Predictor Of Sudden Cardiac Death Research Articles

Acquired long QT interval syndrome as a predictor of sudden cardiac death: Causes, diagnostic criteria, and management strategy

Despite medical advances in diagnosis and treatment, cardiovascular disease remains the leading cause of death worldwide. Sudden cardiac death, an extreme and irreversible outcome of cardiac arrest, is observed in 50% of cases and is often the first suddenly detected pathology. Long QT syndrome is a variant of electrical heart disease resulting from both congenital and acquired dysfunction and/or regulation of cardiomyocyte ion channels, accompanied by impaired depolarization and repolarization of the ventricular myocardium and characterized by QT prolongation and T abnormalities on the electrocardiogram, clinically accompanied by syncope, bidirectional spindle ventricular tachycardia of the “pirouette” type (torsade de pointes), associated with a high risk of sudden cardiac death or sudden arrhythmic death syndrome. Effective management of these patients requires an individualized approach: careful examination, calculation of corrected QT, determination of typology (hereditary or acquired syndrome), risk assessment using the Tisdale scale, and selection of optimal therapy. Treatment should be comprehensive and include pharmacological (beta-adrenergic blockers, correction of electrolyte balance) and surgical (implantable cardioverter-defibrillator and left-sided thoracic sympathetic denervation of the heart), lifestyle (avoidance of drugs that contribute to prolongation of the QT interval, avoidance of specific arrhythmia triggers (active swimming, exposure to loud noises)) and regular consultation with a cardiologist. Early diagnosis and a comprehensive approach are key to successful prevention of sudden cardiac death in patients with Long QT syndrome.

Quantifying late gadolinium enhancement improved sudden cardiac death risk prediction in non-ischemic dilated cardiomyopathy

Abstract Background While previous studies have proved the late gadolinium enhancement (LGE) was associated with poor prognosis in non-ischemic dilated cardiomyopathy (DCM), the optimal method for LGE quantification and the ability of LGE extent in identifying sudden cardiac death (SCD) remain less clarified. Purpose This study sought to compare the reproducibility of LGE quantification methods and explore the asssociation between LGE extent and SCD endpoint in DCM patients. Methods In this prospective study, LGE was quantified by the 2-6 standard deviations (SD) above the remote myocardium and full-wide half-maximum (FWHM) methods. The primary endpoint was SCD and arrythmia endpoint included SCD and aborted SCD. Reproducibility of LGE quantification was measured by Bland-Altman analysis and intra-class correlation coefficients (ICC). Competing risk regression analysis, C-index and area under the curve (AUC) were performed to identify the prognostic value. Results Among the 770 patients, 336 (44%) patients had LGE. FWHM demonstrated the best reproducibility compared with other quantification methods (intraobserver variability: ICC = 0.94, mean bias: -0.43 ± 4.89%; interobserver variability: ICC = 0.90, mean bias: -2.65 ± 6.40%). After a median follow-up of 49 months, SCD occurred in 61 (8%) patients and arrythmia endpoint occurred in 87 (11%) patients. Among all quantification method, LGE extent measured by FWHM showed the highest predictive value of SCD (C index: 0.72) and arrythmia events (C index: 0.71) than other methods. In the competing risk analysis, LGE extent was independently association with SCD (Hazard ratio [HR] 1.05, 95% confidence interval [CI] 1.03 - 1.07, P < 0.001) and arrythmia endpoint (HR 1.05, 95% CI 1.03 - 1.06, P < 0.001). LGE extent >8% showed significantly higher risk of SCD and arrythmia endpoint than those with LGE extent ≤8% (P < 0.001). Incoporating LGE exent and 35% left ventricular ejection fraction (LVEF) significantly improved the SCD (C index: 0.74 vs 0.61, P < 0.001) and arrythmia endpoint (C index: 0.73 vs 0.59, P < 0.001) prediction compared with LVEF. Conclusions FWHM demonstrated best reproducibility and highest SCD prediction ability among LGE quantification methods. Adding LGE extent to LVEF significantly improved the predictive value of SCD and arrythmia endpoint.Figure 1

