34 research outputs found

    Prąd uszkodzenia pęczka Hisa i czynny rytm węzłowy — rozważania kliniczne i techniczne

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    We present a case of dual chamber cardioverter-defibrillator placement with atrial lead implanted in His-bundle area, resulting in current of injury and accelerated nodal rhythm recorded in pacing system analyzer, rarely seen in clinical practice. Such presentation resulted in excellent pacing and sensing conditions, despite primarily doubtful setting.Zaprezentowano przypadek implantacji dwujamowego kardiowertera-defibrylatora z elektrodą przedsionkową lokowanąw okolicy pęczka Hisa, skutkującą prądem uszkodzenia i czynnym rytmem węzłowym, rzadko obserwowanym w praktyceklinicznej. Rezultatem były znakomite parametry stymulacji i czucia, mimo pierwotnie wątpliwej lokalizacji

    Long-term clinical results of biodegradable vascular scaffold ABSORB BVS™ using the PSP-technique in patients with acute coronary syndrome

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    Background: The PSP (predilatation, sizing, post-dilatation)-technique was developed to improve the prognosis of patients after bioresorbable vascular scaffold (BVS) implantation. In acute coronary syndrome (ACS) the use of BVS is particularly demanding and carries some potential risk regarding aggressive lesion preparation, proper vessel sizing due to spasm and thrombus inside the artery. The aim herein, was to determine the long-term results of BVS stenting in ACS patients depending on the scaffold implantation technique.Methods: The present study is a prospective, two-center study, which consisted of 182 patients who underwent percutaneous coronary intervention (PCI) with BVS (Absorb, Abbott Vascular, Santa Clara, California, USA) implantation for the ACS. All patients were divided into two groups. The first consisted of 52 patients treated with the PSP-technique (PSP group). The second group enrolled 130 patients treated with a non-PSP procedure (non-PSP group).Results: The procedure was successful in all patients. The mean observation time was 28.8 ± 16.5 months (median 28.3 months, interquartile range 24.0 [17.0–41.0] months). It was found that target vessel failure (TVF) was consistently reduced in patients using the PSP-technique as compared with the non-PSP group (5.8% vs. 17.7%, p = 0.03). Moreover, PSP-technique was superior to non-PSP-technique concerning major adverse cardiac events (MACE) (3.7% vs. 22.3%, p = 0.02). Logistic regression analysis revealed that the use of PSP technique significantly decreased the risk of target vessel revascularization (odds ratio [OR] 0.11, p = 0.01), TVF (OR 0.28, p = 0.03) and MACE (OR 0.29, p = 0.02).Conclusions: The PSP-technique for BVS implantation improves long-term results and should also be recommended for newer generations of the bioresorbable scaffold

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

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    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

    Niewydolność serca i zapalenie płuc jako kliniczna maska guza lewego przedsionka

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    We present a case of heart failure and pneumonia which masked the tumour that filled the whole left atrium.Autorzy prezentują przypadek niewydolności serca i zapalenia płuc, które maskowały rzadki guz wypełniający cały lewyprzedsionek

    Evaluation of bioresorbable vascular scaffolds in acute coronary syndrome: A two-center, one-year follow-up analysis

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    Background: Bioresorbable vascular scaffolds (BVS) have emerged as a new treatment option in cardiovascular medicine. Nonetheless, there is still limited data on the use of these novel devices in patients with acute coronary syndromes (ACS). The purpose of this study was to evaluate the feasibility and efficacy of BVS implantation in patients with ACS.Methods: The present report is a prospective, two-center registry that involved 165 consecutive patients hospitalized with the diagnosis of ACS and treated with the Absorb BVS (Abbot Vascular, Santa Clara, USA). During 1-year, all patients were monitored for the following endpoints: death, myocardial infarction (MI), scaffold thrombosis (ST), target lesion revascularization (TLR), target vessel revascularization (TVR) and target vessel failure (TVF), defined as cardiac death, target vessel MI, and TVR.Results: A total of 165 patients underwent 179 BVS implantations. 94 patients were diagnosed with unstable angina (UA; 57.6%), 45 with non-ST-segment elevation myocardial infarction (NSTEMI; 27.3%) and 26 with ST-segment elevation myocardial infarction (STEMI; 15.7%). Procedural success was achieved in all patients with thrombolysis in myocardial infarction flow 3. During a follow-up of 14.1 ± 8.5 months (median 12.4 months, IQR 8.7 [8.4 to 12.1] months) death occurred in 4 (2.4%) patients, including 2 (1.3%) cardiac deaths. There was only 1 case of subacute ST (0.66%), without late ST. The incidence of MI, TLR, TVR and TVF were: 2.65%, 2.65%, 7.95%, 9.3%, respectively.Conclusions: The present results suggest that BVS implantation in ACS patients is feasible and safe inhighly experienced centers. One-year clinical results are encouraging with a low rate of stent thrombosis

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

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    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
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