Cardiac Arrest Research Articles

Hydrocortisone improves post-resuscitation myocardial dysfunction by inhibiting the NF-κB pathway.

Myocardial dysfunction is a major cause of early mortality after successful cardiopulmonary resuscitation (CPR) following cardiac arrest (CA). Following the return of spontaneous circulation, myocardial ischemia-reperfusion injury can activate the NF-κB pathway, leading to the transcription of inflammatory genes that impair myocardial function. While clinical studies show hydrocortisone (HC) improves outcomes in CA patients during CPR, its specific role in modulating the NF-κB pathway is unclear. In this study, we established an in vitro model by inducing hypoxia/reoxygenation (H/R) injury in H9C2 cardiomyocytes using Na2S2O4, followed by HC treatment. The results showed that HC treatment of H/R-injured cardiomyocytes promoted proliferation, inhibited apoptosis, and suppressed the NF-κB pathway, thereby reducing IL-6, IL-8, and TNF-α levels. Moreover, inhibition of the NF-κB pathway enhanced the proliferative capacity of H/R cardiomyocytes, decreased apoptosis rates, and reduced IL-6, IL-8, and TNF-α expression levels, with these effects being further amplified by HC treatment. These findings were further supported by in vivo experiments. In conclusion, our study suggests that HC may promote H/R cardiomyocyte proliferation, inhibit apoptosis, and alleviate inflammatory responses by suppressing the NF-κB pathway, providing new evidence to support its potential clinical application in CA management.

Are the cardiovascular benefits and potential risks of physical activity and exercise dependent on race, ethnicity or sex?

Physical activity (PA) is established as a cornerstone of cardiovascular health, however, disparities in participation exist across sociocultural groups, which in turn impacts cardiovascular outcomes. Evidence suggests that while the positive cardiovascular effects of exercise are consistent across populations, notable differences in the magnitude of these benefits exist for racial and ethnic minorities and female sex. Females derive greater protection from PA compared to males, with reduced rates of sudden cardiac death (SCD). This review examines the complex interplay of race/ethnicity and sex on the cardiovascular benefits associated with PA and exercise, cardiovascular adaptations to exercise, and risks of SCD and "excessive" volume of exercise. Understanding these factors is crucial for developing targeted interventions to promote cardiovascular health and offset disparities.

Gain-of-Function and Loss-of-Function Mutations in the RyR2-Expressing Gene Are Responsible for the CPVT1-Related Arrhythmogenic Activities in the Heart

Mutations in the ryanodine receptor (RyR2) gene have been linked to arrhythmia and possibly sudden cardiac death (SCD) during acute emotional stress, physical activities, or catecholamine perfusion. The most prevalent disorder is catecholaminergic polymorphic ventricular tachycardia (CPVT1). Four primary mechanisms have been proposed to describe CPVT1 with a RyR2 mutation: (a) gain-of-function, (b) destabilization of binding proteins, (c) store-overload-induced Ca2+ release (SOICR), and (d) loss of function. The goal of this study was to use computational models to understand these four mechanisms and how they might contribute to arrhythmia. To this end, we have developed a local control stochastic model of a ventricular cardiac myocyte and used it to investigate how the Ca2+ dynamics in the mutant RyR2 are responsible for the development of an arrhythmogenic episode under the condition of β-adrenergic (β-AR) stimulation or pauses afterward. Into the model, we have incorporated 20,000 distinct cardiac dyads consisting of stochastically gated L-type Ca2+ channels (LCCs) and ryanodine receptors (RyR2s) and the intervening dyadic cleft to analyze the alterations in Ca2+ dynamics. Recent experimental findings were incorporated into the model parameters to test these proposed mechanisms and their role in triggering arrhythmias. The model could not find any connection between SOICR and the destabilization of binding proteins as the arrhythmic mechanisms in the mutant myocyte. On the other hand, the model was able to observe loss-of-function and gain-of-function mutations resulting in EADs (Early Afterdepolarizations) and variations in action potential amplitudes and durations as the precursors to generate arrhythmia, respectively. These computational studies demonstrate how GOF and LOF mutations can lead to arrhythmia and cast doubt on the feasibility of SOICR as a mechanism of arrhythmia.

