Association between triglyceride-glucose index trajectories and radiofrequency ablation outcomes in patients with stage 3D atrial fibrillation.

BACKGROUND: This study investigates the relationship between triglyceride-glucose (TyG) index trajectories and the results of ablation in patients with stage 3D atrial fibrillation (AF). METHODS: A retrospective cohort study was carried out on patients who underwent AF Radiofrequency Catheter Ablation (RFCA) at the Cardiology Department of the Fourth Affiliated Hospital of Zhejiang University and Taizhou Hospital of Zhejiang Province from January 2016 to December 2022. The main clinical endpoint was determined as the occurrence of atrial arrhythmia for at least 30 s following a 3-month period after ablation. Using a latent class trajectory model, different trajectory groups were identified based on TyG levels. The relationship between TyG trajectory and the outcome of AF recurrence in patients was assessed through Kaplan-Meier survival curve analysis and multivariable Cox proportional hazards regression model. RESULTS: The study included 997 participants, with an average age of 63.21 ± 9.84 years, of whom 630 were males (63.19%). The mean follow-up period for the participants was 30.43 ± 17.75 months, during which 200 individuals experienced AF recurrence. Utilizing the minimum Bayesian Information Criterion (BIC) and the maximum Entropy principle, TyG levels post-AF RFCA were divided into three groups: Locus 1 low-low group (n = 791), Locus 2 low-high-low group (n = 14), and Locus 3 high-high group (n = 192). Significant differences in survival rates among the different trajectories were observed through the Kaplan-Meier curve (P < 0.001). Multivariate Cox regression analysis showed a significant association between baseline TyG level and AF recurrence outcomes (HR = 1.255, 95% CI: 1.087-1.448). Patients with TyG levels above 9.37 had a higher risk of adverse outcomes compared to those with levels below 8.67 (HR = 2.056, 95% CI: 1.335-3.166). Furthermore, individuals in Locus 3 had a higher incidence of outcomes compared to those in Locus 1 (HR = 1.580, 95% CI: 1.146-2). CONCLUSION: The TyG trajectories in patients with stage 3D AF are significantly linked to the outcomes of AF recurrence. Continuous monitoring of TyG levels during follow-up may help in identifying patients at high risk of AF recurrence, enabling the early application of effective interventions.

Post-stroke arrhythmia could be a potential predictor for post-stroke depression.

Post-stroke depression (PSD) is regarded as the consequence of multiple contributors involving the process of cognition, mood and autonomic system, with the specific mechanism unclear yet. As a common type of stroke-heart syndromes, post-stroke arrhythmia shared some common pathogenesis with PSD. We presumed that post-stroke arrhythmia might be an early distinguishable marker for the presence of PSD and aimed to verity their association in this study. Patients with first-ever ischemic stroke were enrolled. The presence of post-stroke ectopic arrhythmia and the symptoms of arrhythmia were recorded with anti-arrhythmia drugs prescribed when necessary. Patients were followed up 3 months later to identify their presence and severity of PSD using Hamilton Depression Scale (HAMD) and also presence and severity of arrhythmia. Characteristics including the prevalence of various types of arrhythmias were compared between PSD and non-PSD groups. The HAMD scores were compared between patients with and without arrhythmia in PSD group. Logistic regression was used to identify the independent predictor of PSD. Patients with PSD had higher prevalence of post-stroke arrhythmia especially newly-detected arrhythmia, symptomatic arrhythmia and poor-controlled arrhythmia. In PSD group, patients of post-stroke arrhythmia had higher scores of HAMD than those without arrhythmia. Presence of newly-detected, symptomatic and poor-controlled arrhythmias were independent predictor of PSD. post-stroke arrhythmia especially newly-detected arrhythmia and symptomatic arrhythmia could be an early predictor of PSD. Successful control of arrhythmia was associated with reduced prevalence and severity of PSD.

Shared genetic architecture and causal relationship between liver and heart disease.

