Although the genetic origins underpinning congenital heart disease (CHD) have been extensively studied, genes, by themselves, do not entirely predict phenotypes, which result from the complex interplay between genes and the environment. Consequently, genes merely suggest the potential occurrence of a specific phenotype, but they cannot predict what will happen in reality. This task can be revealed by metabolomics, the most promising of the “omics sciences”. Though metabolomics applied to CHD is still in its infant phase, it has already been applied to CHD prenatal diagnosis, as well as to predict outcomes after cardiac surgery. Particular metabolomic fingerprints have been identified for some of the specific CHD subtypes. The hallmarks of CHD-related pulmonary arterial hypertension have also been discovered. This review, which is presented in a narrative format, due to the heterogeneity of the selected papers, aims to provide the readers with a synopsis of the literature on metabolomics in the CHD setting. Congenital heart disease (CHD) represents the most common birth anomaly, with a reported prevalence approximately 8:1000 children who are born alive . More reomics sciences” are very promising tools to study this complex interplay. They are defined as the disciplines in which study molecules have similar qualities. They are named “genomics”, which studies DNA structure and function; “transcriptomics”, which is focused on studying mRNA molecules; “proteomics”, which studies proteins ; and “metabolomics”, the discipline which studies metabolites . Progressing from the first one to the last, their complexity increases whilst the quantity of information decreases [omics sciences”. Metabolomics studies metabolites and the “metabolome” represents the collection of all metabolites. Metabolomics provides a functional picture of an organism, as determined by the sum of his/her genes, RNA, and environmental factors [Genetics and epigenetics interact and lead to multiple metabolic pathways of extremely small molecules, which sometimes cross over one another. The “ecreases . Genes,  factors .This review provides an overview of metabolomics as applied specifically to the field of CHD.The metabolomic analysis of the huge array of chemical compounds that possess different physical and chemical properties is carried out by two techniques, namely nuclear magnetic resonance spectroscopy and mass spectrometry. Mass spectrometry, combined with separation techniques such as gas or liquid chromatography, estimates the mass-to-charge ratio of ions in a molecule of a biological sample and represents them in the form of a spectrum see .On the other hand, proton nuclear magnetic resonance spectroscopy is able to characterise low-molecular-weight metabolites containing a nuclear magnetic resonance nucleus in a biological sample and represent them in a spectrum .Some of the advantages of proton nuclear magnetic resonance spectroscopy include that it is a fast, highly reproducible and non-destructive method. Furthermore, it usually does not require sample preparation and can be used for the analysis of tissue samples. However, this technique has quite a low sensitivity, may encounter trouble in quantifying co-resonant metabolites, and is relatively expensive .metabolomic(s), metabonomics, congenital heart disease, congenital heart defect, paediatric cardiology.The electronic databases including Pubmed/Medline, Scopus, and Web of Science were investigated from their initial existence up to 8 October 2022. All searches were conducted using the following MeSH terms: All studies relevant to metabolomics in the field of CHD were included in this review, with no limitation on the period of publication. However, some specific exclusion criteria were employed, including: (1) studies not focused on the selected topic (2) papers in a language other than English (3) duplicated studies and (4) those studies not available from libraries for full-text assessment.Two hundred and twelve studies were identified through the database search. After excluding those studies that did not reach criteria mentioned above, 39 papers were included in the review.Due to the limited number of papers, this review is in the form of a narrative review.Although significant research has focused on the genetic and environmental factors underlying CHD, very few studies have been focused on teratogenic mechanisms.In one study of metabolomic biomarkers in maternal first-trimester blood, mass spectrometry and nuclear magnetic-resonance-spectrometry-based metabolomic analyses were performed between 11 weeks’ and 13 weeks 6 days’ gestation. One hundred and twenty-three metabolites were detected to occur in significant quantities in those mothers with babies affected by CHD compared to normal controls. Significant differences in sphingomyelin, acylcarnithine, and other metabolite levels were detected in pregnancies affected by CHD. Predictive algorithms for CHD showed a high sensitivity (0.929) and specificity (0.932) [The prenatal diagnosis of CHD typically relies on the use of foetal echocardiography, but some cases may go undetected despite this investigation. An untargeted metabolomic study performed through a combination of gas chromatography and mass spectrometry showed that amniotic fluid uric acid level had a moderate predictive power in identifying those cases with CHD. This may represent a potential diagnostic biomarker to identify pregnancies at risk of CHD .When extending this analysis to the third trimester, by using the same tools to analyse urine samples, dysregulation was detected in lipid  and folate metabolism. This dysregulation in lipid metabolism may explain, at least in part, the elevated risk of CHD in offspring of diabetic mothers .Another study evaluating the potential biomarkers of maternal urine metabolomics for foetuses with CHD, based on modified gas chromatography-mass spectrometry (during the 2nd and 3rd trimesters), revealed that the most important potential biomarkers linked to CHD were not only uric acid, but also 4-hydroxybenzeneacetic acid, 5-trimethylsilyloxy-n-valeric acid, propanedioic acid, and hydracrylic acid. These results suggest that aromatic amino acid and short-chain fatty acid metabolism may also be crucial in the development of CHD .In another metabolomics study on maternal blood, the metabolomic signature of the second trimester in mothers with a baby affected by a foetal cardiac abnormality showed significant differences to that of a normal pregnancy. Malonic acid, 3-hydroxybutyric and methyl glutaric acid, urea, androstenedione, fructose, tocopherol, leucine, and putrescine were the most relevant metabolites detected between the two groups .When comparing the gut microbiota and plasma metabolic profile of mothers of infants with CHD to healthy controls, differences were identified in 34 bacteria and 53 plasma metabolites. Specifically, the Bifidobacterium and Streptococcus bacteria showed positive correlations with a range of metabolites belonging to the lipid metabolism pathway. Therefore, perturbations of maternal gut microbiota and plasma metabolites may be linked to the risk of occurrence of congenital heart abnormalities in their offspring .On metabolomic analysis, CHD patients showed significant differences in the concentrations of a significant number of metabolites. Differences between CHD and controls were greater in number and degree than those between different CHD aetiologies. A specific group of metabolites containing amino acids and their metabolites  were associated with negative clinical outcomes across all anatomic CHD subgroups. .A similar metabolomics study showed that the blood amino acid and choline metabolite levels in CHD patients were significantly different in comparison with healthy control children. In particular, four metabolites, betaine, taurine, glutamine, and phenylalanine, could be used as potential biomarkers in the screening and diagnosis of CHD .Bicuspid aortic valve is the most frequent CHD. It is responsible for valvular dysfunction and aortopathy in affected patients. Urine nuclear magnetic resonance metabolomics was employed to search for a molecular fingerprint for this particular cardiac defect. As a consequence, seven metabolites  were identified as potential biomarkers. Among these, glycine, hippurate and taurine individually displayed the highest sensitivity and specificity .Significant differences in arginine and proline metabolisms were also identified between patients with bicuspid aortic valve and a normal trileaflet aortic valve. The observed augmented arachidonic acid metabolism may be a consequence of more severe baseline haemodynamics and worse left ventricular reserve remodelling after transcatheter aortic valve replacement in those patients with bicuspid aortic valve .By using a serum-based metabolomics profiling method as a tool for bicuspid aortic valve screening, two metabolites were identified: monoglyceride and glycerophospho-N-oleoyl ethanolamine. A predictive model was established with a sensitivity of 76.7%, specificity of 90.0%, positive predictive value of 80% and negative predictive value of 85.0% .Blood samples and thymus tissue collected from patients with ventricular septal defect underwent liquid chromatography-mass spectrometry-based metabolomics. This showed that two metabolites, uric acid and sphingomyelin, were increased in the serum of these patients .Blood samples on case (n = 550) and control mothers (n = 221) were obtained from the National Birth Defects Prevention Study. A metabolomic profile of 22 pregnant females with Tetralogy of Fallot (TOF) and hypoplastic left heart syndrome (HLHS) was studied. Overall, there were 9 significant metabolites detected in the mothers of HLHS patients and 30 metabolites in the mothers of the TOF patients. Among them, 2 sphingolipids were significantly increased in the HLHS group, while 2 triglycerides were decreased and cholesterol esters were elevated in TOF females. These metabolites were hypothesised to possibly play a pivotal role in both cellular signalling and disease development. It was also proposed that dietary modifications may potentially prevent these two forms of CHD .Metabolomic analysis performed using right atrial biopsy samples in patients with TOF discovered that carbohydrate and heme metabolism were upregulated, whereas bile acid metabolism, lipid droplet, and lipid binding were downregulated. Additionally, the butanoate metabolism-related hub regulators ALDH5A1 and EHHADH were significantly downregulated, potentially contributing to myocardial tissue damage in those patients with TOF .Combining transcriptomics and metabolomics analysis, by means of liquid chromatography coupled with mass spectrometry, on right atrial specimens in TOF patients, showed a downregulation of sphingolipids. This may reflect the chronic hypoxia experienced in many patients with TOF .Dextro-transposition of the great arteries is a life-threatening CHD requiring surgery, requiring the use of cardiopulmonary bypass, usually within the first month of life. Fourteen newborns with dextro-transposition of the great arteries with intact ventricular septum underwent arterial switch operation. Urine samples were collected pre- and post-surgery. Three hundred and seventy-one different metabolites were identified between the preoperative state and following surgery. Among these, 13 belonged to the kynurenine pathway of tryptophan degradation. These metabolites could be potential biomarkers of brain injury in cyanotic CHD .In complex CHD, such as single ventricle hearts, which have been surgically palliated with the Fontan technique, abnormalities in the tricarboxylic acid cycle and amino acid metabolism were identified as distinguishing the metabolomic profiles of these patients .The targeted metabolomic analysis of serum amino acids in adult Fontan patients, with a dominant left ventricle, was undertaken in one study. Blood levels of asymmetric dimethylarginine, methionine sulfoxide, glutamic acid, and trans-4-hydroxyproline and the methionine sulfoxide/methionine ratio were shown to be significantly elevated and blood levels of asparagine, histidine, taurine, and threonine were significantly reduced in the Fontan patient cohort compared to the control group.The methionine sulfoxide/methionine ratio values had a significant negative correlation with oxygen uptake on exertion. These