Risk stratifiers for arrhythmic and non-arrhythmic mortality after acute myocardial infarction

Open Access. Publicado online: 2-Jul-2018The effective discrimination between patients at risk of Arrhythmic Mortality (AM) and Non-Arrhythmic Mortality (NAM) constitutes one of the important unmet clinical needs. Successful risk assessment based on Electrocardiography (ECG) records is greatly improved by the combination of different indices reflecting not only the pathological substrate but also the autonomic regulation of cardiac electrophysiology. This study assesses the cardiac risk stratification capacity of two new Heart Rate Variability (HRV) parameters, Breath Concurrence 6 (BC6) -sinusoidal RR variability of 6 heart beats per breath cycle- and Primary Ectopia (PE) -presence of early ventricular contractions of any etiology- together with the Deceleration Capacity (DC). While BC6 characterizes the response to physiological and pathophysiological stimuli, PE qualifies autonomic cardiac electrophysiology. The analysis of the European Myocardial Infarct Amiodarone Trial (EMIAT) database indicates that BC6 is related with the risk of Arrhythmic Mortality (AM) and PE with the risk of Non-Arrhythmic Mortality. BC6 is the only single parameter that significantly discriminates between AM and NAM. While the combination of BC6 and DC contributes to the identification of AM risk, PE together with DC improves the prediction of NAM in patients with severe ischemic heart disease

Heart Rate Variability: A possible machine learning biomarker for mechanical circulatory device complications and heart recovery

Cardiovascular disease continues to be the number one cause of death in the United States, with heart failure patients expected to increase to \u3e8 million by 2030. Mechanical circulatory support (MCS) devices are now better able to manage acute and chronic heart failure refractory to medical therapy, both as bridge to transplant or as bridge to destination. Despite significant advances in MCS device design and surgical implantation technique, it remains difficult to predict response to device therapy. Heart rate variability (HRV), measuring the variation in time interval between adjacent heartbeats, is an objective device diagnostic regularly recorded by various MCS devices that has been shown to have significant prognostic value for both sudden cardiac death as well as all-cause mortality in congestive heart failure (CHF) patients. Limited studies have examined HRV indices as promising risk factors and predictors of complication and recovery from left ventricular assist device therapy in end-stage CHF patients. If paired with new advances in machine learning utilization in medicine, HRV represents a potential dynamic biomarker for monitoring and predicting patient status as more patients enter the mechanotrope era of MCS devices for destination therapy

Advances in computational modelling for personalised medicine after myocardial infarction

Myocardial infarction (MI) is a leading cause of premature morbidity and mortality worldwide. Determining which patients will experience heart failure and sudden cardiac death after an acute MI is notoriously difficult for clinicians. The extent of heart damage after an acute MI is informed by cardiac imaging, typically using echocardiography or sometimes, cardiac magnetic resonance (CMR). These scans provide complex data sets that are only partially exploited by clinicians in daily practice, implying potential for improved risk assessment. Computational modelling of left ventricular (LV) function can bridge the gap towards personalised medicine using cardiac imaging in patients with post-MI. Several novel biomechanical parameters have theoretical prognostic value and may be useful to reflect the biomechanical effects of novel preventive therapy for adverse remodelling post-MI. These parameters include myocardial contractility (regional and global), stiffness and stress. Further, the parameters can be delineated spatially to correspond with infarct pathology and the remote zone. While these parameters hold promise, there are challenges for translating MI modelling into clinical practice, including model uncertainty, validation and verification, as well as time-efficient processing. More research is needed to (1) simplify imaging with CMR in patients with post-MI, while preserving diagnostic accuracy and patient tolerance (2) to assess and validate novel biomechanical parameters against established prognostic biomarkers, such as LV ejection fraction and infarct size. Accessible software packages with minimal user interaction are also needed. Translating benefits to patients will be achieved through a multidisciplinary approach including clinicians, mathematicians, statisticians and industry partners

Surgical embolectomy for acute massive pulmonary embolism: state of the art

Massive pulmonary embolism (PE) is a severe condition that can potentially lead to death caused by right ventricular (RV) failure and the consequent cardiogenic shock. Despite the fact thrombolysis is often administrated to critical patients to increase pulmonary perfusion and to reduce RV afterload, surgical treatment represents another valid option in case of failure or contraindications to thrombolytic therapy. Correct risk stratification and multidisciplinary proactive teams are critical factors to dramatically decrease the mortality of this global health burden. In fact, the worldwide incidence of PE is 60–70 per 100,000, with a mortality ranging from 1% for small PE to 65% for massive PE. This review provides an overview of the diagnosis and management of this highly lethal pathology, with a focus on the surgical approaches at the state of the art

Hypertrophic Cardiomyopathy: Treatment, Risk Stratification, and Implantable Defibrillators

Hypertrophic cardiomyopathy (HCM) affects 1:500 individuals, and in majority of cases, a mutation in sarcomere proteins can explain the disease. Phenotype is heterogeneous and thus the prognosis. Many patients suffer from dyspnoea, especially at exercise. Unfortunately, sudden cardiac death (SCD) does occur at all ages and is a major cause of death in young adults. There is no proven pharmacological treatment to reduce hypertrophy or fibrosis, but beta-blockers are first-line treatment. In patients with obstruction, myectomy is preferred in the young, but in older patients, alcohol septal ablation is tried to reduce symptoms and possibly prognosis. Risk stratification of sudden cardiac death is challenging. The major established risk factors are extreme myocardial thickness, non-sustained ventricular tachycardia, unexplained syncope, abnormal exercise blood pressure response, and family history of sudden cardiac death. In 2014, a novel risk calculator was developed that also takes age, outflow gradient, and left atrial seize into account. Implantable defibrillator treatment is effective in HCM, but complications requiring surgery and inappropriate shocks remain a problem

