Wpływ terapii skojarzonej opartej na ezetimibie i statynie na metabolizm cholesterolu w świetle aktualnej wiedzy medycznej

Artykuł stanowi próbę syntezy poglądów dotyczących fizjologii krążenia cholesterolu, a także roli zaburzeń lipidowych i ich wpływu na ryzyko sercowo-naczyniowe w różnych populacjach pacjentów. Jednocześnie w pracy opisano mechanizmy i sposoby terapeutycznej ingerencji w metabolizm lipoprotein osocza ze szczególnym uwzględnieniem w tym procesie skojarzonego wpływu statyn i ezetimibu. W ostatniej części artykułu zweryfikowano zasadność zastosowania powyższego schematu hipolipemizującego na podstawie dowodów uzyskanych w wieloośrodkowych randomizowanych badaniach klinicznych

Czy regresja zmian miażdżycowych jest możliwa tylko przez obniżanie stężenia LDL i czy jest to działanie wystarczające, by zredukować skłonność tych lipoprotein do oksydacyjnej modyfikacji?

The process of atherogenesis can be conditioned by the imbalance between the tendency to oxidative modification of the lipoproteins containing apoprotein B100 and antioxidant plasma properties. The paper discusses the situations leading to disruption of oxidative homeostasis on the basis of available data. Kardiol Pol 2011; 69, 8: 834–83

Zaburzenia genetycznych struktur mitochondrialnych i procesów energetycznych oraz ich rola w etiologii choroby niedokrwiennej serca

Apart from the theory of local inflammation in etiopathogenesis of the arteriosclerosis, hypotheses concerning the role of mitochondria in this process arise growing interest. Some proteins of the respiratory chain (OXPHOS) are coded on mitochondrial DNA. Their damage leads to interruption of oxidative phosphorylation, what in turns raises the free oxygen radicals (ROS) generation. The relationship of insufficient mechanism of mitochondrial ROS elimination with the initiation of the atherosclerosis was confirmed in experimental data. The mutagenesis of mitochondrial DNA is tied with the etiology of coronary artery disease (CAD). Some disturbances of the structure of mt-DNA are primal. The second group is probably determined by the effect of CAD influence on the structure of mt-DNA in cardiomyocytes. The mitochondrial energetic transformations are described in the article, with special regard on their potential influence on the process of mt-DNA mutagenesis and secondarily on the formation of CAD. Kardiol Pol 2010; 68, 8: 947-95

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.Peer Reviewe

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

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

Strategies and results of oncofertility counseling in young breast cancer patients

Introduction. Breast cancer (BC) is the most common female neoplasm in Poland and worldwide, yet up to 7% of all cases is diagnosed &lt; 40 years of age. The increased BC morbidity rate in this age group as well as hope for late maternity need special attention. Material and methods. The data concerning the number of children and further procreation needs in women (n = 68), aged 18–40, diagnosed and treated for early breast cancer at the Greater Poland Cancer Center in 2018–2019, were taken from patients’ histories by an oncologist before (neo-)adjuvant systemic therapy. Results. Out of the 68 females surveyed, aged 18–40 (median age 36), 14 (21%) were childless at the moment of diagnosis. After being informed about the therapy, prognosis, side effects and oncofertility, 12 patients (18%) decided to have a consultation with a specialist in reproductive medicine; 5 of them (7%) already had children. In 2 women (3%), hormonal stimulation in combination with tamoxifen was used; then, oocytes were collected and cryopreserved. In 19 (28%), gonadotropine analogues were added to (neo-)adjuvant chemotherapy. In 17 patients (25%) pathogenic mutations in BRCA1/2 genes were found. Conclusions. Oncofertility counseling in young BC patients should be one of the fundamental elements of complex patient care