Background: Perfect heart valve prostheses have optimized hemodynamics, reduced surgical morbidity, long-lasting durability, and extended patient survival with greater quality of life. Mechanical valves are recommended; however, young children may need anticoagulant medication for life. In this study, we looked at the success rate and viability of aortic valve neocuspidization (AVNeo) surgery for a variety of aortic disorders.

Methods: A methodical search strategy was used to fully evaluate the AVNeo results. Boolean operators were used to combine important words like “Ozaki Procedure,” “Aortic Valve Neocuspidization,” “AVNeo,” and associated terms. Reputable databases such as PubMed, MEDLINE, Embase, Web of Science, and Scopus were the focus of our search. Study quality was assessed using a critical evaluation created with the Critical Appraisal Skills Programme tool.

Results: The findings are summarized in the ”Results” section that contains descriptive and critical analysis, ramifications, and explanations. According to research, AVNeo improved valve function and had few side effects. Aortic valve neocuspidization has a lower mean pressure gradient and a larger mean efficient orifice area than Trifecta. Aortic valve neocuspidization surgery reduces aortic valve regurgitation and pressure gradients. Postoperative echocardiograms indicated a decrease in peak and a rise in mean pressure gradient.

Conclusion: The Ozaki method restores a healthy laminar flow pattern while preventing bivalvular disease. Ozaki procedure should be explored for valve repair in infants with truncal valve and congenital aortic disease. Aortic valve tricuspidization with glutaraldehyde-treated autologous pericardium results in considerable effective orifice area, modest pressure gradients, and little regurgitation.

Background: Hypertrophic cardiomyopathy is a common genetic heart disease and up to 40%-60% of patients have mutations in cardiac sarcomere protein genes. This genetic diagnosis study aimed to detect pathogenic or likely pathogenic sarcomeric and non-sarcomeric gene mutations and to confirm a final molecular diagnosis in patients diagnosed with hypertrophic cardiomyopathy.

Methods: A total of 392 patients with hypertrophic cardiomyopathy were included in this nationwide multicenter study conducted at 23 centers across Türkiye. All samples were analyzed with a 17-gene hypertrophic cardiomyopathy panel using next-generation sequencing technology. The gene panel includes ACTC1, DES, FLNC, GLA, LAMP2, MYBPC3, MYH7, MYL2, MYL3, PLN, PRKAG2, PTPN11, TNNC1, TNNI3, TNNT2, TPM1, and TTR genes.

Results: The next-generation sequencing panel identified positive genetic variants (variants of unknown significance, likely pathogenic or pathogenic) in 12 genes for 121 of 392 samples, including sarcomeric gene mutations in 30.4% (119/392) of samples tested, galactosidase alpha variants in 0.5% (2/392) of samples and TTR variant in 0.025% (1/392). The likely pathogenic or pathogenic variants identified in 69 (57.0%) of 121 positive samples yielded a confirmed molecular diagnosis. The diagnostic yield was 17.1% (15.8% for hypertrophic cardiomyopathy variants) for hypertrophic cardiomyopathy and hypertrophic cardiomyopathy phenocopies and 0.5% for Fabry disease.

Conclusions: Our study showed that the distribution of genetic mutations, the prevalence of Fabry disease, and TTR amyloidosis in the Turkish population diagnosed with hypertrophic cardiomyopathy were similar to the other populations, but the percentage of sarcomeric gene mutations was slightly lower.

Background: Gender-related clinical variations in patients with acute heart failure have been described in previous studies. However, there is still a lack of research on gender differences in patients hospitalized for acute heart failure in Türkiye. The aim of this study is to compare the clinical features, in-hospital approaches, and outcomes of male and female patients hospitalized for acute heart failure.

Methods: Differences in clinical characteristics, medication prescription, hospital management, and outcomes between males and females with acute heart failure were investigated from the Journey Heart Failure—Turkish Population study.

Results: Nine hundred eighteen patients (57.2%) were men and 688 (42.8%) were women. Women were older than men (70.48 ± 13.20 years vs. 65.87 ± 12.82 years; P <.001). The frequency of comorbidities such as hypertension (72.7% vs. 62.4%, P <.001), diabetes (46.5% vs. 38.5%, P = .001),  atrial  fibrillation  (46.5%  vs.  33.4%,  P <.001), New York Heart Association class III-IV symptoms (80.6% vs. 71.2%, P =.001), and dyspnea in the rest (73.8% vs. 68.3%, P =.044) were more common in women on admission. Male patients were more frequently hospitalized with reduced left ventricular ejection fraction (51.0% vs. 72.4%, P <.001). In-hospital mortality was higher among female patients (9.3% vs. 6.4%, P =.022). Higher  New  York  Heart  Association  class,  lower  estimated  glomerular  filtration  rate, higher N-terminal pro-B type natriuretic peptide on admission, and mechanical ventilation usage were the independent parameters of in-hospital mortality, whereas the female gender was not.

Conclusion: Our study clearly demonstrated the diversity in presentation, management, and in-hospital outcomes of acute heart failure between male and female patients. Although left ventricular systolic functions were better in female patients, in-hospital mortality was higher. Recognizing these differences in the management of heart failure in different sexes will serve better results in clinical practice.