This study investigates the relationship and genetic mechanisms of liver and heart diseases, focusing on the liver-heart axis (LHA) as a fundamental biological basis. Through genome-wide association study analysis, we explore shared genes and pathways related to LHA. Shared genetic factors are found in 8 out of 20 pairs, indicating genetic correlations. The analysis reveals 53 loci with pleiotropic effects, including 8 loci exhibiting shared causality across multiple traits. Based on SNP-p level tissue-specific multi-marker analysis of genomic annotation (MAGMA) analysis demonstrates significant enrichment of pleiotropy in liver and heart diseases within different cardiovascular tissues and female reproductive appendages. Gene-specific MAGMA analysis identifies 343 pleiotropic genes associated with various traits; these genes show tissue-specific enrichment primarily in the liver, cardiovascular system, and other tissues. Shared risk loci between immune cells and both liver and cardiovascular diseases are also discovered. Mendelian randomization analyses provide support for causal relationships among the investigated trait pairs.

Entity relationship extraction from Chinese electronic medical records based on feature augmentation and cascade binary tagging framework.

Extracting entity relations from unstructured Chinese electronic medical records is an important task in medical information extraction. However, Chinese electronic medical records mostly have document-level volumes, and existing models are either unable to handle long text sequences or exhibit poor performance. This paper proposes a neural network based on feature augmentation and cascade binary tagging framework. First, we utilize a pre-trained model to tokenize the original text and obtain word embedding vectors. Second, the word vectors are fed into the feature augmentation network and fused with the original features and position features. Finally, the cascade binary tagging decoder generates the results. In the current work, we built a Chinese document-level electronic medical record dataset named VSCMeD, which contains 595 real electronic medical records from vascular surgery patients. The experimental results show that the model achieves a precision of 87.82% and recall of 88.47%. It is also verified on another Chinese medical dataset CMeIE-V2 that the model achieves a precision of 54.51% and recall of 48.63%.

Glasgow prognostic score and its derived scores predicts contrast-associated acute kidney injury in patients undergoing coronary angiography.

Background: Glasgow prognostic score (GPS) is a reliable scoring system reflecting both nutritional and inflammatory factors. The association of inflammation and nutrition with contrast-associated acute kidney injury (CA-AKI) has been validated. This study set out to determine the impact of GPS and its derived scores on CA-AKI incidence. Methods: Populations treated with coronary angiography with/without percutaneous coronary intervention were screened retrospectively. According to C-reactive protein and albumin, three kinds of GPSs were involved: GPS, modified GPS (mGPS), and the cutoff-based GPS (cGPS) which was derived by calculating the optimal cutoff values of two parameters. Primary endpoint was CA-AKI. Pearson' r correlation, linear/logistic regression, receiver operating characteristic curve as well as subgroup analyses were conducted. Results: Totally, 3150 patients were valid for analysis, and the mean age was 67.5 years old, with 66.4 % male. Of these, 610 patients suffered CA-AKI. All three kinds of GPSs were independently associated with the SCr elevation proportion (GPS: ß = 4.850, 95%CI [3.700 to 8.722], P < 0.001; mGPS: ß = 3.450, 95%CI [1.896 to 6.888], P = 0.001; cGPS: ß = 3.992, 95%CI [2.368 to 6.940], P < 0.001). GPS, mGPS and cGPS were proved to be the independent risk factors for CA-AKI risk (all P for trend <0.05). Compared with GPS and mGPS, cGPS was of greater prognostic value for predicting CA-AKI incidence (cGPS: AUC = 0.633; mGPS: AUC = 0.567; GPS: AUC = 0.611). Main findings were also consistent in all subgroup analysis. Conclusion: Preprocedural GPS and its derived scores (mGPS and cGPS), especially cGPS, were correlated with the incidence of CA-AKI, which might assist in clinical decision making in treating CA-AKI.

Gender Disparities in Patients' Decisions about the Management of Myocardial Infarction in East Chinese Province.

