Day-of-the-week variation of sudden unexpected cardiac death.

Background: There has been some controversy about the day-of-the-week (septadian) variation of unexpected sudden cardiac death (SCD). Methods: We evaluated the incidence of unexpected SCD on different days of the week in a consecutive series of 5869 SCD victims from Northern Finland [the FINGESTURE study (Finnish Genetic Study of Arrhythmic Events)]. As it is mandatory in Finland, a medico-legal autopsy was performed on all unexpected sudden death victims. The autopsies were performed between the years 1998-2017. Results: The mean incidence of unexpected SCD was higher at weekends (during the days from Friday to Sunday, peaking on Saturday) than during the days from Monday to Thursday (8.54 ± 0.72 vs. 7.22 ± 0.19 SCDs per day of the week per 100,000 inhabitants per year, p < 0.001). Regardless of sex or ischemic versus non-ischemic etiology of SCD, the distribution of the occurrence of SCD among the days of the week was similar compared with the whole SCD cohort. Conclusion: The incidence of unexpected SCD was highest at weekends (during the days from Friday to Sunday, peaking on Saturday).

An ECG-based artificial intelligence model for assessment of sudden cardiac death risk.

BACKGROUND: Conventional ECG-based algorithms could contribute to sudden cardiac death (SCD) risk stratification but demonstrate moderate predictive capabilities. Deep learning (DL) models use the entire digital signal and could potentially improve predictive power. We aimed to train and validate a 12 lead ECG-based DL algorithm for SCD risk assessment. METHODS: Out-of-hospital SCD cases were prospectively ascertained in the Portland, Oregon, metro area. A total of 1,827 pre- cardiac arrest 12 lead ECGs from 1,796 SCD cases were retrospectively collected and analyzed to develop an ECG-based DL model. External validation was performed in 714 ECGs from 714 SCD cases from Ventura County, CA. Two separate control group samples were obtained from 1342 ECGs taken from 1325 individuals of which at least 50% had established coronary artery disease. The DL model was compared with a previously validated conventional 6 variable ECG risk model. RESULTS: The DL model achieves an AUROC of 0.889 (95% CI 0.861-0.917) for the detection of SCD cases vs. controls in the internal held-out test dataset, and is successfully validated in external SCD cases with an AUROC of 0.820 (0.794-0.847). The DL model performs significantly better than the conventional ECG model that achieves an AUROC of 0.712 (0.668-0.756) in the internal and 0.743 (0.711-0.775) in the external cohort. CONCLUSIONS: An ECG-based DL model distinguishes SCD cases from controls with improved accuracy and performs better than a conventional ECG risk model. Further detailed investigation is warranted to evaluate how the DL model could contribute to improved SCD risk stratification.

Sudden cardiac death (SCD) occurs when there are problems with the electrical activity within the heart. It is a common cause of death throughout the world so it would be beneficial to be able to easily identify individuals that are at high risk of SCD. Electrocardiograms are a cheap and widely available way to measure electrical activity in the heart. We developed a computational method that can use electrocardiograms to determine whether a person is at increased risk of having a SCD. Our computational method could allow clinicians to screen large numbers of people and identify those at a higher risk of SCD. This could enable regular monitoring of these people and might enable SCDs to be prevented in some individuals.

Artificial Intelligence Model Predicts Sudden Cardiac Arrest Manifesting With Pulseless Electric Activity Versus Ventricular Fibrillation.

