Annals of the American Thoracic Society

Background. Diagnosis and risk stratification in rare hematologic malignancies such as myeloproliferative neoplasms (MPNs) - polycythemia vera (PV), essential thrombocythemia (ET), and myelofibrosis (MF) - require expert review of longitudinal, heterogeneous clinical records. This process is cognitively demanding, inconsistently applied, and difficult to scale beyond tertiary centers. No automated phenotyping workflow currently exists for hematologic malignancies. Methods. A HIPAA-compliant large language model (LLM) framework for phenotyping MPN was developed to integrate (i) rule-based retrieval of bone marrow biopsy reports, clinical notes, and structured laboratory results from the electronic health record (EHR); (ii) zero-shot extraction of diagnostic and prognostic variables from unstructured text using GPT-4 Turbo; (iii) a clinician-informed source-prioritization algorithm to reconcile conflicting multi-source data; (iv) WHO/ICC-criteria-based diagnostic classification; and (v) NCCN-based risk stratification using the conventional risk model for PV, IPSET-thrombosis for ET, and DIPSS, DIPSS-plus, and MIPSS70/MIPSS70+ v2 for MF. Patients were identified via MPN-related ICD-9/10 codes; cases met 2017 WHO criteria or had a hematologist-documented diagnosis, and controls did not. The cohort was split into a prompt-development set (n = 60) and a held-out test set (n = 450; 75 cases and 75 controls per disease). Ground truth was established by independent dual-clinician chart review with consensus adjudication. LLM performance was evaluated against the ground truth: variable-level extraction using accuracy, F1 score, and Cohen's kappa; patient-level diagnostic classification using sensitivity, specificity, and Cohen's kappa; and prognostic risk stratification (among confirmed cases) using accuracy, weighted F1 score, and quadratic-weighted Cohen's kappa. Wilson 95% confidence intervals (CIs) were used for proportions and bootstrap 95% CIs with 500 resamples for F1 scores. Results. The held-out test set included 450 patients (PV: 150; ET: 150; MF: 150) with pathology reports and structured laboratory results, and 172 patients (PV: 52; ET: 55; MF: 65) with clinical notes. From pathology reports, overall variable extraction accuracy and F1 score were 99% (95% CI, 98-100) and 1.00 (0.99-1.00) for PV, 100% (99-100) and 0.99 (0.96-1.00) for ET, and 100% (99-100) and 0.99 (0.97-1.00) for MF. From clinical notes, overall accuracy and F1 score were 96% (91-100) and 0.94 (0.85-1.00) for PV, 100% (100-100) and 1.00 (1.00-1.00) for ET, and 100% (99-100) and 0.98 (0.95-1.00) for MF. Diagnostic sensitivity was 100% (95% CI, 95.1-100.0) for PV, ET, and MF; specificity was 98.7% (92.8-99.8) for PV and 100% (95.1-100.0) for both ET and MF, with Cohen's kappa of 0.99 for PV and 1.00 for ET and MF. Risk stratification accuracy was 100% with weighted F1 score of 1.00 and quadratic-weighted Cohen's kappa of 1.00 across all three diseases. A pre-specified source-ablation analysis showed that pathology reports alone were sufficient for diagnosis (sensitivity 98.7% for PV, 100% for ET, 96.0% for MF; specificity 100% across all three subtypes) but inadequate for prognostication (accuracy 69.3% for PV, 93.3% for ET, 77.3% for MF). Adding clinical notes to pathology reports recovered full prognostic accuracy of 100% across all three diseases. Conclusions. This first-in-class automated framework achieved expert-level performance for MPN diagnosis and risk stratification from real-world EHR data, establishing a foundation for scalable, standardized phenotyping in rare hematologic malignancies. Prospective, multi-site validation is warranted before clinical deployment.

Background: Transthyretin amyloidosis cardiomyopathy (ATTR-CM) is an infiltrative cardiomyopathy and an increasingly recognized cause of heart failure. With improved survival from disease-modifying therapies, an increasing number of patients are presenting for surgery and may be at increased risk of adverse postoperative outcomes. This study reports outcomes of ATTR-CM patients undergoing surgery and evaluates the utility of the Revised Cardiac Risk Index (RCRI), a perioperative risk tool. Methods: A total of 145 ATTR-CM patients were included, among which 51 patients underwent at least one eligible surgical procedure. Preoperative risk was assessed using the RCRI, analyzed both as a categorical and as a dichotomized ({greater than or equal to}3 vs <3) variable. Postoperative outcomes included unplanned hospital admission, length of stay (LOS), prolonged hospitalization (>48 hours), and major adverse cardiac events. Models were adjusted for frailty (Clinical Frailty Scale {greater than or equal to}5) and major surgery, using multivariable, ordinal, and Firth penalized logistic regression analyses. Results: Patients were predominantly male (86%) with a mean age of 76 {plus minus} 9 years, and 61% were frail. Higher RCRI scores were associated with unplanned postoperative hospital admission (RCRI {greater than or equal to}3: adjusted OR 48.9, 95% CI 4.8-502.2) and longer LOS (RCRI {greater than or equal to}3: adjusted OR 40.7, 95% CI 4.3-382.8). RCRI {greater than or equal to}3 was also associated with prolonged hospitalization (>48 hours) in Firth penalized logistic regression, whereas frailty was not independently associated. Conclusions: In a real-world ATTR-CM cohort undergoing major non-cardiac surgery, the overall risk of adverse outcomes was low, and higher RCRI scores were associated with increased postoperative hospital admission and longer LOS, including hospitalization exceeding 48 hours. The RCRI retains prognostic utility in this high-risk cohort and may support peri-operative risk stratification.

