Background/Objectives: Cardiovascular diseases (CVDs) remain the leading cause of morbidity and mortality worldwide and increasingly are recognized as a continuum of interconnected conditions rather than isolated entities. Methods: A structured narrative literature search was performed in PubMed, Scopus, and Google Scholar for publications from 2015 to 2025 using combinations of different keywords: “cardiovascular disease spectrum”, “multi-omics”, “precision cardiology”, “machine learning”, and “artificial intelligence in cardiology”. Results: Evidence was synthesized across seven major clusters of cardiovascular conditions, and across these domains, common biological pathways were mapped onto heterogeneous clinical phenotypes, and we summarize how multi-omics integration, AI-enabled imaging and digital tools contribute to improved risk prediction and more informed clinical decision-making within this spectrum. Conclusions: Interpreting cardiovascular conditions as components of a shared disease spectrum clarifies cross-disease interactions and supports a shift from organ- and syndrome-based classifications toward mechanism- and data-driven precision cardiology. The convergence of multi-omics, and AI offers substantial opportunities for earlier detection, individualized prevention, and tailored therapy, but requires careful attention to data quality, equity, interpretability, and practical implementation in routine care.

Background/Objectives: Cardiovascular diseases are a major cause of morbidity and mortality worldwide. Acute chest pain is a frequent reason for emergency department presentation and requires structured evaluation to identify life-threatening conditions. This study evaluated clinical characteristics, cardiovascular risk profile, risk stratification patterns, and hospitalization status in adults with acute chest pain. Methods: We conducted a retrospective study using registry data from Arad County Clinical Emergency Hospital between January 2021 and December 2024. Adult patients with documented acute chest pain were included according to predefined criteria. Demographics, comorbidities, clinical presentation, troponin values, hospitalization status, and HEART, and EDACS categories were extracted when available. The Marburg Heart Score was also assessed as an exploratory complementary score. Statistical analysis used descriptive statistics, contingency tables, and chi-square testing, with available-case analysis. Results: Overall, 2070 patients were included. Most patients were aged 35–54 or 55–69 years. Hypertension and diabetes mellitus were the most common comorbidities, and pressure-like chest pain predominated. In unadjusted analyses, HEART and EDACS categories were significantly associated with hospitalization status across all study years. Score categories were significantly associated with hospitalization status across all study years. Age was consistently associated with cardiovascular comorbidity burden and higher-risk score categories. Conclusions: Structured risk stratification scores were associated with hospitalization status, while age was associated with cardiovascular risk burden.

Background: Pulmonary embolism (PE) is a major cause of morbidity and in-hospital mortality and requires rapid clinical assessment for diagnosis, risk stratification, and management. Several validated clinical prediction tools, including the Pulmonary Embolism Severity Index (PESI), WELLS, PADUA, and IMPROVE scores, are commonly used to evaluate thromboembolic risk and predict clinical outcomes. Methods: A retrospective observational study was conducted on a cohort of 538 patients diagnosed with PE, all recruited between January 2020–December 2025. Group comparisons between survivors and non-survivors were performed using independent samples t-tests and Mann–Whitney U tests. Receiver operating characteristic (ROC) curve analysis was used to evaluate the predictive performance of clinical scores. Multivariable logistic regression analysis was performed to identify independent predictors of in-hospital mortality. Results: Mean age was 69 years, and overall death-rate was 18.4%. Significant differences between survivors and non-survivors were observed for age and clinical scores. White blood cell count, neutrophils, lymphocytes, platelet count, total bilirubin, aspartate aminotransferase (AST), alanine aminotransferase (ALT), gamma-glutamyl transferase (GGT), procalcitonin, and international normalized ratio (INR), were significantly associated with in-hospital mortality. ROC curve analysis demonstrated predictive performance of the evaluated clinical scores. Logistic regression identified PESI score, procalcitonin levels, and white blood cell count as independent predictors of unfavorable outcome. mortality. Conclusions: Clinical risk scores and specific laboratory biomarkers were associated with in-hospital mortality in patients with pulmonary embolism. The PESI score, procalcitonin, and white blood cell count showed independent predictive value for death rate in this cohort.

Background: Pulmonary embolism (PE) remains a major cause of cardiovascular morbidity and mortality, and associated infections may modify its clinical course. This study evaluated the impact of viral infections on pulmonary embolism severity, mortality, and the performance of established risk scores. Methods: This retrospective observational study included 729 patients with confirmed PE admitted between January 2020 and December 2025. Patients were grouped according to the presence of associated infections. We analysed clinical data, comorbidities, thrombus localization, laboratory parameters, infection type, treatment, mortality, and Pulmonary Embolism Severity Index (PESI), Wells, Padua, and International Medical Prevention Registry on Venous Thromboembolism (IMPROVE) scores. We performed between-group comparisons, logistic regression, and ROC curve analyses. Results: Patients with associated infections had higher crude mortality than those without infections (27.7% vs. 18.4%, p = 0.006). They also showed higher PESI and IMPROVE scores for venous thromboembolism (VTE), increased white blood cell (WBC) count, markedly elevated D-dimers, longer prothrombin time, and different thrombus localization. Viral infection category was independently associated with mortality, with the IMPROVE score showing the highest ROC discrimination among evaluated scores. Conclusions: Associated infections define a higher-risk PE phenotype. Risk stratification should integrate infection status, laboratory markers, and conventional PE/VTE scores.