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Abstract Background The prevalence of combined heart failure (HF) and atrial fibrillation (AF) is rising, and these patients suffer from high rates of mortality. This study aims to provide robust data on factors associated with death, uniquely supported by post-mortem examination. Methods A retrospective cohort study of hospitalized adults with a clinical diagnosis of HF and AF at a tertiary centre in Romania between 2014 and 2017. A standardized post-mortem examination was performed where death occurred within 24 h of admission, when the cause of death was not clear or by physician request. National records were used to collect mortality data, subsequently categorized and analysed as HF-related death, vascular death and non-cardiovascular death using Cox proportional hazards regression. Results A total of 1009 consecutive patients with a mean age of 73 ± 11 years, 47% women, NYHA class 3.0 ± 0.9, left ventricular ejection fraction (LVEF) 40.1 ± 11.0% and 100% anticoagulated were followed up for 1.5 ± 0.9 years. A total of 291 (29%) died, with post-mortems performed on 186 (64%). Baseline factors associated with mortality were dependent on the cause of death. HF-related death in 136 (47%) was associated with higher NYHA class (hazard ratio [HR] 2.45 per one class increase, 95% CI 1.73–3.46; p

Heart failure with preserved ejection fraction (HFpEF) and AF are two common cardiovascular conditions that are inextricably linked to each other’s development and progression, often in multimorbid patients. Current management is often directed to specific components of each disease without considering their joint impact on diagnosis, treatment and prognosis. The result for patients is suboptimal on all three levels, restricting clinicians from preventing major adverse events, including death, which occurs in 20% of patients at 2 years and in 45% at 4 years. New trial evidence and reanalysis of prior trials are providing a glimmer of hope that adverse outcomes can be reduced in those with concurrent HFpEF and AF. This will require a restructuring of care to integrate heart failure and AF teams, alongside those that manage comorbidities. Parallel commencement and non-sequential uptitration of therapeutics across different domains will be vital to ensure that all patients benefit at a personal level, based on their own needs and priorities.

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IntroductionThe echocardiographic measurement of left ventricular ejection fraction (LVEF) is fundamental to the diagnosis and classification of patients with heart failure (HF).MethodsThis paper aimed to quantify LVEF automatically and accurately with the proposed pipeline method based on deep neural networks and ensemble learning. Within the pipeline, an Atrous Convolutional Neural Network (ACNN) was first trained to segment the left ventricle (LV), before employing the area-length formulation based on the ellipsoid single-plane model to calculate LVEF values. This formulation required inputs of LV area, derived from segmentation using an improved Jeffrey’s method, as well as LV length, derived from a novel ensemble learning model. To further improve the pipeline’s accuracy, an automated peak detection algorithm was used to identify end-diastolic and end-systolic frames, avoiding issues with human error. Subsequently, single-beat LVEF values were averaged across all cardiac cycles to obtain the final LVEF.ResultsThis method was developed and internally validated in an open-source dataset containing 10,030 echocardiograms. The Pearson’s correlation coefficient was 0.83 for LVEF prediction compared to expert human analysis (p < 0.001), with a subsequent area under the receiver operator curve (AUROC) of 0.98 (95% confidence interval 0.97 to 0.99) for categorisation of HF with reduced ejection (HFrEF; LVEF

IntroductionRecent advances in machine learning provide new possibilities to process and analyse observational patient data to predict patient outcomes. In this paper, we introduce a data processing pipeline for cardiogenic shock (CS) prediction from the MIMIC III database of intensive cardiac care unit patients with acute coronary syndrome. The ability to identify high-risk patients could possibly allow taking pre-emptive measures and thus prevent the development of CS.MethodsWe mainly focus on techniques for the imputation of missing data by generating a pipeline for imputation and comparing the performance of various multivariate imputation algorithms, including k-nearest neighbours, two singular value decomposition (SVD)—based methods, and Multiple Imputation by Chained Equations. After imputation, we select the final subjects and variables from the imputed dataset and showcase the performance of the gradient-boosted framework that uses a tree-based classifier for cardiogenic shock prediction.ResultsWe achieved good classification performance thanks to data cleaning and imputation (cross-validated mean area under the curve 0.805) without hyperparameter optimization.ConclusionWe believe our pre-processing pipeline would prove helpful also for other classification and regression experiments.

Objectives: To synthesize and characterize the inclusion complexes of fusidic acid with β – cyclodextrin, followed by the evaluation of their antimicrobial activity against pure strain (Staphylococcus aureus ATCC 25,923) and isolated Staphylococcus from clinical cases. Methods: The desired compounds were synthesized using molar ratio of fusidic acid: β-cyclodextrin of 1:1. Synthesized compounds were analyzed by Fourier Transform Infrared Spectroscopy (FTIR), X-Ray Diffraction (XRD), Scanning Electron Microscopy (SEM), and Thermal Analysis, and the results confirmed the formation of inclusion compounds by fusidic acid with β-cyclodextrin. Results: Physical–chemical characterization confirmed the preparation of desired inclusion compounds, and the antimicrobial test confirmed that all compounds obtained have antimicrobial activity. Antimicrobial activity of freeze-drying complex againstS. aureus is similar with pure fusidic acid activity, being better than the cefoxitin one. Similar behavior was observed against methicillin-resistant S. aureus and S. epidermidis. Conclusions: In the present work, three different inclusion complexes of fusidic acid were prepared using three different preparation methods. All inclusion complexes obtained presented good antimicrobial activity against differentS. aureus strains. Antimicrobial activity of these new prepared compounds was observed to be better than that of cefoxitin.