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Background: Immune checkpoint inhibitors (ICIs) have transformed cancer therapy but may increase thrombotic risk through immune-mediated inflammation. While venous thromboembolism is a known complication in cancer, real-world data quantifying the incidence of pulmonary embolism (PE) and deep venous thrombosis (DVT) specifically in ICI-treated patients remain limited. Methods: We conducted a retrospective cohort study of cancer patients treated with ICIs at a tertiary institution from 2011 to 2022. Electronic health records were reviewed to identify cases of PE and DVT, along with baseline characteristics and clinical outcomes at one-year follow-up. Rates of PE and DVT were compared to those reported in the literature. Time-dependent Cox regression analyses were performed to assess associations of PE and DVT with one-year all-cause mortality and heart failure (HF), defined as a new diagnosis and/or HF exacerbation/hospitalization. Results: Among patients treated with ICI, the incidence of PE and DVT was 10.6% and 8.3%, respectively. These rates are higher than those reported in several prior studies, where PE incidence in patients treated with ICI typically ranged from 3% to 6% and DVT incidence from 3% to 5%. A history of valve disease, lung cancer, and treatment with pembrolizumab or durvalumab were more frequently observed among patients with PE, whereas pembrolizumab use was most prevalent among those who developed DVT. In time-dependent Cox regression analyses, PE was significantly associated with increased one-year all-cause mortality (HR: 2.62, 95% CI: 2.12–3.23, p < 0.001) and showed a trend toward increased HF (HR: 1.63, 95% CI: 0.96–2.75, p = 0.070). Similarly, DVT was also associated with significantly higher one-year all-cause mortality (HR: 2.63, 95% CI: 2.10–3.31, p < 0.001) and a significantly greater risk of HF (HR: 1.80, 95% CI: 1.03–3.15, p = 0.040). Conclusion: In this real-world cohort study, PE and DVT were more frequent than previously recognized in ICI-treated cancer patients and were associated with significantly increased risks of all-cause mortality and heart failure. Conditions such as valve disease, lung cancer, and specific ICI therapies (e.g., pembrolizumab or durvalumab) may help identify patients at higher thrombotic risk. These findings underscore the importance of vigilant thrombotic risk assessment and may support the need for proactive monitoring or prevention strategies in high-risk patients receiving ICI therapy.

Background: A key determinant of prognosis in pulmonary hypertension (PH) is the right ventricle’s (RV) ability to adapt to increased pulmonary vascular resistance. Consequently, markers of RV-pulmonary artery (PA) coupling may improve prognostic prediction in PH. The ratio of tricuspid annular plane systolic excursion (TAPSE) to systolic PA pressure (sPAP) has been proposed as a surrogate for RV-PA coupling and a reliable prognostic marker, however, the existing data on the prognostic value of the ratio between RV longitudinal strain (RVLS) and sPAP as a surrogate for RV-PA coupling is scarce. Hypothesis: To evaluate the prognostic role of RVLS/RVSP in PH patients and to compare this novel marker with established markers of RV-PA coupling. Methods: This is a single-center study that included adult patients with PH classified as group I or IV, all of whom had undergone a transthoracic echocardiogram (TTE) within one year prior to the initiation of PH-specific treatment. Certified sonographers procured RV strain measurements using a specific software (Epsilon Imaging, Ann Arbor, MI). Receiver operating characteristic (ROC) analysis was used to determine the best cutoff for predicting all-cause mortality at one-year for RVLS/RVSP and TAPSE/RVSP. The association of RVLS/RVSP and TAPSE/RVSP with mortality at one year was assessed using the Kaplan-Meier analysis and multivariate Cox regression analysis (adjusted for REVEAL LITE 2.0 risk score). Results: A total of 160 patients were included with a median age of 67.2 (56.8, 73.7) years and 47 (29.2%) were male . ROC analysis revealed optimal cutoffs of 0.265 (AUC = 0.83) for RVLS/RVSP and 0.185 (AUC = 0.77) for TAPSE/RVSP to predict all-cause mortality at one year. One-year cumulative incidence of all-cause mortality was lower among patients with RVLS/RVSP ≥ 0.265 (0 % vs 13.7 %, p = 0.001) and patients with TAPSE/RVSP ≥ 0.185 (1.8 % vs 16.7 %, p = 0.001) (Figure 1) . In the multivariate analysis, RVLS/RVSP < 0.265 was associated with higher risk of mortality at one year (HR: 2.38, 95 % CI: 1.05 to 5.38, p = 0.036) while TAPSE/RVSP < 0.185 was not (HR: 2.01, 95 % CI: 0.93 to 4.31, p = 0.072). Conclusions: RVLS/ RVSP may be a valuable prognostic marker in PH patients and could outperform conventional TTE parameters as a surrogate for detecting RV-PA coupling.