Background: Outpatient chronic heart failure (HF) assessment relies on NYHA class and distance-based testing that can obscure physiological heterogeneity. Near-infrared spectroscopy (NIRS) enables tissue oxygenation phenotyping but is underexplored during standardized stressors in outpatient HF. We tested whether wearable NIRS-derived oxygenation kinetics during a vascular occlusion test (VOT) and six-minute walk test (6MWT) differ across NYHA classes. Methods: In this prospective, single-center pilot study, 44 chronic HF outpatients (mean age 70.9 {+/-} 8.7 years, 75% male; NYHA I [n=19], II [n=12], III [n=13]) were monitored with a novel wearable NIRS device (NIRSense Envello Core) during a VOT and 6MWT. Primary endpoints were the post-occlusion net area under the curve (net AUC; VOT) and post-walk recovery net AUC (modified 6MWT). Secondary endpoints included the exertional tissue oxygenation (Oxy) nadir, VOT reperfusion kinetics, gait metrics, and tolerability. Results: Despite NYHA I and II walking identical median distances (420 m), post-walk recovery net AUC was lower in NYHA II (-16.3 a.u.xs) and III (-12.8 a.u.xs) than NYHA I (46.1 a.u.xs, p=0.004). The exertional Oxy nadir did not differ (p=0.722), but NYHA III walked 27% and 38% slower than NYHA II and I (p<0.001). NYHA II had higher VOT net AUC (134.2 a.u.xs) than NYHA I (71.9; p=0.018) and III (61.1; p=0.011). Post-walk recovery net AUC correlated with gait velocity (rs=0.44) and distance (rs=0.39; both p<0.05). VOT net AUC did not correlate with functional metrics, but resting reperfusion kinetics correlated with 6MWT performance (rs=0.41-0.46, p<0.05). The sensor was well tolerated. Conclusions: Wearable NIRS-derived recovery kinetics differentiated NYHA I from NYHA II despite these classes walking identical median distances. Coupled with distinct resting VOT hyperemic differences, these preliminary findings indicate wearable NIRS may capture physiological heterogeneity in outpatient HF not reflected by NYHA class and standard functional metrics.

Background: Biologically distinct asthma subgroups may obscure genetic effects when analyzed as a single phenotype. We examined whether asthma susceptibility signals are shared, heterogeneous, or stratum-specific across ancestry, obesity status, and sex. Methods: We performed ancestry-specific GWAS meta-analyses in African ancestry participants (9,965 asthma cases; 37,391 controls) and European ancestry participants (6,074 cases; 116,255 controls), followed by obesity- and sex-stratified analyses. Analyses used imputed dosages and fixed-effect meta-analysis within ancestry. Results: Stratification detected asthma association signals that were less apparent in the combined phenotype. Shared cross-ancestry loci implicated epithelial antiviral susceptibility and immune regulation, represented by signals near CDHR3 and FOXO1. An ancestry-heterogeneous signal at the 17q21 locus, harboring ORMDL3/GSDMB, supported population-dependent effects at an epithelial inflammatory locus. Obesity stratification mapped the genome-wide significant burden to asthma without obesity. Sex stratification detected genome-wide significant signals in AFR females with asthma and obesity and in both sex strata with asthma without obesity, with the strongest signal burden in EU females without obesity. Conclusions: Asthma genetic architecture differed by ancestry, obesity status, and sex. Stratified analyses identified group-specific susceptibility related to epithelial and immune regulation, airway inflammation, remodeling, and neural signaling, supporting precision approaches to asthma.

