Predictor Of Sudden Cardiac Death Research Articles

Turbulence and deceleration capacity of the heart rate in patients with coronary artery disease and implanted cardioverter defibrillator

Aim. To study the possibility of using of heart rate turbulence (HRT) and deceleration capacity (DC) in the risk stratification of fatal events in patients with coronary artery disease (CAD) and implanted cardioverter defibrillator (ICD). Material and methods. The study included 45 patients with sinus rhythm and verified CAD, who had indications for ICD. A 24-hour ECG monitoring was performed with the evaluation of non-invasive predictors of sudden cardiac death. The minimum observation period was 30 months (the average observation period was 48 (42;51) months). Two endpoints were assessed: the onset of deaths and the justified ICD actuations. Results. During the observation period, 10 patients died (5 from progression of heart failure, 2 from recurrent myocardial infarction, 2 from non-cardiac pathology, and 1 died suddenly before implantation of a defibrillator). Arrhythmic events occurred in 13 people. Compared to the surviving patients, deceased patients’ mean DC value was significantly lower (1,25 (0,48;2,23) ms vs 3,3 (1,85;5,40) ms, p=0,016). There were significantly more frequent DC in the high-risk zone (80% vs 34%, p=0,024) and type 2 HRT disorders (80% vs 29%, p=0,009). When comparing patients with arrhythmic events and without them, no significant differences in DC and HRT were detected. According to univariate regression analysis, HRT and DC were risk factors for death (p=0,004 and p=0,006, respectively). Conclusion. Electrophysiological markers reflecting vegetative imbalance (HRT and DC) in patients with CAD and ICD can identify patients with high risk of overall mortality, but it is not associated with the risk of ventricular tachyarrhythmias development.

The Risk for Sudden Cardiac Death Among Patients Living With Heart Failure and Human Immunodeficiency Virus

ObjectivesThe aim of this study was to determine the incidence of sudden cardiac death (SCD) among persons living with human immunodeficiency virus infection (PHIV) with heart failure (HF), who were hospitalized for HF, and the risk factors associated with it. BackgroundHF is associated with an increased risk for SCD. PHIV are at heightened risk for HF. MethodsThis was a retrospective study of 2,578 patients hospitalized with HF from a single academic center, of whom 344 were PHIV. The outcome of interest was SCD. Subgroup analyses were performed by strata of viral load (VL) and left ventricular ejection fraction (LVEF) <35%, 35% to 49%, and ≥50%. ResultsOf 2,578 patients with HF, 2,149 (86%) did not have implantable cardioverter-defibrillators; of these, there were 344 PHIV and 1,805 uninfected control subjects. Among PHIV with HF, 313 (91%) were prescribed antiretroviral therapy and 64% were virally suppressed. There were 191 SCDs over a median follow-up period of 19 months. Compared with control subjects, PHIV had a 3-fold increase in SCD (21.0% vs. 6.4%; adjusted odds ratio: 3.0; 95% confidence interval: 1.78 to 4.24). Among PHIV, cocaine use, lower LVEF, absence of beta-blocker prescription, and VL were predictors of SCD. The SCD rate among PHIV with undetectable VL was similar to the rate among uninfected subjects. Similar findings were observed by LVEF strata. Among PHIV with HF without conventional indications for an implantable cardioverter-defibrillator, the rate of SCD was 10% per year. ConclusionsPHIV hospitalized with HF are at a markedly increased risk for SCD. SCD risk was increased in patients with lower LVEFs, lower CD4 counts, and higher VL.

QT and P-wave dispersion during the manic phase of bipolar disorder.