Early prediction of sudden cardiac death using multimodal fusion of ECG Features extracted from Hilbert–Huang and wavelet transforms with explainable vision transformer and CNN models

Background and Objective:Sudden cardiac death (SCD) is a critical health issue characterized by the sudden failure of heart function, often caused by ventricular fibrillation (VF). Early prediction of SCD is crucial to enable timely interventions. However, current methods predict SCD only a few minutes before its onset, limiting intervention time. This study aims to develop a deep learning-based model for the early prediction of SCD using electrocardiography (ECG) signals. Methods:A multimodal explainable deep learning-based model is developed to analyze ECG signals at discrete intervals ranging from 5 to 30 min before SCD onset. The raw ECG signals, 2D scalograms generated through wavelet transform and 2D Hilbert spectrum generated through Hilbert–Huang transform (HHT) of ECG signals were applied to multiple deep learning algorithms. For raw ECG, a combination of 1D-convolutional neural networks (1D-CNN) and long short-term memory networks were employed for feature extraction and temporal pattern recognition. Besides, to extract and analyze features from scalograms and Hilbert spectra, Vision Transformer (ViT) and 2D-CNN have been used. Results:The developed model achieved high performance, with accuracy, precision, recall and F1-score of 98.81%, 98.83%, 98.81%, and 98.81% respectively to predict SCD onset 30 min in advance. Further, the proposed model can accurately classify SCD patients and normal controls with 100% accuracy. Thus, the proposed method outperforms the existing state-of-the-art methods. Conclusions:The developed model is capable of capturing diverse patterns on ECG signals recorded at multiple discrete time intervals (at 5-minute increments from 5 min to 30 min) prior to SCD onset that could discriminate for SCD. The proposed model significantly improves early SCD prediction, providing a valuable tool for continuous ECG monitoring in high-risk patients.

Reduction in FEV1 following spinal anesthesia is associated with intraoperative complications: A prospective study.

Although Spinal Anesthesia (SA) remains the technique of choice for many surgeries below the umbilicus, it is associated with multiple intraoperative complications. Sympathetic blockade and Bezold-Jarisch reflex do not fully explain SA-related cardiopulmonary complications. Reduction in FEV1 has been reported as a predictor of sudden cardiac death. This study aimed to determine the association between reduction in FEV1 following SA and adverse intraoperative cardiopulmonary complications. A prospective study of 48 patients of ASA status I and II with no history of primary cardiopulmonary disease scheduled for elective surgery under SA. Spirometry was performed based on ATS/ERS guidelines before induction and 30 min after induction of SA. FEV1% predicted was determined using GLI 2012 equations. Participants were grouped into two (∆FEV1% < 10% and ∆FEV1% ≥ 10%) based on reductions (∆) in FEV1% predicted following SA. Logistic regression analyses were used to examine associations between ∆FEV1% and intraoperative hypoxia, hypotension, bradycardia, and nausea/vomiting, with adjustments for age, gender, and BMI. The mean FEV1% predicted following SA was lower than the mean FEV1% predicted before SA (83.42 vs. 95.31, p = 0.001). In a fully adjusted model, ∆FEV1% ≥ 10% was associated with an increased risk of hypoxia [AOR 13.55; 95% CI, 1.07-171.24, p = 0.044]. The positive associations between ∆FEV1% ≥ 10% and hypotension [2.02 (0.33-12.46), 0.449], bradycardia [1.10 (0.28-4.25), 0.895] and nausea/vomiting [9.74 (0.52-183.94), 0.129] were not statistically significant. Reduction in FEV1% predicted following SA was associated with adverse intraoperative outcomes. FEV1 may play an important role in the association between SA and cardiopulmonary complications.