Background: Acute pulmonary embolism (PE) is a leading cause of morbidity and mortality with persistent difficulties in choosing interventions and predicting outcomes for patients defined clinically as intermediate risk. Computational fluid dynamics (CFD) tools have been used to understand the hemodynamic environment and plan interventions in the pulmonary arteries across a variety of disease conditions. Several biomechanical metrics have been used to evaluate risk in narrowed vessels, including hemodynamic resistance, power dissipation, and fractional flow reserve (FFR). In this study, we evaluate differences in these CFD-derived biomarkers between healthy controls (HC) and intermediate risk, acute PE patients. Additionally, we examine the response of patient hemodynamics to mechanical thrombectomy and compare values of these biomarkers across post-intervention pressure status. Methods: A CFD framework was developed to simulate patient-specific hemodynamics within the pulmonary vasculature identifiable from clinical imaging. The pipeline involved reconstructing three-dimensional (3D) structures of the pulmonary arteries and modeling blood flow with the finite element method. Patient-specific boundary conditions were derived from matching pre-intervention inlet mPAP to the patient's measured value given their measured CO as steady inflow. Converged simulations allowed for precise quantification of primary hemodynamic characteristics (flow and pressure) as well as secondary flow phenomena, primarily wall shear stress (WSS) and simulated pressure metrics such as fractional flow reserve (FFR). Results: Our simulations revealed significant elevations in resistance, power dissipation, and the number of vessels with low FFR in those patients with acute PE (n=6) compared to HC (n=3). Occlusions of hemodynamic significance were generally found in segmental pulmonary arteries. For patients with normalized pulmonary pressures post-thrombectomy (n=3), we found significantly higher proximal power dissipation and counts of low FFR vessels in comparison to those with elevated pressures after intervention (n=3). Distal resistance, which was derived from the portion of resistance attributed to the outflow boundary conditions, was significantly higher in patients with elevated pressures post-intervention. Across all PE patients, FFR count was significantly correlated with post-thrombectomy pulmonary pressure and cardiac index. Discussion: CFD-derived biomarkers offer a promising tool for understanding disease severity in acute PE. Differences between HCs and acute PE patients reveal expected increases in metrics associated with proximal disease burden. Yet, in examining acute PE patients with varying post-intervention hemodynamics, we found that these metrics of proximal disease burden could also be useful to predict the efficacy of mechanical thrombectomy. Those patients with normalized pressures had higher values for proximal disease metrics and lower values for distal disease metrics than those with continued elevations in pressure. This suggests that accessibility of hemodynamically-significant emboli to thrombectomy may be useful as a predictor for outcomes.

Background: Clonal hematopoiesis (CH) is associated with increased risks of diverse cardiovascular diseases, hematologic malignancies and mortality, yet no preventive therapies are approved. As emerging data implicate lipid pathways in CH pathogenesis, we investigated the association of statin use and genetically proxied inhibition of HMG-CoA reductase (HMGCR) with CH risk, and validated findings using primary peripheral blood mononuclear cells (PBMCs). Methods: We performed an observational analysis of 416,118 UK Biobank participants of European ancestry using multivariable logistic regression to compare CH prevalence among statin users and nonusers. Mendelian randomization (MR) analyses evaluated the causal association of genetically proxied lowering of low-density lipoprotein cholesterol (LDL-C) with risk of CH using two instruments; (i) the lead HMGCR variant (rs12916) which proxied LDL-C lowering by statins, and, (ii) 303 genome-wide LDL-C-lowering variants representing polygenic mechanisms. Summary statistics were obtained from the Global Lipid Genetics Consortium genome-wide association study (N = 842,634). Experimentally, primary PBMCs from a DNMT3AR882 hotspot mutation carrier were cultured in methylcellulose with pravastatin or vehicle control to evaluate colony-forming dynamics. Results: Among 416,118 individuals, 20,488 had CH, including 11,550 with single DNMT3A-mutant and 4,375 with single TET2-mutant CH. Pre-recruitment statin users had reduced odds of DNMT3A-mutant CH (OR=0.93; 95% CI:0.88-0.98; P=0.009), driven primarily by associations with DNMT3AR882-mutant (OR=0.78; 95% CI:0.66-0.92; P=0.003), but not TET2-mutant CH (OR=1.05; 95% CI:0.97-1.14; P=0.20). Similarly, genetically predicted HMG-CoA-reductase inhibition equivalent to a 1 SD reduction in circulating LDL-C levels was associated with lower odds of DNMT3A-mutant CH (OR=0.66; 95% CI:0.45-0.95; P=0.03) but not TET2-mutant CH (OR=1.34; 95% CI:0.76-2.36; P = 0.31). By contrast, polygenic estimation of LDL-C lowering was not associated with DNMT3A-mutant CH (OR=1.05; 95% CI:0.97-1.14; P=0.20), suggesting protective effects were independent of LDL-C lowering per se. Genetically predicted HMG-CoA reductase inhibition had wide effects on blood cell counts and indices, suggesting effects on bone marrow cell dynamics. In vitro, pravastatin selectively suppressed colony formation of primary human DNMT3AR882-mutant relative to wild-type cells (P=0.031). Conclusions: Statin therapy and genetically predicted lifelong inhibition of HMG-CoA reductase were significantly associated with reduced risk of DNMT3A-mutant CH, likely via LDL-C-independent mechanisms, which may be specific to DNMT3A-mutant CH. This provides a strong rationale for prospective trials evaluating the effect of statins on risk of developing DNMT3A-mutant CH, subsequent clonal expansion, and associated clinical sequelae.

