Artificial intelligence in cardiovascular prevention: new ways will open new doors

Epigenetic mechanisms underlying the beneficial effects of cardiac rehabilitation. An overview from the working groups of "cellular and molecular biology of the heart" and "cardiac rehabilitation and cardiovascular prevention" of the Italian Society of Cardiology (SIC)

The benefits of cardiac rehabilitation (CR) have been demonstrated in patients after myocardial infarction (MI), and in patients with chronic heart failure (HF). The core components of the CR program include improvement in exercise tolerance and optimization of coronary risk factors (i.e., lipid and lipoprotein profiles, body weight, blood glucose levels, blood pressure levels, and smoking cessation). Indeed, CR has been shown to improve exercise capacity, control of cardiovascular risk factors, quality of life, hospital readmission, and mortality rates. Nonetheless, pre- and clinical CR and exercise training models are an enormous source of potential beneficial mechanisms that can be exploited for cardiac disease therapy. Consequently, in this review, we aim to explore the unique benefits of CR in HF and coronary artery disease, focusing on the epigenetic mechanisms involved and their translational relevance. These mechanisms may represent novel therapeutic targets to promote functional recovery after cardiac injury, and non-coding RNAs could be predictive biomarkers for CR success in patients.

Subclinical left ventricular dysfunction assessed by global longitudinal strain correlates with mild cognitive impairment in hypertensive patients

Prevention of dementia represents a public health priority. Hypertension is a risk factor for mild cognitive impairment (MCI), a precursor to progressive dementia. A great effort is underway to develop accurate and sensitive tools to detect the MCI condition in hypertensive patients. To investigate the potential association of subclinical left ventricular dysfunction expressed by the global longitudinal strain (GLS) with the MCI, defined by the Italian version of the quick mild cognitive impairment (Qmci-I). This multi-centric study included 180 consecutive hypertensive patients without medical diseases and/or drugs with known significant effects on cognition but with a not negligible comorbidity burden to avoid a possible "hyper-normality bias". The study cohort was classified into two main groups concerning the median value of the GLS. A weighted logistic regression model was employed after an inverse probability of treatment weighting (IPTW) analysis to characterize a potential association between GLS and MCI. Almost 41,1% of the whole study population was female. The mean age was 65,6 ± 7,2. 39 patients (21,7%) showed MCI. After IPTW, the GLS was significantly associated with the study endpoint (OR, 1,22; 95% CI: 1,07-1,39, P = 0.003). Our results highlight that the GLS is a potential predictor of MCI and, therefore, a valuable tool for establishing preventive strategies to arrest the progression toward a cognitive decline in hypertensive patients.

Machine learning based prediction models for cardiovascular disease risk using electronic health records data: systematic review and meta-analysis

Cardiovascular disease (CVD) remains a major cause of mortality in the UK, prompting the need for improved risk predictive models for primary prevention. Machine learning (ML) models utilizing electronic health records (EHRs) offer potential enhancements over traditional risk scores like QRISK3 and ASCVD. To systematically evaluate and compare the efficacy of ML models against conventional CVD risk prediction algorithms using EHR data for medium to long-term (5-10 years) CVD risk prediction. A systematic review and random-effect meta-analysis were conducted according to preferred reporting items for systematic reviews and meta-analyses guidelines, assessing studies from 2010 to 2024. We retrieved 32 ML models and 26 conventional statistical models from 20 selected studies, focusing on performance metrics such as area under the curve (AUC) and heterogeneity across models. ML models, particularly random forest and deep learning, demonstrated superior performance, with the highest recorded pooled AUCs of 0.865 (95% CI: 0.812-0.917) and 0.847 (95% CI: 0.766-0.927), respectively. These significantly outperformed the conventional risk score of 0.765 (95% CI: 0.734-0.796). However, significant heterogeneity (I² > 99%) and potential publication bias were noted across the studies. While ML models show enhanced calibration for CVD risk, substantial variability and methodological concerns limit their current clinical applicability. Future research should address these issues by enhancing methodological transparency and standardization to improve the reliability and utility of these models in clinical settings. This study highlights the advanced capabilities of ML models in CVD risk prediction and emphasizes the need for rigorous validation to facilitate their integration into clinical practice.