Quantitative Structure-Activity Relationship Models to Predict Cardiac Adverse Effects.

Drug-induced cardiotoxicity represents one of the most common causes of attrition of drug candidates in preclinical and clinical development. For this reason, the evaluation of cardiac toxicity is essential during drug development and regulatory review. In the present study, drug-induced postmarket adverse event combinations from the FDA Adverse Event Reporting System were extracted for 2002 drugs using 243 cardiac toxicity-related preferred terms (PTs). These PTs were combined into 12 groups based on their clinical relevance to serve as training sets. The optimal classification scheme was determined using a combination of data sources that included drug labeling information, published literature, clinical study data, and postmarket surveillance data. Two commercial QSAR platforms were used to construct 12 models, including general cardiac toxicity, cardiac ischemia, heart failure, cardiac valve disease, myocardial disease, pericardial disease, structural heart disease, cardiac arrhythmia, Torsades de Pointes, long QT syndrome, atrial fibrillation and ventricular arrhythmia, and cardiac arrest. The cross-validated performance for the new models reached a sensitivity of up to 80% and negative predictivity of up to 80%. These new models covering a wide range of cardiac endpoints will provide fast, reliable, and comprehensive predictions of potential cardiotoxic compounds in drug discovery and regulatory safety assessment.

Healthcare Professionals’ Ethical Dilemma in Out-of-Hospital Cardiopulmonary Arrest: A Scoping Review

Healthcare Professionals’ Ethical Dilemma in Out-of-Hospital Cardiopulmonary Arrest: A Scoping Review

Cardiac Implantable Electronic Devices in Cardiac Transplant Patients: A Comprehensive Review

A fraction of patients (approximately 10%) undergoing heart transplantation require permanent pacemaker (PPM) implantation due to sinus node dysfunction or atrioventricular block, occurring either shortly after surgery or later. The incidence of PPM implantation has declined to less than 5% with the introduction of bicaval anastomosis transplantation surgery. Pacing dependency during follow-up varies among recipients. A smaller subset (1.5–3.4%) receives implantable cardioverter-defibrillators (ICDs), but data on their use in transplant recipients are limited, primarily from cohort studies and case series. Sudden cardiac death affects around 10% of transplant recipients, attributed to various nonarrhythmic factors such as acute rejection, late graft failure, and cardiac allograft vasculopathy-induced ischemia. This review offers a comprehensive analysis of the existing data concerning the role of PPMs and ICDs in this population, encompassing leadless PPMs, subcutaneous ICDs, unique considerations, and future directions.

Knowledge, Attitude, and Practice of the Lebanese University Medical Students and Junior Doctors on Basic Life Support Practices

Background: Basic life support (BLS) is the recognition of sudden cardiac arrest and activation of emergency response system, followed by cardiopulmonary resuscitation (CPR), and rapid defibrillation. Aim: Our study aimed to determine the level of awareness of the Lebanese University medical students and trainees on BLS, by assessing the association between knowledge, attitude, and practice on BLS, and between the demographic variables and KAP scores. Methods: This was a cross-sectional study including 330 medical students enrolled at Lebanese University, from year four of general medicine till year five of residency. An online survey was used to collect data about demographic characteristics, knowledge (K), attitudes (A), and practice (P) about BLS. Data was analyzed using SPSS version 25. Results: Participants were 52.7% females, 47.3% males, and their mean age was 24 ± 2 years. Of the 330 participants, 38.8% received formal training regarding BLS. Medical students had low knowledge (90%), moderate to good attitudes (71.5%), and low practice (93%) regarding BLS. Multiple linear regression showed that knowledge was positively associated with age (p = 0.001), knowledge and information regarding BLS (p = 0.016), and any formal training/workshop regarding BLS (p = 0.021). Attitude was positively associated with academic year (p = 0.002) and knowledge (p = 0.003). Practice was positively associated with age (p < 0.001) and knowledge (p < 0.001). Conclusion: Most Lebanese University medical students showed low knowledge, moderate to good attitudes, and low practice regarding BLS. We recommend that CPR/BLS should be a core competency across all health care professional programs.

