Sudden Cardiac Arrest Prediction Research Articles

Abstract Su803: Sudden Cardiac Arrest Prediction via Deep Learning Electrocardiogram Analysis

Background: Sudden cardiac arrest (SCA) is a potentially fatal event that often occurs without prior indications. Hypothesis: It was hypothesized that electrocardiogram (ECG) data could be used in conjunction with Deep Learning (DL) algorithms to predict the occurrence of SCA’s. Aims: To improve SCA outcomes and enable preventative strategies, the ECG data from the Nightingale Open Science - Subtyping Cardiac Arrest dataset were utilized to create a DL model to serve as a screening tool for sudden cardiac arrest. Methods: A publicly-available dataset containing ten seconds of 12-lead ECGs from individuals who had a SCA and persons who did not have a SCA, and information about time from ECG to arrest, and age and sex were utilized for individualized prediction of SCA. Subsets of the initial data were used to train, validate, and test deep convolution neural network models that processed cardiac cycles from single-lead ECG voltage waveforms. Results: The cohort comprised 221 (mean age: 68.5 and #females: 75) patients who experienced SCA and 1046 controls (mean age: 69.7 and #females: 497) who did not have a cardiac arrest. The SCA ECG-DL base model, with ECGs within one day and with age and sex data, had an area under the receiver operating curve (AUROC) of 0.774 during hold-out testing for discrimination of subsequent SCA. With sensitivity set at 95%, the base model specificity of the SCA ECG-DL model was 31%. Gradient-weighted class activation mapping showed that the SCA ECG-DL model relied most heavily on the QRS complex to make predictions. The performance of the SCA ECG-DL model was similar removing age and sex data (AUROC 0.775) and less reliable when predicting SCA within one month (AUROC 0.734) or between one month and one year (AUROC 0.574). Conclusion: DL interpretation of ECG data is a promising means of screening for SCA, and this method explains differences in SCA’s due to age and sex. Model performance improved when ECGs were obtained at shorter intervals to the incident SCA event, and SCA prediction was more dependent upon QRS complex data than other temporal segments of the ECG.

Hardware-accelerated neural network model for early prediction of sudden cardiac arrest based on heart rate variability metrics

AbstractSudden Cardiac Arrest (SCA) constitutes a dire medical condition, marked by the abrupt cessation of effective blood circulation due to the heart's failure to contract properly. This leads to acute circulatory collapse, often culminating in loss of consciousness within an hour and potentially resulting in fatality within minutes if left unattended. Heart rate variability (HRV) serves as a critical biometric, derived from electrocardiogram (ECG) signals with ventricular depolarization waves to further calculate the R-R Intervals (RRIs). These intervals provide the basis for extracting various characteristics of cardiac rhythm, encompassing time-domain, frequency-domain, and nonlinear features. This study presents a neural network-based classification algorithm that leverages HRV metrics to categorize patients into SCA and Normal Sinus Rhythm (NSR) cohorts. The proposed neural network (NN) model showcased impressive results, achieving an accuracy of 96.23%, a sensitivity of 94.35%, and a specificity of 98.12% in detecting SCA, as evaluated through leave-one-subject-out analysis. In order to harness the benefits of hardware acceleration, the algorithm is implemented on a Field-Programmable Gate Array (FPGA). Its computational efficiency is subsequently benchmarked against traditional software-based methodologies. The hardware-level implementation is made possible in Verilog hardware description language (HDL) and was verified successfully with expected performance by register-transfer level (RTL) simulation via Vivado 2020.2.

The Role of Artificial Intelligence in Diagnosing Heart Disease in Humans: A Review

The electrical activity of the heart and the electrocardiogram (ECG) signal are fundamentally related. In the study that has been published, the ECG signal has been examined and used for a number of applications. The monitoring of heart rate and the analysis of heart rhythm patterns, the detection and diagnosis of cardiac diseases, the identification of emotional states, and the use of biometric identification methods are a few examples of applications in the field. Several various phases may be involved in the analysis of electrocardiogram (ECG) data, depending on the type of study being done. Preprocessing, feature extraction, feature selection, feature modification, and classification are frequently included in these stages. Every stage must be finished in order for the analysis to go smoothly. Additionally, accurate success measures and the creation of an acceptable ECG signal database are prerequisites for the analysis of electrocardiogram (ECG) signals. Identification and diagnosis of various cardiac illnesses depend heavily on the ECG segmentation and feature extraction procedure. Electrocardiogram (ECG) signals are frequently obtained for a variety of purposes, including the diagnosis of cardiovascular conditions, the identification of arrhythmias, the provision of physiological feedback, the detection of sleep apnea, routine patient monitoring, the prediction of sudden cardiac arrest, and the creation of systems for identifying vital signs, emotional states, and physical activities. The ECG has been widely used for the diagnosis and prognosis of a variety of heart diseases. Currently, a range of cardiac diseases can be accurately identified by computerized automated reports, which can then generate an automated report. This academic paper aims to provide an overview of the most important problems associated with using deep learning and machine learning to diagnose diseases based on electrocardiography, as well as a review of research on these techniques and methods and a discussion of the major data sets used by researchers.