BACKGROUND: There is no specific treatment for sudden cardiac arrest (SCA) manifesting as pulseless electric activity (PEA) and survival rates are low; unlike ventricular fibrillation (VF), which is treatable by defibrillation. Development of novel treatments requires fundamental clinical studies, but access to the true initial rhythm has been a limiting factor. METHODS: Using demographics and detailed clinical variables, we trained and tested an AI model (extreme gradient boosting) to differentiate PEA-SCA versus VF-SCA in a novel setting that provided the true initial rhythm. A subgroup of SCAs are witnessed by emergency medical services personnel, and because the response time is zero, the true SCA initial rhythm is recorded. The internal cohort consisted of 421 emergency medical services-witnessed out-of-hospital SCAs with PEA or VF as the initial rhythm in the Portland, Oregon metropolitan area. External validation was performed in 220 emergency medical services-witnessed SCAs from Ventura, CA. RESULTS: In the internal cohort, the artificial intelligence model achieved an area under the receiver operating characteristic curve of 0.68 (95% CI, 0.61-0.76). Model performance was similar in the external cohort, achieving an area under the receiver operating characteristic curve of 0.72 (95% CI, 0.59-0.84). Anemia, older age, increased weight, and dyspnea as a warning symptom were the most important features of PEA-SCA; younger age, chest pain as a warning symptom and established coronary artery disease were important features associated with VF. CONCLUSIONS: The artificial intelligence model identified novel features of PEA-SCA, differentiated from VF-SCA and was successfully replicated in an external cohort. These findings enhance the mechanistic understanding of PEA-SCA with potential implications for developing novel management strategies.

Dynamic electrocardiogram changes are a novel risk marker for sudden cardiac death.

BACKGROUND AND AIMS: Electrocardiogram (ECG) abnormalities have been evaluated as static risk markers for sudden cardiac death (SCD), but the potential importance of dynamic ECG remodelling has not been investigated. In this study, the nature and prevalence of dynamic ECG remodelling were studied among individuals who eventually suffered SCD. METHODS: The study population was drawn from two prospective community-based SCD studies in Oregon (2002, discovery cohort) and California, USA (2015, validation cohort). For this present sub-study, 231 discovery cases (2015-17) and 203 validation cases (2015-21) with ≥2 archived pre-SCD ECGs were ascertained and were matched to 234 discovery and 203 validation controls based on age, sex, and duration between the ECGs. Dynamic ECG remodelling was measured as progression of a previously validated cumulative six-variable ECG electrical risk score. RESULTS: Oregon SCD cases displayed greater electrical risk score increase over time vs. controls [+1.06 (95% confidence interval +0.89 to +1.24) vs. -0.05 (-0.21 to +0.11); P < .001]. These findings were successfully replicated in California [+0.87 (+0.7 to +1.04) vs. -0.11 (-0.27 to 0.05); P < .001]. In multivariable models, abnormal dynamic ECG remodelling improved SCD prediction over baseline ECG, demographics, and clinical SCD risk factors in both Oregon [area under the receiver operating characteristic curve 0.770 (95% confidence interval 0.727-0.812) increased to area under the receiver operating characteristic curve 0.869 (95% confidence interval 0.837-0.902)] and California cohorts. CONCLUSIONS: Dynamic ECG remodelling improved SCD risk prediction beyond clinical factors combined with the static ECG, with successful validation in a geographically distinct population. These findings introduce a novel concept of SCD dynamic risk and warrant further detailed investigation.

Artificial Intelligence in Ventricular Arrhythmias and Sudden Death.

Sudden cardiac arrest due to lethal ventricular arrhythmias is a major cause of mortality worldwide and results in more years of potential life lost than any individual cancer. Most of these sudden cardiac arrest events occur unexpectedly in individuals who have not been identified as high-risk due to the inadequacy of current risk stratification tools. Artificial intelligence tools are increasingly being used to solve complex problems and are poised to help with this major unmet need in the field of clinical electrophysiology. By leveraging large and detailed datasets, artificial intelligence-based prediction models have the potential to enhance the risk stratification of lethal ventricular arrhythmias. This review presents a synthesis of the published literature and a discussion of future directions in this field.

Deep learning-based electrocardiographic screening for chronic kidney disease.

