Citations

Abstract Aims The clinical significance of early recurrences during the blanking period following pulmonary vein isolation (PVI) remains uncertain, particularly after pulsed field ablation (PFA). This study aimed to evaluate the prognostic impact of early recurrences at 30-, 60-, and 90-day thresholds on outcomes following PFA for atrial fibrillation (AF). Methods and results Consecutive patients who underwent PFA for AF across multiple centres within the Mayo Clinic health system were included. The primary endpoint was AF-free survival. Kaplan–Meier and multivariate Cox regression analyses were performed to compare outcomes between patients with and without early recurrences using 30-, 60-, and 90-day thresholds. A total of 1162 patients were included. The estimated AF-free survival at 1 year was 70%. Early recurrences within 30-, 60-, and 90-days post-ablation were significantly associated with late recurrences beyond 90 days (hazard ratio [HR] 5.9, 95% confidence interval [CI] 4.4–7.9, P < 0.01; HR 6.6, 95% CI: 5.0–8.6, P < 0.01; and HR 9.1, 95% CI: 7.0–11.7, P < 0.01, respectively). These findings remained significant among patients undergoing continuous rhythm monitoring. Early recurrences within 30 days in this subgroup were significantly associated with a 1-year AF burden >1% or the need for repeat AF ablation (odds ratio 25.0, 95% CI: 6.6–126.8, P < 0.01). Conclusion Early recurrences following PFA are strongly predictive of long-term arrhythmia recurrence and are unlikely to represent transient inflammatory phenomena. Accordingly, the applicability of the conventional blanking interval after PFA should be reconsidered.

Hypertension is the main risk factor for cardiovascular disease (CVD), affecting 20–40% of Latin American (LATAM) adults, and responsible for more than two million deaths annually due to CVD. The different ethnic, economic, geographic, and cultural characteristics of the LATAM population influence the high prevalence of all cardiovascular risk factors (CVRF), particularly metabolic disturbances such as type 2 diabetes (DM2), obesity and the metabolic syndrome. Their main determinants in LATAM includes environment, food quality, social inequity, low education, political aspects, contextual behaviour, and genetics. The prevalence of overweight and obesity in LATAM increased during the last four decades reaching figures of 10–20% in childhood, 30–40% in adolescence, and 60–70% in adults. Many studies in the region have reported the extremely low rates of awareness, treatment, and control of CVRF in the general population of LATAM, particularly in patients with metabolic disorders, and the consequent high cardiovascular morbidity and mortality. This 2026 LATAM consensus is developed by a large group of experts from different LATAM countries, the USA and Europe, representing areas of internal medicine, cardiology, nephrology, endocrinology, geriatrics, paediatrics, pharmacology, and epidemiology. A careful search for novel studies in LATAM, together with new evidence that has emerged since the 2019 LATAM consensus, support the statements and recommendations in the current report. This update aims to provide clear and useful recommendations for health professionals to improve awareness, treatment, and control of hypertension and associated CVRF in the region.

Abstract Background Artificial intelligence (AI)–based, mammography breast cancer risk prediction models show improved discriminatory accuracy relative to clinical risk models. However, data on their calibration are limited. This study compared model performance of 3 clinical breast cancer risk models: Gail, Tyrer–Cuzick v8, and Breast Cancer Surveillance Consortium v3 with the MIRAI AI-risk model. Methods Digital mammograms were ascertained from a screening mammography cohort of 12 308 women within the Mayo Clinic Biobank with 250 incident breast cancers (176 invasive) within 5 years. We predicted 5-year breast cancer risk, estimated discriminatory accuracy (C-index), and calibration (observed-to-expected ratio) of both overall and invasive breast cancer and compared estimates using bootstrapping approaches. Results MIRAI demonstrated similar or improved discriminatory accuracy of overall breast cancer (C-index = 0.71, 95% confidence interval [CI] = 0.68 to 0.74) and invasive breast cancer (C-index = 0.71, 95% CI = 0.67 to 0.75) compared with clinical models (overall breast cancer: C-index = 0.59-0.68; invasive breast cancer: C-index = 0.60-0.68). MIRAI’s calibration for risk of overall breast cancer (observed-to-expected ratio = 0.96, 95% CI = 0.85 to 1.08) was improved compared with Gail (observed-to-expected ratio = 1.22, 95% CI = 1.07 to 1.38) and Breast Cancer Surveillance Consortium (observed-to-expected ratio = 1.38, 95% CI = 1.22 to 1.56) but similar to TC with volumetric percent density and polygenic risk score (observed-to-expected ratio = 0.99, 95% CI = 0.87 to 1.13). However, for low-risk women (approximately 50%), MIRAI overestimated risk of overall breast cancer. MIRAI also overestimated risk of invasive breast cancer across the risk spectrum (observed-to-expected ratio = 0.68, 95% CI = 0.58 to 0.78), whereas clinical models had good calibration (observed-to-expected ratio = 0.86–0.99). Conclusion MIRAI demonstrated stronger discriminatory accuracy than clinical models for 5-year overall and invasive breast cancer risk prediction but overestimated risk for both breast cancer endpoints. AI-based risk models should consider discriminatory accuracy and calibration for invasive cancer before implementation.