Background and aimPatients with bipolar disorder (BD) are at increased risk for cardiovascular diseases and complications. This increased risk is considered to be associated with the autonomic nervous system (ANS) abnormalities. However, there is little or no documentation of the relationship between this increased risk and the phases of BD. In this study, we aimed to compare the changes in the QT dispersion (QTd) and P-wave dispersion (Pd), which are predictors of sudden cardiac death and atrial fibrillation, between the patients with manic BD and healthy controls.Participants and methodsThe study included a patient group of 44 patients (26 female and 18 male) that were hospitalized due to a diagnosis of manic BD and met the inclusion criteria, and a control group of 34 age- and gender-matched healthy individuals (21 female and 13 male) with no history of psychiatric and neurological disorders. The QTd and Pd values were determined in each participant by performing the standard 12-lead body surface electrocardiography (ECG).ResultsThe principal electrocardiographic indicators including corrected maximum QT interval, corrected QT dispersion, and minimum P-wave duration significantly increased in the patient group compared to the control group (t=2.815, p<0.01; t=4.935, p<0.001; t=3.337, p<0.001, respectively).ConclusionThe results indicated that patients with manic BD are at increased risk for cardiovascular autonomic dysfunction. Therefore, clinicians should be more careful about ECG changes and related possible cardiac conduction problems such as cardiac arrthythmias in patients with manic BD. Further longitudinal studies are needed to investigate BD and its phases (depression, mania, and hypomania) with regard to ANS and cardiac abnormalities.

Heart Rate Variability Analysis to Predict Onset of Ventricular Tachyarrhythmias in Implantable Cardioverter Defibrillators.

Implantable cardioverter defibrillators (ICDs) are commonly used in patients at high risk of sudden cardiac death (SCD) to help prevent and treat life-threatening arrhythmia. Up to 80% of cases of sudden cardiac death are caused by ventricular tachyarrhythmias (VTA) and the accurate prediction of VTA in patients with ICDs can help prevent SCD. Early prediction allows tiered and less invasive therapies to be used to help prevent VTA which are more easily tolerated by the patient and are less battery intensive. In this work, a comparative study of three types of frequency domain features (spectral, bispectrum, and Fourier-Bessel) for VTA prediction is presented based on heart rate variability (HRV) signals between one and five minutes prior to known SCD. Using Fourier-Bessel features and a standard classification approach resulted in the best performance of 87.5% accuracy, 89.3% sensitivity and 85.7% specificity. These results suggest that Fourier-Bessel features are a promising approach for SCD prediction, and that new feature development can help improve both the sensitivity and specificity of SCD prediction in ICDs.

A novel multi-class approach for early-stage prediction of sudden cardiac death

Sudden cardiac death (SCD) is a complex issue that may occur in population groups with either known or unknown cardiovascular disease (CVD). Given the complex nature of SCD, the discovery of a suitable biomarker will prove essential in identifying individuals at risk of SCD, while discriminating it from patients with other cardiac pathologies as well as healthy individuals. Thus, this study aimed to develop an efficient approach to support a better comprehension of heart rate variability (HRV) as a predictive biomarker to identify SCD patients at an early stage. The present study proposed a novel multi-class classification approach using signal processing methods of HRV to predict SCD 10min before its occurrence. The developed algorithm was qualitatively and quantitatively analyzed in terms of discriminating SCD patients from patients of heart failure and normal people. A total of 51 HRV signals of all three classes obtained from PhysioBank were processed to extract 32 features in each subject. The optimal feature selection was performed by a hybrid approach of sequential feature selection-random under sampling boosting algorithms. Multi-class classifiers, namely decision tree, support vector machine, and k-nearest neighbors were used for classification. An average classification accuracy of SCD prediction 10min before occurrence was obtained as 83.33%. Therefore, this study suggests a new efficient approach for the early-stage prediction of SCD that is considerably different from that reported in the literature to date. However, to generalize the findings, the algorithm needs to be tested for a larger population group.

A bio-clinical approach for prediction of sudden cardiac death in outpatients with heart failure: The ST2-SCD score

BackgroundSudden cardiac death (SCD) is one of the main modes of death in heart failure (HF) patients and its prediction remains a real challenge. Our aim was to assess the incidence of SCD at 5 years HF contemporary managed outpatients, and to find a simple prediction model for SCD. MethodsSCD was considered any unexpected death, witnessed or not, occurring in a previously stable patient with no evidence of worsening HF or any other cause of death. A competing risk strategy was adopted using the Fine-Gray method of Cox regressions analyses that considered other causes of death as the competing event. ResultsThe derivation cohort included 744 consecutive outpatients (72% men, age 67.9 ± 12.2 years, left ventricular ejection fraction [LVEF] 36% ± 14). During follow-up, 312 deaths occurred, 40 SCDs (5.4%). Age, haemoglobin, eGFR, HF duration, high-sensitivity troponin T, NTproBNP, and ST2 were associated with SCD in univariate analyses; HF duration (p = 0.006), eGFR (p < 0.001), LVEF <45% (p = 0.03), and ST2 (p = 0.006) remained in multivariable analysis. A predictive score (ST2-SCD) including dichotomous variables (ST2 > 45, LVEF <45%, HF duration >3 years, eGFR < 55, age ≥ 60 years and male sex) provided a Harrell's C-statistic of 0.82 (0.76-0.89)), reaching 0.87 (0.80–0.95) in the validation cohort (n = 149). ConclusionsIn contemporary managed HF, SCD occurred in 5.4% of outpatients, accounting for 12.8% of all deaths at 5 years. Of the 3 studied biomarkers, only ST2 remained independently associated with SCD. A model containing age, sex, ST2, eGFR, LVEF, and HF duration reasonably predicted 5-years risk of SCD.

