Noninvasive Visualization of the Atrioventricular Conduction System Using Cardiac Computed Tomography.

Importance: Noninvasive localization of the compact atrioventricular node and the proximal specialized conduction system (AVCS) would enhance planning for transcatheter aortic valve and complex or congenital heart disease surgical procedures. Objective: To test the hypothesis that preprocedure contrast-enhanced cardiac computed tomography (CECT) can accurately localize the AVCS by identification of the fat that insulates the conductive myocardium. Design, Setting, and Participants: This was a prospective cohort study that took place at an academic tertiary care center. Included in the study were patients with CECT acquired less than 1 month before atrial fibrillation ablation and electroanatomic localization of the His electrogram signal on electroanatomic mapping (EAM) between January 2022 and January 2023. Exposures: Preprocedure CECT. Main Outcomes and Measures: The distance from the His electrogram signal to the fat segmentation encompassing the AVCS on CECT, after registration of the images to EAM. Results: Among 20 patients (mean [SD] age, 66 [10] years; 15 male [75%]) in the cohort, the mean (SD) attenuation of the AVCS fat segmentation was 2.9 (21.5) Hounsfield units. The mean (SD) distance from the His electrogram to the closest AVCS fat voxel was 3.3 (1.6) mm. Conclusions and Relevance: Results of this cohort study suggest that CECT could accurately localize the fatty tissue that insulates the AVCS from surrounding atrial and ventricular myocardium and may enhance the efficacy and safety of procedures targeting the conduction system and structures in its proximity.

Facile synthesis of MoS2@red phosphorus heterojunction for synergistically photodynamic and photothermal therapy of renal cell carcinoma.

The therapy of the clear cell renal cell carcinoma (ccRCC) is crucial for the human healthcare due to its easy metastasis and recurrence, as well as resistance to radiotherapy and chemotherapy. In this work, we propose the synthesis of MoS2@red phosphorus (MoS2@RP) heterojunction to induce synergistic photodynamic and photothermal therapy (PDT/PTT) of ccRCC. The MoS2@RP heterojunction exhibits enhanced spectra absorption in the NIR range and produce local heat-increasing under the NIR laser irradiation compared with pure MoS2 and RP. The high photocatalytic activity of the MoS2@RP heterojunction contributes to effective transferring of the photo-excited electrons from the RP to MoS2, which promotes the production of various types of radical oxygen species (ROS) to kill the ccRCC cells. After the NIR irradiation, the MoS2@RP can effectively induce the apoptosis in the ccRCC cells through localized hyperthermia and the generation of ROS, while exhibiting low cytotoxicity towards normal kidney cells. In comparison to MoS2, the MoS2@RP heterojunction shows an approximate increase of 22 % in the lethality rate of the ccRCC cells and no significant change in toxicity towards normal cells. Furthermore, the PDT/PTT treatment using the MoS2@RP heterojunction effectively eradicates a substantial number of deep-tissue ccRCC cells in vivo without causing significant damage to major organs. This study presents promising effect of the MoS2@RP heterojunction-based photo-responsive therapy for effective ccRCC treatment.

Personalized prediction of mortality in patients with acute ischemic stroke using explainable artificial intelligence.

BACKGROUND: Research into the acute kidney disease (AKD) after acute ischemic stroke (AIS) is rare, and how clinical features influence its prognosis remain unknown. We aim to employ interpretable machine learning (ML) models to study AIS and clarify its decision-making process in identifying the risk of mortality. METHODS: We conducted a retrospective cohort study involving AIS patients from January 2020 to June 2021. Patient data were randomly divided into training and test sets. Eight ML algorithms were employed to construct predictive models for mortality. The performance of the best model was evaluated using various metrics. Furthermore, we created an artificial intelligence (AI)-driven web application that leveraged the top ten most crucial features for mortality prediction. RESULTS: The study cohort consisted of 1633 AIS patients, among whom 257 (15.74%) developed subacute AKD, 173 (10.59%) experienced AKI recovery, and 65 (3.98%) met criteria for both AKI and AKD. The mortality rate stood at 4.84%. The LightGBM model displayed superior performance, boasting an AUROC of 0.96 for mortality prediction. The top five features linked to mortality were ACEI/ARE, renal function trajectories, neutrophil count, diuretics, and serum creatinine. Moreover, we designed a web application using the LightGBM model to estimate mortality risk. CONCLUSIONS: Complete renal function trajectories, including AKI and AKD, are vital for fitting mortality in AIS patients. An interpretable ML model effectively clarified its decision-making process for identifying AIS patients at risk of mortality. The AI-driven web application has the potential to contribute to the development of personalized early mortality prevention.