Anesthesia in Obese Patients and Complications

Background: Almost all ages and socioeconomic categories are affected by the complex illness known as obesity, which has major social and psychological ramifications. Global obesity rates have reached epidemic proportions and obesity plays a significant role in the burden of chronic illnesses and disabilities worldwide. The aim of the study was to assess the Anesthesia in obese patient and its Complications in a tertiary care hospital. Materials and mathods: This cross-sectional observational study was carried out in the Department of Anesthesiology, Bangabandhu Memorial Hospital (BBMH) from July 2020 to June 2022.Total sample size was 200. Adult in-patients who had undergone non-cardiac surgery and were older than 18 with a BMI greater than 30 kg/m2 met the inclusion criteria. Pregnancy, ambulatory surgery and missing or insufficient data on the desired outcomes were the exclusion criteria. Results: Mean age ±SD was 32 ± 0.9 years. About 55% of the patients were female and 45% were male. Regarding ASA classification 53.5% had 2, 45.5% had 3 and 1.0% had 4 score. Regarding coexisting disease 42.5% had hypertension, 14% had dyslipidemia, 18% had DM type-2 and 7.5% had obstructive sleep apnea. Here, type of anesthesia was 55% general anesthesia, 6% normal mask ventilation, 25% regional anesthesia, 4% total intravenous anesthesia and 10% LMA (Laryngeal Mask Airway). Regarding complication 4.5% was Reintubation, 5.5 %Pneumonia, 7.5% Upper airway obstruction, 0.5% Delay emergence respectively. 2.5% Urinary retention, 5% Oliguria, 2% Wound infection. 0.5% myocardial infarction and 0.5% Cardiac arrest occurred and no death occur inpatient respectively. Conclusion: Obesity has long been seen from a surgical standpoint as a risk factor for unfavorable post-surgical outcomes. Patients who are morbidly obese may experience respiratory adverse effects following surgery as a result of physiological disruption. The undesirable effects range depending on the surgical population. IAHS Medical Journal Vol 7(1), June 2024; 97-101

Emergency Medical Service Agency Cardiac Arrest Practices.

Emergency Medical Service Agency Cardiac Arrest Practices.

Emergency Medical Service Agency Cardiac Arrest Practices-Reply.

Emergency Medical Service Agency Cardiac Arrest Practices-Reply.

Amplitude spectrum area is dependent on the electrocardiogram magnitude: evaluation of different normalization approaches.

Amplitude Spectrum Area (AMSA) of the electrocardiogram (ECG) waveform during ventricular fibrillation (VF) has shown promise as a predictor of defibrillation success during cardiopulmonary resuscitation (CPR). However, AMSA relies on the magnitude of the ECG waveform, raising concerns about reproducibility across different settings that may introduce magnitude bias. This study aimed to evaluate different AMSA normalization approaches and their impact on removing bias while preserving predictive value. ECG were recorded in 118 piglets (1-2 months old) during a model of asphyxia-associated VF cardiac arrest and CPR. An initial subset (91/118) was recorded using one device (Device 1), and the remaining piglets were recorded in the second device (Device 2). Raw AMSA and three ECG magnitude metrics were estimated to assess magnitude-related bias between devices. Five AMSA normalization approaches were assessed for their ability to remove detected bias and to classify defibrillation success. Device 2 showed significantly lower ECG magnitude and raw AMSA compared to Device 1. CPR-based AMSA normalization approaches mitigated device-associated bias. Raw AMSA normalized by the average AMSA in the 1st minute of CPR (AMSA1m-cpr) exhibited the best sensitivity and specificity for classification of successful and unsuccessful defibrillation. While the optimal AMSA1m-cpr thresholds for balanced sensitivity and specificity were consistent across both devices, the optimal raw AMSA thresholds varied between the two devices. The area under the receiver operating characteristic curve for AMSA1m-cpr did not significantly differ from raw AMSA for both devices (Device 1: 0.74 vs. 0.88, P=0.14; Device 2: 0.56 vs. 0.59, P=0.81). Unlike raw AMSA, AMSA1m-cpr demonstrated consistent results across different devices while maintaining predictive value for defibrillation success. This consistency has important implications for the widespread use of AMSA and the development of future guidelines on optimal AMSA thresholds for successful defibrillation.&#xD.