alterations in amino acid metabolome, identified in Fontan patients, suggest relationships between Fontan pathophysiology, altered cell energy metabolism, oxidative stress, and endothelial dysfunction similar to those found in biventricular patients with congestive heart failure . In the Metabolomics is not routinely used when quantifying amino acids in CHD. In one study, liquid chromatography and mass spectrometry were compared when sequencing them. Both methods proved to be reliable, although, in the Fontan patient group, concentrations significantly differed between the two techniques. Mass spectrometry is time-saving compared to liquid chromatography .In relation to cyanotic CHD, 12 specific metabolites were detected that differentiated between hypoxemic and non-hypoxemic patients, including lower methylmalonic acid, glutamate and hypoxanthine, and higher thymine and myo-inositol. No significant variations were observed in relation to the degree of hypoxemia between these groups . One stuThere is strong evidence that hypoxemia in cyanotic CHD has the potential to alter metabolic pathways. This is supported by the metabolomic mass spectrometry analysis of early postnatal amino acid and carnitine/acylcarnitine profiles in newborns with cyanotic CHD in comparison to healthy controls. The former had higher levels of alanine, phenylalanine, leucine/isoleucine, citrulline, ornithine, C5, C5-OH, but lower levels of C3, C10, C12, C14, C14:1, C16, C16.1, C18, C5-DC, C6-DC, C16-OH, C16:1-OH, when compared with the control group .In cyanotic CHD, significant metabolic shifts were detected during puberty through a combination of positron emission tomography/computed tomography and magnetic resonance imaging. Heart tissue samples were collected. Prepubertal patients with cyanotic CHD had glucose-dominant cardiac metabolism and normal cardiac function. In contrast, during puberty, myocardial glucose uptake and glycolysis were significantly decreased, whereas fatty acids were significantly increased, along with decreased left ventricular ejection fraction. These clinical phenotypes were confirmed in an animal model of chronic hypoxia. HIF-1α was identified as the cornerstone of cardiac metabolic switching. It was upregulated during childhood but significantly downregulated during puberty. Chronic hypoxia hampered the switch of myocardial substrates from fatty acids to glucose, thus inhibiting ATP production and impairing ejection fraction .Arrhythmogenic right ventricular cardiomyopathy (ARVC) is a potentially life-threatening inherited disease, which may cause ventricular arrhythmias and sudden cardiac death. Mutations in at least eight different genes were identified as potentially responsible for ARVC. A rare subtype of the disease is that caused by the mutation TMEM43 p.S358L in males. TMEM43 p.S358 is similar to the mutation CG8111 p.S333 found in the fly Drosophila melanogaster.Metabolomics proved that the S333 in CG8111 is essential in lipid metabolism in the insect. Interestingly, this lipid metabolism impairment was also found in a murine model of ARVC, with fibrofatty replacement being the classic hallmark of the disease. Taken together, these findings may shed greater light on the molecular basis of the human TMEM43 p.S358L variant of ARVC .Corticosteroid Therapy in Neonates Undergoing Cardiopulmonary Bypass trial was performed to determine possible links with surgical outcomes. Blood was sampled three times, i.e., before skin incision, just after cardiopulmonary bypass, and 12 h following surgery. Single and composite outcomes were evaluated: morbidity, mortality, cardiac injury, hepatic injury, and acute kidney injury. The metabolomics profile was determined by a combination of high-resolution tandem liquid chromatography and mass spectrometry. A total of 34 metabolites changed significantly between all the measured timepoints, in terms of either an immediate postoperative decrease or increase, with a trend toward the preoperative baseline. Many of these metabolites were capable of predicting the occurrence of morbidity, mortality, cardiac, and hepatic injuries. However, the metabolomics plasma analysis failed to find any predictor of kidney injury [Clinical risk factors often fail to fully explain the different outcomes after cardiac surgery. Targeted metabolomic analysis of plasma from newborns enrolled in the y injury .On the other hand, acute kidney injury remains a significant cause of morbidity and mortality following CHD surgery. Progress in the scientific knowledge of this field is limited because of the poor evidence for the underlying altered metabolic pathways. As such, metabolomics may help tot identify the mechanisms of renal failure and provide potential therapeutic targets. In a piglet model of cardiopulmonary bypass with deep hypothermic circulatory arrest, targeted metabolic profiling of kidney tissue, urine, and plasma was performed to detect the specific metabolic changes in animals with acute kidney injury on histology. The tissue metabolic profile in injured kidneys proved to be different in comparison to uninjured kidneys, as evidenced by dysregulation in tryptophan and purine metabolism. On urine analysis, nine metabolites differed significantly in piglets with acute kidney injury, with a pattern suggesting increased aerobic glycolysis. No significant differences were found following plasma analysis. No overlap was found concerning dysregulated metabolites in kidney tissue and urine .Not only did marked metabolomic differences exist between infants with or without acute kidney injury after cardiothoracic surgery with cardiopulmonary bypass, but a portion of patients with mild injury showed metabolic changes similar to those seen in the moderate-to-severe stage of the disease. Prominent changes to purine were confirmed, but methionine and kynurenine/nicotinamide metabolism were also involved, suggesting a potential role for metabolomic analysis in the evaluation of lower-stage injury .Liquid-chromatography-tandem mass spectrometry metabolomics showed that, in infants undergoing cardiopulmonary bypass, circulating serum kynurenic acid metabolites may explain adverse neurologic and immunologic activities . In an IMortality for infants undergoing complex CHD surgery is over 10%, with a risk of complications up to 40%. The early identification of high-risk infants may prove challenging. Metabolomic fingerprinting of newborns ≤120 days undergoing cardiopulmonary bypass showed significant shifts in metabolic pathways, which were associated with mortality and length of cardiac intensive care unit stay. Non-survivors and individuals requiring a longer intensive care unit stay showed distinct modifications in specific metabolites, including aspartate and methylnicotinamide, which may discriminate sicker patients from those experiencing a more benign course .Dysregulated metabolism occurs following surgery for CHD, contributing to organ failure and morbidity. Based on this premise, the effect of postoperative tight glycemic control compared to conventional blood glucose management on metabolism in children undergoing CHD surgery was studied. Blood plasma metabolome was profiled using proton nuclear magnetic resonance spectroscopy. Changes in metabolic profile were seen over the time course from surgery to recovery, compared with the preoperative state. Interestingly, tight glycemic control did not significantly alter the metabolic profile, while conventional glucose management did. Eight metabolites  were identified as associated with surgical and disease severity .Unrepaired CHD with a long-standing left-to-right shunt may cause irreversible pulmonary arterial hypertension (PAH) (Eisenmenger syndrome). The initial increase in blood flow to the lungs triggers vasoconstriction, and then vascular changes due to intimal layer hyperplasia, hypertrophy of smooth cells, and thrombosis. Ultimately, due to a significant increase in pulmonary vascular resistance, the left-to-right shunt becomes a right-to-left shunt, resulting in marked hypoxemia and cyanosis and, eventually, right heart failure and death .Metabolome profiling data showed that metabolites which are involved in purine, glycerophospholipid, galactose, and pyrimidine metabolisms, were significantly modified in PAH with CHD and left-to-right shunt. Alterations in microbial flora from the lung microbiota were also found. Based on these premises, pulmonary microbes and metabolites may prove to be effective biomarkers and provide valuable insights into clinical diagnosis, treatment, and prognosis of the secondary disease .2α, fructose 6-phosphate, l-glutamine, dehydroascorbate, hydroxypyruvate, threonine, l-cystine, and 1-aminocyclopropane-1-carboxylate levels. By combining the results obtained through these different techniques, a risk score was suggested as a predictor of PAH development in patients with ventricular septal defects [The molecular mechanisms underlying PAH in ventricular septal defects are unclear. A multi-omics study involving proteomics, metabolomics, and bioinformatics discovered 191 differential metabolites between patients with and without PAH. These included an increase in serotonin, taurine, creatine, sarcosine, and 2-oxobutanoate, and a decrease in vanillylmandelic acid, 3,4-dihydroxymandelate, 15-keto-prostaglandin F defects .Nuclear magnetic resonance analysis of plasma in adult patients with CHD and associated PAH showed a significant correlation between metabolites such as lactate and threonine and mean pulmonary arterial pressure, pulmonary vascular resistance, and N-terminal pro-B-type natriuretic peptide. Lactate and threonine have the potential to become biomarkers of this disease, thus providing a non-invasive diagnosis in addition to predicting the prognosis of PAH associated with CHD .Explanted lung tissue from 13 idiopathic PAH patients, 5 PAH associated with CHD patients, and 16 controls were analysed for untargeted metabolomic analysis with liquid chromatography associated with mass spectrometry. Significant differences were detected in metabolites and metabolic pathways between the PAH subgroups and control tissues. Spermine levels were significantly correlated with the patients’ cardiac output, while (2e)-2,5-dichloro-4-oxo-2-hexenedioic acid was notably correlated with serum creatinine levels. PAH subjects with higher thymine levels were found to have a better prognosis .In another study, blood samples were used instead of lung specimens. Forty subjects with idiopathic PAH, 20 individuals with CHD-associated PAH, and 20 healthy controls were enrolled. Analyzing their samples through liquid chromatography along with mass spectrometry, distinct metabolite signatures distinguished idiopathic PAH and CHD PAH patients from healthy controls. Twenty-six and 15 distinct metabolites were detected, respectively, between the groups. Dysregulation involved lipid, glucose, amino acid, and phospholipid metabolisms in PAH patients in comparison with their healthy counterparts. Among these metabolites, a group from lipid metabolism and fatty acid oxidation demonstrated a close association with PAH .2 in radial artery blood samples. The metabolites and clinical parameters were, in turn, correlated with mean arterial pressure. These metabolomic patterns may be used as suitable non-invasive markers in disease monitoring [Again, following a mass spectrometry metabolomics analysis of plasma, seventeen metabolites were identified that responded to shunt surgical or interventional closure. These correlated with clinical measurements including diastolic pulmonary arterial pressure, bicarbonate content in radial artery, bicarbonate content in superior vena cava, and the partial pressure of dissolved COnitoring .The presented scientific reports show that metabolomics has been widely used in attempts to make a prenatal diagnosis of CHD. Three maternal samples, namely urine, blood, and amniotic fluid, have been used. Many different metabolomic biomarkers have been identified depending on the metabolomic technique, the type of maternal sample, and the trimester of pregnancy. However, some overlapping results have been found concerning derangements in lipid metabolism and increased uric acid. This is consistent with previous reports concerning the decreased metabolic capacity to utilize released free fatty acids in infants with CHD, mostly in those with a cyanotic cardiac defect. Lipid metabolism