A panel of blood biomarkers unique to sudden cardiac arrest

The identification of circulating biomarkers specific for sudden cardiac arrest (SCA) could enhance risk prediction. Of particular interest are biomarkers specific to SCA, independent of coronary artery disease (CAD). The purpose of this study was to identify biomarkers of SCA obtained close to the SCA event. Twenty cases (survivors of SCA) and 40 age- and sex-matched controls were compared, with a replication analysis of 29 cases matched to 57 controls. A secondary analysis compared 20 SCA cases to 20 controls with CAD. Blood samples were obtained from SCA survivors at a median of 11 months after the SCA event. Proteins were analyzed on a mass spectrometer using data-independent acquisition; a subset of cytokines were analyzed using immunoassays; and 1153 lipids (13 classes) were analyzed. A false discovery rate P value of <.05 identified associated proteins. Patients had a mean age of 58 years (range 25-87 years), and 70% were male. A total of 26 protein biomarkers associated with SCA when cases were compared with controls, of which 20 differentiated SCA from CAD. The replication analysis identified 8 of 26 biomarkers, of which 6 were not overlapping with CAD. The top identified biological processes involved the extracellular matrix, coagulation cascades, and platelet activation. Lipids in the lysophosphatidylcholine class were implicated in SCA through the CAD pathway. We identified a panel of novel blood biomarkers specifically associated with SCA, including several that may be involved outside the CAD pathway. These biomarkers could have mechanistic significance and the potential to enhance clinical prediction of SCA.

Abstract 15034: Prediction of Imminent Out-of-Hospital Sudden Cardiac Arrest Based on Warning Symptoms

Introduction: At least 50% of individuals with sudden cardiac arrest (SCA) have warning symptoms. However, such symptoms are also commonly experienced in the community. Comparisons with a control group, currently unavailable, are needed to establish utility of these symptoms for prediction of imminent SCA. Hypothesis: Symptoms documented by emergency medical services (EMS) prior to SCA vs. other 911 calls are distinct. Methods: We performed a case-control analysis from the ongoing Ventura, CA PRESTO study. Cases (ages 18-85) had witnessed SCA attended by EMS (2015-2021). SCA was defined as a sudden, unexpected pulseless condition of likely cardiac origin. Controls were identified from 911 calls that required transport by Ventura County EMS for emergency care but were not SCA. All cases and controls were required to have at least one symptom recorded by EMS that has been previously associated with acute coronary syndrome or SCA (Figure). Results: Among 553 witnessed cases with detailed EMS narratives, 411 (74%) had at least one inclusion symptom (mean age 66 y, 31% female). From a random subset of eligible EMS calls (n=2,794), 1171 controls had ≥1 symptom (mean age 61 y, 55% female). Among cases, dyspnea (41%) and chest pain (33%) were most common; prevalence of other symptoms was &lt;15%. Compared to controls, odds of chest pain, dyspnea, diaphoresis, and seizure-like activity were ≥45% higher in cases (p&lt;0.01) (Figure). Controls’ symptoms were less specific (nausea/vomiting, weakness, dizziness, abdominal symptoms), and palpitations (p&lt;0.01) (Figure). The sensitivity of chest pain (0.33) and dyspnea (0.41) for prediction of SCA was low, but specificities were higher (0.75 and 0.78, respectively). Conclusions: The prevalence of warning symptoms differed significantly between SCA cases and controls. However, sensitivity of individual symptoms was low; additional factors should be investigated to combine with warning symptoms to improve prediction of imminent SCA.

