Extracellular Vesicles to Predict Outcomes After Transcatheter Aortic Valve Implantation - a Prospective, Multicenter Cohort Study.

INTRODUCTION: Transcatheter aortic valve implantation (TAVI) is an established treatment for aortic stenosis (AS) in patients at intermediate and high surgical risk. Circulating extracellular vesicles (EVs) are nanoparticles involved in cardiovascular diseases. We aimed to (i) determine the effect of TAVI on plasma concentrations of five EV subtypes and (ii) evaluate the predictive value of EVs for post-TAVI outcomes. METHODS: Blood samples were collected 1 day before TAVI and at hospital discharge. Concentrations of EVs were evaluated using flow cytometry. RESULTS: Concentration of leukocytes EVs decreased after TAVI, compared to the measurement before (p = 0.008). Among 123 patients discharged from the hospital, 19.5% experienced MACCE during the median of 10.3 months. Increased pre-TAVI concentration of phosphatidylserine-exposing EVs was an independent predictor of MACCE in multivariable analysis (OR 5.313, 95% CI 1.164-24.258, p = 0.031). CONCLUSIONS: Patients with increased pre-TAVI concentration of procoagulant, PS-exposing EVs have over fivefold higher odds of adverse outcomes.

Can transesophageal echocardiography be safely omitted in patients scheduled for elective ablation of atrial arrhythmias? Data based on the LATTEE registry.

BACKGROUND: According to the present guidelines, transesophageal echocardiography (TEE) before scheduled catheter ablation (CA) for atrial arrhythmias (atrial fibrillation [AF] or atrial flutter [AFL]) is not deemed obligatory for optimally anticoagulated patients. However, daily clinical practice significantly differs from the recommendations. AIMS: We aimed to identify transthoracic echocardiographic parameters that could be useful in identifying patients without left atrial thrombus (LAT), which makes it possible to avoid unnecessary TEE before scheduled CA. METHODS: This is a sub-analysis of a multicenter, prospective, observational study - the LATTEE registry. A total of 1346 patients referred for TEE before scheduled CA of AF/AFL were included. RESULTS: LAT was present in 44 patients (3.3%) and absent in the remaining 1302, who were younger, more likely to have paroxysmal AF, and displayed sinus rhythm during TEE. Additionally, they exhibited a lower incidence of heart failure, diabetes, systemic connective tissue disease, and chronic obstructive pulmonary disease. Furthermore, they had a lower CHA2DS2-VASc score and a higher prevalence of direct oral anticoagulants. Echocardiographic parameters, including left ventricular ejection fraction (LVEF) >65%, left atrial diameter (LAD) <40 mm, left atrial area (LAA) <20 cm2, left atrial volume (LAV) <113 ml, and left atrial volume index (LAVI) <51 ml/m2, demonstrated 100% sensitivity and 100% negative predictive value for the absence of LAT and were met by 417 patients. Additional echocardiographic indices: LVEF/LAD ≥1.4, LVEF/LAVI ≥1.6, and LVEF/LAA ≥2.7 identified 57 additional patients, bringing the total of predicted LAT-free patients to 474 (35%). CONCLUSIONS: Simple echocardiographic parameters could help identify individuals for whom TEE could be safely omitted before elective CA due to atrial arrhythmias.

Predictors of Revascularization in Patients with Unstable Angina.

BACKGROUND: The factors that determine the necessity of coronary artery revascularization in patients with unstable angina (UA) have been supported by limited data. Therefore, this study aimed to identify the predictors of revascularization in patients with UA. METHODS: The study included the recorded data of 3668 patients with UA who underwent cardiac catheterization (age 66 ± 9.2, men 70%); 2615 of them (71%) underwent revascularization (percutaneous transluminal coronary angioplasty (PTCA), coronary artery bypass graft (CABG), or hybrid revascularization. The remaining 1053 patients (29%) had no significant coronary stenosis and were regarded as controls. Multivariable logistic regression analysis was performed to separate the predictors of revascularization. RESULTS: It was found that severe angina (OR 2.7, 95%CI 1.9-3.7), male gender (OR 1.4, 95%CI 1.1-1.7), and hyperlipidemia were the predictors of revascularization. It was also noted that intraventricular conduction disorders including left and right bundle branch blocks and a history of previous revascularization and myocardial infarction were associated with lower odds of revascularization. CONCLUSION: Overall, however, the predictive value of the studied factors proved to be poor and may still point to the multifactorial nature of significant coronary artery stenosis and the need for revascularization in patients with UA.