While exposure to volatile organic compounds such as ethylene dichloride and vinyl chloride monomer is a well-established cause of liver disease, particularly hepatic hemangiosarcoma, characterizing real-world exposure profiles in communities surrounding industrial centers remains challenging. Calvert City, Kentucky (population ~2,500), provides a unique setting characterized by both active industrial emissions and legacy sources of air toxics. To address these complexities, this method paper describes the framework for the Biomonitoring and Environmental Assessment for Community Outreach and Neighborhood Safety (BEACON) study. By utilizing a novel, multi-dimensional exposure assessment strategy, BEACON aims to characterize air toxic exposures and provide actionable data for community health and safety. For the BEACON study, we will leverage Kentucky Department of Air Quality measures of air toxics, analyze urine samples in a small cohort of community volunteers, analyze community urine via wastewater in an adjacent community, geocode citizen odor reporting, assess blood markers in wildlife, survey small and large animal veterinarians in the area for anomalies in morbidity and mortality, and work with the regional health system to enhance vigilance for health issues associated with toxicants present in the area. In addition, blood samples will be collected at three time points and biobanked for future analyses. Efforts will be made to link this study to additional large-scale long-term cohorts where possible. Throughout the project, community engagement will play a critical role by raising awareness, fostering collaboration, and ensuring that the voices of affected residents are heard.

Background: Heart failure with preserved ejection fraction (HFpEF) is a heterogeneous syndrome comprising multiple pathophysiological phenotypes. HFpEF trials have largely enrolled diverse populations and reported average treatment effects, consistently yielding neutral results that may obscure drug-specific benefits within distinct subgroups. To address this issue, we employ an interaction-based that incorporates treatment-by-variable interactions to uncover drug-specific responses. Methods: We leveraged four HFpEF clinical trials (TOPCAT, RELAX, NEAT-HFpEF, INDIE-HFpEF) and developed a framework comprising two complementary approaches. The first employed a prognostic responder model to evaluate whether conventional responder definitions reflect treatment-specific benefit or instead capture favorable clinical trajectories common to both treatment and placebo groups. The second used an interaction-based individual treatment effect (ITE) modeling to identify baseline variables that modify therapy effect, distinguishing drug-specific response from prognostic phenotypes. Results: Although the prognostic responder model demonstrated good discrimination, further analisys suggested it primarily captured a prognostic signal associated with favorable clinical trajectories common to both treatment and placebo arms. In contrast, the ITE model identified distinct, drug-specific effect modifiers across trials (cardiorenal-inflammatory for spironolactone (TOPCAT), NO-mediated anti-inflammatory for isosorbide mononitrate (NEAT-HFpEF), afterload-reducing for inorganic nitrite (INDIE-HFpEF), and anti-volume-overload for sildenafil (RELAX). Each ITE model demonstrated significance only within its own trial suggesting drug-specific signal. Conclusions: The proposed method identifies mechanism-specific effect modifiers, and uncovers clinically meaningful heterogeneity in treatment response, which is not captured by conventional MCID-based approaches. Although exploratory, these findings support phenotype-guided therapy in HFpEF and argue for phenotype-informed trial design to enhance treatment-effect detection and therapy targeting.

_ SURPASS-HF: Safety and Utility of Remote Pulmonary Artery Sensor Shared-management in Heart Failure --Background-- Insulin-dependent diabetics self-titrate therapy to self-obtained glucose values as standard of care, yet heart failure (HF) patients with implanted pulmonary artery (PA) pressure sensors never see their own readings; clinicians interpret and execute every dose change - a model that does not scale to a ~200-patient HF panel. To our knowledge, SURPASS-HF is the first prospective feasibility study applying the insulin-titration paradigm to PA-pressure-guided HF care: patients executing a prescribed loop-diuretic sliding scale, supported by ARTHUR, a domain-trained large language model, with clinician confirmation of every adjustment. --Methods-- Non-randomized, prospective, single-arm, single-center 90-day feasibility study (January 14-April 14, 2026; 60.1 patient-months). Twenty-one adults with implanted PA sensors enrolled (intention-to-treat, ITT); 19 completed full follow-up (per-protocol, PP). Regimens and individual PA diastolic (PAD) targets were explicitly prescribed; when daily pressures met published serial-reading thresholds, the software prepared the pre-determined adjustment, the clinician confirmed it, and the patient executed it. ARTHUR reinforced dose ceilings, prompted surveillance labs, and escalated edge cases for review. Pre-specified outcomes: adverse events, escalations, time in optimal PA range (TIR-PAP, +/- 5 mmHg of goal), reading adherence, provider overrides, and paired delta_PAD (first vs last 7-day windows). Confidence intervals are descriptive; the study was not powered for significance. --Results-- Mean age was 69+/-11 years, 52% women, mean baseline PAD 14.8 mmHg. No pre-specified safety event (KDIGO >or=1 AKI, hyperkalemia, hyponatremia, symptomatic hypotension) was detected (0/8 post-adjustment draws in 5/21 patients; exact 95% CI 0-37%); laboratory ascertainment was sparse, so a meaningful harm rate cannot be excluded. Seventeen of 19 PP patients (89%) required no protocol-triggered escalation; 4 escalations occurred in 2 patients. TIR-PAP was 88.4% (ITT)/91.3% (PP); reading adherence 92.1%; 53 provider alerts (0.88/patient-month) all resolved (median 24 h) with no overrides. delta_PAD was -0.89 mmHg (ITT; 95% CI -2.60 to +0.82) in a cohort already at goal at baseline. Two non-cardiac hospitalizations occurred. --Conclusions-- LLM-mediated, clinician-confirmed patient execution of a published deterministic PA-pressure-guided diuretic algorithm was feasible over 90 days, with high time-in-range and adherence and no detected safety events. Findings from this prospective, single arm, non-randomized, small cohort are descriptive. The study was not designed or powered to demonstrate evidence of a treatment effect; a randomized, well powered prospective comparison study against provider-led PA-pressure management is the next ideal step.