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.

Abstract Su602: Categorizing Witnessed Out-of-Hospital Cardiac Arrest by Bystander Type

Witnessed out-of-hospital cardiac arrests (OHCA) are associated with improved outcomes with increased likelihood of rapid activation of the emergency response system thus decreasing no-flow or low-flow times. Current literature has largely treated witnessed cardiac arrest as binary (i.e. was a cardiac arrest witnessed Yes/No). We hypothesize there is a need to further delineate and categorize distinct bystander types; these differences may have important downstream contributions to outcomes and survival and targets for improving training and recognition. For example, a bystander who is a healthcare provider with formal training or has experience performing resuscitation may be associated with better outcomes than a non-medical layperson. We examined a national-level database of emergency medical services (EMS) activations in the United States (NEMSIS). Inclusion criteria were any adult (18+ years) cardiac arrest activation between January 2022 and December 2023. Cardiac arrests taking place after EMS arrival (EMS-witnessed) were excluded. Witnessed status was described as unwitnessed, witnessed by healthcare provider, witnessed by family member, or witnessed by bystander. Across basic demographic variables, we compared overall witnessed rates, family member witnessed rates, and healthcare worker witnessed rates. A total of 791,217 cardiac arrests met inclusion criteria. Overall, 62.5% of arrests were unwitnessed, 22.8% were witnessed by a family member, and 6.3% were witnessed by a healthcare provider. Across sex, cardiac arrests in male patients were more likely to be witnessed, however, cardiac arrests in female patients were more likely to be witnessed by a family member or healthcare provider (p<0.05). Significant differences in witnessed status were also seen across race/ethnicity. For instance, cardiac arrests in Black/African American patients were less likely to be witnessed by family members and cardiac arrests in Hispanic/Latino patients were less likely to be witnessed by healthcare workers (p<0.05). Our analysis of a large and nationally-representative database of cardiac arrest suggests differences in rates of witnessed cardiac arrest and – perhaps, more importantly – the existence of different types of bystanders. Moreover, when considering these differences, there is heterogeneity among demographic variables. Future research is needed to understand whether these differences in bystander type may contribute to differences in outcomes.

Abstract 4119472: Predictors of Sudden Cardiac Death in Patients with Atrial Fibrillation and No Apparent Risk Factor of Sudden Cardiac Death

Background: Atrial fibrillation (AF) is associated with sudden cardiac death (SCD). It is known that AF is associated with multiple traditional risk factors of SCD, such as coronary artery disease (CAD), history of myocardial infarction (MI), left ventricular hypertrophy (LVH) and low left ventricular ejection fraction (LVEF). However, AF per se could predispose SCD not mediated by these cardiovascular risk factors, and predictors of SCD in AF patients without these apparent SCD risk factors are unknown. Methods: Using the NTUH iMED database, AF patients without apparent SCD risk factors were longitudinal followed up from Jan. 2010 to Dec. 2021. The primary outcome was implantation of implantable cardioverter defibrillator (ICD) due to SCD. Cox regression was used to identify the predictors of SCD. Results: A total of 11250 AF patients without cardiovascular SCD risk factors and LVEF ≥55 % were longitudinally followed up. We first found CHA2DS2-VASc score did not predict SCD event. We found male gender (hazard ratio [HR ] 1.71 [1.05-2.78 ], p =0.032), hyperthyroidism (HR 3.50 [1.40-8.77 ], p=0.007）and paroxysmal AF (PAF)(HR 2.86 [1.63-5.04 ], p<0.001）were associated with higher SCD risks. We created a AFSCD score using these parameters. We found SCD risk incrementally increased with increasing AFSCD score (p <0.001 for trend). Patients with highest score (5 points) had more than 3-fold SCD risk compared with those with 0-point score (1.18 % person-year vs 0.36 %, p<0.001). The C statistic of AFSCD score was significantly higher than CHA2DS2-VASc score (0.64 [0.57-0.70] vs 0.40 [0.33-0.48], p <0.01). Conclusions: Male gender, hyperthyroid AF and PAF were predictors of incident SCD in AF patients and AFSCD scoring scheme based on these risk factors could possibly be implemented in clinical practice.