Recommendations of the Experts of the Polish Cardiac Society (PCS) and the Polish Lipid Association (PoLA) on the diagnosis and management of elevated lipoprotein(a) levels.

Lipoprotein(a) [Lp(a)] is made up of a low-density lipoprotein (LDL) particle and a specific apolipoprotein(a). The blood concentration of Lp(a) is approximately 90% genetically determined, and the main genetic factor determining Lp(a) levels is the size of the apo(a) isoform, which is determined by the number of KIV2 domain repeats. The size of the apo(a) isoform is inversely proportional to the blood concentration of Lp(a). Lp(a) is a strong and independent cardiovascular risk factor. Elevated Lp(a) levels ≥ 50 mg/dl (≥ 125 nmol/l) are estimated to occur in more than 1.5 billion people worldwide. However, determination of Lp(a) levels is performed far too rarely, including Poland, where, in fact, it is only since the 2021 guidelines of the Polish Lipid Association (PoLA) and five other scientific societies that Lp(a) measurements have begun to be performed. Determination of Lp(a) concentrations is not easy due to, among other things, the different sizes of the apo(a) isoforms; however, the currently available certified tests make it possible to distinguish between people with low and high cardiovascular risk with a high degree of precision. In 2022, the first guidelines for the management of patients with elevated lipoprotein(a) levels were published by the European Atherosclerosis Society (EAS) and the American Heart Association (AHA). The first Polish guidelines are the result of the work of experts from the two scientific societies and their aim is to provide clear, practical recommendations for the determination and management of elevated Lp(a) levels.

Artificial intelligence in detecting left atrial appendage thrombus by transthoracic echocardiography and clinical features: the Left Atrial Thrombus on Transoesophageal Echocardiography (LATTEE) registry.

AIMS: Transoesophageal echocardiography (TOE) is often performed before catheter ablation or cardioversion to rule out the presence of left atrial appendage thrombus (LAT) in patients on chronic oral anticoagulation (OAC), despite associated discomfort. A machine learning model [LAT-artificial intelligence (AI)] was developed to predict the presence of LAT based on clinical and transthoracic echocardiography (TTE) features. METHODS AND RESULTS: Data from a 13-site prospective registry of patients who underwent TOE before cardioversion or catheter ablation were used. LAT-AI was trained to predict LAT using data from 12 sites (n = 2827) and tested externally in patients on chronic OAC from two sites (n = 1284). Areas under the receiver operating characteristic curve (AUC) of LAT-AI were compared with that of left ventricular ejection fraction (LVEF) and CHA2DS2-VASc score. A decision threshold allowing for a 99% negative predictive value was defined in the development cohort. A protocol where TOE in patients on chronic OAC is performed depending on the LAT-AI score was validated in the external cohort. In the external testing cohort, LAT was found in 5.5% of patients. LAT-AI achieved an AUC of 0.85 [95% confidence interval (CI): 0.82-0.89], outperforming LVEF (0.81, 95% CI 0.76-0.86, P < .0001) and CHA2DS2-VASc score (0.69, 95% CI: 0.63-0.7, P < .0001) in the entire external cohort. Based on the proposed protocol, 40% of patients on chronic OAC from the external cohort would safely avoid TOE. CONCLUSION: LAT-AI allows accurate prediction of LAT. A LAT-AI-based protocol could be used to guide the decision to perform TOE despite chronic OAC.

Machine learning model for predicting late recurrence of atrial fibrillation after catheter ablation.