Early screening for chronic obstructive pulmonary disease (COPD) is critical due to the progressive and debilitating nature. Preliminary diagnosis typically relies on pulmonary function tests, particularly the ratio of forced expiratory volume in one second (FEV1) to forced vital capacity (FVC). However, conventional spirometers are often bulky and non-portable, while most existing portable devices can only measure a single parameter, such as FVC, thereby limiting comprehensive assessment. To address these limitations, an integrated wearable system was proposed for both monitoring and rehabilitation training. This system is based on the innovative thermoelectric-airflow inversion (TAI) model, which quantitatively correlates convective heat transfer with thermoelectric voltage to reconstruct airflow velocity and volume in real time. The developed thermoelectric smart mask enables simultaneous measurement of two key obstructive indicators (FVC and FEV1) and automatically evaluates COPD risk via the FEV1/FVC ratio, alerting users to seek medical consultation when abnormalities are detected. In terms of performance, the device demonstrates a measurement accuracy of 99.10% and a coefficient of determination (R2) of 0.9947 compared to a commercial spirometer. Furthermore, the incorporated virtual reality assisted rehabilitation system was developed, yielding an average FVC improvement of 5.87% across three participants after one week of interactive training. Enabled by the TAI framework and a closed-loop multi-parameter design, this platform provides an intelligent, quantitative, and continuous solution for respiratory healthcare and rehabilitation.

Background: Heart failure with reduced ejection fraction (HFrEF) remains a major global health burden. Most electrocardiogram (ECG)-based artificial intelligence models are limited to diagnostic tasks or fixed-horizon prognostic classification and provide little insight into the temporal evolution of risk. In addition, concerns regarding model interpretability continue to impede clinical adoption. Whether deep learning applied to ECGs can deliver individualized, time-resolved, and biologically interpretable risk estimates for incident HFrEF across diverse populations remains uncertain. Methods: We developed a convolutional neural network-based survival model using raw 12-lead ECGs from Zhongshan Hospital (SHZS) and externally validated it in independent cohorts from Shanghai Tenth People's Hospital (SHTP) and Beth Israel Deaconess Medical Center (BIDMC). The model generated individualized, day-by-day probabilities of incident HFrEF over a 5-year horizon. Performance was comprehensively evaluated using discrimination, calibration, precision-recall characteristics, clinical utility, and risk stratification metrics, with subgroup analyses across age, sex, and race to assess generalizability. Model interpretability was examined using complementary representation and attention-based frameworks. Results: In 458,884 patients, the survival model demonstrated strong and stable discrimination across cohorts, with overall C-indices of 0.971 (95% CI, 0.965-0.976) in SHZS, 0.945 (95% CI, 0.938-0.950) in SHTP, and 0.855 (95% CI, 0.850-0.860) in BIDMC, and consistently high time-dependent AUROC values across the 1-5-year horizons. Calibration showed close agreement between predicted and observed risks, and decision curve analyses indicated meaningful net clinical benefit across a broad range of thresholds. Kaplan-Meier curves showed clear stratification across predicted risk groups. Interpretability analyses identified physiologically coherent ECG features related to QRS duration, heart rate, and QT interval that were associated with predicted risk. Conclusion: This ECG-based deep learning survival model provides individualized, time-resolved, and clinically interpretable estimates of future HFrEF risk with robust performance across multinational cohorts. These findings support the potential of AI-enabled ECG analysis as an accessible tool for early HFrEF risk stratification within routine clinical workflows.

Background. Heart failure with preserved ejection fraction (HFpEF) accounts for more than half of heart failure cases and is characterized by substantial clinical and biological heterogeneity. Sex differences are central to HFpEF pathophysiology, yet current phenotyping approaches often aggregate women and men, potentially obscuring distinct molecular mechanisms of disease progression. Molecularly resolved, sex-specific stratification is therefore needed to identify divergent risk pathways and improve biological understanding of HFpEF heterogeneity. Methods. In 698 HFpEF participants from the prospective MyoVasc cohort (379 females, 319 males), we run separate analyses on sex-specific cohorts. For each cohort, we integrated 92 circulating proteins (Olink Inflammation panel) and 49 clinical variables using Similarity Network Fusion to construct sex-stratified patient-patient similarity networks. Spectral clustering identified sex-specific prognostic subgroups related to the primary endpoint, i.e. worsening of Heart Failure (WHF). XGBoost models characterizing cluster-defining features were validated in an independent cohort of 342 HFpEF patients from the Gutenberg Health Study (GHS; 194 females, 148 males). Results. Two clusters emerged in each sex, with high-risk and low-risk clusters, showing the difference in WHF risk (MyoVasc females: HR 2.45, 95% CI 1.32-4.54, p=0.005; males: HR 2.77, 95% CI 1.27-6.04, p=0.011; C-index 0.62-0.63). Kaplan-Meier analyses confirmed separation (p<0.02 females, p<0.01 males). Clusters were reproduced in GHS using MyoVasc-trained XGBoost (females p=0.0082, males p=0.037). Shared top-ranking features included VEGF-A, TNFRSF9, and TGF-. Females were characterized by inflammatory (CD40, HGF, TNF) and glycemic signatures, whereas males showed prominence of immune-regulatory markers (IL-10RB, PD-L1) and renal function indicators (eGFR, creatinine). Conclusions. Sex-stratified molecular-clinical networks define prognostically distinct HFpEF subgroups with robust external validation. Shared protein biomarkers alongside sex-specific drivers reveal complementary progression mechanisms, supporting precision medicine strategies targeting high-risk cluster patients in sex-specific manner.