Using dynamic spatio-temporal graph pooling network for identifying autism spectrum disorders in spontaneous functional infrared spectral sequence signals.

BACKGROUND: Autism classification work on fNIRS data using dynamic graph networks. Explore the impact of the dynamic connection relationship between brain channels on ASD, and compare the brain channel connection diagrams of ASD and TD to explore potential factors that influence the development of autism. METHOD: Using dynamic graph construction to mine the dynamic relationships of fNIRS data, obtain spatio-temporal correlations through dynamic feature extraction, and improve the information extraction capabilities of the network through spatio-temporal graph pooling to achieve classification of ASD. RESULT: A classification effect with an accuracy of 97.2% was achieved using a short sequence of 1.75s. The results showed that the dynamic connections of channel 5 and 19, channel 12 and 25, and channel 7 and 34 have a greater impact on the classification of autism. Comparison with previously used method(s): Compared with previous deep learning models, our model achieves efficient classification using short-term fNIRS data of 1.75s, and analyzes the impact of dynamic connections on classification through dynamic graphs. CONCLUSION: Using Dynamic Spatio-Temporal Graph Pooled Neural Networks (DSTGPN), dynamic connectivity between brain channels was found to have an impact on the classification of autism. By modeling the brain channel relationship maps of ASD and TD, hyperlink clusters were found to exist on the brain channel connections of ASD.

Lipomatous Metaplasia Is Associated With Ventricular Tachycardia Recurrence Following Ablation in Patients With Nonischemic Cardiomyopathy.

BACKGROUND: Ventricular tachycardia (VT) recurrence rates remain high following ablation among patients with nonischemic cardiomyopathy (NICM). OBJECTIVES: This study sought to define the prevalence of lipomatous metaplasia (LM) in patients with NICM and VT and its association with postablation VT recurrence. METHODS: From patients who had ablation of left ventricular VT, we retrospectively identified 113 consecutive NICM patients with preprocedural contrast-enhanced cardiac computed tomography (CECT), from which LM was segmented. Nested within this cohort were 62 patients that prospectively underwent CECT and cardiac magnetic resonance from which myocardial border zone and dense late gadolinium enhancement (LGE) were segmented. A control arm of 30 NICM patients without VT with CECT was identified. RESULTS: LM was identified among 57% of control patients without VT vs 83% of patients without VT recurrence and 100% of patients with VT recurrence following ablation. In multivariable analyses, LM extent was the only independent predictor of VT recurrence, with an adjusted HR per 1-g LM increase of 1.1 (P < 0.001). Patients with LM extent ≥2.5 g had 4.9-fold higher hazard of VT recurrence than those with LM <2.5 g (P < 0.001). In the nested cohort with 32 VT recurrences, LM extent was independently associated with VT recurrence after adjustment for border zone and LGE extent (HR per 1 g increase: 1.1; P = 0.036). CONCLUSIONS: Myocardial LM is prevalent in patients with NICM of a variety of etiologies, and its extent is associated with postablation VT recurrence independent of the degree of fibrosis.

Evolutionary dynamics and comparative pathogenicity of clade 2.3.4.4b H5 subtype avian influenza viruses, China, 2021-2022.