seems to be most frequently disturbed in this setting . TypicalHyperuricaemia, due to reduced renal excretion in concert with uric acid overproduction, is often encountered in cyanotic CHD patients, most commonly in those over 15 years of age . Blood lWhen comparing cyanotic and non-cyanotic CHD, the most relevant differences are that, in the latter, protein synthesis is reduced, whilst Krebs circle is up-regulated at the expense of aerobic energy metabolism. Protein synthesis requires significant energy, which is unfortunately limited in this setting. As the oxygen supply to the heart is decreased, mitochondrial oxidative phosphorylation declines. However, lactate levels are high in cyanotic CHD patients, suggesting that anaerobic energy metabolism is up-regulated amid hypoxia.Metabolomics also has the potential to predict outcomes after cardiac surgery. This is important, given that clinical risk factors themselves are often unable to fully explain the different outcomes after cardiac surgery. Specific metabolites are associated with morbidity/mortality, length of intensive care unit stay and cardiac, brain, hepatic, and acute kidney injuries. Renal injury by metabolomics is not detected with plasma analysis, but requires renal tissue specimens for analysis.Peculiar metabolomic fingerprints for some specific CHD subtypes have been identified. They are very heterogeneous. At times, they are related to secondary heart or other organ failure. At other times, they are likely to be linked to the metabolic error, which is responsible for that specific CHD. Further research is required before this intriguing relationship is fully elucidated. Characteristic metabolomic patterns in repaired and unrepaired cardiac defects with PAH have also been discovered. They differ significantly from other PAH aetiologies, indicating that this is a multifaceted disease process.In conclusion, recent advances in the “omics sciences” have revealed new opportunities in CHD research, with multiple potential applications . Untarge


We found a statistically significant difference between the two groups for the mean values of the LVpGLS parameter (adjusted p = 0.0047), with impaired LV myocardium function being observed in the TGA group after adjusting for other covariates. Regarding segmental strain, the mean medial and apical inter-ventricular septum strain values were found to be significantly lower in the neonates with TGA than in the controls . The results of an ROC analysis showed that LVpGLS had a significant ability to differentiate between neonates with TGA and controls . In conclusion, LVpGLS is a parameter with a significant discriminatory ability for LV dysfunction, and it is useful in the evaluation of ventricular myocardial function in newborns with TGA.The transposition of great arteries (TGA) is one of the most frequent and severe congenital heart diseases. After newborn stabilization and while pending surgical correction, echocardiographic monitoring with a careful evaluation of left ventricle (LV) performance is warranted. In this study, our objectives were (i) to compare myocardial function, assessed via speckle-tracking echocardiography, between neonates with TGA and neonates without TGA and (ii) to identify a strain parameter with a good discriminatory ability for TGA. We conducted a retrospective, single-center study. A total of 90 neonates were examined, of whom 66 were included (16 comprised the TGA group and 50 comprised the control group). The results of a bivariate analysis showed that classic echocardiography parameters displayed no significant differences between the two studied groups ( The lesion is defined by ventriculoarterial discordance in the setting of atrioventricular concordance. The most common form of TGA is D-TGA, in which the right ventricle (RV) is positioned to the right of the left ventricle (LV), and the origin of the aorta is situated anterior and to the right of the origin of the pulmonary artery ,2. With o stages .Single-stage ASO is currently considered the procedure of choice for newborns with simple TGA. There is a potential risk of myocardial injury after the reimplantation of the coronary arteries, as well as a sudden pressure overload, to which the LV is subjected after the interruption of the cardiopulmonary bypass .We sought to evaluate myocardial function using 2D-strain analyses with the following objectives: (i) to compare longitudinal and segmental strain parameters between two groups, namely, newborns with TGA and newborns without TGA, and (ii) to identify strain parameters with a sensitivity towards the detection of myocardial dysfunction in the group of newborns with TGA (in order to identify a strain parameter with a good discriminatory ability for TGA).This retrospective study was performed between January 2020 and September 2022 in the Children’s Cardiology Clinic, the Emergency Institute of Cardiovascular Diseases and Heart Transplantation, in Târgu Mureș.Inclusion criteria: newborns diagnosed with simple TGA and with a complete echocardiographic evaluation performed preoperatively under the conditions of hemodynamic stability . Thus, 20 newborns were included.Exclusion criteria: newborns diagnosed with complex TGA, with severe hypoxia (O2 saturation below 75%), with metabolic acidosis and/or with inefficient interatrial mixing. Thus, 2 newborns with hemodynamic instability and inefficient mixing due to pulmonary hypertension, which required the administration of nitric oxide, and 2 newborns whose echocardiographic images were of poor quality for strain analyses were excluded.Control group (or neonates without TGA) inclusion criteria: full-term, healthy newborns  with no major congenital anomalies, with no history of maternal gestational diabetes and with normal echocardiography  were not excluded). Thus, 70 newborns were included in the control group, and premature newborns, as well as those with cardiac and non-cardiac congenital anomalies, were excluded from the study  .In order to echocardiographically evaluate the patients, we used an EPIQ 7 ultrasound machine. The monitored echocardiographic parameters included mitral and tricuspid annular plane systolic excursion , LV EF-Teichholz and the indexed LV mass. Two-dimensional acquisitions from an apical four-chamber view recorded at a frame rate exceeding 70 Hz and optimal quality were stored in the Digital Imaging and Communications in Medicine (DICOM) file format and analyzed offline with Philips QLAB 15 software using LV and RV autostrain functions. In order to analyze an entire cardiac cycle, the M-mode function was used to determine the motion (closing and opening) of the mitral valve, automatically generated using the software. Three points were tagged at the endocardium level . The endocardial margin was automatically traced and then manually checked and corrected.Thus, the pre-procedural images of the 16 included newborns were analyzed by a single investigator, measuring the LV peak longitudinal strain (LVpGLS), RV free-wall longitudinal strain (RVFWSL), RV 4-chamber strain (RV4SLC) and the biventricular segmental strain. The interventricular (InterV) septum and the walls were divided automatically into 3 segments . The same echocardiographic parameters were examined in the control group.Descriptive measures, including arithmetic mean values, sample standard deviations, medians with interquartile ranges  and frequencies, are presented for each studied group (TGA group vs. control group). The fitting of the sample data to normal distribution was tested using the Shapiro–Wilk test and by visually inspecting quantile plots (Q-Q plots).p-value < 0.001. Because there were three response variables, when reporting the MANOVA results, we used Wilks’ lambda test statistic as a more robust test for the unbalanced number of observations in the groups [To test for significant differences between the groups (TGA group vs. control group), we compared the mean scores for the overall longitudinal strain variables  of the neonates with TGA with those of the controls by conducting a one-way multivariate analysis of variance (MANOVA). The MANOVA assumption concerning the homogeneity of covariance matrices was tested using Box’s M test, with a significant result being achieved when e groups .In order to estimate the group effect size in the MANOVA analysis, we used the partial eta squared  was estimated. All tested independent variables showed a value of VIF lower than 3.In order to assess the ability of the longitudinal strains to differentiate between the neonates with TGA and the neonates without TGA, a univariate analysis based on the Receiver Operating Characteristic (ROC) curve was used. The indices for the performances of the longitudinal strains, such as specificity, sensitivity, positive predictive value, negative predictive value, cut-off value and area under the curve (AUC), were derived from ROC analyses using the Youden’s index.A comparison between regional strain variables  were performed using Student’s t test, assuming equal variances, or the Welch two-sample t-test.p-value) lower than 0.05.All two-sided statistical tests used a significance level (α) chosen a priori at 0.05, a significant result being defined using an estimated significance level .A sample of 66 subjects, comprising 50 neonates without TGA (controls) and 16 neonates with TGA, was recruited. The neonates’ characteristics are described in p > 0.05), although a lower body surface area (BSA) mean was observed  in the TGA group.Both studied groups were comparable regarding all demographic and anthropometric characteristics (p = 0.027) and RV4CSL (p = 0.002), while the RV-related longitudinal strain variables had a strong significant linear correlation between them (p < 0.0001). After stratification by group, the linear correlations remained statistically significant in the neonates with TGA but not in the controls  and in each group (p > 0.05).The results of Mardia’s test confirmed the multivariate normality in the total sample . The measure of effect size  = 6.23, The post hoc one-way ANOVAs carried out following the MANOVA highlighted that the neonates with TGA differed significantly from the controls regarding LVpGLS but not in terms of RVFWSL or RV4CSL . FurtherAfter performing the multiple linear regression analysis and adjusting for other covariates, namely, age, BMI, systolic blood pressure, heart rate and indexed left ventricular mass, the association between the left ventricle peak longitudinal strain (LVpGLS) and TGA remained significant despite the significant effect of BMI . The magp = 0.011), with a sensitivity of 75% , a specificity of 76% , a positive predictive value (PPV) of 50%  and a negative predictive value (NPV) of 90%  at an optimal cutoff value of −18.40%.The results of the ROC analysis showed that LVpGLS had a significant ability to differentiate between neonates with TGA and controls  and apical locations (p = 0.002). Moreover, the inter-ventricular septum strain mean measured from the medial portion was significantly lower in the neonates with TGA than in the controls . We also found that the inter-ventricular septum strain mean measured from the apical location was significantly lower in the neonates with TGA than in the controls  can be used to predict the clinical evolution of TGA ,20.Our study provides evidence that a speckle-tracking analysis is useful for evaluating the ventricular myocardial function in newborns with TGA, representing a promising approach for evaluations of cardiac function. LVpGLS differentiated those with TGA from those without TGA, but conclusions concerning the magnitude of its diagnostic ability require further studies in larger samples. The use of LVpGLS as a predictive parameter for LV dysfunction could be applied in the perioperative management of patients with TGA.Our study has several limitations: (i) The data analysis was based on a relatively small number of patients with TGA in a retrospective, single-center study, which might have limited the power to detect subtle cardiac dysfunction; therefore, a prospective, multicenter, multivendor study is required to validate our results for clinical application. (ii) The diagnostic ability of LVpGLS was based on a univariate analysis; therefore, a multivariate analysis taking into consideration other conventional cardiac imaging markers is required to test its independent discriminatory capacity.


