Heart failure (HF) is the end-stage of all cardiac diseases, characterized by high prevalence, high mortality, and heavy social and economic burden. Early warning of HF exacerbation is of great value for outpatient management and reducing readmission rates. Currently, remote dynamic monitoring technology, which captures changes in hemodynamic and physiological parameters of HF patients, has become the primary method for early warning and is a hot research topic in clinical studies. This paper systematically reviews the progress in this field, which was categorized into invasive monitoring based on implanted devices, non-invasive monitoring based on wearable devices, and other monitoring technologies based on audio and video. Invasive monitoring primarily involves direct hemodynamic parameters such as left atrial pressure and pulmonary artery pressure, while non-invasive monitoring covers parameters such as thoracic impedance, electrocardiogram, respiration, and activity levels. These parameters exhibit characteristic changes in the early stages of HF exacerbation. Given the clinical heterogeneity of HF patients, multi-source information fusion analysis can significantly improve the prediction accuracy of early warning models. The results of this study suggest that, compared with invasive monitoring, non-invasive monitoring technology, with its advantages of good patient compliance, ease of operation, and cost-effectiveness, combined with AI-driven multimodal data analysis methods, shows significant clinical application potential in establishing an outpatient management system for HF.
Humans ; Heart Failure/physiopathology* ; Monitoring, Physiologic/methods* ; Wearable Electronic Devices ; Remote Sensing Technology ; Early Diagnosis ; Electrocardiography ; Hemodynamics

Heart failure is a complex clinical syndrome and pediatric heart failure (PHF) has a high mortality rate. Early diagnosis is crucial for treatment and management of PHF. In clinical practice, various tests and examinations play a key role in the diagnosis of PHF, including continuously updated biomarkers, echocardiography, and cardiac magnetic resonance imaging. This article focuses on summarizing relevant research on biomarkers, examinations, combined testing, clinical models, and the grading and staging of PHF diagnosis, aiming to provide insights and directions for the diagnosis of PHF.
Humans ; Heart Failure/diagnosis* ; Child ; Biomarkers/blood* ; Echocardiography ; Magnetic Resonance Imaging

OBJECTIVES: To analyze the differentially expressed exosomal miRNAs in patients with chronic heart failure (CHF) complicated by hyperuricemia (HUA) and explore their potential as novel diagnostic molecular markers and their target genes. METHODS: This study was conducted among 30 CHF patients with HUA (observation group) and 30 healthy volunteers (control group) enrolled between September, 2020 and September, 2023. Peripheral blood samples were collected from 6 CHF patients with HUA for analyzing exosomal miRNAs by high-throughput sequencing, and the results were validated in the remaining 24 patients using qRT-PCR. GO and KEGG enrichment analyses were performed to predict the the target genes of the identified differential miRNAs. We also validated the differentially expressed miRNAs by animal experiment. RESULTS: A total of 42 differentially expressed exosomal miRNAs were detected in observation group by high-throughput sequencing; among them, miR-27a-5p was significantly upregulated (P=0.000179), and miR-139-3p was significantly downregulated (P=0.000058). In the 24 patients with both CHF and PUA, qRT-PCR validated significant upregulation of miR-27a-5p (P=0.004) and downregulation of miR-139-3p (P=0.005) in serum exosomes. When combined, miR-27a-5p and miR-139-3p had a maximum area under the curve (AUC) of 0.899 (95% CI: 0812-0.987) for predicting CHF complicated by HUA. GO and KEGG enrichment analyses suggested that the differential expressions of miR-27a-5p and miR-139-3p was associated with the activation of the AMPK-mTOR signaling pathway to activate the autophagic response. We obtained the same conclusion from animal experiment. CONCLUSIONS Upregulated exosomal miR-27a-5p combined with downregulated exosomal miR-139-3p expression can serve as a novel molecular marker for diagnosis of CHF complicated by HUA, and their differential expression may promote autophagy in cardiomyocytes by activating the AMPK-mTOR signaling pathway.
Humans ; Hyperuricemia/diagnosis* ; Heart Failure/genetics* ; MicroRNAs/metabolism* ; Exosomes/metabolism* ; Chronic Disease ; Male ; Female ; Middle Aged ; Animals