Enhanced American College of Cardiology/American Heart Association Strategy for Prevention of Sudden Cardiac Death in High-Risk Patients With Hypertrophic Cardiomyopathy

Strategies for reliable selection of high-risk patients with hypertrophic cardiomyopathy (HCM) for prevention of sudden cardiac death (SCD) with implantable cardioverter/defibrillators (ICDs) are incompletely resolved. To assess the reliability of SCD prediction methods leading to prophylactic ICD recommendations to reduce the number of SCDs occurring in patients with HCM. In this observational longitudinal study, 2094 predominantly adult patients with HCM consecutively evaluated over 17 years in a large HCM clinical center were studied. All patients underwent prospective ICD decision making relying on individual major risk markers derived from the HCM literature and an enhanced American College of Cardiology/American Heart Association (ACC/AHA) guidelines-based risk factor algorithm with complete clinical outcome follow-up. Data were collected from June 2017 to February 2018, and data were analyzed from February to July 2018. Arrhythmic SCD or appropriate ICD intervention for ventricular tachycardia or ventricular fibrillation. Of the 2094 study patients, 1313 (62.7%) were male, and the mean (SD) age was 51 (17) years. Of 527 patients with primary prevention ICDs implanted based on 1 or more major risk markers, 82 (15.6%) experienced device therapy-terminated ventricular tachycardia or ventricular fibrillation episodes, which exceeded the 5 HCM-related SCDs occurring among 1567 patients without ICDs (0.3%), including 2 who declined device therapy, by 49-fold (95% CI, 20-119; P = .001). Cumulative 5-year probability of an appropriate ICD intervention was 10.5% (95% CI, 8.0-13.5). The enhanced ACC/AHA clinical risk factor strategy was highly sensitive for predicting SCD events (range, 87%-95%) but less specific for identifying patients without SCD events (78%). The C statistic calculated for enhanced ACC/AHA guidelines was 0.81 (95% CI, 0.77-0.85), demonstrating good discrimination between patients who did or did not experience an SCD event. Compared with enhanced ACC/AHA risk factors, the European Society of Cardiology risk score retrospectively applied to the study patients was much less sensitive than the ACC/AHA criteria (34% [95% CI, 22-44] vs 95% [95% CI, 89-99]), consistent with recognizing fewer high-risk patients. A systematic enhanced ACC/AHA guideline and practice-based risk factor strategy prospectively predicted SCD events in nearly all at-risk patients with HCM, resulting in prophylactically implanted ICDs that prevented many catastrophic arrhythmic events in this at-risk population.

Machine learning for prediction of sudden cardiac death in heart failure patients with low left ventricular ejection fraction: study protocol for a retroprospective multicentre registry in China

IntroductionLeft ventricular ejection fraction (LVEF) ≤35%, as current significant implantable cardioverter-defibrillator (ICD) indication for primary prevention of sudden cardiac death (SCD) in heart failure (HF) patients, has been widely recognised...

Arrhythmic risk stratification in patients with dilated cardiomyopathy and intermediate left ventricular dysfunction.

