Heart failure (HF) stands as a prevalent chronic ailment, imposing a substantial burden on global healthcare systems due to recurrent hospitalizations, intricate management, persistent symptoms, and polypharmacy challenges. The augmentation of patient safety and treatment efficacy across various care stages, facilitated by a multidisciplinary HF team inclusive of a clinical pharmacist, emerges as paramount. Evidence underscores that the collaborative engagement of a physician and a clinical pharmacist engenders proficient and secure management, forestalling avoidable adversities stemming from drug reactions and prescription inaccuracies. This synergistic approach tailors treatments optimally to individual patients. Post-discharge, the vulnerability of HF patients to re-hospitalization looms large, historically holding sway as the foremost cause of 30-day readmissions. Diverse strategies have been instituted to fortify patient well-being, leading to the formulation of specialized transitional care programs that shepherd patients effectively from hospital to outpatient settings. These initiatives have demonstrably curtailed readmission rates. This review outlines a spectrum of roles assumed by clinical pharmacists within the healthcare cohort, spanning inpatient care, transitional phases, and outpatient services. Moreover, it traverses a compendium of studies spotlighting the affirmative impact instigated by integrating clinical pharmacists into these fields.

The prevalence of heart failure (HF) is increasing, necessitating accurate diagnosis and tailored treatment. The accumulation of clinical information from patients with HF generates big data, which poses challenges for traditional analytical methods. To address this, big data approaches and artificial intelligence (AI) have been developed that can effectively predict future observations and outcomes, enabling precise diagnoses and personalized treatments of patients with HF. Machine learning (ML) is a subfield of AI that allows computers to analyze data, find patterns, and make predictions without explicit instructions. ML can be supervised, unsupervised, or semi-supervised. Deep learning is a branch of ML that uses artificial neural networks with multiple layers to find complex patterns. These AI technologies have shown significant potential in various aspects of HF research, including diagnosis, outcome prediction, classification of HF phenotypes, and optimization of treatment strategies. In addition, integrating multiple data sources, such as electrocardiography, electronic health records, and imaging data, can enhance the diagnostic accuracy of AI algorithms. Currently, wearable devices and remote monitoring aided by AI enable the earlier detection of HF and improved patient care. This review focuses on the rationale behind utilizing AI in HF and explores its various applications.

Body fluid monitoring and management are essential to control dyspnea and prevent re-hospitalization in patients with chronic heart failure (HF). There are several methods to estimate and monitor patient's volume status, such as symptoms, signs, body weight, and implantable devices. However, these methods might be difficult to use for reasons that are slow to reflect body water change, inaccurate in specific patients' condition, or invasive. Bioelectrical impedance analysis (BIA) is a novel method for body water monitoring in patients with HF, and the value in prognosis has been proven in previous studies. We aim to determine the efficacy and safety of home BIA body water monitoring-guided HF treatment in patients with chronic HF. This multi-center, open-label, randomized control trial will enroll patients with HF who are taking loop diuretics. The home BIA group patients will be monitored for body water using a home BIA device and receive messages regarding their edema status and direction of additional diuretics usage or behavioral changes through the linked application system once weekly. The control group patients will receive the usual HF management. The primary endpoint is the change in N-terminal prohormone of brain natriuretic peptide levels from baseline after 12 weeks. This trial will provide crucial evidence for patient management with a novel home BIA body water monitoring system in patients with HF.

Background and Objectives: Takotsubo syndrome (TTS) is a form of reversible cardiomyopathy often preceded by mental or physical stressors and predominantly affects elderly women. Several cardiac and inflammatory biomarkers are involved in the pathogenesis of the disease. We aimed to investigate the correlation of C-reactive protein (CRP) level with left ventricular ejection fraction (LVEF) and clinical outcomes in patients with TTS. Methods: The study included patients with discharge-diagnosis of Takotsubo through 2017-2022 from the cardiology department. Demographic, laboratory, echocardiographic, and clinical outcomes were retrospectively obtained. We investigated the relation between CRP and LVEF, length of stay (LOS), in-hospital complications, and recurrence. Results: A total of 86 patients (93% female, mean age 68.8±12.3 years) were included in the study. The median CRP level was 17.4 (interquartile range [IQR], 6.1-40.1) mg/L, and the mean LVEF was 41.5%, (IQR, 38-50%). Complications occurred in 24 (27.9%) of the patients, and the median LOS was 3 (IQR, 3-5) days. The level of CRP was associated with lower LVEF (r=-0.39, p<0.001), longer hospital stay (r=0.25, p=0.021), and recurrence. There was no correlation between CRP and in-hospital complications. In multivariate logistic regression, poor LVEF was associated with TTS recurrence (odds ratio, 1.22; 95% confidence interval, 1.08-1.37; p=0.001). Using linear regression, only CRP was correlated with longer LOS and lower LVEF (p<0.001). Conclusions: Among patients hospitalized with TTS, CRP level was associated with poor LVEF and prolonged hospital stay but not with in-hospital complications. Poor LVEF was also associated with TTS recurrence.

Background and Objectives: Atrial fibrillation is common in patients with cardiac amyloidosis. However, the optimal anticoagulation strategy to prevent thromboembolic events in patients with cardiac amyloidosis and atrial fibrillation is unknown. This systematic review and meta-analysis compares direct oral anticoagulants (DOACs) vs. vitamin K antagonists (VKAs) in patients with cardiac amyloidosis and atrial fibrillation. Methods: We performed a systematic literature review to identify clinical studies of anticoagulation therapies for patients with cardiac amyloidosis and atrial fibrillation. The primary outcomes of major bleeding and thrombotic events were reported using random effects risk ratios (RRs) with 95% confidence interval (CI). Results: Our search yielded 97 potential studies and evaluated 14 full-text articles based on title and abstract. We excluded 10 studies that were review articles or did not compare anticoagulation. We included 4 studies reporting on 1,579 patients. The pooled estimates are likely underpowered due to small sample sizes. There was no difference in bleeding events for patients with cardiac amyloidosis and atrial fibrillation treated with DOACs compared to VKAs with a RR of 0.64 (95% CI, 0.38-1.10; p=0.10). There were decreased thrombotic events for patients with cardiac amyloidosis and atrial fibrillation treated with DOACs compared to VKAs with a RR of 0.50 (95% CI, 0.32-0.79; p=0.003). Conclusions: This systematic review and meta-analysis suggests that DOACs are as safe and effective as VKAs in patients with cardiac amyloidosis and atrial fibrillation. However, more data are needed to investigate clinical differences in anticoagulation therapy in this patient population.