Background: Previous studies showed that there are gender disparities in various respects of acute myocardial infarction (AMI), including risk factors, symptoms, and outcomes. However, few of them noticed the gender disparities in patients' decision about the management of AMI, which might also be associated with the outcome. Aims: To identify gender disparities in patients' decisions about the management of myocardial infarction. Methods: In this cohort study, the critical time points including the time of symptom onset, visiting hospital, diagnosis of AMI, consent to coronary angiography (CAG), beginning of CAG, and balloon dilation were recorded. Medication and major adverse cardiac event (MACE) within 6 months were also recorded. Results: Female patients took more time from symptom onset to visiting hospital (P = 0.001), from diagnosis of AMI to consent to CAG (P < 0.05), and from door to needle/balloon than male (P < 0.05). Less female patients accepted CAG (P < 0.05) and coronary intervention/bypass grafting (P < 0.05). Less female patients kept good inherence to antiplatelet therapy (P < 0.05) and statins (P < 0.05) than male, more female preferred traditional Chinese medicine (TCM) than male patient (P < 0.05), and most of them had MACE within 6 months (P < 0.05). Patients' good adherence to antiplatelet therapy and statins and accepting coronary intervention/bypass grafting were associated with a reduced risk of MACE. Conclusion: Female patients were more reluctant to make decisions about emergency management of AMI and tended to choose conservative treatment. More female patients preferred TCM than evidence-based medicine. Their reluctance about the critical management of AMI and poor adherence to evidence-based medicine were associated with an elevated risk of MACE.

Emerging biomolecules for practical theranostics of liver hepatocellular carcinoma.

Most cases of hepatocellular carcinoma (HCC) are able to be diagnosed through regular surveillance in an identifiable patient population with chronic hepatitis B or cirrhosis. Nevertheless, 50% of global cases might present incidentally owing to symptomatic advanced-stage HCC after worsening of liver dysfunction. A systematic search based on PUBMED was performed to identify relevant outcomes, covering newer surveillance modalities including secretory proteins, DNA methylation, miRNAs, and genome sequencing analysis which proposed molecular expression signatures as ideal tools in the early-stage HCC detection. In the face of low accuracy without harmonization on the analytical approaches and data interpretation for liquid biopsy, a more accurate incidence of HCC will be unveiled by using deep machine learning system and multiplex immunohistochemistry analysis. A combination of molecular-secretory biomarkers, high-definition imaging and bedside clinical indexes in a surveillance setting offers a comprehensive range of HCC potential indicators. In addition, the sequential use of numerous lines of systemic anti-HCC therapies will simultaneously benefit more patients in survival. This review provides an overview on the most recent developments in HCC theranostic platform.

Machine Learning Assisted Wearable Wireless Device for Sleep Apnea Syndrome Diagnosis.

Sleep apnea syndrome (SAS) is a common but underdiagnosed health problem related to impaired quality of life and increased cardiovascular risk. In order to solve the problem of complicated and expensive operation procedures for clinical diagnosis of sleep apnea, here we propose a small and low-cost wearable apnea diagnostic system. The system uses a photoplethysmography (PPG) optical sensor to collect human pulse wave signals and blood oxygen saturation synchronously. Then multiscale entropy and random forest algorithms are used to process the PPG signal for analysis and diagnosis of sleep apnea. The SAS determination is based on the comprehensive diagnosis of the PPG signal and blood oxygen saturation signal, and the blood oxygen is used to exclude the error induced by non-pathological factors. The performance of the system is compared with the Compumedics Grael PSG (Polysomnography) sleep monitoring system. This simple diagnostic system provides a feasible technical solution for portable and low-cost screening and diagnosis of SAS patients with a high accuracy of over 85%.

Risk investigation of in-stent restenosis after initial implantation of intracoronary drug-eluting stent in patients with coronary heart disease.