Arrhythmic risk stratification is a challenging issue in patients with dilated cardiomyopathy (DCM), particularly when left ventricular ejection fraction (LVEF) is more than 35%. We studied the prevalence and predictors of sudden cardiac death or malignant ventricular arrhythmias (SCD/MVAs) in DCM patients categorized at low arrhythmic risk because of intermediate left ventricular dysfunction under optimal medical treatment (OMT). DCM patients considered at low arrhythmic risk (LVEF >35% and New York Heart Association class I-III after 6 ± 3 months of OMT) were analysed. An arrhythmogenic profile was defined as the presence of at least one among a history of syncope, nonsustained ventricular tachycardia, at least 1000 premature ventricular contractions/24 h, at least 50 ventricular couplets/24 h at Holter ECG monitoring. SCD/MVAs was considered as the study end-point. During a median follow-up of 152 months (interquartile range 100-234), 30 out of 360 (8.3%) patients at low arrhythmic risk (LVEF 47 ± 7%) experienced the study end-point [14 (3.9%) SCD and 16 (4.4%) MVA]. Compared with survivors, patients who experienced SCD/MVAs had more frequently an arrhythmogenic profile and a larger left atrium. Their LVEF at the last available evaluation before the arrhythmic event was 36 ± 12%. At multivariable analysis, left atrial end-systolic area [hazard ratio 1.107; 95% confidence interval (95% CI) 1.039-1.179, P = 0.002 for 1 mm increase] and arrhythmogenic profile (hazard ratio 3.667; 95% CI 1.762-7.632, P = 0.001) emerged as predictors of SCD/MVAs during follow-up. A consistent quota of DCM patients with intermediate left ventricular dysfunction receiving OMT experienced SCD/MVA during follow-up. Left atrial dilatation and arrhythmogenic pattern were associated with a higher risk of SCD/MVA.

Improving military personnel resiliency through recreational trail rides

Cardiorespiratory fitness (CRF) has been widely studied as a powerful and independent predictor of all-cause and disease-specific mortality. Sudden cardiac death (SCD) is recognized as a significant cause of mortality among the general population, including the general population without previous symptoms of any coronary heart disease (CHD). Consequently, SCD is an important public health problem, which constitutes a clinical challenge. Thus, prevention of SCD by detecting early risk factors could be a useful tool, contributing to the American Heart Association‘s goal of decreasing the incidence of SCD at the population level. The identification of these risk factors for CVD would facilitate the large-scale screening of those participants at higher risk of SCD. This systematic review collects information about the role of CRF on the risk of SCD at the available evidence, and analyzes the long-term influence of CRF as a risk factor and independent predictor of SCD.

Incidence and Predictors of Sudden Cardiac Death After a Major Non-Fatal Cardiovascular Event

Sudden cardiac death (SCD) still accounts for the majority of deaths from the four major cardiovascular events (myocardial infarction (MI), heart failure (HF), atrial fibrillation (AF), and stroke) despite substantial progress on prevention. Four separate cohorts (one for each of the four major cardiovascular conditions) were captured through person-linked hospital morbidity and mortality data collections between 2000 and 2009 and followed-up for 11.5 years. The incidence rate for each cohort was total SCD cases divided by sum of follow-up time for each individual alive. Kaplan-Meier survival curve was used to calculate unadjusted risk of SCD. Predictors of SCD were identified by fitting multivariable adjusted Cox regression models in each of the cohorts. There were 1,174 cases of SCD from 53,614 total CVD events across the cohorts (35.6% for MI, 15.6% for HF, 22.4% for AF, 26.4% for stroke). The incidence rate and unadjusted risk of SCD were both highest after incident hospitalisation for HF, followed by MI, stroke and AF. The elevated risk of SCD was independently associated with MI, HF, arrhythmias, peripheral artery disease, diabetes, chronic kidney disease, and prior coronary heart disease (hazard ratios ranging from 1.1 to 2.8). Early revascularisation is protective in 28-day survivors after an incident MI event. An appreciable incidence of SCD following an incident event of MI, HF, AF and stroke deserves greater prevention efforts. Major medical conditions such as MI, HF, peripheral artery disease, and arrhythmias are risk markers of SCD and coronary revascularisation is protective.