Abstract 4137646: Transcriptomic Profiling of Human Myocardium at Sudden Death to Define Vulnerable Substrate for Lethal Arrhythmias

Background: While some chronic pathological substrates for sudden cardiac death (SCD) are well-known (e.g., coronary disease and left ventricular [LV] dysfunction), the vulnerable myocardial state predisposing to fatal arrhythmia remains a critical barrier to near-term SCD prevention. Hypothesis: Myocardium of autopsy-defined SCDs exhibit distinct expression profiles vs. non-cardiac sudden deaths and trauma deaths that reflect its acute vulnerable state in the hours to days before SCD, as expression arrests upon death. Goals/Aims: Define the vulnerable myocardial substrate for lethal arrhythmia by targeted RNA profiling. Methods: We used autopsy to adjudicate arrhythmic from non-arrhythmic causes in 1,265 sudden deaths in San Francisco from 2011-2018. We performed a transcriptomic evaluation of LV sampled at the time of SCD from 245 consented cases using a curated panel of 448 genes with known or hypothesized association with SCD. Results: Comparison between Arrhythmic (n=129) and Non-Arrhythmic (n=116) cohorts revealed 31 differentially upregulated and 36 downregulated genes (p-adj<0.05), related to collagen-containing extracellular matrix (upregulation of FAP , FMOD , LTBP2 ), ion transport (upregulation of KCNA5 and KCNN3 , and downregulation of KCNJ8, KCNK1, KCNJ5 ), and contraction (downregulation of MYH6 ). Fibrosis-related genes showed the highest magnitude increased expression in Arrhythmic vs. Non-arrhythmic deaths and vs. published transcriptomes from end-stage heart failure. After molecular stratification by known markers for mature ( COL1A1, COL1A2, COL3A1) and active ( POSTN , MEOX1 ) fibrosis, cases with highest expression of both had the highest proportion of arrhythmic cause of death (27/36 [75%]) vs. cases with low expression of both (87/181 [38%], p=0.006) or vs. mature only (10/14 [71%]) or active only (5/14 [36%]). Activated fibroblast gene expression was enriched in Arrhythmic female vs. Arrhythmic male cases, among other sex-specific differences in ion channel and myosin (upregulation of SCN4B , SCN8A , KCNAB1 in females, KCNJ4 and MYH7B in males) expression. Conclusions: RNA profiling of myocardium at SCD identifies active fibrosis, undetectable by conventional clinical methods, in the presence of fixed scar and selected ion channel dysregulation (more pronounced among female cases) as an acute vulnerable substrate for fatal arrhythmias, and may identify patients at elevated near-term risk for SCD and novel pathways for intervention.

Abstract 4136643: Automated cardiac arrest detection using a photoplethysmography wristband: Evaluation of the time to detection of the circulatory arrest event

Introduction: Out-of-hospital cardiac arrest survival depends on immediate recognition and early cardiopulmonary resuscitation. For victims of unwitnessed cardiac arrest, accounting for 30-50% of cases worldwide, survival chances are dismal as help often comes too late. Automated cardiac arrest detection and alarming would offer a solution to shorten treatment delays. Within the DETECT project, a wristband with the functionality of automated cardiac arrest detection is developed. During the DETECT-1 study, we developed a photoplethysmography (PPG) algorithm to detect circulatory arrest in patients with induced circulatory arrest. Research question: In this sub analysis of the DETECT-1 study, we aim to study the delay between the actual circulatory arrest and the algorithms’ cardiac arrest alarm. Methods: Patients undergoing circulatory arrest induction during transcatheter aortic valve implantation (TAVI), subcutaneous ICD (S-ICD) implantation or ventricular tachycardia (VT) ablation wore a PPG wristband. Continuous ECG and arterial blood pressure data were collected as reference standard. PPG data were used to develop a circulatory arrest detection algorithm in three training iterations and one validation. Time to detection was defined as the difference between the algorithm’s cardiac arrest alarm and the circulatory arrest annotated based on the arterial blood pressure data. Results: The validation set consisted of 86 patients (77 TAVI, 3 S-ICD, 6 VT ablation) with 97 induced circulatory arrest events of which 95 were correctly identified by the algorithm (sensitivity 97% [95% CI 90-100%]). The median time to circulatory arrest detection was -1 second (IQR -2 – 0). In total, 50 (53%) events were detected before the annotated time (median; -1 sec), 33 (35%) events were detected at t=0, and 12 (13%) events were detected after the annotation (median; +2 sec). A total of 65 (68%) induced circulatory arrest events were detected within one second before and after the annotated start time of the circulatory arrest. Return of PPG pulsations were detected in all patients, with a median of 4 seconds (IQR 3 – 7) after pulsations returned (Figure 1 and 2). There were no differences in detection time between the three subgroups. Conclusion: The PPG algorithm detects the induced circulatory arrest events directly when blood pressure lowers. Further research is needed to validate the circulatory arrest detection algorithm and detection interval in spontaneous cardiac arrest.