Prediction of Sudden Cardiac Arrest After Alcohol Septal Ablation for Hypertrophic Obstructive Cardiomyopathy: ASA-SCARRE Risk Score

This study aimed to derive a new score, the Alcohol Septal Ablation-Sudden Cardiac ARREst (ASA-SCARRE) risk score, that can be easily used to evaluate the risk of sudden cardiac arrest events (sudden cardiac death, resuscitation, or appropriate implantable cardioverter-defibrillator discharge) after alcohol septal ablation (ASA) in patients with hypertrophic obstructive cardiomyopathy. We analyzed 1,834 patients from the Euro-ASA registry (49% men, mean age 57 ± 14years) who were followed up for 5.0 ± 4.3years (9,202 patient-years) after ASA. A total of 65 patients (3.5%) experienced sudden cardiac arrest events, translating to 0.72 events per 100 patient-years. The independent predictors of sudden cardiac arrest events were septum thickness before ASA (hazard ratio 1.09 per 1mm, 95% confidence interval 1.04 to 1.14, p <0.001) and left ventricular outflow tract (LVOT) gradient at the last clinical checkup (hazard ratio 1.01 per 1mm Hg, 95% confidence interval 1.01 to 1.02, p=0.002). The following ASA-SCARRE risk scores were derived and independently predicted long-term risk of sudden cardiac arrest events: "0" for both LVOT gradient <30mmHg and baseline septum thickness <20mm; "1" for LVOT gradient ≥30mm Hg or baseline septum thickness ≥20mm; and "2" for both LVOT gradient ≥30mm Hg and baseline septum thickness ≥20mm. The C statistic of the ASA-SCARRE risk score was 0.684 (SE 0.030). In conclusion, the ASA-SCARRE risk score may be a useful and easily available clinical tool to predict risk of sudden cardiac arrest events after ASA in patients with hypertrophic obstructive cardiomyopathy.

Sudden cardiac arrest prediction and resuscitation network: improving the quality of care (PARQ)

Sudden cardiac arrest prediction and resuscitation network: improving the quality of care (PARQ) PARQ aims to create a network of researchers across Europe whose overall aim is to reduce the societal burden of sudden cardiac arrest (SCA). To reach this aim, PARQ will conduct studies that focus on two dimensions: (1) prevention of SCA through the discovery of its inherited, acquired, and environmental causes, and their interaction, and improved recognition of individuals at risk; and (2) improvement of survival chances after SCA through the development and implementation of resuscitation treatments in the community.

Prediction of Sudden Cardiac Arrest in the General Population: Review of Traditional and Emerging Risk Factors.

Sudden cardiac death (SCD) is the most common and devastating outcome of sudden cardiac arrest (SCA), defined as an abrupt and unexpected cessation of cardiovascular function leading to circulatory collapse. The incidence of SCD is relatively infrequent for individuals in the general population, in the range of 0.03%-0.10% per year. Yet, the absolute number of cases around the world is high because of the sheer size of the population at risk, making SCA/SCD a major global health issue. On the basis of conservative estimates, there are at least 2 million cases of SCA occurring worldwide on a yearly basis. As such, identification of risk factors associated with SCA in the general population is an important objective from a clinical and public health standpoint. This review will provide an in-depth discussion of established and emerging factors predictive of SCA/SCD in the general population beyond coronary artery disease and impaired left ventricular ejection fraction. Contemporary studies on the association of age, sex, race, socioeconomic status, and the emerging contribution of diabetes and obesity to SCD risk beyond their role as atherosclerotic risk factors are reviewed. In addition, the role of biomarkers, particularly electrocardiographic ones, on SCA/SCD risk prediction in the general population are discussed. Finally, the use of machine learning as a tool to facilitate SCA/SCD risk prediction is examined.

Prediction of Sudden Cardiac Death Manifesting With Documented Ventricular Fibrillation or Pulseless Ventricular Tachycardia.

This study aimed to develop a novel clinical prediction algorithm for avertable sudden cardiac death. Sudden cardiac death manifests as ventricular fibrillation (VF)/ ventricular tachycardia (VT) potentially treatable with defibrillation, or nonshockable rhythms (pulseless electrical activity/asystole) with low likelihood of survival. There are no available clinical risk scores for targeted prediction of VF/VT. Subjects with out-of-hospital sudden cardiac arrest presenting with documented VF or pulseless VT (33% of total cases) were ascertained prospectively from the Portland, Oregon, metro area with population ≈1 million residents (n=1,374, 2002-2019). Comparisons of lifetime clinical records were conducted with a control group (n=1,600) with ≈70% coronary disease prevalence. Prediction models were constructed from a training dataset using backwards stepwise logistic regression and applied to an internal validation dataset. Receiver operating characteristic curves (Cstatistic) were used to evaluate model discrimination. External validation was performed in a separate, geographically distinct population (Ventura County, California, population ≈850,000, 2015-2020). A clinical algorithm (VFRisk) constructed with 13 clinical, electrocardiogram, and echocardiographic variableshad very good discrimination in the training dataset (C statistic=0.808; [95% CI: 0.774-0.842]) and was successfully validated in internal (C statistic=0.776 [95% CI: 0.725-0.827]) and external (C statistic=0.782 [95% CI: 0.718-0.846]) datasets. The algorithm substantially outperformed the left ventricular ejection fraction (LVEF)≤35% (Cstatistic=0.638) and performed well across the LVEF spectrum. An algorithm for prediction of sudden cardiac arrest manifesting with VF/VT was successfully constructed using widely available clinical and noninvasive markers. These findings have potential to enhance primaryprevention, especially in patients with mid-range or preserved LVEF.