Late recurrence of atrial fibrillation (LRAF) in the first year following catheter ablation is a common and significant clinical problem. Our study aimed to create a machine-learning model for predicting arrhythmic recurrence within the first year since catheter ablation. The study comprised 201 consecutive patients (age: 61.8 ± 8.1; women 36%) with paroxysmal, persistent, and long-standing persistent atrial fibrillation (AF) who underwent cryoballoon (61%) and radiofrequency ablation (39%). Five different supervised machine-learning models (decision tree, logistic regression, random forest, XGBoost, support vector machines) were developed for predicting AF recurrence. Further, SHapley Additive exPlanations were derived to explain the predictions using 82 parameters based on clinical, laboratory, and procedural variables collected from each patient. The models were trained and validated using a stratified fivefold cross-validation, and a feature selection was performed with permutation importance. The XGBoost model with 12 variables showed the best performance on the testing cohort, with the highest AUC of 0.75 [95% confidence interval 0.7395, 0.7653]. The machine-learned model, based on the easily available 12 clinical and laboratory variables, predicted LRAF with good performance, which may provide a valuable tool in clinical practice for better patient selection and personalized AF strategy following the procedure.

Cognitive Impairment in Cardiovascular Patients after Myocardial Infarction: Prospective Clinical Study.

(1) Background: Assessment of cognitive function is not routine in cardiac patients, and knowledge on the subject remains limited. The aim of this study was to assess post-myocardial infarction (MI) cognitive functioning in order to determine the frequency of cognitive impairment (CI) and to identify factors that may influence it. (2) Methods: A prospective study included 468 patients hospitalized for MI. Participants were assessed twice: during the first hospitalization and 6 months later. The Mini-Mental State Examination was used to assess the occurrence of CI. (3) Results: Cognitive dysfunction based on the MMSE was found in 37% (N-174) of patients during the first hospitalization. After 6 months, the prevalence of deficits decreased significantly to 25% (N-91) (p < 0.001). Patients with CI significantly differed from those without peri-infarction deficits in the GFR, BNP, ejection fraction and SYNTAX score, while after 6 months, significant differences were observed in LDL and HCT levels. There was a high prevalence of non-cognitive mental disorders among post-MI patients. (4) Conclusions: There is a high prevalence of CI and other non-cognitive mental disorders, such as depression, sleep disorders and a tendency to aggression, among post-MI patients. The analysis of the collected material indicates a significant impact of worse cardiac function expressed as EF and BNP, greater severity of coronary atherosclerosis expressed by SYNTAX results, and red blood cell parameters and LDL levels on the occurrence of CI in the post-MI patient population.

Echocardiographic predictors of thrombus in left atrial appendage-The role of novel transthoracic parameters.

Introduction: The left atrium appendage thrombus (LAAT) formation is a complex process. A CHA2DS2-VASc scale is an established tool for determining the thromboembolic risk and initiation of anticoagulation treatment in patients with atrial fibrillation or flutter (AF/AFL). We aimed to identify whether any transthoracic echocardiography (TTE) parameters could have an additional impact on LAAT detection. Methods: That is a sub-study of multicenter, prospective, observational study LATTEE (NCT03591627), which enrolled 3,109 consecutive patients with AF/AFL referred for transesophageal echocardiography (TEE) before cardioversion or ablation. Results: LAAT was diagnosed in 8.0% of patients. The univariate logistic regression analysis [based on pre-specified in the receiver operating characteristic (ROC) analysis cut-off values with AUC ≥ 0.7] identified left ventricular ejection fraction (LVEF) ≤ 48% and novel TTE parameters i.e., the ratios of LVEF and left atrial diameter (LAD) ≤ 1.1 (AUC 0.75; OR 5.64; 95% CI 4.03-7.9; p < 0.001), LVEF to left atrial area (LAA) ≤ 1.7 (AUC 0.75; OR 5.64; 95% CI 4.02-7.9; p < 0.001), and LVEF to indexed left atrial volume (LAVI) ≤ 1.1 (AUC 0.75, OR 6.77; 95% CI 4.25-10.8; p < 0.001) as significant predictors of LAAT. In a multivariate logistic regression analysis, LVEF/LAVI and LVEF/LAA maintained statistical significance. Calculating the accuracy of the abovementioned ratios according to the CHA2DS2-VASc scale values revealed their highest predictive power for LAAT in a setting with low thromboembolic risk. Conclusion: Novel TTE indices could help identify patients with increased probability of the LAAT, with particular applicability for patients at low thromboembolic risk.