Intracardiac metastases from gastroenteropancreatic neuroendocrine tumors (NETs) are rare and challenging to diagnose. Lutetium (Lu)-177 DOTATATE therapy has demonstrated significant benefit in patients with metastatic NET; however, there are limited data regarding the direct impact on cardiac metastases. Patients with intracardiac metastasis from NET were evaluated. Patients were stratified by treatment with Lu-177 DOTATATE. Quantitative assessments of the progression of cardiac metastatic size (maximal diameter) and metabolic activity, as measured by maximal standardized uptake value (SUV max ), were performed on serial 68 Ga-DOTATATE PET imaging. In a cohort of 23 patients, 11 patients (47.8%) received Lu-177 DOTATATE therapy. Through a median of 2.2 years of follow-up, these patients showed some reduction in metastatic burden with a median reduction in maximal diameter of 1.0 mm (IQR: 0.0–3.0) and a median change in SUV max of −1.6 (IQR: −4.4 to 1.5). In contrast, patients who did not receive Lu-177 DOTATATE had some increase in maximal diameter of 0.5 mm (−2.0 to 2.0) and an increase in SUV max of 1.4 (−6.2 to 2.3). Overall, Lu-177 DOTATATE therapy may result in modest regression of intracardiac metastases as assessed by molecular imaging, whereas patients with cardiac metastases who did not receive therapy appeared to show progression.

Introduction: Mitral valve transcatheter edge-to-edge repair (M-TEER) has emerged as a minimally invasive option for high-risk surgical candidates with severe and symptomatic mitral regurgitation (MR), but post-procedure residual mitral valve (MV) dysfunction remains a significant concern. This study evaluates the clinical utility of combining artificial intelligence applied to electrocardiograms (ECG-AI) for diastolic dysfunction (DD) grading and the echocardiography-derived velocity time integral of the MV and left ventricular outflow tract ratio (VTIMV/LVOT) in predicting prognosis in patients post-M-TEER. Methods: A retrospective analysis of patients who underwent M-TEER between 2014 and 2021 was conducted. Patients were categorized based on VTIMV/LVOT and ECG-AI scores into three groups: both normal parameters, either abnormal parameter, or both abnormal parameters to compare outcomes (mortality, major adverse cardiovascular events [MACE], and the need for subsequent MV reintervention) using Kaplan–Meier analysis, multivariable Cox regression models, and net reclassification improvement. Results: Overall, 250 patients were included; the median age was 79.5 (IQR: 73.1, 84.6) and 66.4% were male. The combined abnormal VTIMV/LVOT (≥2.5) and ECG-AI score for DD (>1) was associated with higher risk of one-year mortality (adjusted HR: 4.56 [1.04–19.89], p = 0.044) and MACE (adjusted HR: 3.72 [1.09–12.72], p = 0.037) compared to patients with both normal parameters. Conclusions: This study highlights the potential additive value of integrating VTIMV/LVOT and ECG-AI scores as a prognostic tool for a personalized approach to the post-operative evaluation and risk stratification in M-TEER patients.