(LV)EF is a poor predictor of sudden cardiac death : Pro/Contra

For more than two decades the left ventricular ejection fraction (LVEF) has been utilized with practically uncritical absolutism for the risk stratification of patients with ischemic and, historically, also nonischemic cardiomyopathy, in order to identify patients who could be threatened by sudden cardiac death. Based on historical data and in the absence of other better predictive parameters, the LVEF continues to appear in the guidelines unchanged, with cut-off values that lie in the region of the measurement accuracy of LVEF as determined by echocardiography. The basic identification of high-risk patients who then really benefit from an implantable cardioverter defibrillator (ICD) must be re-evaluated under the aspect of a meaningfully altered interventional and pharmaceutical treatment of heart failure.

Prognostic algorithm for early diagnosis of subcritical conditions as predictors of sudden cardiac death

Aim. To develop a method for early diagnosis of subcritical homeostasis disorders leading to sudden cardiac death (SCD). The basis is to improve the efficiency of predictive algorithms.Material and methods. This pilot, controlled, open-label, randomized, prospective clinical trial included 220 patients at risk of SCD and 150 patients without risk of SCD. Main and control groups was formed according to the global cardiovascular risk score. Based on the informative features proposed by specialized experts using multivariate statistics methods (discriminant analysis), two condition classes were formed. The conducted exploratory analysis confirmed the significance of diagnostic criteria in relation to SCD manifestation (manifestation of cardiac arrest — MCA), which is an integral assessment of a fatal complication. The development of decision rules was carried out on the basis of soft computing technology.Results. Taking into account the priority of clinical research, namely, the identification of subcritical stages of MCA, a classifier is proposed according to basic severity of patients — the severity of critical condition risk (SCCR). The discriminant function and intersection areas between MCA subclasses in the conditions of early SCD diagnosis determine the transition to soft computing technology. Membership functions for severe MCA are formed, followed by their iteration according to E. Shortliffe. The final decision rule, using a fuzzy classifier, differentiates the MCA into stages with different SCCR. In parallel with standard protocols for the management of severe somatic patients (chronic obstructive pulmonary disease, chronic kidney disease, hepatocellular failure), based on the proposed algorithm with an integral assessment of critical conditions, using the MCA decision rule in the main group in 30,5% of cases, subcritical stage was revealed, followed by targeted treatment and preventive support. In the first group, a subcritical condition was detected in 67 patients (30,5%); a critical condition without SCD — in 3 patients (1,4%). In all noted cases, early prevention of SCD was successfully carried out (these patients were transferred to a class with a lower SCD degree). Using conventional prognostic scores in this group, 46 patients (20,9%) were identified with a subcritical condition and 1 (0,4%) with a critical condition. In the control group, subcritical condition was determined in 35 patients (23,3%), of which 17 patients (11,3%) had a moderate risk of SCD. Using conventional prognostic scores, 23 patients (15,3%) with subcritical condition were identified.Conclusion. In the conditions of intensive care unit, general medicine departments, hemodialysis department, cardiac surgery, and organ transplantation department, an algorithm for early diagnosis and risk stratification of SCD with an integral assessment (MCA) should be used. The fuzzy classifier MCA according to SCCR makes it possible to carry out timely correction of treatment measures in addition to standard protocols.

Sudden cardiac death after early-onset myocardial infarction: a multicentre longitudinal cohort study with a 20-year follow-up.