Left Atrial Function Using Cardiovascular Magnetic Resonance Imaging Independently Predicts Life-Threatening Arrhythmias in Patients Referred to Receive a Primary Prevention Implantable Cardioverter Defibrillator

BackgroundIn this study we aimed to investigate left atrial (LA) function, measured from routine cine cardiovascular magnetic resonance imaging, to determine its value for the prediction of sudden cardiac death (SCD) or appropriate implantable cardioverter defibrillator (ICD) shock in patients who received primary prevention ICD implantation. MethodsWe studied 203 patients with ischemic or idiopathic nonischemic dilated cardiomyopathy who underwent cardiovascular magnetic resonance imaging before primary prevention ICD implantation. LA volumes were measured at end-diastole and end-systole from 4- and 2-chamber cine images, and LA emptying function (LAEF) calculated. Patients were followed for the primary composite end point of SCD or appropriate ICD shock. ResultsMean age was 61 ± 12 years with a mean left ventricular ejection fraction of 24 ± 7%. The mean LAEF was 27 ± 15% (range, 0.9%-73%). At a median follow-up of 1639 days, 35 patients (17%) experienced the primary composite outcome. LAEF was strongly associated with the primary outcome (P = 0.001); patients with an LAEF ≤ 30% experienced a cumulative event rate of 26.1% vs 5.7% (hazard ratio, 5.5; P < 0.001) in patients above this cutoff. This finding was maintained in multivariable analysis (hazard ratio, 4.7; P = 0.002) and was consistently shown in the ischemic and nonischemic dilated cardiomyopathy subgroups. ConclusionsLAEF is a simple, powerful, and independent predictor of SCD in patients being referred for primary prevention ICD implantation.

Usefulness of Single Nucleotide Polymorphisms as Predictors of Sudden Cardiac Death

The pathophysiology of sudden cardiac death (SCD) remains incompletely understood. Genetic mutations can create a favorable substrate for SCD. Our aim is to evaluate the evidence of single nucleotide polymorphisms (SNPs) as predictors of SCD. We searched the Medline database (2000 to 2017) and selected all case-control or cohort studies that reported associations between SNPs and SCD. Our search terms included "polymorphisms" and "sudden death." We collected the study design, population ethnic background, gene testing strategy, the association between the SNP and SCD, and the cardiovascular comorbidities of the population. Our search yielded 723 studies, of which we included 24 based upon our inclusion criteria. The studies had a total population of 78,165 participants, with a median age of 62.5 years (IQR 56 to 66) and 35% (IQR 13 to 32) were female. Almost all studies were conducted in white patients of European descent and the most commonly used genetic strategy was candidate gene panels. Fifteen of the studies had a case-control design that included SCD patients without known heart disease as the comparison group and the other 9 studies included patients with heart failure and coronary artery disease. The studies evaluated 53 SNPs and the most common genetic loci were SCN5A, RyR2, CASQ2, NOSA1P, and AGTR. SNPs with the 3 strongest statistically significant ORs >1 were: rs6684209 of CASQ2 (odds ratio [OR] 19), rs3814843 of CALM1 (OR 5.5), and rs35594137 of GJA5 (OR 3.6). In Conclusion, many SNPs are associated with SCD, with the strongest associations seen in SNPs of genes related to intracellular calcium handling. These findings were generated primarily using a candidate gene strategy in white patients with European descent.

Identifying Ventricular Arrhythmias and Their Predictors by Applying Machine Learning Methods to Electronic Health Records in Patients With Hypertrophic Cardiomyopathy (HCM-VAr-Risk Model)

Clinical risk stratification for sudden cardiac death (SCD) in hypertrophic cardiomyopathy (HC) employs rules derived from American College of Cardiology Foundation/American Heart Association (ACCF/AHA) guidelines or the HCM Risk-SCD model (C-index ∼0.69), which utilize a few clinical variables. We assessed whether data-driven machine learning methods that consider a wider range of variables can effectively identify HC patients with ventricular arrhythmias (VAr) that lead to SCD. We scanned the electronic health records of 711 HC patients for sustained ventricular tachycardia or ventricular fibrillation. Patients with ventricular tachycardia or ventricular fibrillation (n = 61) were tagged as VAr cases and the remaining (n = 650) as non-VAr. The 2-sample ttest and information gain criterion were used to identify the most informative clinical variables that distinguish VAr from non-VAr; patient records were reduced to include only these variables. Data imbalance stemming from low number of VAr cases was addressed by applying a combination of over- and undersampling strategies. We trained and tested multiple classifiers under this sampling approach, showing effective classification. We evaluated 93 clinical variables, of which 22 proved predictive of VAr. The ensemble of logistic regression and naïve Bayes classifiers, trained based on these 22 variables and corrected for data imbalance, was most effective in separating VAr from non-VAr cases (sensitivity = 0.73, specificity = 0.76, C-index = 0.83). Our method (HCM-VAr-Risk Model) identified 12 new predictors of VAr, in addition to 10 established SCD predictors. In conclusion, this is the first application of machine learning for identifying HC patients with VAr, using clinical attributes. Our model demonstrates good performance (C-index) compared with currently employed SCD prediction algorithms, while addressing imbalance inherent in clinical data.

