Search Results (143)

Background/Objectives: Heart failure (HF) is a growing global health burden, yet early detection remains challenging due to the limitations of traditional diagnostic tools such as electrocardiograms (ECGs). Recent advances in deep learning offer new opportunities to identify left ventricular systolic dysfunction (LVSD), a key indicator of HF, from ECG data. This study validates AiTiALVSD, our previously developed artificial intelligence (AI)-enabled ECG Software as a Medical Device, for its accuracy, transparency, and robustness in detecting LVSD. Methods: This retrospective single-center cohort study involved patients suspected of LVSD. The AiTiALVSD model, based on a deep learning algorithm, was evaluated against echocardiographic ejection fraction values. To enhance model transparency, the study employed Testing with Concept Activation Vectors (TCAV), clustering analysis, and robustness testing against ECG noise and lead reversals. Results: The study involved 688 participants and found AiTiALVSD to have a high diagnostic performance, with an AUROC of 0.919. There was a significant correlation between AiTiALVSD scores and left ventricular ejection fraction values, confirming the model’s predictive accuracy. TCAV analysis showed the model’s alignment with medical knowledge, establishing its clinical plausibility. Despite its robustness to ECG artifacts, there was a noted decrease in specificity in the presence of ECG noise. Conclusions: AiTiALVSD’s high diagnostic accuracy, transparency, and resilience to common ECG discrepancies underscore its potential for early LVSD detection in clinical settings. This study highlights the importance of transparency and robustness in AI-ECG, setting a new benchmark in cardiac care. Full article

Background/Objectives: Dyspnea is one of the main causes of visits to the Emergency Department (ED) and hospitalization, with its differential diagnosis representing a challenge for the clinician. Lung ultrasound (LUS) is a widely used tool in ED. The objective of this study was to evaluate the impact of LUS, performed by a non-expert operator, in determining diagnosis and prognosis of patients with dyspnea. Methods: A total of 60 patients presenting with dyspnea at the ED were prospectively enrolled and underwent LUS examination by a medical student, after brief training, within 3 h of triage. LUS findings were classified into four patterns: N.1, absence of notable ultrasound findings, attributable to COPD/ASMA exacerbation; N.2, bilateral interstitial syndrome, suggestive of acute heart failure; N.3, subpleural changes/parenchymal consolidations, suggestive of pneumoniae; and N.4, isolate polygonal triangular consolidation, attributable to infarction in the context of pulmonary thromboembolism. Results: The diagnostic hypothesis formulated after LUS was compared with the final diagnosis after further investigations in the ED, showing agreement in 90% of cases. The mean LUS score value was higher in patterns N.2 (18.4 ± 8.5) and N.3 (17 ± 6.6), compared to patterns N.1 and N.4 (9.8± 6.7 and 11.5 ± 2.1). Given the high prevalence of pattern N.2, the diagnostic accuracy of LUS in this context was further evaluated, showing a sensitivity of 82% and specificity of 100%. In terms of the prognostic value of LUS, hospitalized patients had a higher LUS score compared to those discharged (17.3 ± 8.1 vs. 8.5 ± 6.8, p value 0.004). A similar trend was obtained in the subgroup of patients requiring non-invasive ventilation (NIV), who present a higher LUS score (21.1 ± 6.6 vs. 13.1 ± 8.1, p value 0.002). When considering a combined outcome (death and NIV), patients with worse outcomes more often had a LUS score > 15 (p value < 0.001). Conclusions: In conclusion, this study confirms that LUS is a very useful tool in the ED, assisting the clinical evaluation for diagnosis, treatment decision, and determination of the appropriate care setting for patients with acute dyspnea. Its short learning curve allows even non-expert staff to use it effectively. Full article