Sudden cardiac death (SCD) is a serious consequence of a myocardial infarction (MI), but identifying patients at risk of developing SCD remains a major clinical challenge, especially in the case of juvenile MI. The aim of this study is to identify predictors of SCD after early-onset MI using long-term follow-up data relating to a large nationwide patient cohort. The Italian Genetic Study on Early-onset MI enrolled 2000 patients experiencing a first MI before the age of 45 years, who were followed up for a median of 19.9 years. Fine-Gray proportional hazard models were used to assess the associations between their clinical, demographic, and index event data and the occurrence of SCD. Sudden cardiac death occurred in 195 patients, who were more frequently males, were hypertensive and/or diabetic, had a history of previous thrombo-embolic events with a greater atherosclerotic burden, and had a lower left ventricular ejection fraction (LVEF) after the index event. A multivariable analysis showed that the independent predictors of SCD were diabetes, hypertension, previous thrombo-embolic events, a higher SYNTAX score, and a lower LVEF. There was no clear evidence of the clustering of SCD events during the follow-up. Sudden cardiac death was the first post-MI clinical event in 101 patients; the remaining 94 experienced SCD after a non-fatal MI or hospitalization for coronary revascularization. Sudden cardiac death frequently occurs during the 20 years after early-onset MI. The nature of the identified predictors and the absence of clustering suggest that the pathophysiological basis of SCD may be related to progressive coronary atherosclerosis.

Arrhythmic Risk Stratification by Cardiovascular Magnetic Resonance Imaging in Patients With Nonischemic Cardiomyopathy

BackgroundMyocardial fibrosis (MF) forms part of the arrhythmic substrate for ventricular arrhythmias (VAs). ObjectivesThis study sought to determine whether total myocardial fibrosis (TF) and gray zone fibrosis (GZF), assessed using cardiovascular magnetic resonance, are better than left ventricular ejection fraction (LVEF) in predicting ventricular arrhythmias in patients with nonischemic cardiomyopathy (NICM). MethodsPatients with NICM in a derivation cohort (n = 866) and a validation cohort (n = 848) underwent quantification of TF and GZF. The primary composite endpoint was sudden cardiac death or VAs (ventricular fibrillation or ventricular tachycardia). ResultsThe primary endpoint was met by 52 of 866 (6.0%) patients in the derivation cohort (median follow-up: 7.5 years; Q1-Q3: 5.2-9.3 years). In competing-risks analyses, MF on visual assessment (MFVA) predicted the primary endpoint (HR: 5.83; 95% CI: 3.15-10.8). Quantified MF measures permitted categorization into 3 risk groups: a TF of >0 g and ≤10 g was associated with an intermediate risk (HR: 4.03; 95% CI: 1.99-8.16), and a TF of >10 g was associated with the highest risk (HR: 9.17; 95% CI: 4.64-18.1) compared to patients with no MFVA (lowest risk). Similar trends were observed in the validation cohort. Categorization into these 3 risk groups was achievable using TF or GZF in combination or in isolation. In contrast, LVEF of <35% was a poor predictor of the primary endpoint (validation cohort HR: 1.99; 95% CI: 0.99-4.01). ConclusionsMFVA is a strong predictor of sudden cardiac death and VAs in NICM. TF and GZF mass added incremental value to MFVA. In contrast, LVEF was a poor discriminator of arrhythmic risk.

Risk prediction of sudden cardiac death needs multiple evaluation

Risk prediction of sudden cardiac death needs multiple evaluation

Ventriculo-arterial (VA) coupling and fQRS as new selection criteria for primary prevention ICD placement

For decades, left ventricular ejection fraction (LVEF < 35%) has been a mainstay for identifying heart failure (HF) patients most likely to benefit from an implantable cardioverter defibrillator (ICD). However, LVEF is a poor predictor of sudden cardiac death (SCD) and ignores 50% of HF patients with mildly reduced and preserved LVEF. The current international guidelines for primary prophylaxis ICD therapy are inadequate. Instead of LVEF, which is not a good measure of LV contractility or hemodynamic characterization, we hypothesize ventriculo-arterial (VA) coupling combined with fragmented QRS (fQRS) will improve risk stratification and patient suitability for an ICD. Quantifying cardiac and aortic mechanics, and predicting active arrhythmogenic substrate, from varying fQRS morphologies, may help to stratify ischemic and non-ischemic patients with different functional capacities and predisposition for lethal arrhythmias. We propose HF patients with a low physiological reserve may not benefit from ICD therapy, whereas those patients with higher reserves and extensive arrhythmogenic substrate may benefit. Our hypothesis combining VA coupling with fQRS changes has the potential to widen HF patient participation (low and high LVEF) and advance personalized medicine for HF patients at high risk of SCD.

