Artificial Intelligence-Based Electrocardiographic Biomarker for Outcome Prediction in Patients With Acute Heart Failure: Prospective Cohort Study.

BACKGROUND: Although several biomarkers exist for patients with heart failure (HF), their use in routine clinical practice is often constrained by high costs and limited availability. OBJECTIVE: We examined the utility of an artificial intelligence (AI) algorithm that analyzes printed electrocardiograms (ECGs) for outcome prediction in patients with acute HF. METHODS: We retrospectively analyzed prospectively collected data of patients with acute HF at two tertiary centers in Korea. Baseline ECGs were analyzed using a deep-learning system called Quantitative ECG (QCG), which was trained to detect several urgent clinical conditions, including shock, cardiac arrest, and reduced left ventricular ejection fraction (LVEF). RESULTS: Among the 1254 patients enrolled, in-hospital cardiac death occurred in 53 (4.2%) patients, and the QCG score for critical events (QCG-Critical) was significantly higher in these patients than in survivors (mean 0.57, SD 0.23 vs mean 0.29, SD 0.20; P<.001). The QCG-Critical score was an independent predictor of in-hospital cardiac death after adjustment for age, sex, comorbidities, HF etiology/type, atrial fibrillation, and QRS widening (adjusted odds ratio [OR] 1.68, 95% CI 1.47-1.92 per 0.1 increase; P<.001), and remained a significant predictor after additional adjustments for echocardiographic LVEF and N-terminal prohormone of brain natriuretic peptide level (adjusted OR 1.59, 95% CI 1.36-1.87 per 0.1 increase; P<.001). During long-term follow-up, patients with higher QCG-Critical scores (>0.5) had higher mortality rates than those with low QCG-Critical scores (<0.25) (adjusted hazard ratio 2.69, 95% CI 2.14-3.38; P<.001). CONCLUSIONS: Predicting outcomes in patients with acute HF using the QCG-Critical score is feasible, indicating that this AI-based ECG score may be a novel biomarker for these patients. TRIAL REGISTRATION: ClinicalTrials.gov NCT01389843; https://clinicaltrials.gov/study/NCT01389843.

ST-segment depression and left ventricular systolic function recovery post-atrial fibrillation ablation in heart failure.

AIMS: Catheter ablation (CA) of atrial fibrillation (AF) improves left ventricular ejection fraction (LVEF) in patients with heart failure with reduced ejection fraction (HFrEF). The impact of ST-segment depression before CA on LVEF recovery and clinical outcomes remains unknown. In the present study, we aimed to investigate the relationship between ST-segment depression during AF rhythm before CA and improvement in the LVEF and clinical outcomes in persistent atrial fibrillation (PerAF) patients with HFrEF. METHODS AND RESULTS: The present study included 122 PerAF patients (male; 98 patients, 80%, mean age: 69 [56, 76] years) from the Osaka Rosai Atrial Fibrillation ablation (ORAF) registry who had LVEF < 50% and underwent an initial ablation. The patients who underwent percutaneous coronary intervention or coronary artery bypass grafting within the past 1 month were not included in the enrolled patients. We assigned the patients based on the presence of ST-segment depression before CA during AF rhythm and evaluated improvement in the LVEF (LVEF ≥ 15%) 1 year after CA and the relationship between ST-segment depression and heart failure (HF) hospitalization/major adverse cardiovascular events (MACE), which are defined as a composite of HF hospitalization, cardiovascular death, hospitalization due to coronary artery disease, ventricular arrhythmia requiring hospitalization and stroke. The percentage of patients with improvement in the LVEF 1 year after CA was significantly lower in the patients with ST-segment depression than those without (58.6% vs. 79.7%, P = 0.012). Multiple regression analysis showed ST-segment depression was independently and significantly associated with improvement in the LVEF 1 year after CA (HR: 0.35; 95% CI: 0.129-0.928, P = 0.035). Kaplan-Meier analysis showed that the patients with ST-segment depression significantly had higher risk of HF hospitalization and MACE than those without (log rank P = 0.022 and log rank P = 0.002, respectively). Multivariable Cox proportional hazards analysis showed that ST-segment depression was independently and significantly associated with a higher risk of MACE (HR: 2.82; 95% CI: 1.210-6.584, P = 0.016). CONCLUSIONS: ST-segment depression before CA during AF rhythm was useful prognostic predictor of improvement in the LVEF and clinical outcomes including HF hospitalization and MACE in PerAF patients with HFrEF.