BACKGROUND: Undiagnosed chronic kidney disease (CKD) is a common and usually asymptomatic disorder that causes a high burden of morbidity and early mortality worldwide. We developed a deep learning model for CKD screening from routinely acquired ECGs. METHODS: We collected data from a primary cohort with 111,370 patients which had 247,655 ECGs between 2005 and 2019. Using this data, we developed, trained, validated, and tested a deep learning model to predict whether an ECG was taken within one year of the patient receiving a CKD diagnosis. The model was additionally validated using an external cohort from another healthcare system which had 312,145 patients with 896,620 ECGs between 2005 and 2018. RESULTS: Using 12-lead ECG waveforms, our deep learning algorithm achieves discrimination for CKD of any stage with an AUC of 0.767 (95% CI 0.760-0.773) in a held-out test set and an AUC of 0.709 (0.708-0.710) in the external cohort. Our 12-lead ECG-based model performance is consistent across the severity of CKD, with an AUC of 0.753 (0.735-0.770) for mild CKD, AUC of 0.759 (0.750-0.767) for moderate-severe CKD, and an AUC of 0.783 (0.773-0.793) for ESRD. In patients under 60 years old, our model achieves high performance in detecting any stage CKD with both 12-lead (AUC 0.843 [0.836-0.852]) and 1-lead ECG waveform (0.824 [0.815-0.832]). CONCLUSIONS: Our deep learning algorithm is able to detect CKD using ECG waveforms, with stronger performance in younger patients and more severe CKD stages. This ECG algorithm has the potential to augment screening for CKD.

Chronic kidney disease (CKD) is a common condition involving loss of kidney function over time and results in a substantial number of deaths. However, CKD often has no symptoms during its early stages. To detect CKD earlier, we developed a computational approach for CKD screening using routinely acquired electrocardiograms (ECGs), a cheap, rapid, non-invasive, and commonly obtained test of the heart's electrical activity. Our model achieved good accuracy in identifying any stage of CKD, with especially high accuracy in younger patients and more severe stages of CKD. Given the high global burden of undiagnosed CKD, novel and accessible CKD screening strategies have the potential to help prevent disease progression and reduce premature deaths related to CKD.

Temporal Trends in Incidence and Survival From Sudden Cardiac Arrest Manifesting With Shockable and Nonshockable Rhythms: A 16-Year Prospective Study in a Large US Community.

STUDY OBJECTIVE: The proportion of nonshockable sudden cardiac arrests (pulseless electrical activity and asystole) continues to rise. Survival is lower than shockable (ventricular fibrillation [VF]) sudden cardiac arrests, but there is little community-based information on temporal trends in the incidence and survival from sudden cardiac arrests based on presenting rhythms. We investigated community-based temporal trends in sudden cardiac arrest incidence and survival by presenting rhythm. METHODS: We prospectively evaluated the incidence of each presenting sudden cardiac arrest rhythm and survival outcomes for out-of-hospital events in the Portland, Oregon metro area (population of approximately 1 million, 2002 to 2017). We limited inclusion to cases of likely cardiac cause with resuscitation attempted by emergency medical services. RESULTS: Out of 3,723 overall sudden cardiac arrest cases, 908 (24%) presented with pulseless electrical activity, 1,513 (41%) with VF, and 1,302 (35%) with asystole. The incidence of pulseless electrical activity-sudden cardiac arrest remained stable over 4-year periods (9.6/100,000 in 2002 to 2005, 7.4/100,000 in 2006 to 2009, 5.7/100,000 in 2010 to 2013, and 8.3/100,000 in 2014 to 2017; unadjusted beta [ß] -0.56; 95% confidence interval [CI], -3.98 to 2.85). The incidence of VF-sudden cardiac arrests decreased over time (14.6/100,000 in 2002 to 2005, 13.4/100,000 in 2006 to 2009, 12.0/100,000 in 2010 to 2013, and 11.6/100,000 in 2014 to 2017; unadjusted ß -1.05; 95% CI, -1.68 to -0.42) and asystole-sudden cardiac arrests (8.6/100,000 in 2002 to 2005, 9.0/100,000 in 2006 to 2009, 10.3/100,000 in 2010 to 2013, and 15.7/100,000 in 2014 to 2017; unadjusted ß 2.25; 95% CI -1.24 to 5.73) did not change significantly over time. Survival increased over time for pulseless electrical activity-sudden cardiac arrests (5.7%, 4.3%, 9.6%, 13.6%; unadjusted ß 2.8%; 95% CI 1.3 to 4.4) and VF-sudden cardiac arrests (27.5%, 29.8%, 37.9%, 36.6%; unadjusted ß 3.5%; 95% CI 1.4 to 5.6), but not for asystole-sudden cardiac arrests (1.7%, 1.6%, 4.0%, 2.4%; unadjusted ß 0.3%; 95% CI, -0.4 to 1.1). Enhancements in the emergency medical services system's pulseless electrical activity-sudden cardiac arrest management were temporally associated with the increasing pulseless electrical activity survival rates. CONCLUSIONS: Over a 16-year period, the incidence of VF/ventricular tachycardia decreased over time, but pulseless electrical activity incidence remained stable. Survival from both VF-sudden cardiac arrests and pulseless electrical activity-sudden cardiac arrests increased over time with a more than 2-fold increase for pulseless electrical activity-sudden cardiac arrests.