Introduction: The biomechanical role of the dorsal scaphotriquetral ligament (DSTL) ligament in scapholunate (SL) stability remains debated. We assessed DSTL contribution using a cadaveric sectioning model. Methods: Twenty-four wrists underwent sequential sectioning: none (phase 0), palmar/proximal scapholunate interosseous ligament (SLIOL) (phase 1), dorsal SLIOL (phase 2), superficial dorsal intercarpal ligament (DICL) (phase 3) and DSTL (phase 4). Under axial loading (60 N; cyclic 60–200 N) and wrist motion, the SL gap and carpal angles, and a CT-derived dorsal instability scaphoid angle (DISA) were measured. Results: Phase 1 produced no significant instability. Dorsal SLIOL sectioning (phase 2) initiated dynamic instability, with increased SL gap and angles under load and dorsal scaphoid translation, but no resting dorsal intercalated segment instability (DISI). Sectioning the superficial DICL fascicle (phase 3) did not add instability. DSTL sectioning (phase 4) produced the largest changes, with marked SL diastasis, increased angles and DISA, and DISI at rest and under load. Conclusion: Dorsal SLIOL sectioning produced load-dependent (dynamic) SL instability, whereas subsequent DSTL sectioning was associated with progression to static instability, supporting the DSTL as an important dorsal restraint. The DISA may provide an additional CT-based measure of dorsal scaphoid subluxation; however, clinical validation is required.

Abstract Context Most of the patients included in the clinical trials and real-world studies with osilodrostat only include patients with adrenocorticotropic hormone dependent Cushing syndrome (CS), while data on the efficacy and safety of osilodrostat in patients with adrenal CS is scarce. Objective To assess the efficacy and safety of osilodrostat in adrenal CS. Methods International study of patients with adrenal CS: patients treated with osilodrostat at any time were enrolled in the safety evaluation and those treated for longer than 4 weeks, in the efficacy evaluation. Patients were classified as responders if they experienced a reduction in urinary free cortisol (UFC) >50% (complete responders when UFC levels were below the upper limit of normal [ULN] and partial responders if there was a reduction >50% but not normalization). Results Twenty-eight patients with adrenal CS were enrolled: 16 with adrenocortical carcinoma and 12 with benign disease. Osilodrostat was used in monotherapy in 22 patients and in combination with metyrapone in 6 cases. In those patients treated for longer than 4 weeks (n = 21), 66.7% were classified as responders (28.6% with complete response and 38.1% with partial response), and for those treated for longer than 12 weeks, the rate of response increased to 87.5% The use of osilodrostat as a nonfirst-line therapy (odds ratio 15.0, P = .010) was a predictor of response. Osilodrostat led to a significant decrease in systolic blood pressure and body weight (P < .05). Nine patients developed one or more adverse events and in 56% (n = 5) led to osilodrostat discontinuation. Conclusion Osilodrostat controls hypercortisolism in 66.7% of patients with adrenal CS treated for longer than 4 weeks and in 87.5% of cases treated for longer than 12 weeks, with a positive impact on blood pressure and body weight. Patients who received osilodrostat after other previous steroidogenesis inhibitors have a higher probability of response.