Objective: To analyze the risk factors of in-stent restenosis (ISR) after the first implantation of drug-eluting stent (DES) patients with coronary heart disease (CHD) and to establish a nomogram model to predict the risk of ISR. Methods: This study retrospectively analyzed the clinical data of patients with CHD who underwent DES treatment for the first time at the Fourth Affiliated Hospital of Zhejiang University School of Medicine from January 2016 to June 2020. Patients were divided into an ISR group and a non-ISR (N-ISR) group according to the results of coronary angiography. The least absolute shrinkage and selection operator (LASSO) regression analysis was performed on the clinical variables to screen out the characteristic variables. Then we constructed the nomogram prediction model using conditional multivariate logistic regression analysis combined with the clinical variables selected in the LASSO regression analysis. Finally, the decision curve analysis, clinical impact curve, area under the receiver operating characteristic curve, and calibration curve were used to evaluate the nomogram prediction model's clinical applicability, validity, discrimination, and consistency. And we double-validate the prediction model using ten-fold cross-validation and bootstrap validation. Results: In this study, hypertension, HbA1c, mean stent diameter, total stent length, thyroxine, and fibrinogen were all predictive factors for ISR. We successfully constructed a nomogram prediction model using these variables to quantify the risk of ISR. The AUC value of the nomogram prediction model was 0.806 (95%CI: 0.739-0.873), indicating that the model had a good discriminative ability for ISR. The high quality of the calibration curve of the model demonstrated the strong consistency of the model. Moreover, the DCA and CIC curve showed the model's high clinical applicability and effectiveness. Conclusions: Hypertension, HbA1c, mean stent diameter, total stent length, thyroxine, and fibrinogen are important predictors for ISR. The nomogram prediction model can better identify the high-risk population of ISR and provide practical decision-making information for the follow-up intervention in the high-risk population.

A Study on the Association between Korotkoff Sound Signaling and Chronic Heart Failure (CHF) Based on Computer-Assisted Diagnoses.

Background: Korotkoff sound (KS) is an important indicator of hypertension when monitoring blood pressure. However, its utility in noninvasive diagnosis of Chronic heart failure (CHF) has rarely been studied. Purpose: In this study, we proposed a method for signal denoising, segmentation, and feature extraction for KS, and a Bayesian optimization-based support vector machine algorithm for KS classification. Methods: The acquired KS signal was resampled and denoised to extract 19 energy features, 12 statistical features, 2 entropy features, and 13 Mel Frequency Cepstrum Coefficient (MFCCs) features. A controlled trial based on the VALSAVA maneuver was carried out to investigate the relationship between cardiac function and KS. To classify these feature sets, the K-Nearest Neighbors (KNN), decision tree (DT), Naive Bayes (NB), ensemble (EM) classifiers, and the proposed BO-SVM were employed and evaluated using the accuracy (Acc), sensitivity (Se), specificity (Sp), Precision (Ps), and F1 score (F1). Results: The ALSAVA maneuver indicated that the KS signal could play an important role in the diagnosis of CHF. Through comparative experiments, it was shown that the best performance of the classifier was obtained by BO-SVM, with Acc (85.0%), Se (85.3%), and Sp (84.6%). Conclusions: In this study, a method for noise reduction, segmentation, and classification of KS was established. In the measured data set, our method performed well in terms of classification accuracy, sensitivity, and specificity. In light of this, we believed that the methods described in this paper can be applied to the early, noninvasive detection of heart disease as well as a supplementary monitoring technique for the prognosis of patients with CHF.

Korotkoff sounds dynamically reflect changes in cardiac function based on deep learning methods.

Korotkoff sounds (K-sounds) have been around for over 100 years and are considered the gold standard for blood pressure (BP) measurement. K-sounds are also unique for the diagnosis and treatment of cardiovascular diseases; however, their efficacy is limited. The incidences of heart failure (HF) are increasing, which necessitate the development of a rapid and convenient pre-hospital screening method. In this review, we propose a deep learning (DL) method and the possibility of using K-methods to predict cardiac function changes for the detection of cardiac dysfunctions.

