Non-invasive Imaging in Women With Heart Failure - Diagnosis and Insights Into Disease Mechanisms

Artificial Intelligence and Its Role in Diagnosing Heart Failure: A Narrative Review

Heart failure (HF) is prevalent globally. It is a dynamic disease with varying definitions and classifications due to multiple pathophysiologies and etiologies. The diagnosis, clinical staging, and treatment of HF become complex and subjective, impacting patient prognosis and mortality. Technological advancements, like artificial intelligence (AI), have been significant roleplays in medicine and are increasingly used in cardiovascular medicine to transform drug discovery, clinical care, risk prediction, diagnosis, and treatment. Medical and surgical interventions specific to HF patients rely significantly on early identification of HF. Hospitalization and treatment costs for HF are high, with readmissions increasing the burden. AI can help improve diagnostic accuracy by recognizing patterns and using them in multiple areas of HF management. AI has shown promise in offering early detection and precise diagnoses with the help of ECG analysis, advanced cardiac imaging, leveraging biomarkers, and cardiopulmonary stress testing. However, its challenges include data access, model interpretability, ethical concerns, and generalizability across diverse populations. Despite these ongoing efforts to refine AI models, it suggests a promising future for HF diagnosis. After applying exclusion and inclusion criteria, we searched for data available on PubMed, Google Scholar, and the Cochrane Library and found 150 relevant papers. This review focuses on AI's significant contribution to HF diagnosis in recent years, drastically altering HF treatment and outcomes.

Gender Effect on Clinical Profiles, Pharmacological Treatments and Prognosis in Patients Hospitalized for Heart Failure

Heart failure (HF) is a significant disease affecting 1-2% of the general population. Despite its general aspects, HF, like other cardiovascular diseases, presents various gender-specific aspects in terms of etiology, hemodynamics, clinical characteristics, therapy, and outcomes. As is well known, HF with preserved ejection fraction more frequently affects females, with diabetes and arterial hypertension representing the most critical determinants of HF. On the other hand, women are traditionally underrepresented in clinical trials and are often considered undertreated. However, it is not clear whether such differences reflect cultural behaviors and clinical inertia or if they indicate different clinical profiles and the impact of sex on hard clinical outcomes. We aimed to review the sex-related differences in patients affected by HF.

Sex-Related Differences in Heart Failure Diagnosis

PURPOSE OF REVIEW

The literature on the importance of sex in heart failure diagnosis is scarce. This review aims to summarize current knowledge on sex differences regarding the diagnosis of heart failure.

RECENT FINDINGS

Comorbidities are frequent in patients with heart failure, and their prevalence differs between sexes; some differences in symptomatology and diagnostic imaging techniques were also found. Biomarkers also usually show differences between sexes but are not significant enough to establish sex-specific ranges. This article outlines current information related to sex differences in HF diagnosis. Research in this field remains to be done. Maintaining a high diagnostic suspicion, actively searching for the disease, and considering the sex is relevant for early diagnosis and better prognosis. In addition, more studies with equal representation are needed.