Strain analysis of cardiac magnetic resonance (CMR) is critical for the diagnosis and prognosis of heart failure (HF) with preserved ejection fraction (HFpEF). Our study aimed to identify the diagnostic and prognostic value of strain analysis revealed by CMR in HFpEF.  Participants in HFpEF and control were recruited according to the guideline. Baseline information, clinical parameters, blood samples were collected, and echocardiography and CMR examination were performed. Various parameters, including global longitudinal strain, global circumferential strain (GCS) and global radial strain in left ventricle (LV), right ventricle (RV), and left atrium, were measured from CMR. Receiver operator curve (ROC) was established to evaluate the diagnostic and prognostic value of strains in HFpEF. t-test. All strains had significant diagnostic value for HFpEF. The area under curve (AUC) of LV strains was greater than 0.7 and the AUC of the combined analysis of LV strains was 0.858 : 0.798–0.919, sensitivity: 0.713, specificity: 0.875, P < 0.001), indicating that they had a higher diagnostic value than individual LV strains. However, individual strains had no predictive value in identifying end-point events in HFpEF, the AUC of coanalysis of LV strains was 0.722 , indicating its prognostic relevance.  Seven strains, with the exception of RVGCS, were employed to generate ROC curves after  Individual strain analysis in CMR may be useful for diagnosing HFpEF, the combination of LV strain analysis had the highest diagnostic value. Moreover, the prognostic value of individual strain analysis in predicting HFpEF outcome was not satisfactory while the combined usage of LV strain analysis was prognostically valuable in HFpEF outcome prediction. Heart failure (HF) with preserved ejection fraction (HFpEF) accounts for ∼50% of hospitalizations for HF  and has Myocardial strain is a dimensionless indicator of the change in distance between two sites that has provided insight into the mechanics of the myocardium and can be classified into three categories: GLS, global circumferential strain (GCS), and global radial strain (GRS) . Strain GLS is the most often used parameter in strain analysis and has been demonstrated to predict hospitalization for HF and cardiovascular death in a few studies . While GHerein, our study sought to evaluate whether strain analysis of CMR on LV, LA, and RV can identify HFpEF at the early stage and can provide additional prognostic value beyond conventional clinical and echocardiographic indices.The study was approved by the institutional review board of the Affiliated Hospital of Xuzhou Medical University .All participants provided written informed consent (Supplementary Data The following criteria were used to determine inclusion in the HFpEF group: (1) typical HF signs and symptoms; (2) N-terminal pro-brain natriuretic peptide (NT-proBNP) >125 pg/ml; (3) left ventricular ejection fraction (LVEF) ≥50%; (4) either LV structural abnormalities or diastolic dysfunction. Controls were recruited based on the following criteria: (1) be free of HF symptoms; (2) LVEF ≥50% and be free of echocardiographic signs of severe diastolic dysfunction.2, and contraindications to CMR were all excluded.Participants with severe native valve disease, noncardiovascular life expectancy <6 months, severe pulmonary disease, estimated glomerular filtration rate (eGFR) of <30 ml/min/1.73 mAll participants underwent a medical history and physical examination, a review of medical records, general condition, medication and blood sample analysis, transthoracic echocardiography, and CMR during the same visit.E) and mitral annular early (eʹ) diastolic velocity, with calculation E/eʹ ratios.Transthoracic echocardiography was performed on all participants using a commercial echocardiographic device . The US probe model was S5-1 with a frequency range of 1–5 MHz. Measurements of LV diastolic function were performed according to the guidelines. The following diastolic indices were obtained; transmitral early  equipped with a phase-array cardiac coil. To ensure the unity of the data, the time window frame between echocardiography and CMR was within 1 week.A retrospectively gated cine-CMR was used to acquire cine images in cardiac short-axis, vertical long-axis, and horizontal long-axis orientations, and a steady-state free precession sequence for volumetry was used. Steady-state free precision images were used for cine imaging  .CMR images were evaluated using CVI42  software by a single observer with more than 3 years of working experience, who was blinded to all data. Ventricular volumes, EF, and LV mass (excluding papillary muscles) were determined using balanced steady-state cine imaging and calculated from the short-axis cine stack. Endocardial borders were traced in end-systole, and end-diastole and volumes were averaged to calculate.All volumetric and mass data were indexed to body surface area to acquire ventricular volumetric index and mass index. The biplane approach was used to compute the left atrial volumes, excluding the appendage and pulmonary veins. Maximum and minimal volumes were calculated. End-diastolic volume and end-systolic volume of the ventricular volumes were assessed from the volume–time curve for the maximal and minimal values and were used to calculate EF .The LV, RV, and LA endocardial and epicardial borders were delineated to enable semiautomatic tracking of the myocardium throughout the heart cycle. To ensure the accuracy of strain analysis, after automatic profile delineation, the tracking performance was checked and manually adjusted if improper. We employed one two-chamber view and one four-chamber view for LA strain analysis. We used a stack of short-axis views and three long-axis views  to depict LV and RV strain. GCS, GLS, and GRS values  were averaged from peak values of all 16 American Heart Association segments as previously described , excludiIn April 2021, all patients were followed up via telephone and interviewed by a cardiologist. The clinical endpoint was a composite of mortality or readmission to hospital for HF . Additiot-test in two groups. Categorical variables were expressed as numbers and percentages using χ2 test.The statistical analyses were conducted using SPSS Version 26.0  and GraphPad Prism Version 8.3.0 . Continuous variables were expressed as mean ± standard deviation (SD) or median  by means of a sample P < 0.05 was considered statistically significant.To evaluate the diagnostic value of strain analysis in different chambers in HFpEF, we depicted the receiver operator curve (ROC) and calculated the area under curve (AUC) and Youden index  to find A total of 166 participants were enrolled in this study, including 97 patients with HFpEF and 69 participants without HF.2) than control. 40.2% of HFpEF patients had a smoking habit, which is higher than control (14.5%). Regarding medical history, the risk of coronary heart disease (CHD), myocardial infarction (MI), hypertension, and cardiomyopathy is statistically higher in HFpEF patients (incidence rate (HFpEF vs. control): CHD: 76.3% vs. 11.6%; MI: 67% vs. 1.4%; hypertension: 51.5% vs. 17.4%; cardiomyopathy: 14.4% vs. 1.4%). Meanwhile, HFpEF patients used more anticoagulants  and anti-HF drugs to maintain or restore cardiac function. eGFR was markedly lower, and NT-proBNP levels were significantly higher in HFpEF patients  vs. 58  pg/ml), indicating the impairment of renal and cardiac function. All the previous clinical parameters were consistent with the characteristic of HFpEF patients.E/eʹ ratio was considerably higher than control , confirming the impairment of cardiac diastolic function. CMR analysis of LV, RV, and LA revealed the impairment of chambers from the chamber volume and mass. HFpEF patients had an enlarged chamber volume and a heavier chamber weight. The results remained unchanged after controlling for body mass index. Ulteriorly, we measured three strains in three chambers (excluding GCS for LA) and found that seven of eight strains were altered compared to control. To be specific, the mean values for GLS in LV, RV, and LA in control were −14.51% ± 1.814%, −16.09% ± 6.751%, −15.05% ± 4.021%, respectively, while these have been changed to −11.33% ± 3.737%, −14.11% ± 4.735%, −12.87% ± 4.054%, respectively, in HFpEF group. Similarly, GRS and GCS generally followed the similar trajectory. However, the change of GCS in RV was not significant in HFpEF; thus, we excluded RVGCS in the subsequent analyses.The baseline imaging characteristics, including echocardiography and CMR measurements, were summarized in As displayed in P = 0.004), and Youden index was 0.459, showing its reliable prognostic value in HFpEF.During the median follow-up ; HFpEF 483  days), 16 composite events occurred in HFpEF group, including 15 readmissions for HF and one death. No events occurred in the control group. To determine if strain analysis might be utilized as a predictive prognostic marker for HFpEF, we developed the ROC curve in the HFpEF group. The findings  indicateHerein, we systematically investigated LV, RV, and LA strains in HFpEF and found the optimal combination of strain analysis in diagnosing and prognosing HFpEF with ROC curve.CMR strain analysis has been widely used in cardiovascular disease (CVD) diagnosis and prognosis. Strain can be divided into four chambers and three categories based on the dimensions . DiffereLV strain analysis was the most widely utilized strain in assessing HF. To the specific, GLS is the most well-known strain in HF diagnosis and prognosis. Beyond clinical and conventional echocardiographic measures, Shah et al.  demonstrLVGRS and LVGCS are the other two strain parameters, which have been less studied in HF diagnosis and prognosis. Recently, scientists have developed an algorithm to classify patients with chronic nonischemic HF using left and right ventricular strain (GLS and GCS), suggesting the effectiveness of LVGCS in diagnosing HF . He et aOur study also had several limitations. First, HF mainly occurs in developing and remote areas and is a relatively common disease; thus, patients frequently seek care in primary hospitals, our study was constructed in the single-center provincial hospital, it is worth investigating whether the conclusions can be generalized to other hospitals. Second, we used death and rehospitalization due to HF as the outcome events, while HFpEF patients often had a favorable overall prognosis; thus, the occurrence of end-point events is insufficient, and we were unable to perform Cox-regression analysis due to the limited end-point events, we should increase the sample or modify the end-point events in the further study. Third, based on the latest HF guideline, it remains unknown whether individual or combined strain analysis is valuable in HF with mildly reduced ejection fraction or HF with improved ejection fraction. Also, RA strain analysis should be considered in further studies.In conclusion, our study revealed the diagnostic and prognostic value of strain analysis in HFpEF. All individual strains had a high diagnostic value, and the combined use of LV strains showed the highest value, while all individual strains are useless in predicting cardiac death or readmission for HF in HFpEF, while the combined use of LV strains had the statistically significant prognostic value.