Cystic fibrosis (CF) is characterized by defective CFTR-mediated chloride transport, resulting in elevated sweat chloride concentrations. As people with CF (PwCF) now live longer due to highly effective CFTR modulators, exercise has become integral to maintaining health, yet it introduces additional physiological demands on salt and fluid balance. In this study, we used a wearable microfluidic biosensor (CF Patch) to quantify sweat rate and chloride loss during exercise performed both in the supervised laboratory and remote free-living in PwCF and healthy volunteers (HV). Participants completed exercise sessions under both conditions, with continuous heart rate monitoring and sweat collection with real-time measurement of sweat characteristics. Sweat volume and chloride concentration were assessed by colorimetric image analysis, enabling estimation of total fluid and chloride loss at the end of each exercise session. PwCF exercised for a longer duration at a lower average heart rate during remote exercise compared to laboratory exercise though exercise volume (average heart rate x duration) was greater during remote exercise. There was a positive association between exercise volume and both fluid and chloride loss for both PwCF and HV. PwCF exhibited greater chloride loss for a given exercise volume compared to HV, though fluid loss was similar. Further, compared to HV, PwCF demonstrated significantly greater intra- and interindividual variability in sweat chloride loss across the remote exercise sessions. Collectively, these findings provide evidence for the feasibility and physiological validity of remote exercise assessment and establish the feasibility and physiological validity of wearable sweat sensing for remote monitoring of fluid and electrolyte dynamics during real-world exercise. In addition, the variability of chloride loss in response to exercise suggests utility of the CF Patch in providing personalized fluid and salt repletion data for PwCF and advances the translational potential of digital sweat diagnostics for personalized CF care.

Background: Neonatal-onset hypertrophic cardiomyopathy (HCM) is a rare condition with limited data regarding clinical presentation, genetic background, and long-term outcomes. We aimed to characterize the phenotype and prognosis of HCM presenting in neonates. Methods: This is a multicenter retrospective study including patients diagnosed with HCM before 1 year of age. Neonatal-onset HCM was defined as presentation {less than or equal to}28 days of life. Clinical, genetic, instrumental data, treatment, and outcomes were collected. Primary outcome included overall and cardiac survival, major arrhythmic events (MAEs), implantable cardioverter-defibrillator (ICD) implantation, and cardiac surgery. Results: Among 321 pediatric HCM, 21% were diagnosed during infancy and 75% were neonates. Median age at diagnosis was 1 day (IQR 0-6), 82% presented within the first week of life. Prenatal suspicion was in 25%. At presentation, 41% were symptomatic. RASopathies represented the most common etiology (41%), followed by gene-elusive (31%), mitochondrial/inborn errors of metabolism (18%), and sarcomeric (8%). Left ventricular outflow tract obstruction was frequent in sarcomeric and RASopathy. Overall survival was 92% and cardiac survival was 96% at 2 years; long-term survival was 88% at 30 years. ICDs were implanted in 8%; 21% required cardiac surgery. Survival free from ICD was 40% at 15 year and 47% from myectomy. All events occurred in patients presenting within the first weeks of life. Conclusions: Neonatal-onset HCM is characterized by etiologic heterogeneity, predominance of syndromic and non-sarcomeric etiologies, and long-term cardiovascular morbidity. Presentation within the first days of life identifies a high-risk subgroup requiring intensive surveillance and specialized multidisciplinary management.

Background: Short and long sleep duration have been linked to atrial fibrillation (AF), but their influence on arrhythmia recurrence after catheter ablation is uncertain. We evaluated the association between nocturnal sleep duration and the risk of recurrent arrhythmias in patients undergoing catheter ablation for AF in the PREDIMAR trial. Methods: The PREDIMAR study is a multicentre, randomized, controlled, single-blind trial evaluating a Mediterranean diet enriched with extra-virgin olive oil for preventing arrhythmia recurrence after catheter ablation for AF. Nocturnal sleep duration was categorized as adequate (6?8 h/day) or inadequate (<6 h/day or >8 h/day). Multivariable Cox regression models estimated the association between sleep duration and the risk of recurrent atrial flutter (AFL) or AF. Results: Among 720 participants, we observed 226 incident cases of AF relapse and 107 cases of AFL. Inadequate nocturnal sleep duration was associated with a significantly higher risk of AFL recurrence compared with adequate sleep (adjusted HR = 1.87; 95% CI 1.18?2.96). No significant association was observed for AF recurrence (HR = 0.99; 95% CI 0.70?1.41). The association with AFL recurrence was particularly evident in patients with persistent AF at baseline before ablation (adjusted HR = 3.42; 95% CI 1.47?7.97), whereas no significant relationship was observed in those with baseline paroxysmal AF. Conclusions: Inadequate nocturnal sleep duration (<6 h/day or >8 h/day) may increase the risk of AFL recurrence following AF ablation. These findings highlight the relevance of sleep habits as a modifiable behavioural factor potentially influencing post-ablation outcomes.

