With its complicated pathophysiology, high incidence and prevalence, heart failure remains a major public concern. In hopes of improving diagnosis, treatment and prognosis, the utility of many different biomarkers is researched vigorously around the world. In this review, biomarkers of myocardial remodeling and fibrosis (galectin-3, soluble isoform of suppression of tumorigenicity 2, matrix metalloproteinases, osteopontin, interleukin-6, syndecan-4, myostatin, procollagen type I C-terminal propeptide, procollagen type III N-terminal propeptide, vascular endothelial growth factor, nitric oxidase synthetase and asymmetric dimethylarginine), myocyte injury (heart-type fatty acid-binding protein, glutathione S-transferase P1 and heat shock protein 60), as well as iron metabolism (ferritin, transferrin saturation, soluble transferrin receptor and hepcidin), are considered in terms of possible clinical applicability and significance. Our short review consists of a summary of the aforementioned cardiovascular biomarkers’ clinical relevance and perspectives.

Heart failure (HF) is one of the leading causes of mortality and hospitalization worldwide. The accurate prediction of mortality and readmission risk provides crucial information for guiding decision making. Unfortunately, traditional predictive models reached modest accuracy in HF populations. We therefore aimed to present predictive models based on machine learning (ML) techniques in HF patients that were externally validated. We searched four databases and the reference lists of the included papers to identify studies in which HF patient data were used to create a predictive model. Literature screening was conducted in Academic Search Ultimate, ERIC, Health Source Nursing/Academic Edition and MEDLINE. The protocol of the current systematic review was registered in the PROSPERO database with the registration number CRD42022344855. We considered all types of outcomes: mortality, rehospitalization, response to treatment and medication adherence. The area under the receiver operating characteristic curve (AUC) was used as the comparator parameter. The literature search yielded 1649 studies, of which 9 were included in the final analysis. The AUCs for the machine learning models ranged from 0.6494 to 0.913 in independent datasets, whereas the AUCs for statistical predictive scores ranged from 0.622 to 0.806. Our study showed an increasing number of ML predictive models concerning HF populations, although external validation remains infrequent. However, our findings revealed that ML approaches can outperform conventional risk scores and may play important role in HF management.

Acute heart failure (AHF) is a state accompanied by lymphatic system dysregulation. Current knowledge of lymphatic system involvement in the episode of AHF is limited. The lymphatic system is known to be highly responsible for edema formation. In our work, we focused on the analysis of the Vascular Endothelial Growth Factor C (VEGF-C), a known factor responsible for lymphatic system development with AHF symptoms and survival. Our studies presented that low VEGF-C serum levels on admission are associated with more severe signs of accompanying AHF fluid accumulation (defined as ascites and lower extremities edema) and higher mortality rates when compared to medium and high VEGF-C serum levels on admission. Our conclusion is that VEGF-C may be used as a parameter that reflects lymphatic system function in AHF. Although VEGF-C is a known lymphangiogenesis modulator, its relationship with congestion formation and outcomes in AHF is unknown. Serum VEGF-C level was measured in 237 patients hospitalized for AHF. The population was stratified by VEGF-C levels and linked with clinical signs of congestion and outcomes. The study population was divided in VEGF-C tertiles: low (median [Q25-Q75]: 33 [15-175]), medium (606 [468-741]) and high (1141 [968-1442] pg/ml). Low VEGF-C group on admission presented the highest prevalence of severe lower extremity edema (low VEGF-C vs medium VEGF-C vs high VEGF-C): 30% vs 13% vs 20%, p=0.02, the highest percentage of patients with ascites: 22% vs 9% vs 6%, p=0.006 and the lowest proportion of patients with pulmonary congestion: 22% vs 30% vs 46%, p=0.004. The one-year mortality was the highest in low VEGF-C tertile: 35% vs 28% vs 18%, respectively, p=0.049; the same pattern was observed for the composite endpoint (death and AHF rehospitalization): 45% vs 43% vs 26%, p=0.029. The risks of death at one-year follow-up and composite endpoint were significantly lower in the high VEGF-C group. Low VEGF-C was associated with more severe signs of congestion (signs of fluid accumulation) and adverse clinical outcomes.