A machine learning-based risk stratification model for ventricular tachycardia and heart failure in hypertrophic cardiomyopathy

Background: Machine learning (ML) and artificial intelligence are emerging as important components of precision medicine that enhance diagnosis and risk stratification. Risk stratification tools for hypertrophic cardiomyopathy (HCM) exist, but they are based on traditional statistical methods. The aim was to develop a novel machine learning risk stratification tool for the prediction of 5-year risk in HCM. The goal was to determine if its predictive accuracy is higher than the accuracy of the state-of-the-art tools. Method: Data from a total of 2302 patients were used. The data were comprised of demographic characteristics, genetic data, clinical investigations, medications, and disease-related events. Four classification models were applied to model the risk level, and their decisions were explained using the SHAP (SHapley Additive exPlanations) method. Unwanted cardiac events were defined as sustained ventricular tachycardia occurrence (VT), heart failure (HF), ICD activation, sudden cardiac death (SCD), cardiac death, and all-cause death. Results: The proposed machine learning approach outperformed the similar existing risk-stratification models for SCD, cardiac death, and all-cause death risk-stratification: it achieved higher AUC by 17%, 9%, and 1%, respectively. The boosted trees achieved the best performing AUC of 0.82. The resulting model most accurately predicts VT, HF, and ICD with AUCs of 0.90, 0.88, and 0.87, respectively. Conclusions: The proposed risk-stratification model demonstrates high accuracy in predicting events in patients with hypertrophic cardiomyopathy. The use of a machine-learning risk stratification model may improve patient management, clinical practice, and outcomes in general

Prediction of Sudden Cardiac Death Using Ensemble Classifiers

Sudden Cardiac Death (SCD) is a medical problem that is responsible for over 300,000 deaths per year in the United States and millions worldwide. SCD is defined as death occurring from within one hour of the onset of acute symptoms, an unwitnessed death in the absence of pre-existing progressive circulatory failures or other causes of deaths, or death during attempted resuscitation. Sudden death due to cardiac reasons is a leading cause of death among Congestive Heart Failure (CHF) patients. The use of Electronic Medical Records (EMR) systems has made a wealth of medical data available for research and analysis. Supervised machine learning methods have been successfully used for medical diagnosis. Ensemble classifiers are known to achieve better prediction accuracy than its constituent base classifiers. In an effort to understand the factors contributing to SCD, data on 2,521 patients were collected for the Sudden Cardiac Death in Heart Failure Trial (SCD-HeFT). The data included 96 features that were gathered over a period of 5 years. The goal of this dissertation was to develop a model that could accurately predict SCD based on available features. The prediction model used the Cox proportional hazards model as a score and then used the ExtraTreesClassifier algorithm as a boosting mechanism to create the ensemble. We tested the system at prediction points of 180 days and 365 days. Our best results were at 180-days with accuracy of 0.9624, specificity of 0.9915, and F1 score of 0.9607

Heart rate differences between symptomatic and asymptomatic Brugada syndrome patients at night

Peer ReviewedPostprint (author's final draft

Feasibility of improving risk stratification in the inherited cardiac conditions

Fatal ventricular arrhythmias can occur in patients with Hypertrophic Cardiomyopathy, Brugada Syndrome and rarely in patients with normal cardiac investigations. Despite very different pathogeneses, we hypothesised that a common electrophysiological substrate precipitates these arrhythmias and could be used as a marker for risk stratification. In Chapter 3 of this thesis, we found that fewer than half the cardiac arrest survivors with Brugada Syndrome would have been offered prophylactic defibrillators based on current risk scoring, highlighting the need for better risk stratification. Our group previously used a commercially available 252-electrode vest which constructs ventricular electrograms onto a CT image of the heart to show exercise related differences in high-risk patients. In Chapter 4, we applied this method to Brugada patients, but could not reproduce prior results. Further investigation revealed periodic changes in activation patterns after exercise that could explain this discrepancy. An alternative matrix approach was developed to overcome this problem. Exercise induced conduction heterogeneity differentiated Brugada patients from unaffected controls, but not those surviving cardiac arrest. However, if considered alongside spontaneous type 1 ECG and syncope, inducible conduction heterogeneity markedly improved identification of Brugada cardiac arrest survivors. In Chapter 5 the method was shown to differentiate idiopathic ventricular fibrillation patients from those fully recovered from acute ischaemic cardiac arrest, implying a permanent electrophysiological abnormality. In Chapter 8, we showed prolonged mean local activation times and activation-recovery intervals in hypertrophic cardiomyopathy cardiac arrest survivors compared to those without previous ventricular arrhythmia. These metrics were combined into both logistic regression and support vector machine models to strongly differentiate the groups. We concluded that electrophysiological changes could identify cardiac arrest survivors in various cardiac conditions, but a single factor common pathway was not established. Prospective studies are required to determine if using these parameters could enhance current risk stratification for sudden death.Open Acces