Background: The PATHFINDER-CHD Registry is a prospective, multicenter, non-interventional registry across tertiary care centers in Germany. The aim is to analyze real-world data on adults with congenital heart defects (ACHD) or hereditary connective tissue disorders who have manifest heart failure (HF), a history of HF, or are at significant risk of developing HF. This analysis investigates the prevalence and clinical impact of overweight and obesity in this unique population. Methods: As of 1st February, 2025, a total of 1490 ACHD had been enrolled. The mean age was 39.4 ± 12.4 years, and 47.9% were female. Patients were categorized according to Perloff’s functional class and the Munich Heart Failure Classification for Congenital Heart Disease (MUC-HF-Class). Results: The most common congenital heart disease (CHD) in this cohort was Tetralogy of Fallot, transposition of the great arteries, and congenital aortic valve disease. Marfan syndrome was the most common hereditary connective tissue disease. Of the patients, 46.1% were classified as overweight (32.8%) or obese (13.3%), while 4.8% were underweight. The highest prevalence of overweight (47.1%) was observed among patients who had undergone palliative surgery, whereas untreated patients showed the highest proportion of normal weight (57.2%). Cyanotic patients were predominantly of normal weight. Patients with univentricular circulation exhibited significantly lower rates of overweight and obesity (35%; p = 0.001). Overweight and obesity were statistically significantly associated with arterial hypertension, diabetes mellitus, and sleep apnea (all p < 0.001). High BMI was linked to increased use of HF-specific medications, including SGLT2 inhibitors (p = 0.040), diuretics (p = 0.014), and angiotensin receptor blockers (p = 0.005). Conclusions: The data highlight the clinical relevance of overweight and obesity in ACHD with HF, emphasizing the need for individualized prevention and treatment strategies. The registry serves as a critical foundation for the optimization of long-term care in this population. Full article

Objective: We aimed to investigate the association between the pan-immune-inflammation value (PIV) and mortality in patients with heart failure with a reduced ejection fraction (HFrEF), along with clinical and biochemical parameters. Methods: In this retrospective cohort study, 419 patients diagnosed with HFrEF between January 2014 and December 2023 were analyzed. Data on demographic features, comorbidities, cardiac parameters [New York Heart Association (NYHA) classification, left ventricular ejection fraction (LVEF), ventricular dimensions], medication use, and laboratory findings (PIV, N-terminal pro-B-type natriuretic peptide [NT-proBNP], electrolytes, and complete blood count) were collected from institutional and national records. Results: Mortality occurred in 22.91% of patients. PIV > 696 was significantly associated with mortality (sensitivity: 37.5%, specificity: 78.64%, p = 0.006), but it was not an independent predictor in multivariate analysis. Instead, low body mass index (BMI), increased end-systolic diameter, reduced LVEF, advanced NYHA class (III/IV), elevated NT-proBNP, hyponatremia, and lymphopenia were identified as independent predictors (all p < 0.001). Conclusions: Although PIV was associated with mortality in patients with HFrEF, it did not independently predict outcomes beyond established risk factors. These results suggest that while inflammation may contribute to HFrEF pathophysiology, traditional clinical and biochemical markers remain more reliable for prognostication. Full article

Growing evidence underscores the pivotal roles of both in situ-resident and -non-resident cardiac cells in the repair mechanisms following myocardial infarction (MI). MI continues to be a predominant cause of death and disability, posing a significant threat to global health and well-being. Despite advances in medical care, current therapies remain insufficient in preventing ventricular remodeling and heart failure post-MI. We seek to clarify the underlying regenerative mechanisms by which distinct cell types contribute to the repair of MI injury and to systematically assess the translational potential and therapeutic efficacy of these cell-based approaches in clinical applications. This review conducts a comprehensive analysis of recent research progress on the roles of non-cardiac stem cells in situ and cardiac cells derived from explants in MI repair. These cells contribute to the repair process through multiple mechanisms, including cell proliferation and differentiation, angiogenesis, paracrine signaling, immune regulation and fibrosis modulation. Our analysis reveals the intricate mechanisms of MI repair and highlights the necessity for developing age-specific therapeutic strategies for certain cell types. This review offers novel insights into cell-based treatment for MI and provides a scientific foundation for future clinical trials of cardiac regenerative medicine. Full article

Background: Iron deficiency (ID) is a commonly seen comorbidity in heart failure (HF) patients. It is often associated with a poor prognosis and impaired physical capacity. The functional limitations linked to ID may lead to cardiac function abnormalities. The functional limitations linked to ID may lead to cardiac function abnormalities, that can be reversible after iron repletion. Some echocardiographic parameters, such as global longitudinal strain (GLS), myocardial work (MW) and its derivatives constructive work (CW), wasted work (WW) and work efficiency (WE), may be of added value in advanced cardiac performance assessment. Methods: IRON-PATH II was a multicenter, prospective and observational study designed to describe the pathophysiological pathways associated with ID. The echo-substudy included 100 HF patients that had undergone a specific pilot echocardiographic evaluation. Patients had a left ventricular ejection fraction (LVEF) ≤50%, were in stable clinical condition and on standard HF medication with hemoglobin ≥11 g/dL. The final cohort included 98 patients. Results: The ID group showed worse cardiac function, with lower GLS (−8.5 ± 9% vs. −10 ± 10%), WE (74 ± 10% vs. 80 ± 10%) and MW (665 [453–1013] vs. 947 [542–1199] mmHg%), as well as higher WW (290 [228–384] vs. 212 [138–305] mmHg%) and lower RV free wall strain (−13 [−20–(−11)]% vs. −17 [−23–(−14)]%). Following iron repletion, ID patients demonstrated improved LV (GLS, MW, WE and WW) and RV performance (RV free wall strain), aligning with non-ID patients (all p-values >0.05 compared to the non-ID group). Conclusions: Among HF patients with reduced LVEF, ID was associated with worse myocardial performance in both the LV and RV. All the alterations seen were reversible after intravenous iron repletion. Full article