Background: Pulmonary hypertension guidelines recommend invasive right heart catheterization for diagnosis and clinical follow-up. Our aim was to compare non-invasive impedance cardiography with invasive techniques for cardiac index measurements and mortality prediction in patients with pulmonary hypertension.

Methods: Between 2008 and 2018, 284 right heart catheterizations were performed for the diagnosis of pulmonary hypertension in 215 patients with mean pulmonary artery pressure >25 mm Hg, and at least 2 methods used for cardiac output measurement were included in the study retrospectively. Patients were evaluated with Pearson’s correlation in 3 groups: estimated Fick (eFick) method and thermodilution (group 1), eFick method and impedance cardiography (group 2), and thermodilution and impedance cardiography (group 3). We also compared the predictive power of cardiac index measured by different methods for 1-year overall mortality and hospitalizations.

Results: There were strong and moderate positive correlations in groups 1 and 3, respectively (r = 0.634, P <.001, r = 0.534, P =.001), and the weakest correlation was in group 2 (r = 0.390, P =.001). The mean difference (bias) between eFick method versus impedance cardiography, impedance cardiography vs. thermodilution, and eFick method vs.
thermodilution was 0.6 mL/min, 0.47 mL/min, and −0.2 mL/min respectively, but limits of agreement were wide. In both groups, cardiac index <2.5 L/min/m2 as measured by thermodilution significantly predicted 1-year mortality. Also, impedance cardiography was better than eFick method in predicting mortality (P =.02).

Conclusions: Our single-center real-life data showed that for cardiac output and cardiac index measurements, impedance cardiography provides a moderate correlation with thermodilution and is fair with eFick method methods. Moreover, thermodilution appeared superior to both eFick method and impedance cardiography, while impedance cardiography was even better than eFick method in predicting 1-year adverse events, including total mortality and hospitalization, in patients with pulmonary hypertension.

Background: The aim of this study was to evaluate the relationship between risk factors causing cardiovascular diseases and their importance with explainable machine learning models.

Methods: In this retrospective study, multiple databases were searched, and data on 11 risk factors of 70 000 patients were obtained. Data included risk factors highly associated with cardiovascular disease and having/not having any cardiovascular disease. The explainable prediction model was constructed using 7 machine learning algorithms: Random Forest Classifier, Extreme Gradient Boost Classifier, Decision Tree Classifier, KNeighbors Classifier, Support Vector Machine Classifier, and GaussianNB. Receiver operating characteristic curve, Brier scores, and mean accuracy were used to assess the model’s performance. The interpretability of the predicted results was examined using Shapley additive description values.

Results: The accuracy, area under the curve values, and Brier scores of the Extreme Gradient Boost model (the best prediction model for cardiovascular disease risk factors) were calculated as 0.739, 0.803, and 0.260, respectively. The most important risk factors in the permutation feature importance method and explainable artificial intelligence–Shapley’s explanations method are systolic blood pressure (ap_hi) [0.1335 ± 0.0045 w (weight)], cholesterol (0.0341 ± 0.0022 w), and age (0.0211 ± 0.0036 w).

Conclusion: The created explainable machine learning model has become a successful clinical model that can predict cardiovascular patients and explain the impact of risk factors. Especially in the clinical setting, this model, which has an accurate, explainable, and transparent algorithm, will help encourage early diagnosis of patients with cardiovascular diseases, risk factors, and possible treatment options.

Background: We evaluated the predictive value of electrocardiographic (ECG) findings for pulmonary hemodynamics assessed by right heart catheterization (RHC).

Methods: Our study population comprised 562 retrospectively evaluated patients who underwent RHC between 2006 and 2022. Correlations between ECG measures and pulmonary arterial systolic and mean pressures (PASP and PAMP) and pulmonary vascular resistance (PVR) were investigated. Moreover, receiver operating characteristic (ROC) curve analysis assessed the predictive value of ECG for pulmonary hypertension (PH) and precapillary PH.

Results: The P-wave amplitude (Pwa) and R/S ratio (r) in V1 and V2, Ra in augmented voltage right (aVR), right or indeterminate axis, but not P wave duration (Pwd) or right bundle branch block (RBBB) significantly correlated with PASP, PAMP, and PVR (P <.001 for all). The partial R2 analysis revealed that amplitude of R wave (Ra) in aVR, R/Sr in V1 and V2, QRS axis, and Pwa added to the base model provided significant contributions to variance for PASP, PAMP, and PVR, respectively. The Pwa > 0.16 mV, Ra in aVR > 0.05 mV, QRS axis > 100° and R/Sr in V1 > 0.9 showed the highest area under curve (AUC) values for PAMP > 20 mm Hg. Using the same cutoff value, Ra in aVR, Pwa, QRS axis, and R/Sr in V1 showed highest predictions for PVR > 2 Wood Units (WU).

Conclusion: In this study, Pwa, Ra in aVR, right or indeterminate axis deviations, and R/Sr in V1 and V2 showed statistically significant correlations with pulmonary hemodynamics, and Ra in aVR, R/Sr in V2 and V1, QRS axis, and Pwa contributed to variance for PASP, PAMP, and PVR, respectively. Moreover, Pwa, Ra in aVR, QRS axis, and R/Sr in V1 seem to provide relevant predictions for PH and precapillary PH.