Carr Mckee (findsugar19)

ulsant seizure prophylaxis started during acute ischemic stroke hospitalization. Our goal was to evaluate the ability of cardiovascular magnetic resonance for detecting and predicting cardiac dysfunction in patients receiving cancer therapy. Left ventricular ejection fraction, global and regional strain utilizing fast-strain-encoded, T1 and T2 mapping, and cardiac biomarkers (troponin and BNP [brain natriuretic peptide]) were analyzed. Sixty-one patients (47 with breast cancer, 11 with non-Hodgkin lymphoma, and 3 with Hodgkin lymphoma) underwent cardiovascular magnetic resonance scans at baseline and at regular intervals during 2 years of follow-up. The percentage of all left ventricular myocardial segments with strain ≤-17% (normal myocardium [%]) was analyzed. Clinical cardiotoxicity (CTX) and sub-CTX were defined according to standard measures. Nine (15%) patients developed CTX, 26 (43%) had sub-CTX. Of the 35 patients with CTX or sub-CTX, 24 (69%) were treated with cardioprotective medications and showed recovery of cardiac function. The amount of normal myocardium (%) exhibitedy in patients with cancer undergoing cardiotoxic chemotherapy not only for the early detection but also for the prediction of those at risk of developing CTX. Registration URL https//; Unique identifier NCT03543228.Over the past few decades, advances in pharmacological, catheter-based, and surgical reperfusion have improved outcomes for patients with acute myocardial infarctions. However, patients with large infarcts or those who do not receive timely revascularization remain at risk for mechanical complications of acute myocardial infarction. The most commonly encountered mechanical complications are acute mitral regurgitation secondary to papillary muscle rupture, ventricular septal defect, pseudoaneurysm, and free wall rupture; each complication is associated with a significant risk of morbidity, mortality, and hospital resource utilization. The care for patients with mechanical complications is complex and requires a multidisciplinary collaboration for prompt recognition, diagnosis, hemodynamic stabilization, and decision support to assist patients and families in the selection of definitive therapies or palliation. However, because of the relatively small number of high-quality studies that exist to guide clinical practice, there is significant variability in care that mainly depends on local expertise and available resources. We have recently tested an automated machine-learning algorithm that quantifies left ventricular (LV) ejection fraction (EF) from guidelines-recommended apical views. However, in the point-of-care (POC) setting, apical 2-chamber views are often difficult to obtain, limiting the usefulness of this approach. Since most POC physicians often rely on visual assessment of apical 4-chamber and parasternal long-axis views, our algorithm was adapted to use either one of these 3 views or any combination. This study aimed to (1) test the accuracy of these automated estimates; (2) determine whether they could be used to accurately classify LV function. Reference EF was obtained using conventional biplane measurements by experienced echocardiographers. In protocol 1, we used echocardiographic images from 166 clinical examinations. Both automated and reference EF values were used to categorize LV function as hyperdynamic (EF>73%), normal (53%-73%), mildly-to-moderately (30%-52%), or severely reduced (<30%). Additely assess LV function. The new machine-learning algorithm allows accurate automated evaluation of LV function from echocardiographic views commonly used in the POC setting. This approach will enable more POC personnel to accurately assess LV function. Recurrence of cardiovascular events remains a substantial cause of mortality and morbidity among patients with previous coronary revascularization. The aim was to assess the prognostic value