Cardiovascular diseases (CVDs) remain the leading cause of morbidity and mortality worldwide. Epidemiological data demonstrate that the overlap between CVD, Type 2 Diabetes (T2DM), chronic kidney disease (CKD) and heart failure (HF) is becoming increasingly apparent, with aging populations making these patient cohorts more difficult to treat. In the last decade, three standout drug classes have emerged with the potential to broaden the treatment options for patients with multi-morbid CVD and heart failure. These are sodium–glucose cotransporter 2 (SGLT2) inhibitors, non-steroidal mineralocorticoid receptor antagonists (MRAs), e.g., Finerenone, and glucagon-like peptide 1 receptor agonists (GLP-1RAs). These medications are now entering UK and European guidelines for the treatment of CVDs including HF whilst crucially providing associated prognostic benefits for patients with T2DM and CKD. The future of these agents for CVD risk stratification may involve primary care at the forefront, alongside tailored, patient-specific medication regimens. This review article aims to discuss these three main drug classes (SGLT2 inhibitors, GLP-1RAs and non-steroidal MRAs) in detail by exploring their current evidence base across heart failure (HF) and CVD management and future clinical implications of their usage as mainstream medical therapies. Full article

Cardiovascular diseases such as coronary heart disease, heart failure, or stroke are the most common cause of death worldwide and are regularly based on risk factors like diabetes mellitus, hypertension, or obesity. At the same time, both diseases and risk factors are significantly influenced by sex hormones. In order to better understand this influence and also specifically improve the therapy of female patients, medical research has recently focused increasingly on gender-specific differences. The goal is to develop personalized, gender-specific therapy concepts for these diseases to further enhance health outcomes. The enzyme adenosine monophosphate-activated protein kinase (AMPK) is a central regulator of energy metabolism, protecting the cardiovascular system from energy depletion, thereby promoting vascular health and preventing cellular damage. AMPK confers cardioprotective effects by preventing endothelial and vascular dysfunction, and by controlling or regulating oxidative stress and inflammatory processes. For AMPK, sex-specific effects were reported, influencing metabolic and cardiovascular responses. Exercise and metabolic stress generally cause higher AMPK activity in males. At the same time, females exhibit protective mechanisms against insulin resistance or oxidative stress, particularly in conditions like obesity. Additionally, males subject to AMPK deficiency seem to experience greater cardiac and mitochondrial dysfunction. In contrast, females show improvement in cardiovascular function after pharmacological AMPK activation. These differences, influenced by hormones, body composition, and gene expression, highlight the potential to develop personalized, sex-specific AMPK-targeted therapeutic strategies for cardiovascular diseases in the future. Here, we discuss the most actual scientific background, focusing on the protective, gender-specific effects of AMPK, and highlight potential clinical applications. Full article

Background/Objectives: Growth failure is a common complication in children with congenital heart disease (CHD), yet its underlying mechanisms and consequences remain incompletely understood. This review aims to provide a comprehensive overview of growth failure in children with CHD and outline a framework of factors contributing to this condition. Methods: To lay the foundation for this narrative review, several databases were searched using broad search terms related to CHD and growth failure. Results: Growth failure is most pronounced during the first year of life, but often improves after achieving hemodynamic stability through surgical or medical interventions. However, children with complex conditions, such as single-ventricle physiology or multiple heart defects, may experience persistent growth impairment due to chronic disease effects. Specific features of CHD—cyanosis, pulmonary hypertension, and low cardiac output—can further hinder growth by disrupting endocrine function and impairing musculoskeletal development. Long-term use of medications and exposure to repeated diagnostic procedures also contribute to growth failure. Beyond physical effects, growth failure profoundly influences neurodevelopment, psychosocial well-being, and survival outcomes. Based on our review, we have developed a knowledge map to better understand the complexities of growth failure in children with CHD. Conclusions: A thorough understanding of the multifaceted contributors to growth failure in CHD is essential for identifying high-risk children and devising strategies to support optimal growth. Integrating this knowledge into clinical practice can improve long-term outcomes for children with CHD. Full article