Retrospective Study on Short-Term Reverse Cardiac Remodeling in Obese Patients Undergoing Sleeve Gastrectomy

Severe obesity is closely associated with an increased risk of comorbidities and alterations in cardiac structure and function. The primary objective of this study was to investigate cardiovascular (CV) risk factors and ventricular remodeling in individuals from an obese population eligible for bariatric surgery. The secondary objective was to evaluate changes in anthropometric, clinical laboratory, and echocardiographic measurements 12 weeks after surgery compared to baseline values. This retrospective observational cohort study involved patients from a single specialized bariatric surgery center. A total of 35 patients were included (mean age 41.5 ± 10.3 years; BMI 43.4 ± 6.6 kg/m2), of whom 34.2% had a family history of coronary artery disease (CAD), 5.7% had a prior history of CAD, 8 had essential hypertension, 11.4% had dyslipidemia, 20% were smokers, and 8.6% were former smokers. Approximately 57% of the patients exhibited concentric left ventricular remodeling, and 14% had grade I diastolic dysfunction. At 12 weeks post-surgery, with an average weight loss of 25 kg and a mean BMI reduction of 8.5 kg/m2, 14% of the patients still exhibited concentric left ventricular remodeling, and about 11% had grade I diastolic dysfunction. Bariatric surgery contributes to the improvement of cardiac function and structure over time as a result of significant weight loss.

The effects of Dapagliflozin in a real-world population of HFrEF patients with different hemodynamic profiles: worse is better

BACKGROUND

Sodium-Glucose Cotransporter-2 inhibitors (SGLT2i) represent a deep revolution of the therapeutic approach to heart failure (HF), preventing its insurgence but also improving the management of the disease and slowing its natural progression. To date, few studies have explored the effectiveness of SGLT2i and, in particular, Dapagliflozin in a real-world population. Therefore, in this observational prospective study, we evaluated Dapagliflozin's effectiveness in a real-world HF population categorized in the different hemodynamic profiles.

METHODS

From January 2022 to June 2023, we enrolled 240 patients with chronic HF and reduced ejection fraction (HFrEF) on optimal medical therapy, according to 2021 ESC guidelines, that added treatment with Dapagliflozin from the HF Clinics of 6 Italian University Hospitals. Clinical, biochemical, and echocardiographic parameters were collected before and after 6 months of Dapagliflozin introduction. Moreover, the HFrEF population was classified according to hemodynamic profiles (A: SV ≥ 35 ml/m2; E/e' < 15; B: SV ≥ 35 ml/m2; E/e' ≥ 15; C: SV < 35 ml/m2; E/e' < 15; D: SV < 35 ml/m2; E/e' ≥ 15). Then, we compared the Dapagliflozin population with two retrospective HF cohorts, hereinafter referred to as Guide Line 2012 (GL 2012) group and Guide Line 2016 (GL 2016) group, in accordance with the HF ESC guidelines in force at the time of patients enrolment. Precisely, we evaluated the changes to baseline in clinical, functional, biochemical, and echocardiographic parameters and compared them to the GL 2012 and GL 2016 groups.

RESULTS

Dapagliflozin population (67.18 ± 11.11 years) showed a significant improvement in the echocardiographic and functional parameters (left ventricular ejection fraction [LVEF], LV end-diastolic volume [LVEDV], LVEDV index, stroke volume index [SVi], left atrium volume index [LAVi], filling pressure [E/e' ratio], tricuspid annular plane systolic excursion [TAPSE], tricuspid annular S' velocity [RVs'], fractional area change [FAC], inferior vena cava [IVC diameter], pulmonary artery systolic pressure [sPAP], NYHA class, and quality of life) compared to baseline. In particular, TAPSE and right ventricle diameter (RVD1) ameliorate in congestive profiles (B and D); accordingly, the furosemide dose significantly decreased in these profiles. Comparing the three populations, the analysis of echocardiographic parameters (baseline vs follow-up) highlighted a significant decrease of sPAP in the Dapagliflozin population (p < 0.05), while no changes were recorded in the GL 2012 and GL 2016 population. Moreover, at the baseline evaluation, the GL 2012 and 2016 groups needed a higher significant dose of furosemide compared to Dapagliflozin group. Finally, Dapagliflozin patients had significantly fewer rehospitalizations (1.25%) compared with the other two groups (GL 2012 18.89%, p 0.0097; GL 2016 15.32%, p 0.0497).