A multimodal parallel method for left ventricular dysfunction identification based on phonocardiogram and electrocardiogram signals synchronous analysis.

Heart failure (HF) is widely acknowledged as the terminal stage of cardiac disease and represents a global clinical and public health problem. Left ventricular ejection fraction (LVEF) measured by echocardiography is an important indicator of HF diagnosis and treatment. Early identification of LVEF reduction and early treatment is of great significance to improve LVEF and the prognosis of HF. This research aims to introduce a new method for left ventricular dysfunction (LVD) identification based on phonocardiogram (ECG) and electrocardiogram (PCG) signals synchronous analysis. In the present study, we established a database called Synchronized ECG and PCG Database for Patients with Left Ventricular Dysfunction (SEP-LVDb) consisting of 1046 synchronous ECG and PCG recordings from patients with reduced (n = 107) and normal (n = 699) LVEF. 173 and 873 recordings were available from the reduced and normal LVEF group, respectively. Then, we proposed a parallel multimodal method for LVD identification based on synchronous analysis of PCG and ECG signals. Two-layer bidirectional gate recurrent unit (Bi-GRU) was used to extract features in the time domain, and the data were classified using residual network 18 (ResNet-18). This research confirmed that fused ECG and PCG signals yielded better performance than ECG or PCG signals alone, with an accuracy of 93.27%, precision of 93.34%, recall of 93.27%, and F1-score of 93.27%. Verification of the model's performance with an independent dataset achieved an accuracy of 80.00%, precision of 79.38%, recall of 80.00% and F1-score of 78.67%. The Bi-GRU model outperformed Bi-directional long short-term memory (Bi-LSTM) and recurrent neural network (RNN) models with a best selection frame length of 3.2 s. The Saliency Maps showed that SEP-LVDPN could effectively learn features from the data.

Systemic Immune-Inflammation Index Predicts Contrast-Induced Acute Kidney Injury in Patients Undergoing Coronary Angiography: A Cross-Sectional Study.

Background and Aims: Systemic immune-inflammation index (SII) is an emerging indicator and correlated to the incidence of cardiovascular diseases. This study aimed to explore the association between SII and contrast-induced acute kidney injury (CI-AKI). Methods: In this retrospective cross-sectional study, 4,381 subjects undergoing coronary angiography (CAG) were included. SII is defined as neutrophil count × platelet count/lymphocyte count. CI-AKI was determined by the elevation of serum creatinine (Scr). Multivariable linear and logistic regression analysis were used to determine the relationship of SII with Scr and CI-AKI, respectively. Receiver operator characteristic (ROC) analysis, structural equation model analysis, and subgroup analysis were also performed. Results: Overall, 786 (17.9%) patients suffered CI-AKI after the intravascular contrast administration. The subjects were 67.1 ± 10.8 years wold, with a mean SII of 5.72 × 1011/L. Multivariable linear regression analysis showed that SII linearly increased with the proportion of Scr elevation (ß [95% confidence interval, CI] = 0.315 [0.206 to 0.424], P < 0.001). Multivariable logistic regression analysis demonstrated that higher SII was associated with an increased incidence of CI-AKI ([≥12 vs. <3 × 1011/L]: odds ratio, OR [95% CI] = 2.914 [2.121 to 4.003], P < 0.001). Subgroup analysis showed consistent results. ROC analysis identified a good predictive value of SII on CI-AKI (area under the ROC curve [95% CI]: 0.625 [0.602 to 0.647]). The structural equation model verified a more remarkable direct effect of SII (ß = 0.102, P < 0.001) on CI-AKI compared to C-reactive protein (ß = 0.070, P < 0.001). Conclusions: SII is an independent predictor for CI-AKI in patients undergoing CAG procedures.

Using deep learning method to identify left ventricular hypertrophy on echocardiography.