Diuretics are a class of pharmacological agents that promote the renal excretion of water and electrolytes, increasing urine output and reducing fluid retention. They play a critical role in the management of edematous syndromes, irrespective of their etiology (cardiac, renal, or hepatic), as well as in the treatment of hypertension (HTA). The mechanism of action of diuretics can be classified as either renal, as seen with saluretic diuretics that inhibit sodium and water reabsorption at various segments of the nephron, or extrarenal, involving alterations in the glomerular filtration pressure or osmotic mechanisms. Based on their site of action and mechanism, diuretics are categorized into multiple classes, including loop diuretics, thiazide and thiazide-like diuretics, potassium-sparing diuretics, carbonic anhydrase inhibitors, and osmotic diuretics. These agents are frequently used in combination with other antihypertensive or heart failure medications to optimize therapeutic efficacy. By reducing the blood volume and peripheral vascular resistance, diuretics improve cardiac function, lower blood pressure, and enhance exercise tolerance. Additionally, they are employed in managing chronic kidney disease (CKD), electrolyte imbalances, and specific metabolic disorders. Given the potential for adverse effects such as electrolyte disturbances and renal dysfunction, diuretic therapy should be individualized, with the careful monitoring of the dosage, patient response, and comorbid conditions. Patient education on adherence, lifestyle modifications, and the recognition of side effects is essential for optimizing the therapeutic outcomes and minimizing the risks associated with diuretic therapy. Full article

Heart transplantation remains the gold-standard treatment for end-stage heart failure, yet long-term graft survival is hindered by chronic rejection and the morbidity and mortality caused by lifelong immunosuppression. While advances in medical and device-based therapies have reduced the overall need for transplantation, patients who ultimately require a transplant often present with more advanced disease and comorbidities. Recent advances in tolerance-inducing strategies offer promising avenues to improve allograft acceptance, while minimizing immunosuppressive toxicity. This review explores novel approaches aiming to achieve long-term immunological tolerance, including co-stimulation blockade, mixed chimerism, regulatory T-cell (Treg) therapies, thymic transplantation, and double-organ transplantation. These strategies seek to promote donor-specific unresponsiveness and mitigate chronic rejection. Additionally, expanding the donor pool remains a critical challenge in addressing organ shortages. Innovations such as ABO-incompatible heart transplantation are revolutionizing the field by increasing donor availability and accessibility. In this article, we discuss the mechanistic basis, clinical advancements, and challenges of these approaches, highlighting their potential to transform the future of heart transplantation with emphasis on clinical translation. Full article

Cancer medications can cause cardiac issues, which are difficult to treat in oncologic patients because of the risk of complications. In some cases, this may significantly impact their well-being and treatment outcomes. Overall, these complications fall under the term “drug induced cardiotoxicity”, mainly due to chemotherapy drugs being specifically toxic to the heart, causing a decrease in the heart’s capacity to pump blood efficiently and leading to a reduction in the left ventricular ejection fraction (LVEF), and subsequently possibly leading to heart failure. Anthracyclines, alkylating agents, and targeted therapies for cancer hold the potential of causing harmful effects on the heart. The incidence of heart-related issues varies from patient to patient and depends on multiple factors, including the type of medication, dosage, duration of the treatment, and pre-existing heart conditions. The underlying mechanism leading to oncologic-drug-induced cardiovascular harmful effects is quite complex. One particular group of drugs, called anthracyclines, have garnered attention due to their impact on oxidative stress and their ability to cause direct harm to heart muscle cells. Reactive oxygen species (ROS) cause harm by inducing damage and programmed cell death in heart cells. Conventional biomarkers alone can only indicate some degree of damage that has already occurred and, therefore, early detection is key. Novel methods like genetic profiling are being developed to detect individuals at risk, prior to the onset of clinical symptoms. Key management strategies—including early detection, personalized medicine approaches, and the use of novel biomarkers—play a crucial role in mitigating cardiotoxicity and improving patient outcomes. Identification of generated genetic alterations and the association to an increased likelihood of cardiotoxicity will allow treatment in a more personalized approach, aiming at decreasing rates of cardiac events while maintaining high oncological efficacy. Oncology drug-induced cardiotoxicity is managed through a combination of preventive strategies and therapeutic interventions from the union of cardiac and oncological knowledge. Full article