The recent concurrent emergence of H5N1, H5N6, and H5N8 avian influenza viruses (AIVs) has led to significant avian mortality globally. Since 2020, frequent human-animal interactions have been documented. To gain insight into the novel H5 subtype AIVs (i.e., H5N1, H5N6 and H5N8), we collected 6102 samples from various regions of China between January 2021 and September 2022, and identified 41 H5Nx strains. Comparative analyses on the evolution and biological properties of these isolates were conducted. Phylogenetic analysis revealed that the 41 H5Nx strains belonged to clade 2.3.4.4b, with 13 related to H5N1, 19 to H5N6, and 9 to H5N8. Analysis based on global 2.3.4.4b viruses showed that all the viruses described in this study were likely originated from H5N8, exhibiting a heterogeneous evolutionary history between H5N1 and H5N6 during 2015-2022 worldwide. H5N1 showed a higher rate of evolution in 2021-2022 and more sites under positive selection pressure in 2015-2022. The antigenic profiles of the novel H5N1 and H5N6 exhibited notable variations. Further hemagglutination inhibition assay suggested that some A(H5N1) viruses may be antigenically distinct from the circulating H5N6 and H5N8 strains. Mammalian challenge assays demonstrated that the H5N8 virus (21GD001_H5N8) displayed the highest pathogenicity in mice, followed by the H5N1 virus (B1557_H5N1) and then the H5N6 virus (220086_H5N6), suggesting a heterogeneous virulence profile of H5 AIVs in the mammalian hosts. Based on the above results, we speculate that A(H5N1) viruses have a higher risk of emergence in the future. Collectively, these findings unveil a new landscape of different evolutionary history and biological characteristics of novel H5 AIVs in clade 2.3.4.4b, contributing to a better understanding of designing more effective strategies for the prevention and control of novel H5 AIVs.

The associated factors for physical activity-related injuries among first-year university students in southern China from a biopsychosocial perspective.

Background: Understanding the diverse factors influencing physical activity-related injuries is crucial for developing effective interventions that enable individuals to participate in physical activity (PA) while minimizing injury risk. Currently, research evidence on the multiple factors associated with PA-related injuries is inadequate. This study aimed to examine the associations between PA-related injuries and various biological, psychological, and social factors among first-year university students in China. Methods: We recruited first-year university students from Shantou University in Guangdong Province, China, to participate in our study. Data collection employed a structured self-administered questionnaire, gathering information on PA-related injuries, as well as relevant biological, psychological, and social factors. Binary logistic regression, using a stepwise modeling approach, was employed for the data analysis. Results: Among 1,051 first-year university students, 28.16% reported having experienced PA-related injuries in the past year. Most of the injuries reported were minor, with the knee or lower leg being the most frequently injured part of the body. Improper posture, environmental conditions, and excessive physical load were the leading causes of PA-related injuries. Multiple logistic regression analysis revealed that female students (OR = 0.67, 95% CI: 0.47-0.94, p = 0.022) had reduced odds of PA-related injuries. Conversely, high neuroticism (OR = 1.61, 95% CI: 1.07-2.41, p = 0.022), being a member of a sports team (OR = 2.09, 95% CI: 1.34-3.27, p < 0.001), PA on the wet ground (OR = 1.73, 95% CI: 1.18-2.54, p = 0.005) increased the odds of PA-related injuries. Conclusion: Our findings underscore the intricate interplay of various factors contributing to PA-related injuries. Identifying high-risk individuals based on physiological and psychological characteristics, coupled with targeted interventions addressing modifiable risk factors, is crucial for effective prevention.

Evolution and Antigenic Differentiation of Avian Influenza A(H7N9) Virus, China.

We characterized the evolution and molecular characteristics of avian influenza A(H7N9) viruses isolated in China during 2021-2023. We systematically analyzed the 10-year evolution of the hemagglutinin gene to determine the evolutionary branch. Our results showed recent antigenic drift, providing crucial clues for updating the H7N9 vaccine and disease prevention and control.

Incidence and prognosis of acute kidney injury versus acute kidney disease among 71 041 inpatients.