BACKGROUND: Left ventricular hypertrophy (LVH) is an independent prognostic factor for cardiovascular events and it can be detected by echocardiography in the early stage. In this study, we aim to develop a semi-automatic diagnostic network based on deep learning algorithms to detect LVH. METHODS: We retrospectively collected 1610 transthoracic echocardiograms, included 724 patients [189 hypertensive heart disease (HHD), 218 hypertrophic cardiomyopathy (HCM), and 58 cardiac amyloidosis (CA), along with 259 controls]. The diagnosis of LVH was defined by two experienced clinicians. For the deep learning architecture, we introduced ResNet and U-net++ to complete classification and segmentation tasks respectively. The models were trained and validated independently. Then, we connected the best-performing models to form the final framework and tested its capabilities. RESULTS: In terms of individual networks, the view classification model produced AUC = 1.0. The AUC of the LVH detection model was 0.98 (95% CI 0.94-0.99), with corresponding sensitivity and specificity of 94.0% (95% CI 85.3-98.7%) and 91.6% (95% CI 84.6-96.1%) respectively. For etiology identification, the independent model yielded good results with AUC = 0.90 (95% CI 0.82-0.95) for HCM, AUC = 0.94 (95% CI 0.88-0.98) for CA, and AUC = 0.88 (95% CI 0.80-0.93) for HHD. Finally, our final integrated framework automatically classified four conditions (Normal, HCM, CA, and HHD), which achieved an average of AUC 0.91, with an average sensitivity and specificity of 83.7% and 90.0%. CONCLUSION: Deep learning architecture has the ability to detect LVH and even distinguish the latent etiology of LVH.

Management of adolescent anorexia with symptomatic bradycardia and frequent premature ventricular contractions: a case report.

Syncope associated with bradycardia and ventricular arrhythmia is an indication of cardiac intervention. However, in adolescent patients with anorexia nervosa, the management of syncope and arrhythmia can be different. We present a case of a 17-year-old boy who was admitted to the hospital because of syncope during exercise. Electrocardiographic monitoring showed that his mean heart rate was 41 beats/minute, with many long pauses and frequent premature ventricular contractions. These results suggested that the syncope was probably caused by arrythmia. He had been on a diet and had lost 20 kg in the past 6 months, with a body mass index of only 15.3 kg/m2. He was diagnosed with anorexia nervosa. Pacemaker implantation or ablation was not performed. Refeeding therapy was performed with mirtazapine. A follow-up showed a stepwise increase in his heart rate and a stepwise decrease in premature ventricular contractions, with an increase in his body weight. The findings from this case show that vagal hyperactivity associated with anorexia nervosa might lead to multiple premature ventricular contractions and bradycardia.

Cannabis Use and the Risk of Cardiovascular Diseases: A Mendelian Randomization Study.

Several observational studies have shown that cannabis use has negative effects on the cardiovascular system, but the causality of this relationship has not been confirmed. The aim of the current study was to estimate the effects of genetically determined cannabis use on risk of cardiovascular diseases. Ten single-nucleotide polymorphisms related to cannabis use were employed as instruments to estimate the association between genetically determined cannabis use and risk of cardiovascular diseases using a two-sample Mendelian randomization (MR) method. Summary statistics data on exposure and outcomes were obtained from different genome-wide association meta-analysis studies. The results of this MR analysis showed no causal effects of cannabis use on the risk of several common cardiovascular diseases, including coronary artery disease, myocardial infarction, stroke and ischemic stroke subtypes, atrial fibrillation (AF), and heart failure. Various sensitivity analyses yielded similar results, and no heterogeneity and directional pleiotropy were observed. After adjusting for tobacco use and body mass index, multivariable MR analysis suggested a causal effect of cannabis use on small vessel stroke (SVS) [odds ratio (OR) 1.17; 95% CI 1.02-1.35; p = 0.03] and AF (OR 1.06; 95% CI 1.01-1.10; p = 0.01), respectively. This two-sample MR study did not demonstrate a causal effect of genetic predisposition to cannabis use on several common cardiovascular outcomes. After adjusting for tobacco use and body mass index, the multivariable MR analysis suggested a detrimental effect of cannabis use on the risk of SVS and AF, respectively.