A Comprehensive Review on the Electrocardiographic Manifestations of Cardiac Sarcoidosis: Patterns and Prognosis.

PURPOSE OF REVIEW: Cardiac sarcoidosis (CS) refers to cardiac involvement in sarcoidosis and is usually associated with worse outcomes. This comprehensive review aims to elucidate the electrocardiographic (ECG) signs and features associated with CS, as well as examine modern techniques and their importance in CS evaluation. RECENT FINDINGS: The exact pathogenesis of CS is still unclear, but it stems from an abnormal immunological response triggered by environmental factors in individuals with genetic predisposition. CS presents with non-cardiac symptoms; however, conduction system abnormalities are common in patients with CS. The most common electrocardiographic (ECG) signs include atrioventricular blocks and ventricular tachyarrhythmia. Distinct patterns, such as fragmented QRS complexes, T-wave alternans, and bundle branch blocks, are critical indicators of myocardial involvement. The application of advanced ECG techniques such as signal-averaged ECG, Holter monitoring, wavelet-transformed ECG, microvolt T-wave alternans, and artificial intelligence-supported analysis holds promising outcomes for opportune detection and monitoring of CS. Timely utilisation of inexpensive and readily available ECG possesses the potential to allow early detection and intervention for CS. The integration of artificial intelligence models into ECG analysis is a promising approach for improving the ECG diagnostic accuracy and further risk stratification of patients with CS.

Virtual Reality Simulation for the Acquisition and Retention of Electrocardiogram Interpretation Skills: A Randomized Controlled Trial Among Undergraduate Medical Students.

Introduction The electrocardiogram (ECG) is one of the most important tools in diagnosing cardiac abnormalities, particularly arrhythmias and myocardial infarction. It is one of the certifiable competencies for final-year medical undergraduate students. We determined virtual reality's effectiveness in acquiring and retaining ECG interpretation skills among medical students compared to traditional teaching. Methods One hundred and forty students were randomized into two groups. Seventy-one students (immersion group) were trained using virtual reality simulation to acquire and retain interpretation skills of normal and abnormal ECG. Sixty-nine students (traditional group) were trained in the classroom using chalk and board. The primary outcome of change in acquiring knowledge of the interpretation of ECG was determined by comparing pre and post-test scores. The secondary outcome of retention of knowledge was determined by comparing pre-test and second post-test scores conducted after eight weeks of intervention. The p-value of <0.05 was considered significant. Results Out of 140 students, 50 (35.7%) were males and 90 (64.3%) were female. The mean age of the students was 22.1 (SD 1.1), with 69.3% of them between the ages of 21 and 22 years. Mean pre-test scores for the interpretation of normal ECG among immersion and traditional groups were 9.8 (SD 8.4) and 8.3 (SD 7.5), respectively, and post-test scores for the acquisition of knowledge were 24.3 (SD 5.5) and 24.8 (SD 6.3), respectively. The post-test scores for retention skills were 25.3 (SD 5.6) and 20.7 (SD 6.9) respectively (p<0.001). The mean pre-test scores for the interpretation of abnormal ECG of both groups were 7.0 (SD 6) and 8.3 (SD 6.6), respectively. Mean post-test scores for acquiring knowledge to interpret abnormal ECG were 23.5 (SD 6.2) and 17.7 (SD 9), respectively (p<0.001), and mean post-test scores for retention of interpretation skills of abnormal ECG were 19.2 (SD - 6.9) and 13.3 (SD 10.2) respectively (p=0.001). The pairwise comparison of the immersion group indicates that all the combinations that changed in score from the pre to post-intervention time points, from pre-to-retention time, and from the post-to-retention time were significant (p<0.001). Conclusion Virtual reality teaching had a better impact on acquiring and retaining the skill for interpreting normal and abnormal electrocardiograms.

Can Hyperthermia Unveil Brugada Pattern?