Abstract 4116148: Seasonal Variation of Unexpected Sudden Cardiac Death

Introduction: Data on the occurrence of unexpected sudden cardiac death (SCD) in different seasons are scanty and needs further exploration. Methods: Consecutive series of all unexpected SCD victims (n=5,869) who underwent medico-legal autopsy obligated by the Finnish law were prospectively collected from the geographically defined area in the Northern Finland during a twenty years period from 1998 to 2017. The seasonal variation of SCD and its possible causes were assessed in this large SCD victim cohort. Results: Unexpected SCD occurred more frequently during the first quarter of a year (from January to March) than during the other quarters (Figure 1). The occurrence of SCD during the twenty years follow-up was 4.5±0.24 during the first quarter of the year per month and 3.9±0.12 during the other quarters of the year per month on average (SCDs during the quarter of a year per month on average per 100,000 inhabitants per year, p<0.001) (Figure 2). The weather was colder during the first quarter of the year (average temperature -9.1 degrees centigrade) than during the other quarters (average temperature +5.7 degrees centigrade). The snow cover was the largest during the first quarter of the year. Conclusion: Unexpected SCD occurred more commonly during the first quarter of a year (from January to March) than during the other quarters (from April to December). Cold weather, and perhaps winter-time physical activities in cold environment such as removal of snow, may have contributed to the increased occurrence of SCD during the first quarter of the year.

Abstract 4136938: Autonomous Personalized Dynamic ECG Analysis Predicts 1-hour Incidence of Sudden Cardiac Arrest

Background: Sudden cardiac arrest (SCA) is a leading cause of death in the USA. Defibrillators prevent SCA by delivering shocks for sustained ventricular tachyarrhythmias (VT). However, current clinical methods for predicting SCA are limited. We have shown that distinct nonlinear fluctuation patterns “hidden” within normal-appearing ECG waveforms have unique prognostic value. Hypothesis: Autonomous personalized dynamic ECG analysis predicts the 1-hour incidence of SCA. Methods: In an established pressure-overload model that exhibits key features of human VT and SCA (e.g., cardiac restitution properties), 30% of 37 freely ambulating animals had spontaneous SCA in 4 weeks. The exposures included a rolling 1-hour window of time, frequency and nonlinear domain measures of heart rate and QT time series during sinus rhythm, and EntropyX QT , a higher-dimensional machine learning-based nonlinear measure of cardiac repolarization dynamics. Results: In multivariate analyses, the major independent predictors of 1-hour incidence of SCA were (1) high EntropyX QT ; (2) high parasympathetic tone, as indexed by the high-frequency component of the heart rate power spectral density; and (3) the degree of coupling between RR and QT intervals. The baseline values of EntropyX QT and parasympathetic tone increased by 3.6-fold and 46.5-fold, respectively, during the hour preceding SCA. The adjusted ROC curve area was > 89%. Conclusion: Continuous autonomous analysis of cardiac repolarization and autonomic dynamics strongly and independently predicts the subacute incidence of SCA. Incorporation of these measures into current strategies of SCA risk stratification has potential to save many lives and warrants further evaluation.