Prediction of Sudden Cardiac Arrest after Alcohol Septal Ablation for Hypertrophic Obstructive Cardiomyopathy (ASA-SCARRE Risk Score)

Prediction of Sudden Cardiac Arrest after Alcohol Septal Ablation for Hypertrophic Obstructive Cardiomyopathy (ASA-SCARRE Risk Score)

Usefulness of cardiac magnetic resonance images for prediction of sudden cardiac arrest in patients with mitral valve prolapse: a multicenter retrospective cohort study

BackgroundAn association has been identified between mitral valve prolapse (MVP) and sudden cardiac arrest (SCA), and ventricular arrhythmias (VA). This study aimed to elucidate predictive factors for SCA or VA in MVP patients.MethodsMVP patients who underwent cardiac magnetic resonance (CMR) were retrospectively included. Patients with other structural heart disease or causes of aborted SCA were excluded. Clinical characteristics (sex, age, body mass index, histories of diabetes, hypertension, and dyslipidemia) and electrocardiographic (PR interval, QRS duration, corrected QT interval, inverted T wave in the inferior leads, bundle branch block, and atrial fibrillation), echocardiographic [mitral regurgitation grade, prolapsing mitral leaflet, and right ventricular systolic pressure (RVSP)], and CMR [left atrial volume index, both ventricular ejection fractions, both ventricular end-diastolic and systolic volume indexes, prolapse distance, mitral annular disjunction, systolic curling motion, presence of late gadolinium enhancement (LGE), LGE volume and proportion] parameters were analyzed.ResultsOf the 85 patients [age, 54.0 (41.0–65.0) years; 46 men], seven experienced SCA or VA. Younger age and wide QRS complex were observed more often in the SCA/VA group than in the no-SCA/VA group. The SCA/VA group exhibited lower RVSP, more systolic curling motion and LGE, greater LGE volume, and higher LGE proportion. The presence of LGE [hazard ratio (HR), 19.8; 95% confidence interval (CI) 2.65–148.15; P = 0.004], LGE volume (HR 1.08; 95% CI 1.02–1.14; P = 0.006) and LGE proportion (HR 1.32; 95% CI 1.08–1.60; P = 0.006) were independently associated with higher risk of SCA or VA in MVP patients together with systolic curling motion in each model.ConclusionsThe presence of systolic curling motion, high LGE volume and proportion, and the presence of LGE on CMR were independent predictive factors for SCA or VA in MVP patients.

Identifying Patients at Risk for Prehospital Sudden Cardiac Arrest at the Early Phase of Myocardial Infarction: The e-MUST Study (Evaluation en Médecine d'Urgence des Stratégies Thérapeutiques des infarctus du myocarde).

In-hospital mortality of ST-segment-elevation myocardial infarction (STEMI) has decreased drastically. In contrast, prehospital mortality from sudden cardiac arrest (SCA) remains high and difficult to reduce. Identification of the patients with STEMI at higher risk for prehospital SCA could facilitate rapid triage and intervention in the field. Using a prospective, population-based study evaluating all patients with STEMI managed by emergency medical services in the greater Paris area (11.7 million inhabitants) between 2006 and 2010, we identified characteristics associated with an increased risk of prehospital SCA and used these variables to build an SCA prediction score, which we validated internally and externally. In the overall STEMI population (n=8112; median age, 60 years; 78% male), SCA occurred in 452 patients (5.6%). In multivariate analysis, younger age, absence of obesity, absence of diabetes mellitus, shortness of breath, and a short delay between pain onset and call to emergency medical services were the main predictors of SCA. A score built from these variables predicted SCA, with the risk increasing 2-fold in patients with a score between 10 and 19, 4-fold in those with a score between 20 and 29, and >18-fold in patients with a score ≥30 compared with those with scores <10. The SCA rate was 28.9% in patients with a score ≥30 compared with 1.6% in patients with a score ≤9 (P for trend <0.001). The area under the curve values were 0.7033 in the internal validation sample and 0.6031 in the external validation sample. Sensitivity and specificity varied between 96.9% and 10.5% for scores ≥10 and between 18.0% and 97.6% for scores ≥30, with scores between 20 and 29 achieving the best sensitivity and specificity (65.4% and 62.6%, respectively). At the early phase of STEMI, the risk of prehospital SCA can be determined through a simple score of 5 routinely assessed predictors. This score might help optimize the dispatching and management of patients with STEMI by emergency medical services.