The prevalence of heart failure (HF) with reduced (r) and preserved (p) ejection fraction (EF) continues to rise globally despite current advances in diagnostics and improvements to medical management. Regardless of the underlying etiology, HF remains a progressive disease, which is largely irreversible and may ultimately require cardiac transplantation. This Special Issue focuses on the challenges and recent advances of diagnosis, treatment, and prevention of HF with or without associated comorbidities.Existing clinical imaging modalities and methods continue to be optimized for HF. These advances allow for earlier initial diagnosis, improved prognostic timelines, and can even be used to provide personalized medical management. Echocardiography remains a clinical mainstay for point-of-care evaluation, identifying disease etiology, and longitudinal monitoring. From our issue, Pescariu, et al. utilized cardiac strain and left ventricular function to accurately predict resynchronization success in HFrEF patients . Left veBlood biomarkers are another well-established component for helping in the diagnosis of HF. While the natriuretic peptides (BNP/NT-proBNP and ANP/NT-proANP) are the most commonly used clinical biomarkers to confirm or exclude a HF diagnosis, other biomarkers are under investigation, Though there were no correlations with serum galectin-3 or copeptin, Ianos, et al. found fibroblast growth factor 21 (FGF21) to be a reliable biomarker for HFpEF with type-2 diabetes mellitus . ConsideEven with proper imaging and great blood biomarkers, common comorbidities  can complicate accurate and timely diagnosis of HF. Lai et al. found that non-alcoholic fatty liver disease without HF is associated with left ventricular diastolic dysfunction and subclinical changes in left atrial contractility . AdamskaPart of the confusion from the comorbidities is the non-cardiac-systemic involvement; notably, the lungs. Ventilatory inefficiency, measured during cardiopulmonary exercise test, was used to stratify left ventricular ejection fraction in HFrEF outpatients for prognostic predictability of HF outcomes . Chen etHF medical management effectiveness can be longitudinally evaluated using all the above methods. This improvement in patient monitoring allows for a personized medicine approach–run the diagnostics and alter therapy according to the patient’s own data. As an example, Oleynikov, et al. were able to generate patient risk stratification using a developed model (formula) to predict chronic HF progression within 48 weeks after STEMI . Though We thank the authors, reviewers, Ms Lassie Shu and the rest of the editorial staff that contributed to this Special Issue. We hope that readers will appreciate the wide breadth of topics and clinical utility of the articles and reviews published within “Diagnosis and Management of Heart Failure”.


New antidiabetic therapy that includes sodium-glucose co-transporter 2 (SGLT2) inhibitors, glucagon-like peptide-1 receptor (GLP-1R) agonists, and dipeptidyl peptidase 4 (DPP-4) inhibitors showed significant benefit on cardiovascular outcomes in patients with and without type 2 diabetes mellitus, and this was particularly confirmed for SGLT2 inhibitors in subjects with heart failure (HF) with reduced ejection fraction (HFrEF). Their role on patients with HF with preserved ejection fraction (HFpEF) is still not elucidated, but encouraging results coming from the clinical studies indicate their beneficial role. The role of GLP-1R agonists and particularly DPP-4 inhibitors is less clear and debatable. Findings from the meta-analyses are sending positive message about the use of GLP-1R agonists in HFrEF therapy and revealed the improvement of left ventricular (LV) diastolic function in HFpEF. Nevertheless, the relevant medical societies still consider their effect as neutral or insufficiently investigated in HF patients. The impact of DPP-4 inhibitors in HF is the most controversial due to conflicting data that range from negative impact and increased risk of hospitalization due to HF, throughout neutral effect, to beneficial influence on LV diastolic dysfunction. However, this is a very heterogeneous group of medications and some professional societies made clear discrepancy between saxagliptin that might increase risk of HF hospitalization and those DPP-4 inhibitors that have no effect on hospitalization. The aim of this review is to summarize current clinical evidence about the effect of new antidiabetic medications on LV diastolic function and their potential benefits in HFpEF patients. Heart failure with preserved ejection fraction (HFpEF) makes almost the half of all patients with heart failure (HF) . The exiThere are no guidelines for specific treatment of HFpEF. The same medications used in the large therapeutic armamentarium in patients with heart failure with reduced ejection fraction (HFrEF) are used in HFpEF. Nevertheless, the significant improvement was made in the treatment of HFrEF in the last decade and some new medications with new mechanisms of actions were approved for clinical usage. One of the most important is the angiotensin receptor II blocker-neprilysin inhibitor (ARNI), sacubitril-valsartan that has significantly changed the practice in HFrEF treatment and became an established part of therapy in these patients . HoweverMore recently new antidiabetic medications showed significant improvement in patients with HFrEF regardless of the presence of DM –7, but dThe aim of this review is to summarize current clinical knowledge about the effect of new antidiabetic medications on LV diastolic function and potential clinical benefits in HFpEF patients.Preclinical and clinical studies showed significant positive pleotropic effects on kidneys, liver, pancreas, blood plasma, blood vessels, and adipose tissue that result with decreased preload and afterload, reduced fibrosis, oxygen demand, and LV hypertrophy, as well as decreased afterload . The effThe diuretic and natriuretic effects of SGLT2 inhibitors induce depletion in plasma volume, which is the most important to prevent fluid retention and HF exacerbation. At difference from traditional diuretics, these medications have been shown to reduce not only intravascular volume but also interstitial fluid; this mechanism may exert adverse effects in HF patients with reduced intravascular volume and induce diuretic resistance.The diuretic effect of SGLT2 inhibitors is associated with reduced preload and LV filling pressure, myocardial stretching, and myocardial interstitial fibrosis. Furthermore, the reduction in extracellular fluid volume is associated with decrease in blood pressure , 11, whiStudies also showed that SGLT2 inhibitors reduced albuminuria, the urinary albumin to creatinine ratio, and slowed the progression from microalbuminuria to macroalbuminuria . Investie′ and E/A between any of the 6 drugs and placebo or in pairwise comparisons between any two of the 6 drugs  disease showed reduction in LV mass index and LV end-diastolic volume, as well as better parameters of LV diastolic function after 3 or 6 months of therapy , 19. MorRecently published data from the IDDIA trial showed that dapagliflozin in addition to standard antihyperglycemic therapy in patients with type 2 DM was related with a significant improvement in LV diastolic dysfunction evaluated with diastolic stress echocardiography as compared with placebo . The useMatsutani et al. reported that 3-month therapy with canagliflozin in DM patients (approximately 30% had CV disease) significantly reduced LV mass index and E/e′ . TofogliData regarding use of SGLT2 inhibitors in HFpEF are scarce and mostly based on studies with limited number of participants. From pathophysiological perspective, the prescription of these medications would be justifiable because of favorable effect on LV filling pressure reduction and LV diastolic function improvement, which are the key points in treatment of HFpEF. In the recently published network meta-analysis, SGLT-2 inhibitors showed the largest risk reduction for HF hospitalization compared with placebo . AdditioSmall investigation reported that 6-month therapy with dapagliflozin significantly improved LV diastolic function  in HFpEF patients with DM, even though there was no significant change in BNP . Re-evalAnother small study that investigated the effect of canagliflozin on LV remodeling in DM patients with HF (majority of patients—33/35 had HFpEF) showed that LV filling pressure, assessed by E/e′, significantly decreased after 6 months of treatment and effect was sustained even after 12 months of therapy  , and two-third with LVEF > 50% (HFpEF) [Secretion of GLP-1 stimulates insulin release by pancreatic beta-cells in a glucose-dependent correlation and reduces glucagon secretion by alpha-cells. GLP-1 reduces postprandial glucose by slowing gastric emptying, reducing intestinal glucose uptake, suppressing hepatic glucose production, and improving insulin sensitivity of muscle and liver . The maiCardiovascular effects of GLP-1R are reduced activity of renin–angiotensin–aldosterone system, reduced oxidative stress, decreased blood pressure, improved endothelial function and microvascular perfusion, and reduced triglycerides and LDL levels. The negative effect might be increased sympathetic nervous system activity and direct sinoatrial node stimulation, which consequently increases heart rate .e′ and decreased E/e′ and LV end-diastolic volume, which reflects reduction in LV filling pressure [Growing body of evidence shows significant beneficial effect of GLP-1R agonists on LV diastolic function in patients with type 2 DM , 33. Stupressure , 33  and reduced LV stiffness, LV diastolic dysfunction, and pulmonary congestion . AnotherDPP-4 (dipeptidyl peptidase-4) inhibitors are responsible for the degradation of two gut-derived incretin hormones, GLP-1 and GIP (glucose-dependent insulinotropic polypeptide). DPP-4 inhibitors progressively replaced sulfonylureas in therapy of type 2 DM in many countries because they are not related with hypoglycemia or weight gain and they have good safety profile and comfortable usage even in patients with chronic renal failure . Even thThe limited data about the effect of DPP-4 inhibitors and LV diastolic function are inconsistent. Some studies showed the significant decrease in E/e′ ratio in poorly controlled DM patients treated with sitagliptin –52 Tabl.Table 3DSmall study that included 25 DM patients treated with different DPP-4 inhibitors  reported significant improvement in LV longitudinal strain and E/e′, surrogates of LV systolic and diastolic functions, and important improvement in endothelial function, after 12 months of treatment despite no significant differences in weight, blood pressure, or lipid parameters . These eMeta-analysis that compared parameters of LV systolic and diastolic function in DM patients treated with different antidiabetic drugs showed no difference in all parameters of LV diastolic function  in patients treated with DPP-4 comparing with those who were treated with competitors   Table 33.Large investigation, which included more than 500,000 DM patients of whom half was treated with DDp-4 inhibitors, compared the effects of DPP-4 inhibitors with sulfonylurea and revealed no difference in the risk of HF between these two groups . The autA randomized placebo-control trial conducted in DM patients with HFrEF patients reported that vildagliptin did not change LVEF during 12-month therapy . HoweverOther meta-analysis that included 4 trials, which investigated the effects of saxagliptin, alogliptin, sitagliptin, and linagliptin, separately analyzed the hospitalization rate due to HF in patients with and without prior history of HF, and found that DPP-4 inhibitors did not elevated risk of hospitalization due to HF in patients with previous HF, but in those DM patients without previous HF . The samNew studies that will be focused on this topic are warranted and particularly in patients with HFpEF in whom LVEF is not the main determinant. Ongoing TOPLEVEL study should determine the effect of DPP-4 inhibitor (teneligliptin) on LV diastolic function, using E/e′ as a main parameter, in type 2 DM patients and should answer the main question about the potential use of DPP-4 inhibitors in HFpEF . Animal New antidiabetic drugs have incremental influence on DM treatment, but their influence on CV outcome and particularly the effects in HF patients are still largely uninvestigated and possibly underestimated. The available data are very encouraging about the use of SGLT2 inhibitors in HFrEF patients, but their role in HFpEF individuals remain to be investigated. The effects of GLP-1R agonists and DPP-4 inhibitors in HF patients are significantly less investigated and results are controversial, particularly in the later group of medications. Therefore, currently ongoing trials should provide more information about effects of these medications in patients with HF and particularly in those with HFpEF. Preclinical studies reported positive effects of new antidiabetic drugs in HFpEF, which is of a great clinical importance due to limited therapeutic options that are currently available in this large group of HF patients.