Beneficial effects of empagliflozin on hematocrit levels in a patient with severe anemia.

INTRODUCTION: Sodium-glucose cotransporter (SGLT2) inhibitors may additionally benefit patients with diabetes by improving their erythropoiesis followed by the elevation of hemoglobin and hematocrit levels. REASON FOR THE REPORT: In the case described, severe normocytic normochromic anemia was resolved when empagliflozin had been introduced to the therapy. A 78-year-old male patient was admitted to our hospital with a non-ST-segment elevation myocardial infarction. His past medical history included diabetes, right coronary artery angioplasty, myocardial infarction and paroxysmal atrial fibrillation which required anticoagulant treatment. When examined, severe normocytic normochromic anemia was also diagnosed. About two years prior to his admission, the patient began suffering from persistent anemia despite the modification of his anticoagulant therapy with warfarin, rivaroxaban and dabigatran. An extensive evaluation failed to provide an explanation for his anemia. OUTCOME: Eventually, only the introduction of empagliflozin successfully increased the values of hemoglobin and hematocrit. Therefore, it transpires that SGLT2 enhances erythropoietin (EPO) secretion which subsequently raises hematocrit levels in patients with severe anemia.

Predictors of Early-Recurrence Atrial Fibrillation after Catheter Ablation in Women and Men with Abnormal Body Weight.

Our study aimed to select factors that affect the rate of early recurrence (up to 3 months) of atrial fibrillation (AF) (ERAF) following pulmonary veins isolation (PVI) in obese women and men. The study comprised 114 patients: 54 women (age: 63.8 ± 6.3, BMI 31 ± 4 kg/m2), and 60 men (age: 60.7 ± 6.7; BMI 31 ± 3 kg/m2) with paroxysmal, persistent and long-standing persistent AF. They had been scheduled to undergo cryoballoon (men n = 30; women n = 30) and radiofrequency (RF) ablation (men n = 30; women n = 24) using the CARTO-mapping. The blood was collected at baseline and 24 h after ablation. The rate of ERAF was comparable after cryoballoon and RF ablation and constituted 18% in women and 22% in men. Almost 70 parameters were selected to perform univariate and multivariate analysis and to create a multivariate logistic regression (MLR) model of ERAF in the obese men and women. The MLR analysis was performed by forward stepwise logistic regression with three variables. It was only possible to create the MLR model for the group of obese men. It revealed a poor predictive value with an unsatisfactory sensitivity of 31%. Men with ERAF: smokers (OR 39.25, 95% CI 1.050-1467.8, p = 0.0021), with a higher ST2 elevation (OR 1.68, 95% CI 1.115-2.536, p = 0.0021) who received dihydropyridine calcium channel blockers (OR 0.042, 95% CI 0.002-1.071, p = 0.0021) less frequently. Our results indicate a complex pathogenesis of ERAF dependent on the patients' gender.

Patients with Non-Obstructive Coronary Artery Disease Require Strict Control of All Cardiovascular Risk Factors: Results from the Polish Local Population Medical Records.

The aim of the project was to compare patients treated with percutaneous transluminal coronary angioplasty (PTCA), who also had undergone PTCA in the past, with a group of people who had had no angiographic stenosis in the lumen of the coronary arteries in the past, and who also required PTCA during index hospitalization. The secondary aim was to compare the obtained data with the characteristics of a group of people who had undergone angiography twice and for whom no significant stenosis had been found in their coronary arteries. The study used registry data concerning 3085 people who had undergone at least two invasive procedures. Acute coronary syndrome (ACS) was significantly more often observed (Non-ST-segment elevation myocardial infarction (NSTEMI) OR 2.76 [1.91-3.99] and ST-segment elevation myocardial infarction (STEMI) OR 2.35 [1.85-2.99]) in patients with no significant coronary stenosis in the past (who required coronary angioplasty at the time of the study), compared to patients who had already had PTCA. They also demonstrated more frequent occurrence of 'multivessel disease'. This was probably most likely caused by inadequate control of cardiovascular risk factors, as determined by higher total cholesterol levels ([mg/dL] 193.7 ± 44.4 vs. 178.2 ± 43.7) and LDL (123.4 ± 36.2 vs. 117.7 ± 36.2). On the other hand, patients in whom no significant stenosis was found in two consecutive angiographies were more likely to be burdened with chronic obstructive pulmonary disease, atrial fibrillation and chronic kidney disease.