Ensemble methods have proven highly effective in enhancing predictive performance by combining multiple models. We introduce a novel ensemble approach, the Random Generalized Additive Logistic Forest (RGALF), which integrates generalized additive models (GAMs) within a random forest framework to improve binary classification tasks. Unlike traditional random forests, which rely on piecewise constant predictions in terminal nodes, RGALF fits GAM logistic regression (LR) models to the data in each terminal node, enabling it to capture complex nonlinear relationships and interactions among predictors. By aggregating these node-specific GAMs, RGALF addresses multicollinearity, enhances interpretability, and achieves superior bias–variance tradeoffs, particularly in nonlinear settings. Theoretical analysis confirms that RGALF achieves Stone’s optimal rates for additive models ($\mathcal{O}(n^{-2k/(2k+d)}$) under appropriate conditions, outperforming the slower convergence of traditional random forests ($\mathcal{O}\left(n^{-2/3}\right)$). Furthermore, empirical results demonstrate RGALF’s effectiveness across both simulated and real-world datasets. In simulations, RGALF demonstrates superior performance over random forests (RFs), reducing variance by up to 69% and bias by 19% in nonlinear settings, with significant MSE improvements (0.032 vs. RF’s 0.054 at $n=1000$), while achieving optimal convergence rates ($\mathcal{O}\left(n^{-0.48}\right)$ vs. RF’s $\mathcal{O}\left(n^{-0.29}\right)$). On real-world medical datasets, RGALF attains near-perfect accuracy and AUC: 100% accuracy/AUC for Heart Failure and Hepatitis C (HCV) prediction, 99% accuracy/100% AUC for Pima Diabetes, and 98.8% accuracy/100% AUC for Indian Liver Patient (ILPD), outperforming state-of-the-art methods. Notably, RGALF captures complex biomarker interactions (BMI–insulin in diabetes) missed by traditional models. Full article

Background: Pharmacotherapy of chronic heart failure (CHF) with a reduced ejection fraction includes a combination of drugs. Often, different groups of drugs are added together for the treatment of concomitant conditions, such as statins, anticoagulants, antiplatelet agents, and cardiac glycosides, which have a narrow therapeutic window. Increased medication intake is a prerequisite for the increased risk of potential adverse drug–drug interactions (DDI), especially those occurring at the pharmacokinetic level. The main objectives of this study are to identify the most common potential pharmacokinetic drug–drug interactions (pPKDDIs), to explore more complex cases, and to simulate and analyze them with appropriate software. Methods: The data selected for the simulations were collected over a two-year period from January 2014 to December 2015. Identification of the pPKDDIs was carried out using Lexicomp Drug interaction, while simulations were performed with Simcyp software (V20, R1). Results: The most common pharmacokinetic mechanisms responsible for the occurrence of drug–drug interactions in the selected drugs with narrow therapeutic windows are those featuring the cytochrome isoforms CYP3A4 and 2C9 and the efflux pump—P-glycoprotein (P-gp). Simulations with the available data in Simcyp software showed possibilities to analyze and evaluate pPKDDIs, which would be difficult to assess without appropriate software, as well as ways to manage them. Conclusions: The frequency and complexity of pPKDDIs in patients with cardiovascular disease are high. Therefore, such patients require a specific approach to reduce these risks as well as to optimize the therapy. An appropriate PBPK software with the necessary database would be suitable in these cases. Full article

The rate of oxygen consumption, which is measured as the volume of oxygen consumed per mass per minute (VO₂) mL/kg/min, is a critical metric for evaluating cardiovascular health, metabolic status, and respiratory function. Specifically, VO₂ is a powerful prognostic predictor of survival in patients with heart failure (HF) because it provides an indirect assessment of a patient’s ability to increase cardiac output (CO). In addition, VO₂ measurements, particularly VO₂ max, are significant because they provide a reliable indicator of your cardiovascular fitness and aerobic endurance. However, traditional VO₂ assessment requires bulky, breath-by-breath gas analysis systems, limiting frequent and continuous monitoring to specialized settings. This study presents a novel wrist-worn multiwavelength photoplethysmography (PPG) device and machine learning algorithm designed to estimate VO₂ continuously. Unlike conventional wearables that rely on static formulas for VO₂ max estimation, our algorithm leverages the data from the PPG wearable and uses the Beer–Lambert Law with inputs from five wavelengths (670 nm, 770 nm, 810 nm, 850 nm, and 950 nm), incorporating the isosbestic point at 810 nm to differentiate oxy- and deoxy-hemoglobin. A validation study was conducted with eight subjects using a modified Bruce protocol, comparing the PPG-based estimates to the gold-standard Parvo Medics gas analysis system. The results demonstrated a mean absolute error of 1.66 mL/kg/min and an R² of 0.94. By providing precise, individualized VO₂ estimates using direct tissue oxygenation data, this wearable solution offers significant clinical and practical advantages over traditional methods, making continuous and accurate cardiovascular assessment readily available beyond clinical environments. Full article