Aims: We aim to identify the most accurate marker for early prediction of poor diuretic response in acute heart failure (AHF) patients with signs of congestion requiring intravenous diuretic treatment. Methods: In this single-centre, prospective observational study, AHF patients with signs of congestion received a standardized intravenous furosemide dose (1 mg/kg of body weight; 40 mg in bolus and remaining dose in 2 h continuous infusion). Subsequently, we assessed spot urine composition at 2 h post-administration, comparing it with total urine output at 6 h. Various potential urine markers were analysed for predicting urine output using receiver operating characteristic (ROC) curves and logistic regression models. We investigated guideline-recommended markers, including spot urine sodium (UNa+) and its cut-off, and introduced the UNa+/UCr (urine creatinine concentration) ratio adjusting UNa+ for urine dilution. Results: Out of 111 patients (85% males, 66.4 ± 13.9 years old, NTproBNP 7290 [4493–14 582] pg/ml), there were 18 (16%) with a poor diuretic response (cumulative urine output <600 ml during the first 6 h). The mean 6 h cumulative diuresis in patients with poor and good diuretic response was 406 ± 142 and 2114 ± 1164 ml, respectively, P < 0.005. After an initial evaluation of several potential biomarkers, only UNa+, UCr and UNa+/UCr were selected as candidates with the highest predictive value. The cut-off for UNa+ adjusted for urine dilution: UNa+/UCr ratio <0.167 mmol/mg × 10−1 was determined by ROC analysis with the highest area under the curve (95% confidence interval): 0.956 (0.915–0.997), P < 0.001. When compared with the guideline-recommended cut-off (UNa+ <50 mmol/L as a reference, specificity—0.97; sensitivity—0.83), the odds ratio (OR) for UNa+/UCreat to identify a poor diuretic response was 2.5 times greater, regardless of kidney function (OR for estimated glomerular filtration rate in the logistic regression model was 0.978 [0.945–1.013, P = 0.222]). Conclusions: The UNa+/UCr ratio in a spot urine sample 2 h after intravenous diuretic administration is a simple, highly predictive marker for the identification of AHF patients with poor diuretic response, surpassing guidelines-recommended markers like UNa+.

Despite the progress of its management, COVID-19 maintains an ominous condition which constitutes a threat, especially for the susceptible population. The cardiac injury occurs in approximately 30% of COVID-19 infections and is associated with a worse prognosis. The clinical presentation of cardiac involvement can be COVID-19-related myocarditis. Our review aims to summarise current evidence about that complication. The research was registered at PROSPERO (CRD42022338397). We performed a systematic analysis using five different databases, including i.a. MEDLINE. Further, the backward snowballing technique was applied to identify additional papers. Inclusion criteria were: full-text articles in English presenting cases of COVID-19-related myocarditis diagnosed by the ESC criteria and patients over 18 years old. The myocarditis had to occur after the COVID-19 infection, not vaccination. Initially, 1588 papers were screened from the database search, and 1037 papers were revealed in the backward snowballing process. Eventually, 59 articles were included. Data about patients’ sex, age, ethnicity, COVID-19 confirmation technique and vaccination status, reported symptoms, physical condition, laboratory and radiological findings, applied treatment and patient outcome were investigated and summarised. COVID-19-related myocarditis is associated with the risk of sudden worsening of patients’ clinical status, thus, knowledge about its clinical presentation is essential for healthcare workers.

Acute heart failure (AHF) is a life-threatening, heterogeneous disease requiring urgent diagnosis and treatment. The clinical severity and medical procedures differ according to a complex interplay between the deterioration cause, underlying cardiac substrate, and comorbidities. This study aimed to analyze the natural phenotypic heterogeneity of the AHF population and evaluate the possibilities offered by clustering (unsupervised machine-learning technique) in a medical data assessment. We evaluated data from 381 AHF patients. Sixty-three clinical and biochemical features were assessed at the admission of the patients and were included in the analysis after the preprocessing. The K-medoids algorithm was implemented to create the clusters, and optimization, based on the Davies-Bouldin index, was used. The clustering was performed while blinded to the outcome. The outcome associations were evaluated using the Kaplan-Meier curves and Cox proportional-hazards regressions. The algorithm distinguished six clusters that differed significantly in 58 variables concerning i.e., etiology, clinical status, comorbidities, laboratory parameters and lifestyle factors. The clusters differed in terms of the one-year mortality (p = 0.002). Using the clustering techniques, we extracted six phenotypes from AHF patients with distinct clinical characteristics and outcomes. Our results can be valuable for future trial constructions and customized treatment.