Aortic stenosis (AS), the most common valvular heart disease, is traditionally graded based on several echocardiographic quantitative parameters, such as aortic valve area (AVA), mean pressure gradient (MPG), and peak jet velocity (Vmax). This systematic review evaluates the clinical significance and prognostic implications of discordant high-gradient AS (DHG-AS), a distinct hemodynamic phenotype characterized by elevated MPG despite a preserved AVA (>1.0 cm2). Although often overlooked, DHG-AS presents unique diagnostic and therapeutic challenges, as high gradients remain a strong predictor of adverse outcomes despite moderately reduced AVA. Sixty-three studies were included following rigorous selection and quality assessment of the key studies. Prognostic outcomes across five key studies were discrepant: some showed better survival in DHG-AS compared to concordant high-gradient AS (CHG-AS), while others reported similar or worse outcomes. For instance, a retrospective observational study including 3209 patients with AS found higher mortality in CHG-AS (unadjusted HR: 1.4; 95% CI: 1.1 to 1.7), whereas another retrospective multicenter study including 2724 patients with AS observed worse outcomes in DHG-AS (adjusted HR: 1.59; 95% CI: 1.04 to 2.56). These discrepancies may stem from delays in intervention or heterogeneity in study populations. Despite the diagnostic ambiguity, the presence of high gradients warrants careful evaluation, aggressive risk stratification, and timely management. Current guidelines recommend a multimodal approach combining echocardiography, computed tomography (CT) calcium scoring, transesophageal echocardiography (TEE) planimetry, and, when needed, catheterization. Anatomic AVA assessment by TEE, CT, and cardiac magnetic resonance imaging (CMR) can improve diagnostic accuracy by directly visualizing valve morphology and planimetry-based AVA, helping to clarify the true severity in discordant cases. However, these modalities are limited by factors such as image quality (especially with TEE), radiation exposure and contrast use (in CT), and availability or contraindications (in CMR). Management remains largely based on CHG-AS protocols, with intervention primarily guided by transvalvular gradient and symptom burden. The variability among the different guidelines in defining severity and therapeutic thresholds highlights the need for tailored approaches in DHG-AS. DHG-AS is clinically relevant and associated with substantial prognostic uncertainty. Timely recognition and individualized treatment could improve outcomes in this complex subgroup.

Cardiovascular diseases and cancer are the leading causes of morbidity and mortality in modern society. Expanding cancer therapies that have improved prognosis may also be associated with cardiotoxicity, and extended life span after survivorship is associated with the increasing prevalence of cardiovascular disease. As such, the field of cardio-oncology has been rapidly expanding, with an aim to identify cardiotoxicity and cardiac disease early in a patient who is receiving treatment for cancer or is in survivorship. Artificial intelligence is revolutionizing modern medicine with its ability to identify cardiac disease early. This article comprehensively reviews applications of artificial intelligence specifically applied to electrocardiograms, echocardiography, cardiac magnetic resonance imaging, and nuclear imaging to predict cardiac toxicity in the setting of cancer therapies, with a view to reduce early complications and cardiac side effects from cancer therapies such as chemotherapy, radiation therapy, or immunotherapy.

Lipoprotein(a) [Lp(a)] is a well-established causal risk factor for cardiovascular diseases (CVDs), as reported by multiple Mendelian randomization studies and large epidemiological studies. When elevated Lp(a) is combined with other risk factors, most notably elevated low-density lipoprotein cholesterol (LDL-C), a synergistic atherogenic effect has been reported. However, the current literature is conflicting regarding how Lp(a) interacts in the context of controlled LDL-C levels (e.g., <70 mg/dL) and whether reducing LDL-C can modify the atherogenic effect of Lp(a). In some studies, elevated Lp(a) was still significantly associated with a higher risk of cardiovascular events, despite controlled levels of LDL-C. In contrast, multiple studies have reported attenuation of the cardiovascular risk mediated by elevated Lp(a) with lower LDL-C levels. Moreover, the relationship between Lp(a) and triglycerides, high-density lipoprotein, and very low-density lipoprotein remains unclear. In this literature review, we summarize and discuss the current evidence regarding the interactions between Lp(a) and other lipid molecules, how they contribute to the pathogenesis of CVD, and future perspectives, particularly in the current era where promising targeted Lp(a)-lowering therapies are under development.

Background: Pulmonary hypertension (PH) is a complex condition associated with significant morbidity and mortality. Traditional diagnostic and management approaches for PH often face limitations, leading to delays in diagnosis and potentially suboptimal treatment outcomes. Artificial intelligence (AI), encompassing machine learning (ML) and deep learning (DL) offers a transformative approach to PH care. Materials and Methods: We systematically searched PubMed, Scopus, and Web of Science for original studies on AI applications in PH, using predefined keywords. Out of more than 500 initial articles, 45 relevant studies were selected. Risk of bias was evaluated using PROBAST (Prediction model Risk of Bias Assessment Tool). Results: This review examines the potential applications of AI in PH, focusing on its role in enhancing diagnosis, disease classification, and prognostication. We discuss how AI-powered analysis of medical data can improve the accuracy and efficiency of detecting PH. Furthermore, we explore the potential of AI in risk stratification, leading to treatment optimization for PH. Conclusions: While acknowledging the existing challenges and limitations and the need for continued exploration and refinement of AI-driven tools, this review highlights the significant promise of AI in revolutionizing PH management to improve patient outcomes.