Causes and characteristics of unexpected sudden cardiac death in octogenarians/nonagenarians.

INTRODUCTION: The risk for sudden cardiac death (SCD) increases with ageing. METHODS: We evaluated causes and characteristics of unexpected SCD in SCD victims aged ≥ 80 years in a consecutive series of 5,869 SCD victims in Northern Finland. All the victims underwent medico-legal autopsy as medico-legal autopsy is mandatory in cases of unexpected sudden death in Finland. All the non-cardiac deaths such as pulmonary embolism and cerebral hemorrhage were excluded from the study, as were unnatural deaths such as intoxications. RESULTS: Among SCD victims ≥ 80 years, 91.0% of SCDs were due to ischemic heart disease (IHD) determined in autopsy and 9.0% due to non-ischemic heart disease (NIHD), whereas among those < 80 years, only 72.6% of SCDs were due to IHD and 27.4% due to NIHD (P < .001). Severe fibrosis in myocardium was more common whereas heart weight and liver weight, body mass index and abdominal fat thickness, were lower among SCD victims aged ≥ 80 years than among victims aged < 80 years. In those with IHD as etiology of SCD, at least 75% stenosis in one or more major coronary vessels was more common in SCD victims aged ≥ 80 years than among victims aged < 80 years (P = .001). SCD victims 80 years or older were less likely to die during physical activity than those under 80 years old (5.6% vs. 15.9%, P < .001). Dying in sauna was more common among those ≥ 80 years than among those < 80 years (5.5% vs. 2.6%, P < .001). CONCLUSION: In victims of unexpected SCD aged ≥ 80 years, the autopsy-based etiology of SCD was more commonly IHD than in those aged < 80 years. In SCD victims aged ≥ 80 years, severe fibrosis in myocardium, representing arrhythmic substrate, was more common than in the younger ones.

Sudden Cardiac Arrest During Sports Activity in Older Adults.

BACKGROUND: Sports activity among older adults is rising, but there is a lack of community-based data on sports-related sudden cardiac arrest (SrSCA) in the elderly. OBJECTIVES: In this study, the authors investigated the prevalence and characteristics of SrSCA among subjects ≥65 years of age in a large U.S. METHODS: All out-of-hospital sudden cardiac arrests (SCAs) were prospectively ascertained in the Portland, Oregon, USA, metro area (2002-2017), and Ventura County, California, USA (2015-2021) (catchment population ∼1.85 million). Detailed information was obtained for SCA warning symptoms, circumstances, and lifetime clinical history. Subjects with SCA during or within 1 hour of cessation of sports activity were categorized as SrSCA. RESULTS: Of 4,078 SCAs among subjects ≥65 years of age, 77 were SrSCA (1.9%; 91% men). The crude annual SrSCA incidence among age ≥65 years was 3.29/100,000 in Portland and 2.10/100,000 in Ventura. The most common associated activities were cycling, gym activity, and running. SrSCA cases had lower burden of cardiovascular risk factors (P = 0.03) as well as comorbidities (P < 0.005) compared with non-SrSCA. Based on conservative estimates of community residents ≥65 years of age who participate in sports activity, the SrSCA incidence was 28.9/100,000 sport participation years and 18.4/100,000 sport participation years in Portland and Ventura, respectively. Crude survival to hospital discharge rate was higher in SrSCA, but the difference was nonsignificant after adjustment for confounding factors. CONCLUSIONS: Among free-living community residents age ≥65 years, SrSCA is uncommon, predominantly occurs in men, and is associated with lower disease burden than non-SrSCA. These results suggest that the risk of SrSCA is low, and probably outweighed by the high benefit of exercise.