Brugada syndrome (BrS) is characterized by ST segment elevations in the right precordial leads, V1 - V3, with additional findings of ventricular arrhythmias and family history (FH) of sudden cardiac death (SCD) at a young age. Here, we describe a case of hyperthermia, unveiling the Brugada electrocardiography (EKG) pattern and the resolution of EKG findings with appropriate hyperthermia management. It is important to distinguish the Brugada EKG pattern from other causes of ST elevations and treat appropriately to prevent patients from developing ventricular fibrillation and SCD. It is key to identify environmental triggers in patients presenting with Brugada EKG pattern and closely monitor for ventricular fibrillation. Educating patients on prompt fever treatment with antipyretics and avoiding medications like sodium channel blockers during the febrile event is paramount to counter patients going into ventricular fibrillation. It is also crucial for close follow-up of these patients, offering them genetic testing for BrS and screening families of patients with BrS.

The role of routine cardiac investigations before hyperbaric oxygen treatment.

Cardiac complications are a rare but potentially serious consequence of hyperbaric oxygen treatment (HBOT), resulting from increased blood pressure and decreased heart rate and cardiac output associated with treatment. These physiologic changes are generally well-tolerated by patients without preexisting cardiac conditions, although those with known or undetected cardiac disease may be more vulnerable to treatment complications. Currently, there are no universally accepted guidelines for pre-HBOT cardiac screening to identify these patients at heightened risk, leading to variability in practice patterns. In the absence of HBOT-specific evidence, screening protocols might be adapted from the diving medicine community; however, given the important differences in physiological stressors, these may not be entirely applicable to patients undergoing HBOT. Traditional cardiac investigations such as electro- and echo-cardiograms are limited in their ability to detect relevant risk modifying states in the pre-HBOT patient, stymieing their cost-effectiveness as routine tests. In the absence of strong evidence to support routine cardiac investigation, we argue that a comprehensive history and physical exam - tailored to identify high-risk patients based on clinical parameters - may serve as a more practical screening tool. While certain unique patient groups such as those undergoing dialysis or with implanted cardiac devices may warrant specialised assessment, thorough evaluation may be sufficient to identify many patients unlikely to benefit from cardiac investigation in the pre-HBOT setting. A clinical decision-making tool based on suggested low-risk and high-risk features is offered to guide the use of targeted cardiac investigation prior to HBOT.

The Effect of Electroconvulsive Therapy on Frontal QRS-T Angle in Psychiatric Patients.

Introduction: Electroconvulsive therapy (ECT) is one of the biological therapies that is well tolerated and has a low risk of complications. Acute cardiovascular complications related to ECT such as ventricular arrhythmia, myocardial infarction and cardiac arrest have been recorded. Increased frontal QRS-T (fQRS-T) angle was associated with ventricular arrhythmia, sudden cardiac death and total mortality. In this study, we aimed to evaluate the effect of ECT on the myocardium using electrocardiography (ECG) parameters such as fQRS-T angle, QRS duration, QT and QTc interval. Methods: A total of 108 patients diagnosed with bipolar disorder (n=36), depressive disorder (n=70) and schizophrenia (n=2) who underwent ECT were included in this study. 12-lead surface ECG of all patients were taken before the ECT, 15 min. after ECT and 24 hour after ECT. Results: QRS duration, QT interval and corrected QT (QTc) interval were not changed significantly during the follow-up period. However, we found that, fQRS-T angle was significantly increased 15 minutes after ECT compared to baseline angle (p<0.001). We also detected that this increase in fQRS-T angle 15 minutes after ECT was significantly reduced 24 hours after ECT (p=0.031). Meanwhile, there was no significant difference between baseline and 24th hour fQRS-T angle (p=0.154). Conclusions: In our study, a significant increase in fQRS-T angle was observed 15 min after ECT. However, the fQRS-T angle was found to return to normal after 24 hours. Our findings may indicate that ECT does not have a permanent side effect on the risk of cardiovascular events according to the fQRS-T angle.

Vectorcardiography signs of a failing Fontan: Heart rate, PR interval, RtQRSvm, QRSvm and SPQRS-T angle as noninvasive markers of late Fontan complications and mortality.