Background: Acute kidney disease (AKD) defines patients with acute kidney injury (AKI) or subacute loss of kidney function lasting for >7 days. Little is known about the prognosis of AKD in hospitalized patients. The aim of this study was to investigate the risk factors and prognosis of AKD and to compare different types of acute/subacute renal impairment among Chinese inpatients. Methods: Complete data were available for 71 041 patients for a range of 5-63 months. AKI and AKD were diagnosed based on the Acute Disease Quality Initiative criteria of 2017. Results: Of 71 041 inpatients, 16 098 (22.7%) patients developed AKI or AKD; 5895 (8.3%) AKI patients recovered within 7 days, 5623 (7.9%) AKI patients developed AKD and 4580 (6.4%) patients developed AKD without AKI. Mortality was proportional to stages of AKI and AKD (P < .05), while AKI followed by AKD was associated with a higher risk of long-term mortality [hazard ratio (HR) 4.51] as compared with AKD without AKI (HR 2.25) and recovery from AKI (HR 1.18). The AKD criteria were robustly associated with overall survival [area under the receiver operating characteristic curve (AUROC) 0.71] and de novo CKD (AUROC 0.71), while the AKI criteria showed a relatively lower ability to fit the risk of overall survival (AUROC 0.65) and CKD (AUROC 0.63). Conclusions: AKD and AKD stages are useful clinical definitions for clinical practice, as they predict unfortunate clinical outcomes such as overall long-term mortality and CKD. Research activities should focus on AKD.

Hospitalized patients received furosemide undergoing acute kidney injury: the risk and prediction tool.

PURPOSE: Furosemide, a frequently prescribed diuretic for managing congestive heart failure and edema, remains a topic of debate regarding its potential risk of inducing acute kidney injury (AKI) in patients. Consequently, this study aims to examine the occurrence of hospital-acquired AKI (HA-AKI) in hospitalized patients who are administered furosemide and to investigate potential risk factors associated with this outcome. METHODS: This study encompassed a cohort of 22374 hospitalized patients who either received furosemide treatment or not from June 1, 2012, to December 31, 2017. Propensity score matching was employed to establish comparability between the two groups regarding covariates. Subsequently, a nomogram was constructed to predict the probability of AKI occurrence among patients who underwent furosemide treatment. RESULTS: The regression analysis identified the single-day total dose of furosemide as the most significant factor for AKI, followed by ICU administration, estimated glomerular filtration rate, antibiotic, statin, NSAIDs, ß-blockers, proton pump inhibitor, chronic kidney disease, and 7 other indicators. Subgroup analysis revealed a synergistic effect of furosemide with surgical operation, previous treatment with ß-blockers, ACEI/ARB and antibiotics, leading to an increased risk of AKI when used in combination. Subsequently, a visually represented prognostic nomogram was developed to predict AKI occurrence in furosemide users. The predictive accuracy of the nomogram was assessed through calibration analyses, demonstrating an excellent agreement between the nomogram predictions and the actual likelihood of AKI, with a probability of 77.40%. CONCLUSIONS: Careful consideration of factors such as dosage, concurrent medication use, and renal function of the patient is necessary for clinical practice when using furosemide. Our practical prognostic model for HA-AKI associated with furosemide use can be utilized to assist clinicians in making informed decisions about patient care and treatment.

Hexarelin alleviates apoptosis on ischemic acute kidney injury via MDM2/p53 pathway.

INTRODUCTION: Hexarelin exhibits significant protection against organ injury in models of ischemia/reperfusion (I/R)-induced injury (IRI). Nevertheless, the impact of Hexarelin on acute kidney injury (AKI) and its underlying mechanism remains unclear. In this study, we investigated the therapeutic potential of Hexarelin in I/R-induced AKI and elucidated its molecular mechanisms. METHODS: We assessed the protective effects of Hexarelin through both in vivo and in vitro experiments. In the I/R-induced AKI model, rats were pretreated with Hexarelin at 100 µg/kg/d for 7 days before being sacrificed 24 h post-IRI. Subsequently, kidney function, histology, and apoptosis were assessed. In vitro, hypoxia/reoxygenation (H/R)-induced HK-2 cell model was used to investigate the impact of Hexarelin on apoptosis in HK-2 cells. Then, we employed molecular docking using a pharmmapper server and autodock software to identify potential target proteins of Hexarelin. RESULTS: In this study, rats subjected to I/R developed severe kidney injury characterized by tubular necrosis, tubular dilatation, increased serum creatinine levels, and cell apoptosis. However, pretreatment with Hexarelin exhibited a protective effect by mitigating post-ischemic kidney pathological changes, improving renal function, and inhibiting apoptosis. This was achieved through the downregulation of conventional apoptosis-related genes, such as Caspase-3, Bax and Bad, and the upregulation of the anti-apoptotic protein Bcl-2. Consistent with the in vivo results, Hexarelin also reduced cell apoptosis in post-H/R HK-2 cells. Furthermore, our analysis using GSEA confirmed the essential role of the apoptosis pathway in I/R-induced AKI. Molecular docking revealed a strong binding affinity between Hexarelin and MDM2, suggesting the potential mechanism of Hexarelin's anti-apoptosis effect at least partially through its interaction with MDM2, a well-known negative regulator of apoptosis-related protein that of p53. To validate these findings, we evaluated the relative expression of MDM2 and p53 in I/R-induced AKI with or without Hexarelin pre-administration and observed a significant suppression of MDM2 and p53 by Hexarelin in both in vivo and in vitro experiments. CONCLUSION: Collectively, Hexarelin was identified as a promising medication in protecting apoptosis against I/R-induced AKI.