Clinical applications of artificial intelligence in cardiology on the verge of the decade.

Artificial intelligence (AI) has been hailed as the fourth industrial revolution and its influence on people's lives is increasing. The research on AI applications in medicine is progressing rapidly. This revolution shows promise for more precise diagnoses, streamlined workflows, increased accessibility to healthcare services and new insights into ever-growing population-wide datasets. While some applications have already found their way into contemporary patient care, we are still in the early days of the AI-era in medicine. Despite the popularity of these new technologies, many practitioners lack an understanding of AI methods, their benefits, and pitfalls. This review aims to provide information about the general concepts of machine learning (ML) with special focus on the applications of such techniques in cardiovascular medicine. It also sets out the current trends in research related to medical applications of AI. Along with new possibilities, new threats arise - acknowledging and understanding them is as important as understanding the ML methodology itself. Therefore, attention is also paid to the current opinions and guidelines regarding the validation and safety of AI-powered tools.

The risk for subsequent coronary interventions in a local Polish population.

INTRODUCTION: Paradoxically, the literature lacks an assessment of the impact of various factors on subsequent coronary interventions in patients with coronary artery disease (CAD). AIM: To assess the impact of various factors on subsequent percutaneous transluminal coronary angioplasty (PTCA), as well as to characterize the clinical profile of people undergoing repeated diagnostic coronary angiography without significant coronary artery changes. MATERIAL AND METHODS: We investigated retrospective data from 4041 subjects according to the clinical factors which may affect the occurrence of unplanned future PTCA. RESULTS: The strongest risk factors for subsequent PTCA were significant stenosis of left descending artery (OR = 2.17, 95% CI: 1.09-4.32) during baseline coronary angiography, the atherosclerotic burden (number of critically narrowed vessels) (OR for narrowing lesions in 3 epicardial arteries 12.13, 95% CI: 5.40-27.27), and restenosis in a previously implanted stent (OR = 4.34, 95% CI: 1.96-9.62). A strong positive relationship between total mortality and the number of critically narrowed coronary arteries (during baseline hospitalization) was observed. Patients without significant coronary artery stenosis in two diagnostic angiographies (control group) differed from subjects with hemodynamic relevant CAD in: higher creatinine levels, more frequent presence of chronic obstructive pulmonary disease and more frequent symptoms of intermittent claudication. CONCLUSIONS: The results of the study are in accord with real clinical practice. The arteriosclerotic burden is a major cause of recurrent PTCA, but an important clinical issue is the qualification for recurrent coronary-angiography in those patients whose previous coronary angiography did not show significant stenosis, because other clinical causes may explain their symptoms.

Predicting Long-Term Mortality after Acute Coronary Syndrome Using Machine Learning Techniques and Hematological Markers.

INTRODUCTION: Hematological indices including red cell distribution width and neutrophil to lymphocyte ratio are proven to be associated with outcomes of acute coronary syndrome. The usefulness of machine learning techniques in predicting mortality after acute coronary syndrome based on such features has not been studied before. OBJECTIVE: We aim to create an alternative risk assessment tool, which is based on easily obtainable features, including hematological indices and inflammation markers. PATIENTS AND METHODS: We obtained the study data from the electronic medical records of 5053 patients hospitalized with acute coronary syndrome during a 5-year period. The time of follow-up ranged from 12 to 72 months. A machine learning classifier was trained to predict death during hospitalization and within 180 and 365 days from admission. Our method was compared with the Global Registry of Acute Coronary Events (GRACE) Score 2.0 on a test dataset. RESULTS: For in-hospital mortality, our model achieved a c-statistic of 0.89 while the GRACE score 2.0 achieved 0.90. For six-month mortality, the results of our model and the GRACE score on the test set were 0.77 and 0.73, respectively. Red cell distribution width (HR 1.23; 95% CL 1.16-1.30; P < 0.001) and neutrophil to lymphocyte ratio (HR 1.08; 95% CL 1.05-1.10; P < 0.001) showed independent association with all-cause mortality in multivariable Cox regression. CONCLUSIONS: Hematological markers, such as neutrophil count and red cell distribution width have a strong association with all-cause mortality after acute coronary syndrome. A machine-learned model which uses the abovementioned parameters can provide long-term predictions of accuracy comparable or superior to well-validated risk scores.

