven A. Lubitz

Right ventricular (RV) size and function inform prognosis in many cardiovascular diseases, but assessment by two-dimensional transthoracic echocardiography is challenging. Here we show that a deep learning model, Dimensional Reconstruction of Imaging Data-Right Ventricle (DROID-RV), can estimate RV end-diastolic diameter and identify RV systolic dysfunction from transthoracic echocardiography videos. We trained DROID-RV in 17,141 ambulatory cardiology patients (51,188 studies and 383,881 video clips) and validated model predictions in 9,112 primary care patients and 831 patients with RV ejection fraction assessed by three-dimensional transthoracic echocardiography. Across multiple test samples, DROID-RV accurately estimates RV end-diastolic diameter and identifies RV dysfunction, although model prediction error increases at extremes of RV size and function. We additionally show that RV size and function predictions are associated with incident mortality, atrial fibrillation and heart failure. Our work demonstrates how deep learning can automate RV assessment and risk stratification at scale using two-dimensional echocardiography.

Rare coding genetic variants may exert large effects on risk of common disease, yet their contribution to disease architecture and their utility in gene prioritization remain limited by inadequate sample sizes. Here, we performed a massive-scale rare variant association study (RVAS), analyzing over 1.1 million sequenced participants among which 130,000 had atrial fibrillation (AF). Through a multi-mask burden testing approach, we identified 15 genes significantly associated with AF through rare large-effect variation. Integrative analyses revealed strong convergence between genes implicated by rare and common variation, and highlighted instances where RVAS data may aid in GWAS prioritization. Nevertheless, several RVAS genes were not among GWAS loci ( FAM189A2 , ACTC1 , FNIP1 , FBN1 ), or were not nominated through contemporary GWAS prioritization ( KDM5B , ZFP36L2 ). Finally, we observed that ultra-rare protein-disrupting variants - concentrated in a small number of large-effect size genes - explained at least 2% of AF susceptibility across European and African ancestry groups. These findings refine the genetic architecture of AF, while highlighting the value and cost of RVAS for genomic discovery in common disease.

Background: Stressor‐associated atrial fibrillation (AF) refers to new‐onset AF that occurs with a reversible, acute stressor. Identifying individuals at highest risk for AF recurrence is essential to guide management. Although clinical factors have shown limited value, the utility of contemporary artificial intelligence (AI)‐enabled models using the 12‐lead ECG to estimate recurrence risk remains unknown. Methods: We retrospectively analyzed consecutive primary care and cardiology patients with stressor‐associated AF occurring during hospitalization. We quantified the cumulative incidence of recurrence accounting for death as a competing risk. We investigated the relationship between time‐varying recurrence and a composite end point of AF‐related adverse events (stroke, heart failure, all‐cause death) using Cox models. We then developed and validated a penalized regression model to predict recurrence using clinical factors, stressor type, and AF risk estimates from a previously validated ECG‐based AI model. Results: We analyzed 3371 patients with stressor‐associated AF (mean age, 69±12 years; 40% women). Over a median of 3.7 years (interquartile range, 1.8–7.2), the 10‐year cumulative incidence of AF recurrence was 41% (95% CI, 39–44). AF recurrence was strongly associated with AF‐related adverse events (hazard ratio, 2.24 [95% CI, 1.81–2.76]). A model incorporating clinical factors, stressor type, and ECG‐based AI model AF risk estimates (clinical–AI) discriminated AF recurrence (area under the receiver operating characteristic curve, 0.768 [95% CI, 0.707–0.830]) favorably compared with clinical features (area under the receiver operating characteristic curve, 0.707 [95% CI, 0.642–0.772]; P <0.05). Conclusions: AF recurrence rates following stressor‐associated AF are considerable and are associated with substantially higher risk of adverse cardiovascular events. Models incorporating ECG‐based AI risk estimates may prioritize individuals for intensive monitoring and preventive interventions.