To test the hypothesis of a relationship between a specific genetic lesion (T2:ERG) and imaging scores, such as PI-RADS and PRI-MUS, and to test the effectiveness of these parameters for the diagnosis of prostate cancer (PCa) and clinically significant PCa (csPCa).This is a prospective study of men with suspected PCa enrolled between 2016 and 2019 at a high-volume tertiary hospital. Patients underwent systematic US-guided biopsy, plus targeted biopsy if they were presenting with >=1 suspicious lesion (PI-RADS>2) at mpMRI or PR-IMUS >2 at micro-ultrasound assessment. For each patient, one core from the highest PI-RADS or PRI-MUS lesion was collected for T2:ERG analysis. Multivariable logistic regression models (LRMs) were fitted for csPCa with a clinical model , a clinical plus PI-RADS, clinical plus T2:ERG, clinical plus PI-RADS plus T2:ERG, and T2:ERG plus PI-RADS alone.The cohort consists of 158 patients: 83.5% and 66.2% had respectively a diagnosis of PCa and csPCa after biopsy. A T2:ERG fusion was found in 37 men and 97.3% of these patients harbored PCa, while 81.1% were diagnosed with csPCa. SE of T2:ERG assay for csPCa was 28.8%, SP 87.0%, NPV 38.8%, and PPV 81.1%. Of 105 patients who performed mpMRI 93.% had PIRADS ≥3. SE of mpMRI for csPCa was 98.5%, SP was 12.8%, NPV was 83.3%, and PPV was 65.7%. Among 67 patients who were subjected to micro-US, 90% had a PRI-MUS ≥3. SE of micro-US for csPCa was 89.1%, SP was 9.52%, NPV was 28.6%, and PPV was 68.3%. At univariable LRM T2:ERG was confirmed as independent of mpMRI and micro-US result . At multivariable LRM the clinical model alone had an AUC for csPCa of 0.74 while the clinical model including PI-RADS and T2:ERG achieved an AUC of 0.83.T2:ERG translocation and imaging results are independent of each other, but both are related csPCa. To evaluate the best diagnostic work-up for PCa and csPCa detection, all available tools (T2:ERG detection and imaging techniques) should be employed together as they appear to have a complementary role. TMPRSS2:ERG) gene fusion has been studied as a PCa-specific biomarker since 2005 (TMPRSS2:ERG gene fusion (T2:ERG) is found in up to 50% of PCa and results in androgen-dependent overexpression of ERG, which has been demonstrated to be a key regulator of differentiation, apoptosis, embryonic development, cell proliferation, and inflammation  was introduced for diagnosis of prostate cancer (PCa). However, due to its poor specificity for clinically significant PCa (csPCa), the use of PSA as a standalone test still leads to diagnosis of many indolent tumors . The idence 2005 , 9. The ammation . Therefond urine –12. Sincver time . The detver time , 15.On the other hand, in recent decades, imaging has also been used for detection of PCa and multiparametric magnetic resonance imaging (mpMRI) has become the preferred method, allowing for high quality visualization of the prostate and identification of PCa , 17.For this reason, both the American College of Radiology (ACR) and the European Society of Uroradiology (ESUR) proposed the Prostate Imaging Reporting and Data System (PI-RADS) score with the intent to standardize prostate MRI , 19. ThuIn addition to biomarkers and imaging, the use of risk calculators has been widely suggested by the authors and many of these risk calculators have the advantage of using easy to retrieve clinical variables and being freely accessible as a web tool/mobile application . DespiteSince the association between imaging and clinical results remains vague, this study aims to explore the hypothesis that a correlation between a specific genetic lesion (T2:ERG) and imaging scores, such as PI-RADS and PRI-MUS, exists, and may be useful for improving PCa and csPCa diagnosis.T2:ERG gene fusion. We excluded patient who were under hormonal therapy for PCa or who underwent radiotherapy for PCa. Type of imaging, biopsy decision and timing of the biopsy were at the discretion of the treating provider, patient preference.This is a prospective cohort study of men admitted at a tertiary university hospital, for scheduled prostate biopsy, between 2017 and 2019. The study was approved by the local ethics committee . All patients signed a written informed consent. We included all patients  for PCa) referred to our institution for prostate biopsy who performed a genetic test to detect T2:ERG analysis. In those patients with 2 or more lesions with the same score, the largest one was sampled. In those patients who had a negative mpMRI or micro-US, a core from the right lobe was collected as standard protocol and control. All histological analyses were performed by experienced genitourinary pathologists at our institution.All patients underwent systematic US-guided biopsy plus targeted biopsy only if they were presenting with ≥1 suspicious lesion (PI-RADS V.2 >2) at mpMRI or PRI-MUS >2 at micro-US assessment. Biopsies were either transrectal or transperineal depending on lesions’ location. Targeted mpMRI\TRUS biopsy has been performed with BioJet™ fusion system. For each patient, one prostatic core from the highest PI-RADS or PRI-MUS lesion was collected for T2:ERG and PI-RADS/PRI-MUS scores. The secondary endpoint was to explore the additional value of T2:ERG next to imaging for the detection of csPCa.The primary endpoint was to evaluate the correlation between All routine clinical mpMRI acquisitions were 1.5 Tesla, with endorectal coil, and 3.0 Tesla without endorectal coil. A dedicated experienced radiologist (>500 readings) reviewed all the mpMRIs. The PI-RADS v.2 score was used for this study , 19. All® G2 Flexi DNA Polymerase (Promega). RT-PCRs were performed with different primer couples to detect the most frequent T2:ERG gene fusion events. To ensure the detection of T2:ERG fusion transcripts involving TMPRSS2 exon 1 and ERG exon 4, a nested PCR was performed by using 1.5 µl of the first PCR amplification, previously diluted 1:5. All amplified products were checked by direct Sanger sequencing using the BigDye Terminator Cycle Sequencing Ready Reaction Kit v1.1 (Thermo Fisher Scientific). Sequencing reactions were run on an ABI-3500 and analyzed using the FinchTV software. All oligonucleotides used in RT-PCR and sequencing steps were purchased from Sigma. Their sequences, as well as thermal profiles used for RT-PCRs, are available on request.For RNA extractions, we used dedicated biopsy specimens, immediately immersed into the RNAlater stabilization solution  after withdrawal. RNA was extracted using the Maxwell RSC miRNA Tissue Kit  on a Maxwell RSC instrument (Promega). Random primers (Promega) and the Superscript-IV Reverse Transcriptase  were used to perform first-strand cDNA synthesis, following the manufacturer’s instructions. PCR reactions were accomplished starting from 2 µl of the reverse-transcription (RT) reaction and the GoTaqT2:ERG results. For 5 patients who performed biopsy and genetic test twice, the most recent results were considered for the analysis.Mean and standard deviation (SD) and median and interquartile ranges (IQRs) were used to compare normal or skewed continuous variables, respectively; frequencies were used for categorical variables. Demographic and clinical characteristics were compared by stratifying patients upon T2:ERG gene fusion, mpMRI and micro-US for detection of any PCa  and csPCa, defined as GG ≥2. Positive mpMRI was defined as PI-RADS >2 and positive micro-US was defined as PRI-MUS >2.To test categorical variables “Chi-squared test of independence” and “Fisher exact test” were applied. To test categorical and continuous variable, ANOVA test or Kruskal-Wallis test were applied. We defined sensitivity (SE), specificity (SP), positive predictive value (PPV), and negative predictive value (NPV) of T2:ERG, positive mpMRI, or positive micro-US. Furthermore, the univariable LRM was used to test the mpMRI and micro-US correlation with T2:ERG. Multivariable LRMs were fitted for csPCa with a clinical model , a clinical plus PI-RADS, clinical plus T2:ERG, clinical plus PI-RADS plus T2:ERG, and T2:ERG plus PI-RADS alone. A model with PRI-MUS was not run due to the small sample size.Univariable logistic regression models (LRM) were performed to test the association with PCa or csPCa for the presence of Statistical significance was set at p <0.05. Statistical analysis was done with STATA 16.1 .T2:ERG gene fusion. Mean age was 66.4 ( ± 8.42) years old, while median PSA was 7.87 ng/ml (IQR 5.34-14.5). Family history was positive in 26 (16.5%) patients, 98 (62.0%) patients were biopsy naïve, 41 (25.9%) had a prior negative biopsy and 19 (12.0%) were on AS  men: GG1 was found in 28 (21.2%) men, GG2 in 42 (31.8%), and ≥GG3 in 62 (47.0%). Median number of cores taken at each biopsy was 12 (IQR 10-14). In total, 34 (16.5%) were systematic and 124 (83.5%) were fusion biopsies Table 2.T2:ERG fusion was found in 37 (23.4%) men and 36 (97.3%) had a positive biopsy (p=0.010): 6 (16.2%) with GG1, 13 (35.1%) with GG2 and 17 (45.9%) with ≥GG3. Of these patients, 29 (78.4%) underwent a fusion biopsy, of whom 96.4% had a positive target core and 90.9% had also a positive random one.A T2:ERG fusion, 26 (20%) had a negative biopsy versus 101 (80%) with positive one, 22 (17%) GG1, 31 (24%) GG2 and 48 (38%) ≥GG3.Among 127 (81%) men without the T2:ERG assay for any PCa was 27.3% (95%CI 19.9-35.7), SP 96.2% (95%CI 80.4-99.9), NPV 20.7% (95%CI 13.8-29.0), and PPV 97.3% (95%CI 85.8-99.9). SE of T2:ERG assay for csPCa was 28.8% (95%CI 20.4-38.6), SP 87.0% (95CI% 75.1-94.6), NPV 38.8% (95%CI 30.1-48.1), and PPV 81.1% (95%CI 64.8-92.0) (SE of the .8-92.0) Figure 1T2:ERG was associated with PCa  and csPCa .At univariable LRM, Of 105 patients who performed mpMRI, 29 (27.6%) had a PI-RADS 3, while 69 (65.7%) had a PI-RADS 4-5. A fusion biopsy was performed in 95% of men with a PI-RADS ≥3. SE of the mpMRI for any PCa was 96.5% (95%CI 90.1-99.3), SP was 15.8% (95%CI 3.38-39.6), NPV was 50.0% (95%CI 11.8-88.2), and PPV was 83.8% (95%CI 75.1-90.5). SE of mpMRI for csPCa was 98.5% (95%CI 91.8-100), SP was 12.8% (95%CI 4.3-27.4), NPV was 83.3% (95%CI 35.9-99.6), and PPV was 65.7% (95%CI 55.4-74.9).At univariable LRM mpMRI was not associated with PCa  while it was with csPCa . In addition there was an association of csPCa with PI-RADS 4  and PI-RADS 5 Among 67 patients who were subjected to micro-US, 60 (90%) had a