Predictive value of glycemic gap and stress glycemia ratio among critically ill patients with acute kidney injury: a retrospective analysis of the MIMIC-III database.

BACKGROUND AND AIMS: Acute hyperglycemia has been identified as a risk factor for acute kidney injury occurrence and mortality in various diseases. The aim of the current study was to investigate the relationship between stress-induced hyperglycemia and adverse outcomes in critically ill patients with AKI. METHODS: We extracted clinical data from Multiparameter Intelligent Monitoring in Intensive Care III version 1.4. Blood glucose and glycosylated hemoglobin during the first 24 h of ICU admission were used to calculate glycemic gap and stress hyperglycemia ratio (SHR). The outcomes included ICU mortality and need for renal replacement therapy. The association of the glycemic gap and SHR with outcomes were determined via logistic regression model and receiver-operating curves. The subgroup analysis of patients with and without diabetes was performed separately. RESULTS: Higher glycemic gap and SHR were observed in patients who had increased need of RRT, higher mortality rates and longer ICU stay. Multivariate analysis demonstrated that higher glycemic gap (OR 1.01, 95%CI 1.00-1.02, P = 0.015), as well as SHR (OR 1.32; 95%CI 1.07-1.64, P = 0.009), were independently associated with ICU mortality after adjusting for potential covariates. In subgroup analysis, the association of glycemic gap and SHR were only significant in the non-diabetic population as for the outcome of ICU mortality (OR 2.25, 95%CI 1.64-3.08, P < 0.001 and OR 1.99; 95%CI 1.46-2.72, P < 0.001, respectively). CONCLUSIONS: The glycemic gap and SHR might serve as a potential prognostic indicator of ICU mortality in critically ill patients with AKI, especially in the non-diabetic population.

Noninvasive Assessment of Lipomatous Metaplasia as a Substrate for Ventricular Tachycardia in Chronic Infarct.

Myocardial lipomatous metaplasia (LM) has been increasingly reported in patients with prior myocardial infarction. Cardiac magnetic resonance and cardiac contrast-enhanced computed tomography have been used to noninvasively detect and quantify myocardial LM in postinfarct patients, and may provide useful information for understanding cardiac mechanics, arrhythmia susceptibility, and prognosis. This review aims to summarize the advantages and disadvantages, clinical applications, and imaging features of different cardiac magnetic resonance sequences and cardiac contrast-enhanced computed tomography for LM detection and quantification. We also briefly summarize LM prevalence in different cohorts of postinfarct patients and review the clinical utility of cardiac imaging in exploring myocardial LM as an arrhythmogenic substrate in patients with prior myocardial infarction.

Does cardiac imaging surveillance strategy influence outcomes in patients with early breast cancer?