Abstract Su706: New risk markers of sudden cardiac death in the young : insights from machine learning on EHR data

Background: Sudden Cardiac Death (SCD) in young adults is a significant public health issue, with survival rates often less than 15%. Effective prevention is critical as many SCD cases go undetected due to nonspecific symptoms and ineffective risk stratification methods, especially among the youth. Objective: To explore risk markers specific to SCD in younger populations using Artificial Intelligence (AI) trained on systematically collected Electronic Health Records (EHR) data, and to compare these markers with those in older populations. Methods: We conducted a retrospective cohort study using data from 46,060 participants in Paris and its inner suburbs, spanning 2011 to 2020. The cohort included 1,100 SCD cases and 1,100 controls aged 40 years or younger, and 21,930 SCD cases and 21,930 controls aged over 40. A machine learning model was developed, trained, and validated on these cohorts. We used the Catboost algorithm and evaluated model performance using AUC, sensitivity, and specificity. The SHAP algorithm was employed to elucidate the relationship between variables and predicted risk. Results: The model achieved on validation sets an AUC of 0.75 (95% CI 0.67-0.83) for the younger cohort and AUC of 0.82 (95% CI: 0.70-0.83), for the older cohort. Neuropsychiatric drugs were the most influential predictors for the younger cohort, whereas age and cardiovascular drugs were most predictive for the older cohort. The results indicate that traditional cardiovascular risk factors play a secondary role in predicting SCD among younger individuals. Conclusion: In analyzing predictors of sudden death in individuals under 40, we developed a personalized SCD prediction model using EHR data tailored to this demographic. Psychiatric medications, and possibly underlying psychiatric conditions, emerged as significant predictors. Our findings emphasize the need for a multidisciplinary approach to SCD prevention, integrating neuropsychiatric and cardiovascular considerations.

Abstract Su805: Early-Latency Evoked Brain Response Revealed using Novel Filtering Technique

Background: Cardiac arrest (CA) can cause coma in resuscitated patients. Accuracy of prognosis is limited. Presence of brain signals such as somatosensory evoked potential (SSEP) reveals integrity of the thalamocortical pathway, which is necessary for arousal but insufficient to predict positive outcome. The N10 is a SSEP component that diminishes in CA. The N7, which can be obscured by stimulus artifact or other components, is associated with subcortical structures. Phase space area (PSA) quantifies SSEP morphology and is associated with CA recovery. It has been measured at least 5ms after stimulus. Hypothesis: Early SSEP and PSA diminish under anesthesia and during CA but may recover afterwards. Aims: By removing stimulus artifact, we capture early SSEP (N7 and early PSA) to enable improved prognostication. Methods: Six rats received 1.2, 1.8, and 2.5% isoflurane anesthesia for 30min. Two underwent 5 or 7min CA and were observed over 2hr. SSEP was recorded every 2s. An adaptive filter removed the artifact. Peaks were measured near 7 (N7) and 15 (N10) ms post-stimulus. PSA, the area occupied by the phase space curve, was recorded over 0-9ms (“early”) and 0-30ms (“total”). Signal-to-noise ratio (SNR) is the ratio of N10 amplitude to standard deviation of the signal. T tests were used for comparisons between SNR values and between experimental conditions. Results: We identified 4153 N7 peaks with latency (mean±95% C.I.) 8.6±1.3ms in the filtered data, compared to 2464 with latency 8.5±1.4ms in the unfiltered data. SNR was greater in the filtered signal (2.3±1.5) than unfiltered (0.3±0.4). N7 amplitude decreased at the highest anesthesia level; N10 decreased for each level. N7 and N10 decreased during CA and recovered after, although N10 remained below baseline during 2hr recovery. Early PSA was lower under the higher anesthesia levels, while total PSA decreased for each level. PSA decreased during CA and recovered after 5min CA; total PSA recovered to baseline and early PSA exceeded it after 2hr.Early PSA did not recover from 7min CA, while total PSA recovered but remained below baseline. Conclusion: We demonstrate a novel technique to discover an elusive early-latency response, adding detail to a neural pathway involved in arousal. We reduce noise, capture N7 consistently, and measure PSA beginning at 0ms, providing additional diagnostic dimensions responsive to anesthesia and CA. Future study will investigate outcomes in resuscitated patients.