Worsening chronic heart failure (HF) is responsible for recurrent hospitalization and increased mortality risk after discharge, irrespective to the ejection fraction. Symptoms and signs of pulmonary and systemic congestion are the most common cause for hospitalization of acute decompensated HF, as a consequence of increased cardiac filling pressures. The elevated cardiac filling pressures, also called hemodynamic congestion, may precede the occurrence of clinical congestion by days or weeks. Since HF patients often have comorbidities, dyspnoea, the main symptom of HF, may be also caused by respiratory or other illnesses. Recent studies underline the importance of the diagnosis and treatment of hemodynamic congestion before HF symptoms worsen, reducing hospitalization and improving prognosis. In this paper we review the role of integrated evaluation of biomarkers and imaging technics, i.e., echocardiography and pulmonary ultrasound, for the diagnosis, prognosis and treatment of congestion in HF patients.

Our study objective was to construct models using 20 routine laboratory parameters on admission to predict disease severity and mortality risk in a group of 254 hospitalized COVID-19 patients. Considering the influence of confounding factors in this single-center study, we also retrospectively assessed the correlations between the risk of death and the routine laboratory parameters within individual comorbidity subgroups. In multivariate regression models and by ROC curve analysis, a model of three routine laboratory parameters (AUC 0.85; 95% CI: 0.79–0.91) and a model of six laboratory factors (AUC 0.86; 95% CI: 0.81–0.91) were able to predict severity and mortality of COVID-19, respectively, compared with any other individual parameter. Hierarchical cluster analysis showed that inflammatory laboratory markers grouped together in three distinct clusters including positive correlations: WBC with NEU, NEU with neutrophil-to-lymphocyte ratio (NLR), NEU with systemic immune-inflammation index (SII), NLR with SII and platelet-to-lymphocyte ratio (PLR) with SII. When analyzing the routine laboratory parameters in the subgroups of comorbidities, the risk of death was associated with a common set of laboratory markers of systemic inflammation. Our results have shown that a panel of several routine laboratory parameters recorded on admission could be helpful for early evaluation of the risk of disease severity and mortality in COVID-19 patients. Inflammatory markers for mortality risk were similar in the subgroups of comorbidities, suggesting the limited effect of confounding factors in predicting COVID-19 mortality at admission.

Congestion represents a defining hallmark of heart failure (HF) leading to increased morbidity and mortality in HF patients. While it was traditionally viewed as a simple and uniform state of volume overload, contemporary understanding has emphasized its complexity, distinguishing between intravascular, interstitial, and tissue congestion. Congestion contributes to overt clinical manifestation of HF. However, subclinical congestion often goes undetected, increasing the risk of adverse outcomes. Residual congestion, in particular, remains a frequent and challenging issue, with its persistence at discharge being strongly linked to rehospitalization and poor prognosis. Clinical evaluation often fails to reliably identify the resolution of congestion, highlighting the need for supplementary diagnostic methods. Improvement in imaging modalities, including lung ultrasound, venous Doppler, and echocardiography, have significantly enhanced the detection of congestion. Moreover, biomarkers such as natriuretic peptides, bioactive adrenomedullin, soluble CD146, and carbohydrate antigen 125 offer valuable, complementary insights into fluid distribution and the severity of HF congestion. Therefore, a comprehensive, multimodal strategy that integrates clinical evaluation with imaging and biomarker data is crucial for optimizing the management of congestion in HF. Future approaches should prioritize personalized decongestive therapy, addressing both intravascular and tissue congestion, while aiming to preserve renal function and limit neurohormonal activation. Refinement of these strategies holds promise for improving long-term outcomes, reducing rehospitalizations, and enhancing overall patient prognosis.

Acute decompensated heart failure (ADHF) represents a major healthcare burden, with residual congestion at discharge being a critical determinant of poor outcomes. Despite its prognostic significance, the assessment of decongestion status before discharge remains suboptimal, highlighting the need for a more comprehensive evaluation approach. This descriptive review synthesizes current evidence on congestion assessment methods in ADHF, focusing on their role in discharge decision-making and prognostic value. We describe various evaluation tools, including clinical examination, biomarkers, imaging techniques, and congestion scores, presenting their integration into a practical assessment algorithm. A comprehensive algorithm for congestion assessment before discharge is presented, incorporating multimodal evaluation techniques, with the aim of highlighting the practical utility of various assessment methods in guiding treatment decisions and determining optimal discharge timing. Integration of multiple parameters provides superior accuracy in evaluating decongestion status compared to single-method approaches. A standardized, multimodal approach to congestion assessment before discharge is essential for optimal ADHF management. The proposed assessment algorithm, combining clinical, biochemical, and imaging parameters, offers a practical framework for more reliable discharge decision-making, potentially improving patient outcomes.

Introduction: Tuberculosis (TB) remains a global health challenge, requiring enhanced active case finding (ACF) through screening strategies. This study assesses the effectiveness of such an approach in locating TB cases among vulnerable groups, such as homeless persons, injecting drug users, those detained in prison, and people living in rural areas. Methods: The study focuses on socio-economic characteristics and TB detection rates across Romanian counties using modern techniques including computer-aided detection of lesions on chest X-ray and GeneXpert tests. Results: The results highlight the disproportionate burden of TB in vulnerable groups, by revealing significant differences in TB detection rates between regions. Notably, the TB detection rates among these vulnerable groups (250.85 per 100,000 population) are five times higher than the national incidence rate (46.1). Discussion: These findings underscore the imperative integration of ACF into National TB Program to provide customized and efficient solutions for diverse vulnerable groups, thereby informing crucial public health initiatives and interventions.