PRI-MUS 3-4-5, and 83% of these underwent a fusion biopsy. SE of micro-US for any PCa was 89.7% (95%CI 78.8-96.1), SP was 11.1% (95%CI 0.281-48.2), NPV was 14.3% (95%CI 0.361-57.9), and PPV was 86.7% (95%CI 75.4-94.1). SE of micro-US for csPCa was 89.1% (95%CI 76.4-96.4), SP was 9.52% (95%CI 1.17-30.4), NPV was 28.6% (95%CI 3.67-), and PPV was 68.3% (95%CI 55.0-79.7) Figure 1At univariable LRM micro-US was not associated with PCa (p=0.944).T2:ERG fusion performed mpMRI and all had positive findings (PI-RADS>2), although the correlation was not significant (p=0.170). Of those with translocation who performed micro-US, 14/15 (93.3%) had positive results (PRI-MUS<2), but as for mpMRI there was no significant correlation (p=0.587).All men carrying the At univariable LRM T2:ERG was confirmed as independent of mpMRI and micro-US result .T2:ERG achieved an AUC of 0.84 or 0.78, respectively. The clinical model including PI-RADS and T2:ERG achieved an AUC of 0.83, and the AUC of a model with PI-RADS and T2:ERG was 0.73  without translocation (p=0.077). In patients with translocation, GG was ≥2 for all men, however we did not find a statistically significant difference in GG compared to those without translocation (p=0.870).Forty-two men underwent radical prostatectomy: 14 (33.3%) with the Lymphadenectomy dissection was performed in 39 men, and we found a positive finding in 5 men, of whom 2 with the translocation (p=0.840). Prostatectomy characteristics are summarized in T2:ERG gene fusion and PCa, however we did not find an association of the gene mutation with mpMRI and/or micro-US. Those data lead speculating that T2:ERG gene fusion does not determine specific tissue rearrangement which could be specifically detected by MRI or micro-US.In this study, we observed a strong correlation between ERG in PCa was firstly described in 2005   , 31.T2:ERG had cancer while no mutations were found in men with benign histological prostatic hyperplasia  of translocation among 140 men diagnosed with PCa. Of note, all men with erplasia . As repoerplasia .T2:ERG has been proposed as a biomarker for PCa with rather low SE, 37%, but high SP, 93%, and PPV, 94% in post- digital rectal examination (DRE) urine specimens  and reported a SE of 73%. Conversely, Stephan et al. reported an AUC of 0.63 for the gene fusion alone versus 0.74 for PCA3 alone, and interestingly the combination of both biomarkers did not result in a significant increase of the accuracy .Moreover, a further limitation of the study could be a “selection bias” as the PI-RADS score on mpMRI is an indication for biopsy and could consequently be associated with T2: ERG. Furthermore, when we estimated the clinical model, due to the lack of complete volume values, we did not estimate the model added by PSA density, which is a well-known and crucial component for PCa risk prediction.There are some limitations to be acknowledged. First, the sample size is relatively small and not all patients received a standardized diagnostic work-up. Additionally, the presence of T2:ERG with germline mutations is arousing more and more interest: in the era of selective and targeted medicine with dedicated screening pathways, PCa management should include specific machine learning algorithms implemented with the detection of specific gene mutations  and by intramural funding (Fondazione Humanitas per la Ricerca).The authors would like to thank Nadia Lo Iacono as study coordinator. In addition, the authors thank and warmly remember Professor Stefano Duga, geneticist and co-investigator, who passed away of prostate cancer on October 10, 2021 at the age of 53 yr.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. 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Heart failure (HF) is a serious end-stage condition of various heart diseases with increasing frequency. Few studies have combined clinical features with high-throughput echocardiographic data to assess the risk of major cardiovascular events (MACE) in patients with heart failure. In this study, we assessed the relationship between these factors and heart failure to develop a practical and accurate prognostic dynamic nomogram model to identify high-risk groups of heart failure and ultimately provide tailored treatment options.We conducted a prospective study of 468 patients with heart failure and established a clinical predictive model. Modeling to predict risk of MACE in heart failure patients within 6 months after discharge obtained 320 features including general clinical data, laboratory examination, 2-dimensional and Doppler measurements, left ventricular (LV) and left atrial (LA) speckle tracking echocardiography (STE), and left ventricular vector flow mapping (VFM) data, were obtained by building a model to predict the risk of MACE within 6 months of discharge for patients with heart failure. In addition, the addition of machine learning models also confirmed the necessity of increasing the STE and VFM parameters.p < 0.05), which indicated good identical calibration and clinical efficacy. In multiple datasets, through machine learning multi-model comparison, we found that the area under curve (AUC) of the model with VFM and STE parameters was higher, which was more significant with the XGboost model.Through regular follow-up 6 months after discharge, MACE occurred in 156 patients (33.3%). The prediction model showed good discrimination C-statistic value, 0.876 (In this study, we developed a prediction model and nomogram to estimate the risk of MACE within 6 months of discharge among patients with heart failure. The results of this study can provide a reference for clinical physicians for detection of the risk of MACE in terms of clinical characteristics, cardiac structure and function, hemodynamics, and enable its prompt management, which is a convenient, practical and effective clinical decision-making tool for providing accurate prognosis. Urbanization and the widespread use of cars have shifted many people from active to sedentary lifestyles, increasing the incidence of chronic diseases such as obesity, hypertension, diabetes, and coronary artery disease. Heart failure is a serious manifestation of the late stage of various heart diseases, and its risk factors include coronary diseases, hypertension, and diabetes, lifestyle factors such as smoking and alcohol consumption. Heart failure remains a serious clinical and public health problem as the total number of patients living with heart failure increases, reflecting the chronic course of the disease as well as population growth and aging . With hiAll efforts must be underway to examine clinical, laboratory, and imaging data to better characterize heart failure phenotypes , 6 and tThe 2019 American College of Cardiology expert consensus on heart failure suggests that assessing risk-increasing factors can help inform decisions about preventive interventions . Many riHowever, most of these models are insufficient to reflect the overall situation of the patient. The aim of this study was to develop a predictive model and a predictive nomogram model to estimate the risk of MACE within 6 months of discharge among patients with HF taking into account clinical characteristics, laboratory parameters of blood tests, speckle-tracking echocardiographic analysis, hemodynamic analysis, which can better represent the structure and function of the heart.This was a single center study. Between July 2021 and February 2022, 505 consecutive patients who were previously diagnosed with chronic heart failure referred to our institution for routine evaluation were screened for eligibility for this study. Criteria and definitions used in the diagnosis of heart failure followed the 2021 heart failure guidelines . InclusiAll scans were performed by a VFM imaging specialist, and all images were post-processing and analyzed by two professional VFM researchers. Eligible patients were prospectively followed up at 28 days, 3 months, and 6 months after discharge. The study protocol followed the Declaration of Helsinki and was approved by the Research Ethics Committee. All subjects gave written informed consent for additional research tests and for the use of their data for research purposes.All echocardiographic examinations follow the guidelines of the American Society of Echocardiography and use commercially available ultrasound equipment  , 19.Left atrial (LA) and LV endocardial boundaries were manually determined using QRS early end-diastolic frames as a reference for image analysis . The traImages were analyzed using commercially available off-line software . Using the initial point of the QRS complex as a reference point for image analysis, the LV endocardial border was tracked manually in end-diastolic frames and automatically by the software. Previous studies have limited validation studies –26, but Follow-up information was obtained during clinical consultations and patients who did not attend scheduled consultations were contacted through telephone interviews with family members. The primary endpoint of the study was the incidence of major adverse cardiac events (MACE). MACE was defined as a complex of congestive HF hospitalization, non-fatal myocardial infarction (MI), non-fatal stroke, and cardiovascular (CV) death. If there are multiple events, the first event is timed for analysis. Eligible patients were prospectively followed up at 28 days, 3 months, and 6 months after discharge. Clinical and echocardiographic parameters were tested for prediction of MACE in the study population. Clinical data, including clinical symptoms and signs, comorbidities, laboratory test results, treatment during hospitalization, and clinical outcomes were obtained by reviewing each patient’s medical records.We used R  and Python  for statistical analysis. Continuous variables are expressed as mean ± SD, frequencies (percentages) for categorical variables and medians  for skewed variables.t-test was used to compare the means of continuous variables when the data were normally distributed, else, the Mann-Whitney U test was used. One-way analysis of variance (ANOVA) was used for comparisons among multiple groups. Categorical data comparisons between groups were performed using the χ2 test. The statistical significance level for each test was set at α = 0.05; P < 0.05 (two-tailed) was considered statistically