BACKGROUND: ECG-based artificial intelligence may enable efficient prediction of incident heart failure (HF) risk to facilitate preventive efforts. Prior models are proprietary, with modest or inconsistent accuracy. We sought to develop and validate a generalizable and publicly available convolutional neural network to predict incident HF using the 12-lead ECG waveform (ECG-to-HF [ECG2HF]). METHODS: We developed ECG2HF in 94 636 patients receiving longitudinal ambulatory care at Massachusetts General Hospital (MGH), and validated it in 3 test sets: MGH, Brigham and Women’s Hospital (BWH), and Beth Israel Deaconess Medical Center (BIDMC), among 93 868 individuals aged 30 to 79 years without HF. HF events at 10 years were identified using a validated electronic health record-based natural language processing model. Discrimination was quantified using the area under the receiver operating characteristic curve. We then compared discrimination and net reclassification (at <10%, 10% to 20%, ≥20% 10-year risk categories) using ECG2HF versus the 15-component Pooled Cohorts Equations to Prevent HF score. RESULTS: The test sets comprised MGH (13 954 individuals, 441 events, age 57±13 years, 48% women), BWH (54 396 individuals, 1809 events, age 57±13 years, 55% women), and BIDMC (25 457 individuals, 901 events, age 57±13 years, 53% women). Over 10 years, the cumulative risk of HF was 4.6% (95% CI, 4.1–5.0) in MGH, 5.0% (4.8–5.2) in BWH, and 4.4% (4.1–4.7) in BIDMC. ECG2HF discriminated 10-year incident HF in each test set (area under the receiver operating characteristic curve: MGH 0.86 [0.84–0.87]; BWH 0.85 [0.84–0.86]; BIDMC 0.84 [0.83–0.86]). Compared with the Pooled Cohorts Equations to Prevent HF, ECG2HF provided favorable discrimination (improvement in area under the receiver operating characteristic curve MGH/BWH 0.061 [0.025–0.097]; BIDMC 0.038 [−0.0096 to 0.086]) and net reclassification (NRI MGH/BWH 0.16 [0.077–0.24]; BIDMC 0.23 [0.10–0.35]) of 10-year HF risk. CONCLUSIONS: ECG2HF is a publicly available 12-lead ECG-based artificial intelligence model that discriminates the risk of future HF with favorable and consistent performance across 3 large health care samples from the northeastern United States. ECG2HF may enable efficient prioritization of high-risk individuals for HF-related preventive measures.

Atrial fibrillation (AF) is a growing unmet medical need. To reduce its impact on patients’ lives, improvements in stroke prevention therapy, treatment of concomitant conditions, and rhythm control therapy are actively developed: Innovations in anti-thrombotic agents, new anti-arrhythmic drugs (AADs), and novel interventional rhythm control therapies emerge alongside AF-reducing effects of general cardiometabolic therapies. Simple risk scores are slowly replaced by personalized AF risk estimation using quantifiable features. These developments were discussed by over 80 experts from academia and industry during the 10th Atrial Fibrillation NETwork /European Heart Rhythm Association consensus conference from 5 to 7 May 2025. The emerging consensus, described here, is multi-domain therapy combining stroke prevention, rhythm control, and therapy of concomitant cardiovascular conditions. This combines anti-coagulants, AADs, and AF ablation with old and new cardiometabolic drugs that can reduce AF risk, AF burden, and AF-related complications at scale. The paper furthermore describes quantitative traits that may enable a shift towards risk-driven therapy based on AF phenotypes. These can enable adjusted therapy strategies that are safe, accessible, and patient-centred. Applying modern data science and artificial intelligence methods to quantitative phenotypic and genetic features can further improve risk estimation and personalized therapy selection. At the same time, translational and clinical research into reversing the drivers of AF and into improved stroke prevention through new drugs and through combination therapies is needed. Together, these efforts offer pathways towards personalized, patient-centred, multi-modal, and accessible AF management that integrates rhythm control, stroke prevention, and therapy of concomitant conditions to bridge today’s practical needs with tomorrow’s therapeutic innovation.