Background: Many patients with breast cancer receive therapies with the potential to cause cardiotoxicity. Echocardiography and multiple-gated acquisition (MUGA) scans are the most used modalities to assess cardiac function during treatment in high-risk patients; however, the optimal imaging strategy and the impact on outcome are unknown. Methods: Consecutive patients with stage 0-3 breast cancer undergoing pre-treatment echocardiography or MUGA were identified from a tertiary care cancer center from 2010-2019. Demographics, medical history, imaging data and clinical events were collected from hospital charts and administrative databases. The primary outcome is a composite of all-cause death or heart failure event. Clinical and imaging predictors of outcome were evaluated on univariable and multivariable analyses. Results: 1028 patients underwent pre-treatment MUGA and 1032 underwent echocardiography. The groups were well matched for most clinical characteristics except patients undergoing MUGA were younger, had more stage 3 breast cancer and more HER2 over-expressing and triple negative cases. Routine follow-up cardiac imaging scan was obtained in 39.3% of patients with MUGA and 38.0% with echocardiography. During a median follow-up of 2448 (1489, 3160) days, there were 194 deaths, including 7 cardiovascular deaths, and 28 heart failure events with no difference in events between the MUGA and echocardiography groups. There were no imaging predictors of the primary composite outcome or cardiac events. Patients without follow-up imaging had similar adjusted risk for the composite outcome compared to those with imaging follow-up, hazard ratio 0.8 (95% confidence interval 0.5,1.3), p=0.457. Conclusion: The selection of pretreatment echocardiography or MUGA did not influence the risk of death or heart failure in patients with early breast cancer. Many patients did not have any follow-up cardiac imaging and did not suffer worse outcomes. Cardiovascular deaths and heart failure event rates were low and the value of long-term cardiac imaging surveillance should be further evaluated.

Classification of autism based on short-term spontaneous hemodynamic fluctuations using an adaptive graph neural network.

BACKGROUND: Short-term spontaneous hemodynamic fluctuations were collected by the functional near-infrared spectroscopy (fNIRS) system to classify children with autism spectrum disorder (ASD) and typical development (TD), and to explore abnormalities in the left inferior frontal gyrus in ASD. METHODS: Using the fNIRS data of 25 children with ASD and 22 children with TD, a graph neural network combined with the temporal convolution module and the graph convolution module was used, to extract the spatio-temporal features of the data and achieve accurate classification of ASD. RESULTS: The graph neural network was used to obtain a good classification result in the left inferior frontal gyrus, with an accuracy of 97.1%, precision of 95.1%, and specificity of 93.4%. It was found that the 5th channel (which is located in BA 10) and the 8th channel (which is located in BA 47) in the left inferior frontal gyrus were closely correlated with ASD. COMPARISON WITH PREVIOUSLY USED METHOD(S): Compared with the previous deep learning model using the same input, the accuracy of our model has increased by up to 13%, and the correlation between channels in the left inferior frontal gyrus area with the best classification effect was explored through the graph neural network. CONCLUSION: The adaptive graph neural network (AGNN) model may be able to mine more valuable information to distinguish ASD from TD and in addition, the left inferior frontal gyrus may have greater investigative value.

Conduction Velocity Dispersion Predicts Postinfarct Ventricular Tachycardia Circuit Sites and Associates With Lipomatous Metaplasia.

BACKGROUND: Regional myocardial conduction velocity (CV) dispersion has not been studied in postinfarct patients with ventricular tachycardia (VT). OBJECTIVES: This study sought to compare the following: 1) the association of CV dispersion vs repolarization dispersion with VT circuit sites; and 2) myocardial lipomatous metaplasia (LM) vs fibrosis as the anatomic substrate for CV dispersion. METHODS: Among 33 postinfarct patients with VT, we characterized dense and border zone infarct tissue by late gadolinium enhancement cardiac magnetic resonance, and LM by computed tomography, with both images registered with electroanatomic maps. Activation recovery interval (ARI) was the time interval from the minimum derivative within the QRS complex to the maximum derivative within the T-wave on unipolar electrograms. CV at each EAM point was the mean CV between that point and 5 adjacent points along the activation wave front. CV and ARI dispersion were the coefficient of variation (CoV) of CV and ARI per American Heart Association (AHA) segment, respectively. RESULTS: Regional CV dispersion exhibited a much larger range than ARI dispersion, with median 0.65 vs 0.24; P < 0.001. CV dispersion was a more robust predictor of the number of critical VT sites per AHA segment than ARI dispersion. Regional LM area was more strongly associated with CV dispersion than fibrosis area. LM area was larger (median 0.44 vs 0.20 cm2; P < 0.001) in AHA segments with mean CV <36 cm/s and CoV_CV >0.65 than those with mean CV <36 cm/s and CoV_CV <0.65. CONCLUSIONS: Regional CV dispersion more strongly predicts VT circuit sites than repolarization dispersion, and LM is a critical substrate for CV dispersion.