BACKGROUND: Limited data exists on interpreting vectorcardiography (VCG) parameters in the Fontan population. OBJECTIVE: The purpose of this study was to demonstrate the associations between ECG/VCG parameters and Fontan failure (FF). METHODS/RESULTS: 107 patients with a Fontan operation after 1990 and without significant ventricular pacing were included. FF and Fontan survival (FS) groups were compared. The average follow-up after Fontan operation was 11.8 years ±7.1 years. 14 patients had FF (13.1%) which was defined as having protein-losing-enteropathy (1.9%), plastic bronchitis (2.8%), Fontan takedown (1.9%), heart transplant (5.6%), NYHA class III-IV (2.8%) or death (0.9%). A 12­lead ECG at last follow up or prior to FF was assessed for heart rate, PR interval, QRS duration, Qtc and left/right sided precordial measures (P-wave, QRS and T-wave vector magnitudes, spatial P-R and QRS-T angles). Transthoracic echocardiogram evaluated atrioventricular valve regurgitation and ventricular dysfunction at FF or last follow up. A cox multivariate regression analysis adjusted for LV dominance, ventricular dysfunction, HR, PR, QTc, Pvm, QRSvm, SPQRST-angle, RtPvm, RtQRSvm and RtTvm. Ventricular dysfunction, increased heart rate and prolonged PR interval were significantly associated to FF at the multivariate analysis. ROC analysis and Kaplan-meier analysis revealed an increased total mortality associated with a heart rate > 93 bpm, PR interval > 155 mv, QRSvm >1.91 mV, RtQRSvm >1.8 mV and SPQRST angle >92.3 mV with p values <0.001 to 0.018. CONCLUSION: We demonstrate the importance of ECG/VCG monitoring in the Fontan population and suggest specific indicators of late complications and mortality.

AttBiLFNet: A novel hybrid network for accurate and efficient arrhythmia detection in imbalanced ECG signals.

Within the domain of cardiovascular diseases, arrhythmia is one of the leading anomalies causing sudden deaths. These anomalies, including arrhythmia, are detectable through the electrocardiogram, a pivotal component in the analysis of heart diseases. However, conventional methods like electrocardiography encounter challenges such as subjective analysis and limited monitoring duration. In this work, a novel hybrid model, AttBiLFNet, was proposed for precise arrhythmia detection in ECG signals, including imbalanced class distributions. AttBiLFNet integrates a Bidirectional Long Short-Term Memory (BiLSTM) network with a convolutional neural network (CNN) and incorporates an attention mechanism using the focal loss function. This architecture is capable of autonomously extracting features by harnessing BiLSTM's bidirectional information flow, which proves advantageous in capturing long-range dependencies. The attention mechanism enhances the model's focus on pertinent segments of the input sequence, which is particularly beneficial in class imbalance classification scenarios where minority class samples tend to be overshadowed. The focal loss function effectively addresses the impact of class imbalance, thereby improving overall classification performance. The proposed AttBiLFNet model achieved 99.55% accuracy and 98.52% precision. Moreover, performance metrics such as MF1, K score, and sensitivity were calculated, and the model was compared with various methods in the literature. Empirical evidence showed that AttBiLFNet outperformed other methods in terms of both accuracy and computational efficiency. The introduced model serves as a reliable tool for the timely identification of arrhythmias.

Artificial Intelligence Assists in the Early Identification of Cardiac Amyloidosis.

A 69-year-old female presented with symptomatic atrial fibrillation. Cardiac amyloidosis was suspected due to an artificial intelligence clinical tool applied to the presenting electrocardiogram predicting a high probability for amyloidosis, and the subsequent unexpected finding of left atrial appendage thrombus reinforced this clinical suspicion. This facilitated an early diagnosis by the biopsy of AL cardiac amyloidosis and the prompt initiation of targeted therapy. This case highlights the utilization of an AI clinical tool and its impact on clinical care, particularly for the early detection of a rare and difficult to diagnose condition where early therapy is critical.

Should Physicians Be Aware of Rhythm Disturbances in Adults with Systemic Autoimmune Diseases and Anti-Ro52 Antibodies? A Cross-Sectional Study.