Background: Stressor-associated atrial fibrillation (AF), defined as newly diagnosed AF in the setting of a reversible physiological stressor, is common. However, risk factors, outcomes, and current management practices remain poorly understood. Methods: We analyzed data from VITAL-AF, a pragmatic, cluster-randomized AF screening trial conducted 2018-2019 comprising adults aged ≥65 across 16 primary care practices affiliated with Massachusetts General Hospital. All participants had longitudinal follow-up for adjudicated incident AF (including whether stressor-associated vs non-stressor-associated [‘primary’]) and clinical outcomes. We compared associations between clinical AF risk factors and incident AF group (stressor-associated vs primary) using Fine-Gray models handling death and each AF group as competing risks. We also quantified anticoagulation (OAC) initiation rates following AF diagnosis. We then fit Cox proportional hazards models to quantify associations between incident AF group (as a time-varying covariate) and a composite endpoint of major bleeding, stroke, and all-cause mortality, with adjustment for CHA _₂ DS _₂ -VASc (stroke) and ATRIA (bleeding) scores and time-varying OAC exposure. Results: We analyzed data from VITAL-AF, a pragmatic, cluster-randomized AF screening trial conducted 2018-2019 comprising adults aged ≥65 across 16 primary care practices affiliated with Massachusetts General Hospital. All participants had longitudinal follow-up for adjudicated incident AF (including whether stressor-associated vs non-stressor-associated [‘primary’]) and clinical outcomes. We compared associations between clinical AF risk factors and incident AF group (stressor-associated vs primary) using Fine-Gray models handling death and each AF group as competing risks. We also quantified anticoagulation (OAC) initiation rates following AF diagnosis. We then fit Cox proportional hazards models to quantify associations between incident AF group (as a time-varying covariate) and a composite endpoint of major bleeding, stroke, and all-cause mortality, with adjustment for CHA _₂ DS _₂ -VASc (stroke) and ATRIA (bleeding) scores and time-varying OAC exposure. Conclusions: Among over 30,000 primary care patients with adjudicated AF events, nearly one-third of incident AF cases were stressor-associated. Despite similar risk factor profiles and comparably high rates of adverse outcomes, OAC initiation is lower when AF is stressor-associated. Future work is needed to define optimal approaches to prevention, surveillance, and management of stressor-associated AF.

BACKGROUND: Wrist-worn wearables can detect irregular heart rhythms using photoplethysmography, but ECGs are required to confirm atrial fibrillation (AF). We sought to determine the frequency of a recurrent irregular heart rhythm detection (IHRD; ≥30 minutes of an irregular rhythm), estimate the potential diagnostic yield of different electrocardiographic monitoring strategies for confirming AF, and identify predictors of recurrent IHRDs. METHODS: The Fitbit Heart Study enrolled wrist-worn photoplethysmography device users without diagnosed AF. Of 455 699 participants, 1057 who wore and returned a 1-week ECG patch monitor after receiving an IHRD were analyzed. Baseline clinical data, device-derived metrics, IHRDs during follow-up, and electrocardiographic patch data were used for analysis. RESULTS: A total of 570 (53.9%) participants were aged 40 to 64 years, 422 (39.9%) were aged ≥65 years, and 510 (48.2%) were women. Median follow-up after ECG patch initiation was 80 days (interquartile range, 45–122 days). The frequency of another IHRD was 57.2% (95% CI, 53.1%–60.9%) at 3 months. After an initial IHRD, the estimated diagnostic yield for AF with a 10-second ECG was 7.6% (95% CI, 6.2%–9.0%), twice-daily 30-second ECGs over 1 week 19.0% (95% CI, 16.7%–21.2%), 24-hour monitor 17.4% (95% CI, 15.5%–19.3%), 1-week monitor 32.2% (95% CI, 29.4%–35.0%), 2-week monitor 46.8% (95% CI, 42.7%–50.8%), and 4-week monitor 60.8% (95% CI, 56.5%–65.1%). The risk of a recurrent IHRD was greater with older age ( P P =0.001), vascular disease ( P =0.03), longer initial runs of consecutive IHRDs at detection ( P =0.02), and less nightly sleep ( P =0.03). CONCLUSIONS: Irregular heart rhythms are common after initial detection using a wrist-worn wearable device. Longer electrocardiographic monitoring periods increase the likelihood of confirming AF. REGISTRATION: URL: https://www.clinicaltrials.gov ; Unique identifier: NCT04380415.