CONCLUSIONS

We demonstrate that Dapagliflozin is rapidly effective in an HFrEF real-world population; furthermore, the more significant effect is recorded in HFrEF patients with a congestive profile (B and D), supporting the introduction of Dapagliflozin in patients with a congestive profile and a worse prognosis. In conclusion, our data suggest evaluating the patient's hemodynamic state beyond LVEF in HFrEF.

From the armchair to contemporary cardiac rehabilitation: the remarkable ongoing journey of exercise training in ischemic heart disease

Exercise is an important physiological activity with several health benefits. In the setting of ischemic heart disease (IHD), the view toward exercise has greatly evolved throughout the years, concurrently to several major advances in the management of this complex entity. Currently, exercise training has broad applications across the IHD continuum as a powerful tool in its overall management, being a core component of comprehensive cardiac rehabilitation programs. Beyond this, exercise has also been incorporated as an integral part of contemporary methodologies aiming to provide diagnostic and prognostic data, such as cardiopulmonary exercise stress testing or stress echocardiography. In this article, we provide a pragmatic overview concerning the role of exercise in IHD, with a focus on its incorporation in cardiac rehabilitation frameworks, while also discussing some of the challenges and unmet needs concerning these interventions.

The favourable alliance between CardioMEMS and levosimendan in patients with advanced heart failure

AIMS

We report the results of a real-world study based on heart failure (HF) patients' continuous remote monitoring strategy using the CardioMEMS system to assess the impact of this device on healthcare outcomes, costs, and patients' management and quality of life.

METHODS AND RESULTS

We enrolled seven patients (69.00 ± 4.88 years; 71.43% men) with HF, implanted with CardioMEMS, and daily remote monitored to optimize both tailored adjustments of home therapy and/or hospital infusions of levosimendan. We recorded clinical, pharmacological, biochemical, and echocardiographic parameters and data on hospitalizations, emergency room access, visits, and costs. Following the implantation of CardioMEMS, we observed a 50% reduction in the total number of hospitalizations and a 68.7% reduction in the number of days in the hospital. Accordingly, improved patient quality of life was recorded with EQ-5D (pre 58.57 ± 10.29 vs. 1 year post 84.29 ± 19.02, P = 0.008). Echocardiographic data show a statistically significant improvement in both systolic pulmonary artery pressure (47.86 ± 8.67 vs. 35.14 ± 9.34, P = 0.022) and E/e' (19.33 ± 5.04 vs. 12.58 ± 3.53, P = 0.023). The Quantikine® HS High-Sensitivity Kit determined elevated interleukin-6 values at enrolment in all patients, with a statistically significant reduction after 6 months (P = 0.0211). From an economic point of view, the net savings, including the cost of CardioMEMS, were on average €1580 per patient during the entire period of observation, while the analysis performed 12 months after the implant vs. 12 months before showed a net saving of €860 per patient. The ad hoc analysis performed on the levosimendan infusions resulted in 315 days of hospital avoidance and a saving of €205 158 for the seven patients enrolled during the observation period.

CONCLUSIONS

This innovative strategy prevents unplanned access to the hospital and contributes to the efficient use of healthcare facilities, human resources, and costs.