Background. Septal reduction therapy (SRT) is recommended in drug-refractory, symptomatic obstructive hypertrophic cardiomyopathy (oHCM). We evaluated whether aficamten, a novel cardiac myosin inhibitor, can reliably transition guideline-eligible SRT candidates to ineligibility, and the associated safety profile of aficamten in this group. Methods. We analyzed participants with oHCM enrolled in FOREST-HCM (NCT04848506), the long-term open-label extension study of aficamten, from 28 May 2021 to 9 May 2025. Results. Three hundred and fifteen patients were included, of whom 104 met 2024 ACC/AHA guideline criteria for SRT eligibility at baseline. The SRT-eligible cohort was predominantly female (57%), with mean resting and Valsalva left ventricular outflow tract (LVOT) gradients of 63 {+/-} 39 and 109 {+/-} 42 mmHg, and all were in New York Heart Association (NYHA) class III. All baseline SRT-eligible patients became SRT-ineligible with aficamten therapy during study follow-up over a median of 42 days (IQR: 17, 49), except for one participant who withdrew from the study to pursue SRT (total of 3 participants withdrew). After dose titration, 3/104 (2.9%) remained guideline-eligible; by week 72 no patients met eligibility criteria. At maintenance, resting and Valsalva LVOT gradients improved by a least-squares mean of ?41 mmHg ([95% CI ?44 to ?37]; P<0.0001) and ?56 mmHg ([95% CI ?62 to ?51]; P<0.0001), respectively. Relative to baseline, NT-proBNP improved by 77% (95% CI 74 ? 80%), high-sensitivity cardiac troponin I decreased by 38% (95% CI 30 ? 46%), KCCQ-CSS improved by a mean of 20.2 (SD 19.3) points, and 95.2% of SRT-eligible patients had improved by ?1 NYHA class. Overall, the safety profile was favorable, with 2 occurrences of left ventricular ejection fraction (LVEF) < 50% over 193.7 patient-years of follow-up (1 event per 100 patient-years), managed by down-titration. There were no baseline SRT-eligible patients who died or developed LVEF <40%. Conclusions. Aficamten resolved guideline eligibility for SRT in nearly all baseline-eligible patients, with rapid and durable improvements in hemodynamics, symptoms, biomarkers and health status sustained for up to 3.5 years. Instances of LVEF <50% were rare and without clinical sequelae. These data support aficamten as a safe and effective alternative to SRT in oHCM.

Background: Clinical outcomes of switching versus continuing direct oral anticoagulant (DOAC) among atrial fibrillation (AF) patients who experienced an ischemic stroke despite receiving DOAC therapy are uncertain. Methods: We included patients with AF who were hospitalized for ischemic stroke (index stroke) between January 1, 2016, and June 30, 2022, while receiving DOAC therapy and who resumed DOAC within 90 days after discharge in the Merative MarketScan Commercial and Medicare databases. Patients were classified as DOAC-switched or DOAC-continued according to whether the DOAC agent changed or remained the same after the index stroke; secondary analyses considered individual DOACs. The primary outcome was recurrent ischemic stroke; secondary outcomes included major bleeding and a composite outcome (bleeding or ischemic stroke). Propensity score-based overlap weighting and weighted Cox models were used to estimate adjusted hazard ratios (aHRs). Results: A total of 1175 patients were eligible for the study, of whom 970 (82.6%) continued and 205 (17.4%) switched DOAC therapy. Comparing DOAC-switched to DOAC-continued was not significantly associated with recurrent ischemic stroke (aHR, 1.20; 95% CI, 0.63-2.30), major bleeding (aHR, 0.60; 95% CI, 0.21-1.72), or the composite outcome (aHR, 0.98; 95% CI, 0.56-1.70). However, among patients who received apixaban before stroke, switching to rivaroxaban was associated with a higher risk of recurrent ischemic stroke (aHR, 2.70; 95% CI, 1.05-6.95). Conclusions: Overall, switching DOAC therapy after ischemic stroke was not associated with improved clinical outcomes. Switching from apixaban to rivaroxaban, however, could increase risk of recurrent ischemic stroke.