ABSTRACT Recent advances in the diagnosis and management of reflux disease were the central focus of the inaugural Gatherings in Esophagology (GiE), which convened experts across gastroenterology, surgery, otolaryngology, pulmonology, and basic research. The sessions highlighted innovations in reflux monitoring—including high‐resolution manometry, wireless pH monitoring, and novel salivary biomarkers—while critically evaluating their diagnostic accuracy and clinical utility. Presentations explored the limitations of traditional proton‐pump inhibitor therapy, the emergence of potassium‐competitive acid blockers as a new class of acid suppressants, and the evolving role of adjunctive treatments such as mucosal protectants, reflux reducers, and neuromodulators for refractory symptoms. The discourse extended to advanced interventional procedures, including transoral incisionless fundoplication, magnetic sphincter augmentation, and the RefluxStop device, with discussion of patient selection, efficacy, and complication management. Discussants emphasized the pathophysiology and management of extraesophageal manifestations of reflux, the interplay between reflux and pulmonary disease, and the diagnostic challenges in pediatric populations. The meeting also addressed the integration of behavioral therapies, the role of the microbiome, and the application of artificial intelligence in reflux diagnostics. Collectively, these insights underscore a shift toward precision medicine in reflux disease, emphasizing individualized diagnostic strategies and tailored therapeutic approaches to improve patient outcomes.

Abstract Introduction and Objective This study aims to evaluate the learning curve and perioperative outcomes of single‐port extraperitoneal robotic radical prostatectomy (SP‐EP‐RARP) performed by a single surgeon at a high‐volume academic institution. Methods A retrospective review of a prospectively maintained database was conducted for patients who underwent SP‐EP‐RARP from September 2023 to August 2025. Key metrics included operative time, estimated blood loss, conversion rate, nerve‐sparing status, pathological margin status and continence (0–1 pad for safety), and 30‐day complication rate. Descriptive statistics were used, and outcomes were analysed chronologically to assess for trends suggesting a learning curve. Results The cohort included 53 patients who underwent extraperitoneal SP‐EP‐RARP. Median operative time was 213 min (IQR: 145–281). Median estimated blood loss was 100 mL (IQR: 75–125). Nerve‐sparing was attempted in 75.5% of cases. No assist or plus one ports were utilized. Final pathology revealed pT2 disease in 64.1% (34 cases), pT3a in 32.1% (17 cases) and pT3b in 3.7% (2 cases). Overall positive margin rate was 26.4%. Thirty‐day Clavien–Dindo Grades I–II complication rates were 11.3%, and no major complications or 90‐day complications were reported. Continence rates at 6 weeks, 3 months and 6 months were 72%, 72% and 75%, respectively. Stabilization of operative times and intraoperative outcomes occurred after approximately 25–30 cases. Conclusion SP‐EP‐RARP can be safely implemented by a newly graduated fellowship‐trained robotic surgeon with acceptable oncologic and functional outcomes. Initial learning can be estimated at approximately 25–30 cases.

ABSTRACT Background Acute heart failure (AHF) exhibits marked heterogeneity in diastolic hemodynamics, yet comprehensive echocardiographic assessment of diastolic function (DF) and filling pressure (FP) is often infeasible. We evaluated whether artificial intelligence–enabled electrocardiography (AI-ECG) could provide scalable DF grading and FP estimation in hospitalized AHF patients. Methods We retrospectively studied adults hospitalized for AHF across Mayo Clinic sites (2013–2023) who received ≥1 dose of intravenous loop diuretic and had paired 12-lead ECG and TTE. The previously validated AI-ECG DF model was applied without retraining to generate four DF grades and a continuous FP probability. Clinical outcomes were all-cause mortality and heart failure rehospitalization. Associations with clinical severity markers and echocardiographic indices were examined. Kaplan–Meier survival analysis and adjusted multivariable Cox proportional hazards models were performed. Exploratory analyses examine the kinetics of change in FP probability and impact on mortality. Results Among 11,513 patients (median age 75 years, 39% female), AI-ECG DF grading was feasible in 100%, whereas echocardiographic DF was indeterminate in 44% of clinically eligible patients. In 2,582 patients with determinate echocardiographic DF, AI-ECG FP probability discriminated TTE Grade 2–3 dysfunction with AUC 0.85 (95% CI 0.83 – 0.86). Higher AI-ECG DF grades were associated with higher comorbidity burden, worse NYHA class, elevated NT-proBNP, higher MAGGIC scores, elevated PCWP, and more advanced structural remodeling. After multivariable adjustment, AI-ECG DF remained independently associated with mortality (hazard ratio [HR] 1.25, 95% CI 1.16–1.35 for Grade 2; HR 1.44, 95% CI 1.33–1.56 for Grade 3 versus Normal/Grade 1). Combining AI-ECG DF with MAGGIC scores yielded ordered risk gradients, with highest mortality in patients with both high MAGGIC and Grade 2–3 DF. Among patients with serial ECGs, improvement in FP probability was independently associated with lower mortality (HR 0.85, 95% CI 0.79–0.91), whereas worsening did not show a consistent adverse gradient beyond baseline DF. Conclusions In a large, geographically diverse AHF cohort, AI-ECG DF grading was universally feasible, correlated with established hemodynamic severity markers, and provided independent prognostic information beyond established risk factors, supporting its role as a pragmatic, scalable diastolic biomarker in AHF. CLINICAL PERSPECTIVE What Is New? In 11,513 hospitalized acute heart failure (HF) patients, artificial intelligence-enabled electrocardiography provided diastolic function grading in 100% of patients from a single 12-lead ECG without requiring additional clinical variables, compared with 56% feasibility for guideline-based echocardiography grading. AI-ECG diastolic function grades correlated with established marker of severity (NYHA functional class, NT-proBNP, MAGGIC risk scores, and pulmonary capillary wedge pressure) and remained independently associated with both mortality and HF rehospitalization after multivariable adjustment. Serial AI-ECG measurements identified post-discharge filling pressure trajectories, with improvement independently associated with 15% lower mortality, a first demonstration that longitudinal ECG assessment can track post-discharge hemodynamic recovery. What Are the Clinical Implications? AI-ECG transforms the universally obtained 12-lead ECG into an actionable hemodynamic biomarker that addresses the critical gap when echocardiographic diastolic function assessment is indeterminate or unavailable in acute HF patients. Despite markedly different hemodynamic severity and long-term outcomes across AI-ECG diastolic function grades, hospitalization length of stay did not differ, suggesting advanced diastolic dysfunction represents occult risk not easily recognized during routine acute care and highlighting the need for improved post-discharge risk stratification. The continuous filling pressure probability metric enables longitudinal monitoring of post-discharge hemodynamic status using serial routine ECGs, potentially identifying patients requiring intensified follow-up or specialist referral.