Chemotherapy Related Cardiotoxicity Evaluation-A Contemporary Review with a Focus on Cardiac Imaging

The long-term survivorship of patients diagnosed with cancer has improved due to accelerated detection and rapidly evolving cancer treatment strategies. As such, the evaluation and management of cancer therapy related complications has become increasingly important, including cardiovascular complications. These have been captured under the umbrella term "cardiotoxicity" and include left ventricular dysfunction and heart failure, acute coronary syndromes, valvular abnormalities, pericardial disease, arrhythmia, myocarditis, and vascular complications. These complications add to the burden of cardiovascular disease (CVD) or are risk factors patients with cancer treatment are presenting with. Of note, both pre- and newly developing CVD is of prognostic significance, not only from a cardiovascular perspective but also overall, potentially impacting the level of cancer therapy that is possible. Currently, there are varying recommendations and practices regarding CVD risk assessment and mitigating strategies throughout the cancer continuum. This article provides an overview on this topic, in particular, the role of cardiac imaging in the care of the patient with cancer. Furthermore, it summarizes the current evidence on the spectrum, prevention, and management of chemotherapy-related adverse cardiac effects.

A Neuronal Network-Based Score Predicting Survival in Patients Undergoing Aortic Valve Intervention: The ABC-AS Score

Background: Despite being the most commonly performed valvular intervention, risk prediction for aortic valve replacement in patients with severe aortic stenosis by currently used risk scores remains challenging. The study aim was to develop a biomarker-based risk score by means of a neuronal network. Methods: In this multicenter study, 3595 patients were divided into test and validation cohorts (70% to 30%) by random allocation. Input variables to develop the ABC-AS score were age, the cardiac biomarker high-sensitivity troponin T, and a patient history of cardiac decompensation. The validation cohort was used to verify the scores' value and for comparison with the Society of Thoracic Surgery Predictive Risk of Operative Mortality score. Results: Receiver operating curves demonstrated an improvement in prediction by using the ABC-AS score compared to the Society of Thoracic Surgery Predictive Risk of Operative Mortality (STS prom) score. Although the difference in predicting cardiovascular mortality was most notable at 30-day follow-up (area under the curve of 0.922 versus 0.678), ABC-AS also performed better in overall follow-up (0.839 versus 0.699). Furthermore, univariate analysis of ABC-AS tertiles yielded highly significant differences for all-cause (p < 0.0001) and cardiovascular mortality (p < 0.0001). Head-to-head comparison between both risk scores in a multivariable cox regression model underlined the potential of the ABC-AS score (HR per z-unit 2.633 (95% CI 2.156-3.216), p < 0.0001), while the STS prom score failed to reach statistical significance (p = 0.226). Conclusions: The newly developed ABC-AS score is an improved risk stratification tool to predict cardiovascular outcomes for patients undergoing aortic valve intervention.

Revolutionizing Cardiology through Artificial Intelligence-Big Data from Proactive Prevention to Precise Diagnostics and Cutting-Edge Treatment-A Comprehensive Review of the Past 5 Years

BACKGROUND

Artificial intelligence (AI) can radically change almost every aspect of the human experience. In the medical field, there are numerous applications of AI and subsequently, in a relatively short time, significant progress has been made. Cardiology is not immune to this trend, this fact being supported by the exponential increase in the number of publications in which the algorithms play an important role in data analysis, pattern discovery, identification of anomalies, and therapeutic decision making. Furthermore, with technological development, there have appeared new models of machine learning (ML) and deep learning (DP) that are capable of exploring various applications of AI in cardiology, including areas such as prevention, cardiovascular imaging, electrophysiology, interventional cardiology, and many others. In this sense, the present article aims to provide a general vision of the current state of AI use in cardiology.

RESULTS

We identified and included a subset of 200 papers directly relevant to the current research covering a wide range of applications. Thus, this paper presents AI applications in cardiovascular imaging, arithmology, clinical or emergency cardiology, cardiovascular prevention, and interventional procedures in a summarized manner. Recent studies from the highly scientific literature demonstrate the feasibility and advantages of using AI in different branches of cardiology.