Investigation of the Proarrhythmic Effects of Antidepressants according to QT Interval, QT Dispersion and T Wave Peak-to-End Interval in the Clinical Setting.

ObjectiveSome antidepressants have been implicated as risk factors for QT prolongation, which is a predictor of sudden cardiac death. However, the QT interval is considered an imperfect biomarker for proarrhythmic risk. Therefore, we reevaluated the risk of sudden cardiac death due to antidepressants using improved methods, namely, QT dispersion (QTD), T wave peak-to-end interval (Tp-e), and Tp-e/QT ratio. MethodsWe compared the effects of antidepressants on QTc (QT/RR1/3), QTD, Tp-e, and Tp-e/QT ratio in 378 patients with mood disorder. We also compared each index between 165 healthy controls and 215 randomly selected age-matched patients. ResultsAge (p<0.01), sex (p<0.05), tricyclic antidepressant (TCA) use (p<0.05), and clomipramine (p<0.01) and mianserin (p<0.05) use in particular, significantly associated with a prolonged QTc. We also found that age (p<0.01), TCA use (p<0.05), and clomipramine (p<0.01) and mianserin (p<0.05) use in particular, significantly prolonged QTD. However, there was no correlation between each variable and Tp-e or Tp-e/QT ratio. Significant differences in QTc and QTD were found between the patients and healthy controls. ConclusionFrom our results, prediction of risk of sudden cardiac death by QTD, Tp-e, or Tp-e/QT ratio was inconsistent. Increased QTD may be more suitable for predicting sudden cardiac death due to antidepressants.

Lethal immunoglobulins: Autoantibodies and sudden cardiac death.

Sudden cardiac death (SCD) is an unexpected death due to cardiac causes that occurs in a short time period (generally within 1 h of symptom onset) in a person with known or unknown cardiac disease. Patients with cardiomyopathies, myocarditis, ischemic heart disease and cardiac channelopathies are at risk of SCD. However, a certain percentage of autopsy-negative cases of SCD in the young (<35 years) remain unexplained even after a post-mortem genetic testing. Autoantibodies against cardiac proteins may be potentially involved in the pathogenesis of different heart diseases and in the occurrence of unexplained SCD. In this review we analyze clinical and animal studies that elucidate the prevalence of these autoantibodies in patients with different cardiac diseases and their pathophysiological relevance. We propose a classification of the autoantibodies associated with heart diseases and focus on their molecular and cellular effects. Anti-beta adrenergic receptor antibodies and anti-muscarinic acetylcholine receptor antibodies affect myocardial electrophysiological properties and were reported to be the independent predictors of SCD in patients with different heart diseases. Autoimmune mechanism is proposed for cardiac-related adverse reactions following human papillomavirus (HPV) vaccination. The pentapeptid sharing between HPV's antigens, adrenergic receptors and muscarinic acetylcholine receptors supports this assumption. The dysregulating effects of the autoantibodies against calcium and potassium ion channels can be the basis for autoimmune phenocopies of genetic cardiac channelopathies, which are also associated with SCD.

The additive role of echocardiography in the screening for sudden death

A clinically based sudden cardiac death (SCD) risk score has predictive value. Echocardiographic parameters predict SCD. Our aim was to evaluate the effect of adding echocardiographic parameters to the clinical SCD risk score for the prediction of all-cause mortality. We conducted a retrospective cohort of screening echocardiograms performed on primary care patients. We calculated the SCD risk score and added the left ventricular (LV) mass index, LV hypertrophy, diastolic dysfunction, and LV ejection fraction (EF). We calculated the c-statistic, net reclassification index (NRI), and Hosmer-Lemeshow chi-square for the SCD score alone or combined with each echocardiographic parameter in predicting all-cause mortality. We included 6447 primary care patients who underwent a screening echocardiogram and had a SCD score. The c-statistic of the SCD score for mortality was 0.61; 95% CI 0.58-0.62 and the c-statistic for the score combined with LV mass index increased to 0.64; 95% CI 0.63-0.65 and for the score combined with LVEF, the c-statistic was 0.64;95% CI 0.63-0.67. When diastolic dysfunction and LV hypertrophy were added to the SCD score, the c-statistic did not significantly change (P>0.05). The NRI for the addition of LV mass index and LVEF was 0.52±0.02, and the Hosmer-Lemeshow statistic was nonsignificant (P>0.05). Adding LV mass index or LVEF to the SCD risk score improves the ability to predict mortality, but in the primary care setting, the improvement is small and underscores the challenge of SCD prediction and prevention in the community.