Patients with acute heart failure (AHF) often experience dyspnea, and monitoring and quantifying their breathing patterns can provide reference information for disease and prognosis assessment. In this study, 39 AHF patients and 24 healthy subjects were included. Nighttime chest-abdominal respiratory signals were collected using wearable devices, and the differences in nocturnal breathing patterns between the two groups were quantitatively analyzed. Compared with the healthy group, the AHF group showed a higher mean breathing rate (BR_mean) [(21.03 ± 3.84) beat/min vs. (15.95 ± 3.08) beat/min, P < 0.001], and larger R_RSBI_cv [70.96% (54.34%-104.28)% vs. 58.48% (45.34%-65.95)%, P = 0.005], greater AB_ratio_cv [(22.52 ± 7.14)% vs. (17.10 ± 6.83)%, P = 0.004], and smaller SampEn (0.67 ± 0.37 vs. 1.01 ± 0.29, P < 0.001). Additionally, the mean inspiratory time (TI_mean) and expiration time (TE_mean) were shorter, TI_cv and TE_cv were greater. Furthermore, the LBI_cv was greater, while SD1 and SD2 on the Poincare plot were larger in the AHF group, all of which showed statistically significant differences. Logistic regression calibration revealed that the TI_mean reduction was a risk factor for AHF. The BR_ mean demonstrated the strongest ability to distinguish between the two groups, with an area under the curve (AUC) of 0.846. Parameters such as breathing period, amplitude, coordination, and nonlinear parameters effectively quantify abnormal breathing patterns in AHF patients. Specifically, the reduction in TI_mean serves as a risk factor for AHF, while the BR_mean distinguishes between the two groups. These findings have the potential to provide new information for the assessment of AHF patients.
Humans ; Heart Failure/diagnosis* ; Prognosis ; Respiration ; Wearable Electronic Devices ; Acute Disease

Objective To investigate the cardiac structural and functional characteristics in the patients with heart failure with preserved ejection fraction (HFpEF) and type 2 diabetes mellitus (T2DM),and predict the factors influencing the characteristics. Methods A total of 783 HFpEF patients diagnosed in the Department of Geriatric Cardiology,the First Hospital of Lanzhou University from April 2009 to December 2020 were enrolled in this study.Echocardiography and tissue Doppler technique were employed to evaluate cardiac structure and function.According to the occurrence of T2DM,the patients were assigned into a HFpEF+T2DM group (n=332) and a HFpEF group (n=451).Propensity score matching (PSM)(in a 1∶1 ratio) was adopted to minimize confounding effect.According to urinary albumin excretion rate (UAER),the HFpEF+T2DM group was further divided into three subgroups with UAER<20 μg/min,of 20-200 μg/min,and>200 μg/min,respectively.The comorbidities,symptoms and signs,and cardiac structure and function were compared among the groups to clarify the features of diabetes related HFpEF.Multivariate linear regression was conducted to probe the relationship of systolic blood pressure,blood glucose,glycosylated hemoglobin,and UARE with cardiac structural and functional impairment. Results The HFpEF+T2DM group had higher prevalence of hypertension (P=0.001) and coronary heart disease (P=0.036),younger age (P=0.020),and larger body mass index (P=0.005) than the HFpEF group,with the median diabetic course of 10 (3,17) years.After PSM,the prevalence of hypertension and coronary heart disease,body mass index,and age had no significant differences between the two groups(all P>0.05).In addition,the HFpEF+T2DM group had higher interventricular septal thickness (P=0.015),left ventricular posterior wall thickness (P=0.040),and left ventricular mass (P=0.012) and lower early diastole velocity of mitral annular septum (P=0.030) and lateral wall (P=0.011) than the HFpEF group.Compared with the HFpEF group,the HFpEF+T2DM group showed increased ratio of early diastolic mitral filling velocity to early diastolic mitral annular velocity (E/e') (P=0.036).Glycosylated hemoglobin was correlated with left ventricular mass (P=0.011),and the natural logarithm of UAER with interventricular septal thickness (P=0.004),left ventricular posterior wall thickness (P=0.006),left ventricular mass (P<0.001),and E/e' ratio (P=0.049). Conclusion The patients with both T2DM and HFpEF have thicker left ventricular wall,larger left ventricular mass,more advanced left ventricular remodeling,severer impaired left ventricular diastolic function,and higher left ventricular filling pressure than the HFpEF patients without T2DM.Elevated blood glucose and diabetic microvascular diseases might play a role in the development of the detrimental structural and functional changes of the heart.
Humans ; Aged ; Heart Failure/diagnosis* ; Diabetes Mellitus, Type 2 ; Stroke Volume ; Glycated Hemoglobin ; Blood Glucose ; Propensity Score ; Ventricular Function, Left ; Hypertension