Objectives: The association between anti-Ro/SSA antibodies and the appearance of cardiac rhythm disorders in adults is discussed. We aim to study this relationship, together with active treatments and comorbidities, and its impact on daily clinical practice in adults with systemic autoimmune diseases (SADs). Methods: This cross-sectional single-center study was conducted in a tertiary hospital between January 2021 and March 2022. A sample of adult patients followed up in the SAD Unit with a diagnosis of a SAD and previously tested for anti-Ro/SSA and anti-La/SSB were recruited. All of them underwent a 12-lead electrocardiogram. Results: 167 patients were included. 90 (53.9%) were positive for anti-Ro60, 101 (60.5%) for anti-Ro52, and 45 (26.9%) for anti-La/SSB; 52 (31.3%) were triple-negative. 84% were women, and the mean age was 59 years (standard deviation 12.8). The most common SAD was primary Sjögren's syndrome (34.8%), followed by systemic lupus erythematosus (24.6%) and rheumatoid arthritis (22.8%). A statistically significant relationship was found between anti-Ro52 positivity and cardiac rhythm disorders (relative risk = 2.007 [1.197-3.366]), specifically QTc prolongation (relative risk = 4.248 [1.553-11.615]). Multivariate regressions showed a significant association, with diabetes mellitus being the most related comorbidity. The association between anti-Ro52 antibodies and atrioventricular conduction disorders was not significant. Conclusions: The presence of anti-Ro52 antibodies in adult patients with SADs is associated with an increased risk of QTc prolongation. Electrocardiographic screening of patients with SAD, anti-Ro52 antibodies, and other risk factors, like diabetes mellitus or QT-prolonging drugs, seems advisable. Those with baseline electrocardiogram abnormalities or additional risk factors should undergo electrocardiographic monitoring.

The Effect of Obesity on Repolarization and Other ECG Parameters.

Background: Overweight and obesity are important risk factors in the development of cardiovascular diseases. New repolarization markers, such as the Tpeak-Tend interval and JTpeak intervals, have not yet been profoundly studied in obese patients. The study aims to analyze whether, in patients with obesity and overweight, repolarization markers, including the Tpeak-Tend interval, are prolonged and simultaneously check the frequency of other ECG pathologies in a 12-lead ECG in this group of patients. Methods: A study group consisted of 181 adults (90 females and 91 males) with overweight and first-class obesity. The participants completed a questionnaire, and the ECG was performed and analyzed. Results: When analyzing the classic markers, only QT dispersion was significantly higher in obese people. The Tpeak-Tend parameter (97.08 ms ± 23.38 vs. 89.74 ms ± 12.88, respectively), its dispersion, and JTpeak-JTend parameters were statistically significantly longer in the obese group than in the controls. There were also substantial differences in P-wave, QRS duration, and P-wave dispersion, which were the highest in obese people. Tpeak-Tend was positively correlated with body mass and waist circumference, while JTpeak was with BMI, hip circumference, and WHR. Tpeak/JT was positively correlated with WHR and BMI. In backward stepwise multiple regression analysis for JTpeak-WHR, type 2 diabetes and smoking had the highest statistical significance. Conclusions: Only selected repolarization markers are significantly prolonged in patients with class 1 obesity and, additionally, in this group, we identified more pathologies of P wave as well as prolonged QRS duration.

Determining QTc in acute care settings: What we (don't) know.

In an effort to expedite the publication of articles, AJHP is posting manuscripts online as soon as possible after acceptance. Accepted manuscripts have been peer-reviewed and copyedited, but are posted online before technical formatting and author proofing. These manuscripts are not the final version of record and will be replaced with the final article (formatted per AJHP style and proofed by the authors) at a later time.

Alternating anterior and posterior fascicular block and left septal fascicular block.

Electrocardiogram of a patient affected by hypertensive cardiomyopathy showed an anterior fascicular block pattern and in right precordial leads an anterior displacement of QRS complex, characterised by a well evident jump of r wave from V1 to V2. Lead V2 showed qR morphology with embryonic q wave and very tall R wave. Septal q waves were not present in leads I and aVL. A subsequent electrocardiogram showed a posterior fascicular block pattern and the same findings in right precordial leads. Septal q waves were not present in inferior leads. Other causes of anterior displacement of QRS complex were ruled out by clinical/instrumental investigation. These findings are highly suggestive of left septal (middle) fascicular block coexisting with anterior and posterior fascicular block.