Distinctive profile of sudden cardiac arrest in middle-aged vs. older adults: A community-based study

While sudden cardiac arrest (SCA) rates increase with age, middle-aged adults (35-59 years) may comprise a significant proportion of SCA cases in the community (30-40%). However, there is a lack of studies evaluating SCA risk factors specifically associated with this age-group of the population. Using prospective multiple-source surveillance methodology we identified cases of SCA ≥35 years in the ongoing Oregon Sudden Unexpected Death Study (Portland, Oregon metropolitan area, population≈1,000,000). Out-of-hospital SCA cases, aged 35-59 years were compared to older SCA cases (≥60 years) in a comprehensive analysis of clinical profile of SCA. The middle-aged (n=753) compared to older (n=1251) cases were more likely to be male, obese, have sleep apnea and seizure disorder (all p≤0.001); and were less likely to have a history of hypertension, diabetes mellitus, known coronary artery disease, congestive heart failure and syncope (all p<0.01). In multivariable analyses the middle-aged group had higher likelihood of male sex (O.R. 1.67, 95% C.I. 1.29-2.18), obesity (2.20, 1.52-3.19), sleep apnea (2.30, 1.44-3.68) and seizure disorder (2.69, 1.64-4.42); and lower rates of known coronary artery disease (0.57, 0.43-0.74) and congestive heart failure (0.35, 0.25-0.48). SCA in the middle-aged adult was distinguishable from older subjects by higher rates of obesity, sleep apnea and seizure disorder; and lower prevalence of traditional clinical risk markers. With the growing epidemic of obesity, these findings have implications for SCA burden; and suggest the need for a clinical and investigational focus on SCA prediction and prevention in the middle-aged adult, that is distinct from older adults.

Type 2 diabetes mellitus and the risk of sudden cardiac arrest in the community

The reduction of mortality from sudden cardiac arrest (SCA) in the setting of coronary heart disease (CHD) remains a major challenge, especially among patients with type 2 diabetes. Diabetes is associated with an increased risk of SCA, at least in part, from an increased presence and extent of coronary atherosclerosis (macrovascular disease). Diabetes also is associated with microvascular disease and autonomic neuropathy; and, these non-coronary atherosclerotic pathophysiologic processes also have the potential to increase the risk of SCA. In this report, we review the absolute and relative risk of SCA associated with diabetes. We summarize recent evidence that suggests that the increase in risk in patients with diabetes is not specific for SCA, as diabetes also is associated with a similar increase in risk for non-SCA CHD death and non-fatal myocardial infarction. These data are consistent with prior observations that coronary atherosclerosis is a major contributor to the increased SCA risk associated with diabetes. We also present previously published and unpublished data that demonstrates that both clinically-recognized microvascular and autonomic neuropathy also are associated with the risk of SCA among treated patients with diabetes, after taking into account prior clinically-recognized heart disease and other risk factors for SCA. We then discuss how these data might inform research and clinical efforts to prevent SCA. Although the prediction of SCA in this "high" risk population is likely to remain a challenge, as it is in other "high" risk clinical populations, we suggest that current recommendations for the prevention of SCA in the community, related to both lifestyle prescriptions and risk factor reduction, are likely to reduce mortality from SCA among patients with diabetes.

Prediction of sudden cardiac arrest: Risk stratification by anatomic substrate

The prognostic importance of coronary artery anatomy to specific outcomes including ventricular tachycardia/fibrillation was evaluated in 372 consecutive patients undergoing cardiac catheterization at University Hospital at Stony Brook between 1981 and 1984. The hypothesis that proximal left anterior descending artery narrowing before the first septal perforator had a specific relationship to survival was again tested in this cohort. The population was prospectively followed for 8 years, with all clinical management decisions made independently by the patient's primary or referring physician. Multivariate statistical and life table analyses were performed after comprehensive follow-up. Significant narrowing in the proximal left anterior descending artery was associated with an increased risk of sudden cardiac death ( p = 0.0002). Abnormalities of contractility in the diaphragmatic segment of the left ventricle in addition to an elevation of the left ventricular end-diastolic pressure and the presence of congestive heart failure ( p < 0.05) were other contributory variables. Outcome in patients with proximal left anterior descending coronary artery disease who underwent aortocoronary artery bypass to the artery demonstrated improved survival ( p < 0.05). Risk stratification of patients at high risk for sudden cardiac death is possible and may allow identification for an aggressive approach or interventional trials.