Progranulin, sICAM-1, and sVCAM-1 May Predict an Increased Risk for Ventricular Arrhythmias in Patients with Systemic Sclerosis.

In systemic sclerosis (SSc), fibrosis of the myocardium along with ongoing autoimmune inflammation can alter the electric function of the cardiac myocytes, which may increase the risk for ventricular arrhythmias and sudden cardiac death. We analyzed the electrocardiographic (ECG) variables describing ventricular repolarization such as QT interval, QT dispersion (QTd), T wave peak-to-end interval (Tpe), and arrhythmogeneity index (AIX) of 26 patients with SSc and 36 healthy controls. Furthermore, echocardiographic and laboratory parameters were examined, with a focus on inflammatory proteins like C-reactive ptotein (CRP), soluble intracellular adhesion molecule-1 (sICAM-1), soluble vascular adhesion molecule-1 (sVCAM-1), and progranulin (PGRN). The CRP, sICAM-1, and sVCAM-1 levels were positively correlated with the length of the QT interval. Although the serum PGRN levels were not increased in the SSc group compared to the controls, in SSc patients, the PGRN levels were positively correlated with the QT interval and the AIX. According to our results, we conclude that there may be a potential association between autoimmune inflammation and the risk for ventricular arrhythmias in patients with SSc. We emphasize that the measurement of laboratory parameters of inflammatory activity including CRP, PGRN, sVCAM-1, and sICAM-1 could be helpful in the prediction of sudden cardiac death in patients with SSc.

IoT-based emergency cardiac death risk rescue alert system

The use of technology in healthcare is one of the most critical application areas today. With the development of medical applications, people's quality of life has improved. However, it is impractical and unnecessary for medium-risk people to receive specialized daily hospital monitoring. Due to their health status, they will be exposed to a high risk of severe health damage or even life-threatening conditions without monitoring. Therefore, remote, real-time, low-cost, wearable, and effective monitoring is ideal for this problem. Many researchers mentioned that their studies could use electrocardiogram (ECG) detection to discover emergencies. However, how to respond to discovered emergencies in household life is still a research gap in this field.•This paper proposes a real-time monitoring of ECG signals and sending them to the cloud for Sudden Cardiac Death (SCD) prediction.•Unlike previous studies, the proposed system has an additional emergency response mechanism to alert nearby community healthcare workers when SCD is predicted to occur.

Prediction of Sudden Cardiac Death With Ultra-Short-Term Heart Rate Fluctuations

BackgroundConventional measures of heart rate variability (HRV) have shown only modest associations with sudden cardiac death (SCD). Detrended fluctuation analysis (DFA), with novel methodological developments to evaluate the short-term scaling exponent, is a potentially superior method compared to conventional HRV tools. ObjectivesIn this study, the authors studied the analysis of the association between DFA and SCD. MethodsThe investigators studied the predictive value of ultra-short-term heart rate fluctuations (1-minute electrocardiogram samples) with DFA at rest and during different stages of physical exertion for incident SCD among 2,794 participants undergoing clinical exercise testing in the prospective FINCAVAS (Finnish Cardiovascular Study). The novel key DFA measure, the short-scale scaling exponent computed with second-order detrending (DFA2 α1), was the main exposure variable. SCDs were defined by American Heart Association/European Society of Cardiology criteria using death certificates with written accounts of the events. ResultsDuring a median follow-up of 8.3 years (Q1-Q3: 6.4-10.5), 83 SCDs occurred. DFA2 α1 measured at rest (but not in exercise) associated highly significantly with the risk of SCD, with 1-SD lower values associating with a 2.4-fold (Q1-Q3: 2.0-3.0) risk (P < 0.001). The results persisted when adjusting for other major risk factors for SCD, including age, cardiovascular morbidities, cardiorespiratory fitness, heart rate reduction, and left ventricular ejection fraction. Associations between conventional HRV parameters (measured at any stage of exercise or at rest) and SCD were substantially weaker and statistically nonsignificant after adjusting for other risk factors. ConclusionsUltra-short-term DFA2 α1, when measured at rest, is a powerful and independent predictor of SCD. The association between DFA2 α1 and SCD is modified by physical exertion.