Background: Obesity is a major modifiable risk factor for heart failure (HF), but body mass index (BMI) does not distinguish biologically distinct fat depots. Whether imaging-derived adipose tissue depots capture specific cardiometabolic pathways underlying HF risk beyond conventional anthropometric measures remains uncertain. Methods: We performed two-sample Mendelian randomization (MR) and multivariable MR mediation analyses using published genome-wide association study summary statistics. General adiposity traits included BMI, waist-to-hip ratio (WHR), and WHR adjusted for BMI from GIANT and UK Biobank meta-analyses of up to 694,649 individuals. MRI-derived visceral adipose tissue (VAT), abdominal subcutaneous adipose tissue (ASAT), and gluteofemoral adipose tissue (GFAT) were derived from 38,965 UK Biobank participants. HF outcome data were obtained from the HERMES consortium, including 1,946,349 individuals and 153,174 HF cases. Cardiometabolic mediators included type 2 diabetes (T2D), systolic blood pressure (SBP), LDL cholesterol, HDL cholesterol, and triglycerides. Primary analyses used inverse-variance weighted MR, with sensitivity analyses and directionality testing. Mediation was estimated using joint multivariable MR conditioning on significant cardiometabolic mediators. Results: Among MRI-derived adipose depots, ASAT was the only trait significantly associated with HF risk (odds ratio [OR] 1.64 per 1-SD increase; 95% CI 1.40-1.93; FDR q<0.001). VAT showed a positive but imprecise association (OR 1.38; 95% CI 0.87-2.20), and GFAT was not associated with HF. Among general adiposity measures, BMI (OR 1.65; 95% CI 1.58-1.71) and WHR (OR 1.30; 95% CI 1.23-1.38) were robustly associated with HF, whereas WHR adjusted for BMI was not. ASAT was significantly associated with T2D, SBP, HDL cholesterol, and triglycerides, but not LDL cholesterol. In joint multivariable MR, 67.9% of ASAT's HF effect was mediated through T2D, SBP, HDL cholesterol, and triglycerides (95% CI 49.2-86.8%). In contrast, BMI demonstrated only 8.5% mediation (95% CI -7.4 to 24.4%), and WHR showed non-significant mediation of 36.7% (95% CI -8.3 to 81.7%). Conclusions: MRI-derived abdominal subcutaneous adipose tissue captures a biologically coherent cardiometabolic signal underlying HF risk that is diluted by conventional anthropometric measures. ASAT may represent an imaging biomarker of metabolic syndrome-mediated HF risk and could support more precise risk stratification and mechanistically targeted prevention in HF.

Background No systematic review has been conducted to pool the existing evidence and quantify cancer prevalence rates in cardiovascular diseases (CVDs). We aimed to estimate pooled cancer prevalence in coronary artery disease (CAD), heart failure (HF), atrial fibrillation (AF), hypertension, type 2 diabetes mellitus (DM), stroke, peripheral arterial disease (PAD), and valvular heart diseases (VHD). Methods PubMed, Web of Science, and Scopus were searched from 2010 to July 2024. The outcomes were proportions of patients with active, any, previous, blood, solid, and metastatic cancer. The prevalence rates were estimated via one-step generalized linear mixed models. Results Totally, we retrieved 676 studies with enrollment of roughly 180 million participants. The analysis for active cancer included 59 studies with a population of 4,759,695 patients. The pooled prevalence of active cancer was 4.22% (95% confidence interval (CI) 2.18-5.32), 4.43% (95% CI 2.78-6.38), 4.60% (95% CI 1.72-8.13), 4.61% (95% CI, 2.83-6.97), 4.90% (95% CI 3.84-6.37), and 5.55% (95% CI 3.97-7.01) in patients with type 2 DM, chronic HF, any stroke, CAD, VHD, and AF. For any cancer, prevalence rates ranged from 14.10% (95% CI 12.20-15.99) in AF to 7.04% (95% CI 6.05-8.03) in CAD. Conclusion Pooled prevalence rates demonstrate a measurable burden of cancer among patients with a wide range of CVDs, highlighting the need for multidisciplinary management in this population.

Abdominal aortic aneurysm (AAA) patients in the ICU represent a heterogeneous, high-risk population with mortality risk evolving across distinct clinical phases. Existing prognostic tools rely largely on Cox proportional hazards (Cox PH) nomograms with narrow predictor sets and single time horizons, leaving the value of modern machine learning, extended features, and external generalizability uncharacterized. We extracted an ICD-coded AAA ICU cohort from MIMIC-IV v2.2 (858 patients with complete six-predictor admission data: age, BUN, sepsis, antihypertensive use, anion gap, mean SpO2) using a 24-hour admission window. An extended feature set added hemodynamic, laboratory, and comorbidity variables, with feature selection via LASSO and SVM-RFE intersection. Six models (Cox PH, logistic regression, random forest, gradient boosting, XGBoost, MLP) were trained on a 70% split and evaluated at 7-, 14-, and 28-day horizons using ROC-AUC, C-index, Brier score, calibration, and SHAP. External validation used a harmonized eICU-CRD cohort. In-hospital mortality was [~]11.8%. On the six-predictor set, logistic regression led at 7 days (AUC 0.866) and 14 days (AUC 0.872), with XGBoost competitive. Extended features yielded modest gains; random forest achieved the best 28-day AUC (0.892). The MLP consistently underperformed. Discrimination declined monotonically with longer horizons. External validation showed expected attenuation (best 7-day AUC 0.771). SHAP consistently identified anion gap, BUN, and age as top contributors. We conclude that regularized linear models excel under data scarcity, while tree ensembles gain advantage as features and horizons expand. External results motivate local recalibration before deployment.