A predictive model of super response to cardiac resynchronization therapy in short-term period.

BACKGROUND: The left bundle branch block, nonischemic heart failure (HF) and female gender are the most powerful predictors of a super response to cardiac resynchronization therapy (CRT). It is important to identify super responders who can derive most benefits from CRT. We aimed to establish a predicting model that could be used for prognosis of a super response to CRT in short-term period. METHODS: Patients with QRS ≥ 130 ms, New York Heart Association (NYHA) II-III class of HF, left ventricle ejection fraction (LVEF) ≤ 35% and indications for CRT were included in the study. Before and 6 month after CRT the electrocardiography, echocardiography and cardiac scintigraphy were performed. The study's primary endpoint was the NYHA class improvement ≥ 1 and left ventricle end systolic volume decrease > 30% or LVEF improvement > 15% after 6 month CRT. Based on collected data, we developed a predictive model regarding a super response to CRT. RESULTS: Of 49 (100.0%) patients, 32 (65.3%) had a super response to CRT. Patients with a super response were likelier to have a lower cardiac index (p = 0.007), higher rates of interventricular delay (IVD) (p = 0.003), phase standard deviation of left ventricle anterior wall (PSD LVAW) (p = 0.009) and ∆QRS (p = 0.02). Only IVD and PSD LVAW were independently associated with a super response to CRT in univariate and multivariate logistic regression. We created a logistic equation and calculated a cut-off value. The resulting ROC curve revealed a discriminative ability with AUC of 0.812 (sensitivity 90.62%; specificity 70.59%). CONCLUSION: Our predictive model is able to distinguish patients with a super response to CRT.

The Role of Galectin-3 Levels for Predicting Paroxysmal Atrial Fibrillation in Patients with Embolic Stroke of Undetermined Source.

Background/Objectives: Paroxysmal atrial fibrillation (PAF) is an important cause that is thought main potential factor in Embolic stroke of undetermined source (ESUS). Extended Holter ECG is an expensive and time-consuming examination. It needs another tools for predicting PAF in ESUS patients. In this study, serum galectin-3 levels, ECG parameters (PR interval, P wave time and P wave peak time) LA volume index, LA global peak strain and atrial electromechanical conduction time values were investigated for predicting PAF. Methods: 150 patients with ESUS and 30 volunteers for the control group were recruited to study. 48-72 h Holter ECG monitoring was used for detecting PAF. Patients were divided into two groups (ESUS + PAF and ESUS-PAF) according to the development of PAF in Holter ECG monitoring. Results: 30 patients with ESUS whose Holter ECG monitoring showed PAF, were recruited to the ESUS + PAF group. Other 120 patients with ESUS were recruited to the ESUS-PAF group. PA lateral, PA septum, and PA tricuspid were higher in the ESUS + PAF group (p < 0.001 for all). Serum galectin-3 levels were significantly higher in ESUS + PAF than in ESUS-PAF and control groups (479.0 pg/mL ± 435.8 pg/mL, 297.8 pg/mL ± 280.3 pg/mL, and 125.4 ± 87.0 pg/mL, p < 0.001, respectively). Serum galectin-3 levels were significantly correlated with LAVI, PA lateral, and global peak LA strain (r = 0.246, p = 0.001, p = 0.158, p = 0.035, r = -0.176, p = 0.018, respectively). Conclusion: Serum galectin-3 levels is found higher in ESUS patients which developed PAF and Serum galectin-3 levels are associated LA adverse remodeling in patients with ESUS.

A Comprehensive Study on a Deep-Learning-Based Electrocardiography Analysis for Estimating the Apnea-Hypopnea Index.