Heart failure is a disease that seriously threatens human health and has become a global public health problem. Diagnostic and prognostic analysis of heart failure based on medical imaging and clinical data can reveal the progression of heart failure and reduce the risk of death of patients, which has important research value. The traditional analysis methods based on statistics and machine learning have some problems, such as insufficient model capability, poor accuracy due to prior dependence, and poor model adaptability. In recent years, with the development of artificial intelligence technology, deep learning has been gradually applied to clinical data analysis in the field of heart failure, showing a new perspective. This paper reviews the main progress, application methods and major achievements of deep learning in heart failure diagnosis, heart failure mortality and heart failure readmission, summarizes the existing problems and presents the prospects of related research to promote the clinical application of deep learning in heart failure clinical research.
Humans ; Artificial Intelligence ; Deep Learning ; Heart Failure/diagnosis* ; Machine Learning ; Diagnostic Imaging

Objective: To explore the value of the assessment of plasma trimethylamine N-oxide (TMAO) combined with N-terminal pro-B-type natriuretic peptide (NT-proBNP) on predicting the all-cause mortality, length of hospitalization, and hospital cost in ischemic heart failure (IHF) patients. Methods: This prospective cohort study included 189 patients (157 males, mean age (64.0±10.5) years) with a left ventricular ejection fraction<45% caused by coronary artery disease, who hospitalized in our department from March 2016 to December 2020. Baseline data, including demographics, comorbid conditions and laboratory examination, were analyzed. The cumulative rate of all-cause mortality was evaluated using the Kaplan-Meier method and compared between the groups according to the log-rank test. Relative risks were reported as hazard ratios (HR) and 95% confidence interval (95%CI) calculated using the Cox proportional-hazards analysis, with stepwise adjustment for covariables. Spearman correlation analysis was then performed to determine the relationship between TMAO combined with NT-proBNP and length of hospitalization and hospital cost. Results: There were 50 patients in the low TMAO+low NT-proBNP group, 89 patients in high TMAO or high NT-proBNP group, 50 patients in high TMAO+high NT-proBNP group. The mean follow-up period was 3.0 years. Death occurred in 70 patients (37.0%), 27 patients (54.0%) in high TMAO+high NT-proBNP group, 29 patients (32.6%) in high TMAO or high NT-proBNP group and 14 patients (28.0%) in low TMAO+low NT-proBNP group. TMAO, in combination with NT-proBNP, improved all-cause mortality prediction in IHF patients when stratified as none, one or both biomarker(s) elevation, with the highest risk of all-cause mortality in high TMAO+high NT-proBNP group (HR=3.62, 95%CI 1.89-6.96, P<0.001). ROC curve analysis further confirmed that TMAO combined with NT-proBNP strengthened the prediction performance on the risk of all-cause death (AUC=0.727(95%CI 0.640-0.813), sensitivity 55.0%, characteristic 83.1%). Spearman correlation analysis showed that IHF patients with high TMAO and high NT-proBNP were positively associated with longer duration of hospitalization (r=0.191,P=0.009), but not associated with higher hospital cost (r=0.030, P=0.686). Conclusions: TMAO combined with NT-proBNP are valuable prediction tool on risk stratification of patients with IHF, and those with two biomarkers elevation face the highest risk of mortality during follow-up period, and are associated with the longer hospital stay.
Aged ; Biomarkers/blood* ; Female ; Heart Failure/diagnosis* ; Hospitalization ; Humans ; Male ; Methylamines/blood* ; Middle Aged ; Natriuretic Peptide, Brain/blood* ; Peptide Fragments ; Prognosis ; Prospective Studies