Determination of the stability of plasma ATP in vitro.

BACKGROUND: Adenosine 5'-triphosphate (ATP) is the most direct source of energy in organisms. Recently, it is evident that ATP plays an essential role in the immune and inflammatory systems. However, ATP is unstable when it exposed to room temperature in vitro. Therefore, our article is aim to explore the stability of ATP. METHODS AND RESULTS: 28 samples of ATP were detected. Student's t test or one-way ANOVA was used to compare multiple groups. It shows that during the storage process from day 1 to day 70, the overall levels tend to decrease. CONCLUSION: The level of ATP does not reduce at least in the first month when stored at -80°C. On the 70th day, there was a star drop, and the levels were lower than before.

Automatic Lumen Border Detection in IVUS Images Using Deep Learning Model and Handcrafted Features.

In the clinical analysis of Intravascular ultrasound (IVUS) images, the lumen size is an important indicator of coronary atherosclerosis, and is also the premise of coronary artery disease diagnosis and interventional treatment. In this study, a fully automatic method based on deep learning model and handcrafted features is presented for the detection of the lumen borders in IVUS images. First, 193 handcrafted features are extracted from the IVUS images. Then hybrid feature vectors are constructed by combining handcrafted features with 64 high-level features extracted from U-Net. In order to obtain the feature subsets with larger contribution, we employ the extended binary cuckoo search for feature selection. Finally, the selected 36-dimensional hybrid feature subset is used to classify the test images using dictionary learning based on kernel sparse coding. The proposed algorithm is tested on the publicly available dataset and evaluated using three indicators. Through ablation experiments, mean value of the experimental results (Jaccard: 0.88, Hausdorff distance: 0.36, Percentage of the area difference: 0.06) prove to be effective improving lumen border detection. Furthermore, compared with the recent methods used on the same dataset, the proposed method shows good performance and high accuracy.

An Exploratory Study on the Relationship between Brachial Arterial Blood Flow and Cardiac Output.

Background: We have obtained prospective clinical outcomes using the brachial artery largely, such as Korotkoff sound and vasomotor function measurement by ultrasound guidance to predict the prognosis of cardiovascular diseases. Very few reports on the quantitative measurement of the relationship between the brachial artery blood flow and cardiac output have been reported. Purpose: (1) To investigate whether the quantitative relationship between the brachial artery blood flow and cardiac output existed. (2) To provide a theoretical basis for taking advantage of artificial intelligence (AI) using Korotkoff sound analogously as far as possible to predict the cardiac output. Methods: A total of 586 patients who underwent cardiac color ultrasound in our center from 2021.3 to 2021.7 were included for analyses. The vascular parameters of the right upper limb brachial artery (such as the Diameter, Area, Blood Velocity, and Flow) were measured immediately after the cardiac color ultrasound, and some basic clinical parameters (Age, Sex, BMI, and Disease) were recorded subsequently. Ultimately, the Mann-Whitney and independent sample T-test were used to analyze the data. Results: (1) The mean Rate of the brachial arterial blood flow to cardiac output was 1.23%, and the mean 95% CI was (1.18%, 1.29%), indicating that the value was mainly concentrated in the current value interval. The indicator demonstrates that there is no significant difference currently among the patients with hypertension, coronary heart disease, and cardiac dysfunction. (2) The brachial artery wall diameter (Dist) is significantly thicker in patients with coronary heart disease and hypertension compared to patients with other cardiovascular diseases. (3) Cardiac output augments remarkably in patients with hypertension. Conclusion: Our study suggests that the Rate (brachial artery blood flow/cardiac output) is a constant of 1.23% approximately. It provides a theoretical basis for the subsequent application of the artificial intelligence (AI) method to predict heart function using Korotkoff sound, cope with large computational amounts, and improve computational speed. It is also indirectly proved that hypertension can lead to a change in peripheral vascular hyperplasia and increase cardiac output.