significant. The Least Absolute Shrinkage and Selection Operator (LASSO) regression algorithm and 10-fold cross-validation were used to filter out the best variables most associated with 6-month MACE incidence. The importance of variables is sorted by machine learning, a logistic regression model is constructed after screening through Venn diagrams, and generates nomograms. We used the Kaplan-Meier method to compare survival between groups. The concordance index (C-index) was used to measure the discriminative abilities of the nomograms [Harrell et al. . There were 156 cases of MACE, the incidence rate was 33.3%. Patients are divided into four categories based on LVEF: including HFpEF , HFmrEF , HFrEF , HFimpEF . Demographic and baseline criteria are detailed by MACE  and by Lvia the R package glmnet and 10-fold cross-validation. This is consistent with the glmnet package recommendation for choosing λ, as either λ min (minimum mean square error) or this value plus one standard error. When lambda equaled 0.035, twenty-six optimal prognostic variables were identified, including New York Heart Association (NYHA), hyperlipidemia stability (HPLS), ischemic cardiomyopathy (ICM), age, diastolic blood pressure (DBP), left ventricular end diastolic diameter (LVEDd), left ventricular end systolic diameter (LVESd), peak mitral valve blood flow during atrial contraction (A), LV ejection fraction (LVEF), aortic regurgitation (AR), inorganicphosphorus (P), glycatedhemoglobin (GHB), apolipoproteinB (ApoB), sodium ion (Na), N-terminal brain natriuretic peptide (NTproBNP), international normalized ratio (INR), fibrinogen, cardiac index (CI), LV vortex area from 4-chamber images during systolic period (VorAreaC4S), LV energy loss from 3-chamber images during isovolumic relaxation period (ELC3P3), intraventricular pressure differences from 2-chamber images during LV early filling period (IVPDC2P4), LA global longitudinal strain rate during conduit period (GLSRconduitC4LA), LV global longitudinal strain from 3-chamber images during early diastolic period (GLSedC3LV), LV global longitudinal strain from 2-chamber images during atrial systolic period (GLSacC2LV), average of LV global longitudinal strain during atrial systolic period (GLSacLV), peakLA longitudinal peak strain during conduit period (PALSconduitLA)  .Correlation heatmap showing associations between clinical features and ultrasound parameters after lasso dimension reduction analysis . Here, wNomogram for predicting MACE risk, and the point was the selected scoring standard or scale. For each independent variable, by drawing a line (through the ruler) perpendicular to the point axis, the intersection points represent the values of the independent variables. The value of each variable is scored on a scale of 0 to 100, and the scores for each variable are summed to estimate the position perpendicular to the axis. That sum enables us to predict the probability of MACE risk in patient with HF . EstimatPatients were divided into four groups according to the quartile total nomogram scores , and each group had 117 patients. A prediction model with a prediction probability of less than 0.5 is considered a low risk group, and patients were divided into high-risk and low-risk groups . Of these, 342 patients (73.08%) belonged to the low-risk group. The KM survival curves and cumulative survival curves were used to compare survival times among different groups was made by log-rank test. The median survival time (LT50) for different groups. The horizontal axis of the KM survival curve represents time, and the vertical axis represents probability .We divided patients into training and validation sets using MACE as the categorical outcome variable. Parameters include general parameters, STE-related parameters and VFM-related parameters. General parameters include basic characterization parameters, clinical parameters, blood laboratory test results, and conventional ultrasound parameters. Four machine learning models were used to complete the data sample classification task, including: XGBoost Classifier, Random Forest Classifier, Multi-LayerPerceptron (MLP) Classifier, support vector machine (SVM) Classifier. By repeated sampling 10 times and each resampling training validation set of 20.0% of the total sample, training set of 80.0%, the model classification was successively performed and drawed the ROC curve. General Parameters group , General Parameters and STE group , General Parameter, STE and VFM group  were classified in the test set performance .Comparing AUC and accuracy, the XGBoost classifier performed the best among all four learning algorithms. In each group, we found that AUC and accuracy of four learning algorithms were higher when the parameters included general parameters, STE-related parameters and VFM-related parameters, and were more pronounced when using the XGBoost classifier. The inclusion of STE and VFM parameters in the XGBoost classifier model improved its ability to correctly classify MACE patients compared to other learning algorithms . The inclusion of the STE and VFM parameters in the XGBoost classifiers model showed an improved ability to classify patients with MACE correctly compared with the other learning algorithms, which is more beneficial to predicting the occurrence of MACE. The performances (AUC and accuracy) of the model in classifying patients correctly in each group using learning algorithms are shown . CalibraThe primary objective of this study was to develop a scoring system to predict MACE risk in patients with chronic heart failure. This study used clinical trial data from patients with CHF, and age was not included in the risk assessment model due to the short follow-up time. In previous models, the most common parameters used to predict major adverse cardiovascular events were general clinical data, blood test parameters, and conventional ultrasound parameters –16. TherLeft atrial (LA) function is closely related to LV function and plays a key role in maintaining optimal cardiac functional performance. The left atrium, through its reservoir, conduit, and booster pump stages, regulates filling of the left ventricle, while the LV influences LA function throughout the cardiac cycle. Long-term exposure to high LV filling pressures leads to an increase in LA volume, presumably reduced LA function is only a marker of LV deterioration , 34. In Currently, left chamber fluid mechanics is lacking to participate in model construction. There is a growing interest in the imaging and visualization of intracardiac blood flow . VFM is In this study, the left ventricular high-throughput parameters were constructed by collecting mechanics and hydrodynamic parameters, including STE parameters of LV and LA, VFM parameters of LV flow. For the first time, we integrate multiple echocardiographic parameters to meaningfully reflect changes in left ventricular structure and function. Through screening, key indicators are incorporated into the construction of the model. Based on selecting optimal prognostic variables, a logistic regression model and nomogram is constructed, which can well-predict the possibility of MACE within 6 months. Here, we evaluated the relationship between these factors and HF to create a practical and accurate prognostic dynamic nomogram model to identify high-risk groups of heart failure and ultimately develop targeted treatment options. From There were several limitations to this study that require comment. First, this was a single-center study with a limited sample size. A larger study sample from various centers may be helpful to further confirm these observations. The findings of the present study, although showing statistical significance, were based on a relatively small number of patients and are indeed not suitable for data from countries that respond to an aging population. The results should therefore be regarded as preliminary, and further enlarge studies adequately powered for clinical outcomes are warranted to confirm our results. Second, a relatively short follow-up duration was used to detect cardiac events in this study, the effective time follow-up within 6 months is relatively short, the specific time of MACE cannot be clearly defined by patients, therefore logist regression is used to draw nomgraph this time, only to predict the possibility of MACE within 6 months after discharge. In future studies, the continuous follow-up will be summarized to establish an effective cox prediction model. Third, VFM technology relies on strict contour condition provided by the endocardial-blood interface and is limited by the frame rate applied and the Nyquist limit. Aliasing may result in errors in the VFM calculated blood flow data, mild aliasing was manually corrected on the selected frames. However, severe aliasing could not be corrected can underestimate the true velocity, we should minimize this phenomenon by optimizing the Nyquist limit and ensuring appropriate patient selection Patients with valcular heart diseases were not included in the analysis due to severe aliasing produced by the fast valve flow. Fourth, we did not include additional measurements for right ventricular function, which would need to be addressed in future investigations.In this study, we developed a prediction model and nomogram to estimate the risk of MACE within 6 months of discharge among patients with heart failure. The findings may provide a reference for clinical physicians for detection of the risk of MACE in terms of clinical characteristics, cardiac structure and function, hemodynamics, and enable its prompt management, which is a convenient, practical and effective clinical decision making tool for providing accurate prognosis.The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding authors.Written informed consent was obtained from the individual(s) for the publication of any potentially identifiable images or data included in this article.QL-S proposed the hypothesis, designed the experiments, and drafted the manuscript. QL-S, SQ-J, XD-W, and YL made contributions to data collection. QL-S and JC-Z made contributions to the analysis and interpretation of the data. JW-T and HR-L conceived the study, participated in its design, and helped edit the manuscript. All authors contributed to the article and approved the submitted version.