Predictors of atrial fibrillation early recurrence following cryoballoon ablation of pulmonary veins using statistical assessment and machine learning algorithms.

Inflammation, oxidative stress, myocardial injury biomarkers and clinical parameters (longer AF duration, left atrial enlargement, the metabolic syndrome) are factors commonly related to AF recurrence. This study aims to assess the predictive value of laboratory and clinical parameters responsible for early recurrence of atrial fibrillation (ERAF) following cryoballoon ablation (CBA) using statistical assessment and machine learning algorithms. This study group comprised 118 consecutive patients (mean age, 62.5 ± 7.8 years; women 36%) with paroxysmal (54.1%) and persistent (45.9%) AF who underwent their first pulmonary vein isolation (PVI) performed by CBA (Arctic Front Advance 2nd generation 28 mm). The biomarker concentrations were measured at baseline and after CBA in a 24-h follow-up. ERAF was defined as at least a 30-s episode of arrhythmia registered by a 24 h-Holter monitor within the 3 months following the procedure. 56 clinical, laboratory and procedural variables were collected from each patient. We used two classification algorithms: support vector machines, gradient boosted tree. The synthetic minority over-sampling technique (SMOTE) was used to provide a balanced training data set. Within a period of 3 months 21 patients (17.8%) experienced ERAF. The statistical analysis indicated that the lowered levels of post-ablation TnT (p = 0.043) and CK-MB (p = 0.010) with the TnT elevation (p = 0.044) were the predictors of ERAF following CBA. In addition, diabetes and statin treatment were significantly associated with ERAF after CBA (p < 0.05). The machine learning algorithms confirmed the results obtained in the univariate analysis.

Machine-learned models using hematological inflammation markers in the prediction of short-term acute coronary syndrome outcomes.

BACKGROUND: Increased systemic and local inflammation play a vital role in the pathophysiology of acute coronary syndrome. This study aimed to assess the usefulness of selected machine learning methods and hematological markers of inflammation in predicting short-term outcomes of acute coronary syndrome (ACS). METHODS: We analyzed the predictive importance of laboratory and clinical features in 6769 hospitalizations of patients with ACS. Two binary classifications were considered: significant coronary lesion (SCL) or lack of SCL, and in-hospital death or survival. SCL was observed in 73% of patients. In-hospital mortality was observed in 1.4% of patients and it was higher in the case of patients with SCL. Ensembles of decision trees and decision rule models were trained to predict these classifications. RESULTS: The best performing model for in-hospital mortality was based on the dominance-based rough set approach and the full set of laboratory as well as clinical features. This model achieved 81 ± 2.4% sensitivity and 81.1 ± 0.5% specificity in the detection of in-hospital mortality. The models trained for SCL performed considerably worse. The best performing model for detecting SCL achieved 56.9 ± 0.2% sensitivity and 66.9 ± 0.2% specificity. Dominance rough set approach classifier operating on the full set of clinical and laboratory features identifies presence or absence of diabetes, systolic and diastolic blood pressure and prothrombin time as having the highest confirmation measures (best predictive value) in the detection of in-hospital mortality. When we used the limited set of variables, neutrophil count, age, systolic and diastolic pressure and heart rate (taken at admission) achieved the high feature importance scores (provided by the gradient boosted trees classifier) as well as the positive confirmation measures (provided by the dominance-based rough set approach classifier). CONCLUSIONS: Machine learned models can rely on the association between the elevated inflammatory markers and the short-term ACS outcomes to provide accurate predictions. Moreover, such models can help assess the usefulness of laboratory and clinical features in predicting the in-hospital mortality of ACS patients.