CONCLUSIONS

The integration of AI in cardiology offers promising perspectives for increasing accuracy by decreasing the error rate and increasing efficiency in cardiovascular practice. From predicting the risk of sudden death or the ability to respond to cardiac resynchronization therapy to the diagnosis of pulmonary embolism or the early detection of valvular diseases, AI algorithms have shown their potential to mitigate human error and provide feasible solutions. At the same time, limits imposed by the small samples studied are highlighted alongside the challenges presented by ethical implementation; these relate to legal implications regarding responsibility and decision making processes, ensuring patient confidentiality and data security. All these constitute future research directions that will allow the integration of AI in the progress of cardiology.

Cardio-oncology rehabilitation: are we ready?

Cardio-oncology rehabilitation (CORE) is not only an essential component of cancer rehabilitation but also a pillar of preventive cardio-oncology. Cardio-oncology rehabilitation is a comprehensive model based on a multitargeted approach and its efficacy has been widely documented; when compared with an 'exercise only' programme, comprehensive CORE demonstrates a better outcome. It involves nutritional counselling, psychological support, and cardiovascular (CV) risk assessment, and it is directed to a very demanding population with a heavy burden of CV diseases driven by physical inactivity, cancer therapy-induced metabolic derangements, and cancer therapy-related CV toxicities. Despite its usefulness, CORE is still underused in cancer patients and we are still at the dawning of remote models of rehabilitation (tele-rehabilitation). Not all CORE is created equally: a careful screening procedure to identify patients who will benefit the most from CORE and a multidisciplinary customized approach are mandatory to achieve a better outcome for cancer survivors throughout their cancer journey. The aim of this paper is to provide an updated review of CORE not only for cardiologists dealing with this peculiar population of patients but also for oncologists, primary care providers, patients, and caregivers. This multidisciplinary team should help cancer patients to maintain a healthy and active life before, during, and after cancer treatment, in order to improve quality of life and to fight health inequities.

Early Diagnosis of Cardiovascular Diseases in the Era of Artificial Intelligence: An In-Depth Review

Cardiovascular diseases (CVDs) are significant health issues that result in high death rates globally. Early detection of cardiovascular events may lower the occurrence of acute myocardial infarction and reduce death rates in people with CVDs. Traditional data analysis is inadequate for managing multidimensional data related to the risk prediction of CVDs, heart attacks, medical image interpretations, therapeutic decision-making, and disease prognosis due to the complex pathological mechanisms and multiple factors involved. Artificial intelligence (AI) is a technology that utilizes advanced computer algorithms to extract information from large databases, and it has been integrated into the medical industry. AI methods have shown the ability to speed up the advancement of diagnosing and treating CVDs such as heart failure, atrial fibrillation, valvular heart disease, hypertrophic cardiomyopathy, congenital heart disease, and more. In clinical settings, AI has shown usefulness in diagnosing cardiovascular illness, improving the efficiency of supporting tools, stratifying and categorizing diseases, and predicting outcomes. Advanced AI algorithms have been intricately designed to analyze intricate relationships within extensive healthcare data, enabling them to tackle more intricate jobs compared to conventional approaches.

Interventions to Address Cardiovascular Risk in Obese Patients: Many Hands Make Light Work

Obesity is a growing public health epidemic worldwide and is implicated in slowing improved life expectancy and increasing cardiovascular (CV) risk; indeed, several obesity-related mechanisms drive structural, functional, humoral, and hemodynamic heart alterations. On the other hand, obesity may indirectly cause CV disease, mediated through different obesity-associated comorbidities. Diet and physical activity are key points in preventing CV disease and reducing CV risk; however, these strategies alone are not always sufficient, so other approaches, such as pharmacological treatments and bariatric surgery, must support them. Moreover, these strategies are associated with improved CV risk factors and effectively reduce the incidence of death and CV events such as myocardial infarction and stroke; consequently, an individualized care plan with a multidisciplinary approach is recommended. More precisely, this review explores several interventions (diet, physical activity, pharmacological and surgical treatments) to address CV risk in obese patients and emphasizes the importance of adherence to treatments.