Renal dysfunction is a time-varying risk predictor of sudden cardiac death in heart failure.

Sudden cardiac death (SCD) is a common mode of death in patients with congestive heart failure (CHF). Implantable cardioverter defibrillator (ICD) implantation is established treatment for SCD prevention, but current eligibility criteria based on left ventricular ejection fraction (LVEF) and New York Heart Association (NYHA) functional class may be due for reconsideration given the increasing effectiveness of pharmacological therapy. We sought to reconsider the risk stratification of SCD in patients with symptomatic CHF. In total, 1,676 consecutive patients (74±13years old; 56% male) with NYHA class II or III CHF between 2008 and 2015 were enrolled for this prospective study. The endpoint was SCD. During a median (interquartile range) follow-up period of 25 (4-70) months, 198 (11.8%) patients suffered SCD. Of those events, 23% occurred within 3months of discharge. In the adjusted analyses, estimated glomerular filtration rate (eGFR)<30ml/min/1.73m2 [hazard ratio (HR) 1.73, 95% confidence interval (CI) 1.11-2.70, P=0.01] and LVEF≤35% (HR 2.31, 95% CI 1.47-3.66, P<0.01) were independent risk predictors of SCD. Addition of eGFR to LVEF significantly improved prediction of SCD in the C-index (P=0.04), and in two metrics, net reclassification improvement (P=0.01) and integrated discrimination improvement (P=0.03). The predictive power of eGFR declined time-dependently over 2years. The addition of eGFR to current eligibility criteria may be useful for risk assessment of SCD, although its predictive power wanes over time. Roughly a quarter of the SCD occurred within 3months after discharge in patients with CHF.

Sudden cardiac death prediction based on the complete ensemble empirical mode decomposition method and a machine learning strategy by using ECG signals

Cardiovascular diseases are a significant global health problem, often culminating in sudden cardiac death (SCD). Approximately 4 million annual deaths worldwide are attributed to SCD. Although the causes are varied, the heart’s electrical activity is always affected prior to an SCD event. This work explores the capabilities of the complete ensemble empirical mode decomposition (CEEMD) method for extracting relevant time-domain features from electrocardiogram (ECG) signals. CEEMD method is compared with EMD and EEMD methods. Additionally, a machine learning approach incorporating statistical indices, the Kruskal-Wallis test, and a support vector machine (SVM) is utilized for automatic analysis and prediction. SVM results are benchmarked against k-nearest neighbors, decision trees, and neural networks. Results demonstrate that the proposed methodology can predict the SCD event 30 min before it occurs with an accuracy of 97.28%, positively impacting on the improvement of timely interventions and, consequently, the survival rates and quality of life of people.

Cardiac fibrosis as a predictor for sudden cardiac death after transcatheter aortic valve implantation.