Heart failure with preserved ejection fraction (HFpEF) is a syndrome with highly heterogeneous clinical symptoms, and its incidence has been increasing in recent years. Compared with heart failure with reduced ejection fraction (HFrEF), HFpEF has a worse prognosis. Traditional therapies targeting the internal mechanisms of the heart show limited or inefficacy on HFpEF, and new therapeutic targets for HFpEF are expected to be found by focusing on the extracardiac mechanisms. Recent studies have shown that cardiopulmonary pathophysiological interaction exacerbates the progression of HFpEF. Hypertension, systemic vascular injury, and inflammatory response lead to coronary microvascular dysfunction, myocardial hypertrophy, and coronary microvascular remodeling. Acute kidney injury affects myocardial energy production, induces oxidative stress and catabolism of myocardial protein, which leads to myocardial dysfunction. Liver fibrosis mediates heart injury by abnormal protein deposition and inflammatory factors production. Skeletal muscle interacts with the sympathetic nervous system by metabolic signals. It also produces muscle factors, jointly affecting cardiac function. Metabolic syndrome, gut microbiota dysbiosis, immune system diseases, and iron deficiency promote the occurrence and development of HFpEF through metabolic changes, oxidative stress, and inflammatory responses. Therefore, the research on the extracardiac mechanisms of HFpEF has certain implications for model construction, mechanism research, and treatment strategy formulation.
Humans ; Heart Failure/diagnosis* ; Stroke Volume/physiology* ; Myocardium/metabolism* ; Cardiomyopathies/metabolism* ; Hypertension ; Ventricular Function, Left

Cardiovascular homeostasis is regulated by both physical and chemical factors. Vascular stiffness, a physical property of vessel, is crucial in maintaining the physiological function of vasculature. Vascular stiffness has been indicated to be correlated with hypertension, heart failure and other cardiovascular diseases. It has been the most widely accepted clinical index for assessment of vascular function and dysfunction. This paper reviews the commonly used experimental and clinical techniques for evaluating vascular stiffness including direct detection of the Young's modulus and indirect detection method that is based on ultrasound technique and others. Principles of these methodologies, as well as their advantages and disadvantages, are also presented here. Researchers and clinical staff are encouraged to choose the most suitable methods for detecting vascular stiffness according to their purposes and objects, so as to effectively evaluate vascular function.
Humans ; Vascular Stiffness ; Elastic Modulus ; Hypertension/diagnosis* ; Cardiovascular Diseases/diagnosis* ; Heart Failure

BACKGROUND: Epidemiological evidence has shown that serum N-terminal pro-brain natriuretic peptide (NT-proBNP) concentrations, a diagnostic biomarker for heart failure, are positively associated with cardiovascular risk. Since NT-proBNP in serum is excreted in urine, it is hypothesized that urinary NT-proBNP concentrations are correlated with serum concentrations and linked with cardiovascular risk in the general population. METHODS: A total of 3060 community-dwelling residents aged ≥ 40 years without history of cardiovascular disease (CVD) were followed up for a median of 8.3 years (2007-2015). Serum and urinary concentrations of NT-proBNP at baseline were compared. The hazard ratios (HRs) and their 95% confidence intervals (CIs) for the association between NT-proBNP concentrations and the risk of developing CVD were computed using the Cox proportional hazards model. RESULTS: The median values (interquartile ranges) of serum and urinary NT-proBNP concentrations at baseline were 56 (32-104) pg/mL and 20 (18-25) pg/mL, respectively. There was a strong quadratic correlation between the serum and urinary concentrations of NT-proBNP (coefficient of determination [R CONCLUSIONS The present study demonstrated that urinary NT-proBNP concentrations were well-correlated with serum concentrations and were positively associated with cardiovascular risk. Given that urine sampling is noninvasive and does not require specially trained personnel, urinary NT-proBNP concentrations have the potential to be an easy and useful biomarker for detecting people at higher cardiovascular risk.
Adult ; Aged ; Aged, 80 and over ; Biomarkers/urine* ; Cardiovascular Diseases/urine* ; Female ; Heart Failure/diagnosis* ; Humans ; Incidence ; Japan/epidemiology* ; Male ; Middle Aged ; Natriuretic Peptide, Brain/urine* ; Peptide Fragments/urine* ; Prospective Studies ; Risk Assessment