Immune checkpoint inhibitors (ICIs) can cause immune-related adverse events (irAEs), with acute interstitial nephritis (ICI-AIN) being the most common irAE. While the exact mechanism remains unclear, upregulation of IFN-γ and TNF-α pathways has been implicated. This study used a humanized chimeric PD-1/PD-L1 mouse model to assess renal effects of ICIs, alone or combined with proinflammatory cytokines, and to test if selective TNF-α blockade could prevent ICI-AIN. Mice were randomly divided into 4 experimental groups: Control, ICI-Only, ICI-Cytokines (ICI-Cyt), and ICI-Block (ICI-TNF-α blockade). Renal function and cytokine profiles were assessed, while kidney tissue was analyzed using microscopy and single-cell RNA-seq. Histology revealed increased renal infiltration of CD4 + /CD8 + T cells in ICI-treated groups and decreased TNF-α expression following TNF-α blockade. Additionally, kidney tissue ELISA demonstrated reduced IFN-γ levels following TNF-α blockade. Plasma IL-6, MCP-1, and TNF-α were lower in ICI-Block mice. Single-cell RNA-seq revealed shifts in immune cell populations and genes of interest including Bcl2a1 , Icos , Il18r1 , Ccr2 , and Jaml . This humanized model replicates ICI-AIN key features, revealing a synergistic role of ICIs and proinflammatory cytokines. TNF-α blockade demonstrated protective effects, supporting its potential role in mitigating the risk of ICI-AIN.

Evidence-Based Practice (EBP) has increased the availability of psychological treatments, yet many people do not benefit from therapy, some report deteriorations in symptoms, and dropout rates remain high. Precision Mental Health (PMH) is proposed as an extension of EBP by combining systematic measurement with predictive analytics to support the right intervention, at the right time, for the right person. Recent advances in Artificial Intelligence (AI) make PMH increasingly feasible in routine psychotherapy; however, the implementation of these approaches in routine care is still incipient. In this context, the present article has two main aims. First, we summarize key advances in PMH, particularly measurement-based care and data-informed decision making. Second, we introduce the NOVA project (Navigating Outcomes via Analytics), a multi-phase translational program designed to implement PMH in real-world psychological services. Guided by the Implementing Precision Methods framework, NOVA integrates (i) stakeholder-informed work on clinician acceptability and intention to use, (ii) pragmatic evaluation of decision support tools in routine care, (iii) development of robust and interpretable predictive models, and (iv) training and dissemination activities aligned with responsible innovation and professional competencies for AI-supported precision care. By detailing NOVA’s implementation pathway, we aim to provide a concrete roadmap for bridging AI innovation and psychological practice, accelerating the sustainable adoption of PMH in real-world settings.