This study introduces a deep-learning-based automatic sleep scoring system to detect sleep apnea using a single-lead electrocardiography (ECG) signal, focusing on accurately estimating the apnea-hypopnea index (AHI). Unlike other research, this work emphasizes AHI estimation, crucial for the diagnosis and severity evaluation of sleep apnea. The suggested model, trained on 1465 ECG recordings, combines the deep-shallow fusion network for sleep apnea detection network (DSF-SANet) and gated recurrent units (GRUs) to analyze ECG signals at 1-min intervals, capturing sleep-related respiratory disturbances. Achieving a 0.87 correlation coefficient with actual AHI values, an accuracy of 0.82, an F1 score of 0.71, and an area under the receiver operating characteristic curve of 0.88 for per-segment classification, our model was effective in identifying sleep-breathing events and estimating the AHI, offering a promising tool for medical professionals.

School electrocardiography screening program prompts the detection of otherwise unrecognized atrial septal defect in children in Japan.

Background: Atrial septal defect (ASD) is a congenital heart disease that often presents without symptoms or murmurs. If left untreated, children with ASD can develop comorbidities in adulthood. In Japan, school electrocardiography (ECG) screening has been implemented for all 1st, 7th, and 10th graders. However, the impact of this program in detecting children with ASD is unknown. Methods: This is a retrospective study that analyzed consecutive patients with ASD who underwent catheterization for surgical or catheter closure at ≤18 years of age during 2009-2019 at a tertiary referral center in Japan. Results: Of the overall 116 patients with ASD (median age: 3.0 years of age at diagnosis and 8.9 years at catheterization), 43 (37%) were prompted by the ECG screening (Screening group), while the remaining 73 (63%) were by other findings (Non-screening group). Of the 49 patients diagnosed at ≥6 years of age, 43 (88%) were prompted by the ECG screening, with the 3 corresponding peaks of the number of patients at diagnosis. Compared with the non-screening group, the screening group exhibited similar levels of hemodynamic parameters but had a lower proportion of audible heart murmur, which were mainly prompted by the health care and health checkups in infancy or preschool period. Patients positive for a composite parameter (rsR' type of iRBBB, inverted T in V4, or ST depression in the aVF lead) accounted for 79% of the screening group at catheterization, each of which was correlated with hemodynamic parameters in the overall patients. Conclusions: The present study shows that school ECG screening detects otherwise unrecognized ASD, which prompted the diagnosis of the majority of patients at school age and >one-third of overall patients in Japan. These findings suggest that ECG screening program could be an effective strategy for detecting hemodynamically significant ASD in students, who are asymptomatic and murmurless.

Genetic and phenotypic associations of frailty with cardiovascular indicators and behavioral characteristics.

INTRODUCTION: Frailty Index (FI) is a common measure of frailty, which has been advocated as a routine clinical test by many guidelines. The genetic and phenotypic relationships of FI with cardiovascular indicators (CIs) and behavioral characteristics (BCs) are unclear, which has hampered ability to monitor FI using easily collected data. OBJECTIVES: This study is designed to investigate the genetic and phenotypic associations of frailty with CIs and BCs, and further to construct a model to predict FI. METHOD: Genetic relationships of FI with 288 CIs and 90 BCs were assessed by the cross-trait LD score regression (LDSC) and Mendelian randomization (MR). The phenotypic data of these CIs and BCs were integrated with a machine-learning model to predict FI of individuals in UK-biobank. The relationships of the predicted FI with risks of type 2 diabetes (T2D) and neurodegenerative diseases were tested by the Kaplan-Meier estimator and Cox proportional hazards model. RESULTS: MR revealed putative causal effects of seven CIs and eight BCs on FI. These CIs and BCs were integrated to establish a model for predicting FI. The predicted FI is significantly correlated with the observed FI (Pearson correlation coefficient = 0.660, P-value = 4.96 × 10-62). The prediction model indicated "usual walking pace" contributes the most to prediction. Patients who were predicted with high FI are in significantly higher risk of T2D (HR = 2.635, P < 2 × 10-16) and neurodegenerative diseases (HR = 2.307, P = 1.62 × 10-3) than other patients. CONCLUSION: This study supports associations of FI with CIs and BCs from genetic and phenotypic perspectives. The model that is developed by integrating easily collected CIs and BCs data in predicting FI has the potential to monitor disease risk.