Cardiac Magnetic Resonance in Primary Prevention of Sudden Cardiac Death.

Sudden death accounts for 400,000 deaths annually in the United States. Most sudden deaths are cardiac and are related to arrhythmias secondary to structural heart disease or primary electrical abnormalities of the heart. Implantable cardioverter defibrillator significantly improves survival in patients at increased risk of life-threatening arrhythmias, but better selection of eligible patients is required to avoid unnecessary implantation and identify those patients who may benefit most from this therapy. Left ventricular (LV) ejection fraction (EF) measured by echocardiography has been considered the most reliable parameter for long-term outcome in many cardiac diseases. However, LVEF is an inaccurate parameter for arrhythmic risk assessment as patients with normal or mildly reduced LV systolic function could experience sudden cardiac death (SCD). Among other tools for arrhythmic stratification, magnetic resonance (CMR) provides the most comprehensive cardiac evaluation including in vivo tissue characterization and significantly aids in the identification of patients at higher SCD risk. Most of the evidence are related to late gadolinium enhancement (LGE), which was proven to detect cardiac fibrosis. LGE has been reported to add incremental value for prognostic stratification and SCD prediction across a wide range of cardiac diseases, including both ischemic and nonischemic cardiomyopathies. In addition, T1, T2 mapping and extracellular volume assessment were reported to add incremental value for arrhythmic assessment despite suffering from several technical limitations. CMR should be part of a multiparametric approach for patients' evaluation, and it will play a pivotal role in prognostic stratification according to the current evidence.

Novel electrocardiogram markers for prediction of arrhythmia and sudden cardiac death in synthetic cannabinoid users

Novel electrocardiogram markers for prediction of arrhythmia and sudden cardiac death in synthetic cannabinoid users

An Automated Strategy for Early Risk Identification of Sudden Cardiac Death by Using Machine Learning Approach on Measurable Arrhythmic Risk Markers

Early risk identification of an unexpected sudden cardiac death (SCD) in a person who is suffering malignant ventricular arrhythmias is highly significant for timely intervention and increasing the survival rate. For this purpose, we have presented an automated strategy for prediction of SCD with a high-level accuracy by using measurable arrhythmic markers in this paper. The set of arrhythmic parameters includes three repolarization interval ratios, such as T p T e /QT, JT p /JT e , and T p T e /JT p and two conduction-repolarization markers, such as T p T e /QRS and T p T e /(QT×QRS). Each of them is calculated directly from the detected QRS complex waves and T-wave of electrocradiogram (ECG) signals. Then, all calculated markers are used for the automatical classification of normal and SCD risk groups by employing machine learning classifiers, such as k-nearest neighbor (KNN), decision tree (DT), Naive Bayes (NB), support vector machine (SVM), and random forest (RF). The effectiveness and usefulness of the proposed method is evaluated using a database of measured ECG data acquired from 28 SCD and 18 normal patients. For the automated strategy, the set of five arrhythmic risk markers can predict SCD in less than one second with an average accuracy of 98.91% (KNN), 98.70% (SVM), 98.99% (DT), 97.46% (NB), and 99.49% (RF) for 30 minutes before the occurrence of SCD. Moreover, a practical and straightforward SCD index (SCDI) through a judicious integration of these markers is also proposed by using the Student's t-test. The obtained SCDIs are 1.2058 ± 0.0795 and 1.7619 ± 0.1902 for normal and SCD patients, respectively, which provide a sufficient discrimination degree with a p-value of 6.5061e-35. The present results show that both the automated classifier and the integrated SCDI can predict the SCD up to 30 minutes earlier, and that these predictions could be more practical and efficient if applied in portable smart devices with real-time requirements in hospital settings or at home.