Lipomatous Metaplasia Facilitates Slow Conduction in Critical Ventricular Tachycardia Corridors Within Postinfarct Myocardium.

BACKGROUND: Myocardial lipomatous metaplasia (LM) has been reported to be associated with post-infarct ventricular tachycardia (VT) circuitry. OBJECTIVES: This study examined the association of scar versus LM composition with impulse conduction velocity (CV) in putative VT corridors that traverse the infarct zone in post-infarct patients. METHODS: The cohort included 31 post-infarct patients from the prospective INFINITY (Intra-Myocardial Fat Deposition and Ventricular Tachycardia in Cardiomyopathy) study. Myocardial scar, border zone, and potential viable corridors were defined by late gadolinium enhancement cardiac magnetic resonance (LGE-CMR), and LM was defined by computed tomography. Images were registered to electroanatomic maps, and the CV at each electroanatomic map point was calculated as the mean CV between that point and 5 adjacent points along the activation wave front. RESULTS: Regions with LM exhibited lower CV than scar (median = 11.9 vs 13.5 cm/s; P < 0.001). Of 94 corridors computed from LGE-CMR and electrophysiologically confirmed to participate in VT circuitry, 93 traversed through or near LM. These critical corridors displayed slower CV (median 8.8 [IQR: 5.9-15.7] cm/s vs 39.2 [IQR: 28.1-58.5]) cm/s; P < 0.001) than 115 noncritical corridors distant from LM. Additionally, critical corridors demonstrated low-peripheral, high-center (mountain shaped, 23.3%) or mean low-level (46.7%) CV patterns compared with 115 noncritical corridors distant from LM that displayed high-peripheral, low-center (valley shaped, 19.1%) or mean high-level (60.9%) CV patterns. CONCLUSIONS: The association of myocardial LM with VT circuitry is at least partially mediated by slowing nearby corridor CV thus facilitating an excitable gap that enables circuit re-entry.

Prediction of hyperkalemia in ESRD patients by identification of multiple leads and multiple features on ECG.

BACKGROUND: Patients with end-stage renal disease (ESRD) especially those undergoing dialysis have a high prevalence of hyperkalemia, which must be detected and treated immediately. But the initial symptoms of hyperkalemia are insidious, and traditional laboratory serum potassium concentration testing takes time. Therefore, rapid and real-time measurement of serum potassium is urgently needed. In this study, different machine learning methods were used to make rapid predictions of different degrees of hyperkalemia by analyzing the ECG. METHODS: A total of 1024 datasets of ECG and serum potassium concentrations were analyzed from December 2020 to December 2021. The data were scaled into training and test sets. Different machine learning models (LR, SVM, CNN, XGB, Adaboost) were built for dichotomous prediction of hyperkalemia by analyzing 48 features of chest leads V2-V5. The performance of the models was also evaluated and compared using sensitivity, specificity, accuracy, accuracy, F1 score and AUC. RESULTS: We constructed different machine models to predict hyperkalemia using LR and four other common machine-learning methods. The AUCs of the different models ranged from 0.740 (0.661, 0.810) to 0.931 (0.912,0.953) when different serum potassium concentrations were used as the diagnostic threshold for hyperkalemia, respectively. As the diagnostic threshold of hyperkalemia was raised, the sensitivity, specificity, accuracy and precision of the model decreased to various degrees. And AUC also performed less well than when predicting mild hyperkalemia. CONCLUSION: Noninvasive and rapid prediction of hyperkalemia can be achieved by analyzing specific waveforms on the ECG by machine learning methods. But overall, XGB had a higher AUC in mild hyperkalemia, but SVM performed better in predicting more severe hyperkalemia.