Cardiac fibrosis plays a major pathophysiological role in any form of chronic heart disease, and high levels are associated with poor outcome. Diffuse and focal cardiac fibrosis are different subtypes, which have different pathomechanisms and prognostic implications. The total fibrosis burden in endomyocardial biopsy tissue was recently proved to play an independent prognostic role in aortic stenosis patients after transcatheter aortic valve implantation (TAVI). Here, for the first time, we aim to assess the specific impact of different fibrosis subtypes on sudden cardiac death (SCD) as a primary reason for cardiovascular mortality after TAVI. The fibrosis pattern was assessed histologically in the left ventricular biopsies obtained during TAVI interventions in 161 patients, who received a structured follow-up thereafter. Receiver operating characteristic analyses, performed 6, 12, 24 and 48 months after TAVI, showed diffuse, but not focal, fibrosis as a significant predictor for SCD at all timepoints, with the highest area under the curve at the first time point and a decrease in its SCD predictivity over time. In both multivariate Cox proportional hazards and Fine-Gray competing risk models, including both fibrosis subtypes, as well as age, sex and ejection fraction, high diffuse fibrosis remained statistically significant. Accordingly, it represents an independent SCD predictor, most importantly for the occurrence of early events. The burden of diffuse cardiac fibrosis plays an important and independent prognostic role regarding SCD early after TAVI. Therefore, the histological evaluation of fibrosis topography has value as a prognostic tool for TAVI patients and may help to tailor individualised approaches to optimise their postinterventional management.

Arrhythmic risk stratification in non-ischemic cardiomyopathy using cardiovascular magnetic resonance

Abstract Background Myocardial fibrosis (MF) is an essential component of the arrhythmic substrate for ventricular arrhythmias (VAs). Aims To determine whether total myocardial fibrosis (TF) and gray zone fibrosis (GZF), assessed using cardiovascular magnetic resonance, are better than LVEF in predicting ventricular arrhythmias in patients with non-ischemic cardiomyopathy (NICM). Methods and results Patients with NICM in a derivation cohort (n=866) and a validation cohort (n=848) underwent quantification of TF and GZF. The primary composite endpoint was sudden cardiac death (SCD) or VAs (ventricular fibrillation or sustained ventricular tachycardia). The primary endpoint was met by 52/866 (6.0%) patients in the derivation cohort (follow-up of 7.5 years [interquartile range, IQR] 5.2-9.3). In competing risk analyses, MF on visual assessment (MFVA) predicted the primary endpoint (hazard ratio [HR] 5.83, 95% confidence intervals [CI] 3.15-10.8). Quantified MF measures permitted categorization into 3 risk groups: a TF &amp;gt;0 ≤ 10 g was associated with an intermediate risk (HR: 4.03 [95% CI 1.99-8.16]) and a TF &amp;gt; 10g with the highest risk (HR: 9.17 [95% CI 4.64-18.1]) compared to no MFVA (lowest risk) (Figure). Similar trends were observed in the validation cohort. Categorization into 3 risk groups was achievable using TF or GZF in combination or in isolation. In contrast, LVEF&amp;lt;35% was a poor predictor of the primary endpoint (validation cohort HR:1.99, 95% CI 0.99-0.41). Conclusions MFVA is a strong predictor of SCD and VAs in NICM. TF and GZF mass added incremental value to MFVA. In contrast, LVEF was not useful in arrhythmic risk stratification.

Additional factors associated with sudden cardiac death in HCM patients

Abstract Background Hypertrophic cardiomyopathy (HCM) is a primary myocardial disease characterized by an increase in the thickness of the left ventricular (LV) wall, which is not explained only by abnormal loading conditions. Sudden cardiac death (SCD) is the most devastating complication of HCM occurring in asymptomatic or younger patients without warning. Purpose To evaluate additional SCD risk factors in Ukrainian population of HCM patients. Materials and methods We retrospectively evaluated 350 consecutive HCM patients that were followed from 2006 to 2021 in our Cardiological Department. General clinical examination, standard 12-lead ECG, transthoracic echocardiography and 24 hours Holter ECG monitoring data were estimated. The 5-year SCD risk was calculated according to the HCM Risk-SCD score. The endpoint of the study was SCD and its surrogates, including adequate performance of the implantable cardioverter-defibrillator and successful resuscitation. Results Follow-up time was 5.0 (1.5-9.5) years. During this time, 16 patients (4.6%) reached the end point, which was 0.9%/year. According to the HCM Risk-SCD score, 5 (31.3%) of this group had a high risk of SCD (≥ 6%) and 3 (18.8%) had an intermediate risk (≥ 4% and &amp;lt; 6%). In multivariable Cox regression analysis adjusted for conventional SCD risk factors it was found that independent predictors of SCD were unexplained syncope (HR 3.81, 95% CI 1.11-13.12, p=0.034), systolic blood pressure (SBP) (HR 0.97 , 95% CI 0.94- 0.99, p=0.026), "infarct-like" ST elevation on ECG (HR 6.81, 95% CI 2.09-22.16, p=0.001) and PQ interval duration (HR 1.03, 95% CI 1.01-1.05, p=0.002), Harrell's C-index 0.84, 95% CI 0.73-0.95, p &amp;lt; 0.0001). Conclusions In our study HCM Risk-SCD score predicted SCD or surrogates only in 50% of cases. In order to improve the risk stratification some additional independent predictors of SCD can be proposed. ECG parameters ("infarct-like" ST elevation and PQ interval duration) and level of SBP were found to have SCD predictive value.

Development and validation of a machine learning-based prediction model for sudden cardiac death in the general population: insights from the community cardiovascular cohort

Abstract Background/Introduction Accurately predicting and managing the risk of sudden cardiac death (SCD) in the general population is a formidable challenge. Purpose This study aimed to develop and validate a machine learning (ML)-based prediction model for SCD using data from the Community Cardiovascular Cohort (CCCC) in Taiwan. Methods A community-based cohort study was used for model construction . Four ML algorithms, including extreme gradient boosting (XGBoost), random forests (RF), logistic regression, and deep neural network (DNN), underwent meticulous evaluation. Performance assessment encompassed receiver operating characteristic (ROC) curves, area under the curves (AUC), and accuracy metrics. Significant risk factors were identified through feature importance analysis. Results We employed a random partitioning strategy, allocating 1744 participants (55%) to the training dataset, 952 participants (30%) to the test dataset, and 476 participants (15%) to the validation dataset. In the data training stage, the XGBoost algorithm demonstrated outstanding prediction performance for SCD (AUC: 1.000); In the validation stage, the XGBoost algorithm demonstrated good prediction performance for SCD prediction (AUC: 0.791, 95%CI: 0.617-0.964; Accuracy: 0.971). Using XGBoost-generated predicted probabilities (P(XGB)), we identified individuals with P(XGB) ≥ 0.5 (Group 1) who displayed significantly higher SCD risk (HR 32.0; 95% CI: 9.29-109.9) compared to those with P(XGB) &amp;lt; 0.5 (Group 0). Conclusions Our study demonstrates the effectiveness of ML algorithms, particularly XGBoost, in predicting event of SCD in the general population. The identification of these pivotal risk factors holds profound implications, empowering the identification of high-risk individuals and facilitating the implementation of precisely tailored preventive strategies.Figure 1:KM sruvival curveFigure 2:Feature importance analysis

Echocardiography in Hypertrophic Cardiomyopathy

Abstract Hypertrophic cardiomyopathy (HCM) is a common disorder characterized by unexplained left ventricular hypertrophy. The disease can have different phenotypic expressions and can be genetic in nature with autosomal dominant inheritance. It usually runs a benign course in the majority but can lead to heart failure, atrial fibrillation, and sudden cardiac death (SCD). Echocardiography plays a key role in the diagnosis and overall management of the condition. It is useful in diagnosis, differentiation of different types (obstructive vs nonobstructive), in systematic evaluation of associated abnormalities (like mitral regurgitation), helps in selection of appropriate treatment and is helpful in guiding the intervention procedures for the condition. Certain echocardiographic measures are predictors of SCD. The review describes in detail the techniques and nuances of performing a comprehensive echocardiographic evaluation of patients with HCM.