Use of Psychotropic Medication in Victims of Sudden Cardiac Death with Nonischemic Heart Disease.

BACKGROUND: Nonischemic heart disease (NIHD) is the underlying pathology in about 20% of sudden cardiac deaths (SCDs). Psychotropic medication has been reported as a risk factor for SCD among patients with coronary artery disease, but similar information concerning NIHD is scarce. OBJECTIVES: We evaluated the use of psychotropic medication in victims of SCD due to NIHD and compared it to the general medication use in Finland. METHOD: Study population was derived from the Finnish Genetic Study of Arrhythmic Events (Fingesture) (n = 5,869, mean age: 65 ± 12, 79% males; 1,404 victims of SCD due to NIHD, mean age: 57 ± 13, 77% males). All deaths occurred in Northern Finland during 1998-2017. All victims underwent a medicolegal autopsy. Data on use of medication were defined using postmortem toxicology results and patient records. Subjects with neither toxicological analysis nor information of medication use available were excluded. Information on general medication use was derived from Finnish Statistics on Medicines 2018 and presented as defined daily dose/1,000 inhabitants/day. RESULTS: Psychotropic medication was used by 579 (41%) subjects with NIHD, whereas in the general population, only 12% were estimated to use psychotropics. The results were similar in subgroups of psychotropic medication: 27% versus 2.3% for benzodiazepines, 19% versus 7.5% for antidepressants, and 18% versus 2.2% for antipsychotics. CONCLUSIONS: Use of psychotropic medication is common in victims of SCD due to NIHD compared to the general population.

Association of electrocardiographic spatial heterogeneity of repolarization and spatial heterogeneity of atrial depolarization with left ventricular fibrosis.

AIMS: To evaluate the relationship between spatial heterogeneity of electrocardiographic repolarization and spatial heterogeneity of atrial depolarization with arrhythmic substrate represented by left ventricular fibrosis. METHODS AND RESULTS: We assessed the associations of T- and P-wave morphology parameters analysed from the standard 12-lead electrocardiograms with left ventricular fibrosis in 378 victims of unexpected sudden cardiac death (SCD) who underwent medico-legal autopsy. Based on autopsy findings, the SCD victims were categorized into four different groups according to different stages of severity of left ventricular fibrosis (substantial fibrosis, moderate patchy fibrosis, scattered mild fibrosis, no fibrosis). T-wave and P-wave area dispersion (TWAd: 0.0841 ± 0.496, 0.170 ± 0.492, 0.302 ± 404, 0.296 ± 0.476, P = 0.008; PWAd: 0.574 ± 0.384, 0.561 ± 0.367, 0.654 ± 0.281, 0.717 ± 0.257, P = 0.011, respectively; low values abnormal), non-dipolar components of T-wave and P-wave morphology (T_NonDipolarABS: 0.0496 ± 0.0377, 0.0571 ± 0.0487, 0.0432 ± 0.0476, 0.0380 ± 0.0377, P = 0.027; P_NonDipolarABS: 0.0132 ± 0.0164, 0.0130 ± 0.0135, 0.0092 ± 0.0117, 0.0069 ± 0.00472, P = 0.005, respectively, high values abnormal), T-wave morphology dispersion (TMD: 45.9 ± 28.3, 40.5 ± 25.8, 35.5 ± 24.9, 33.0 ± 24.6, P = 0.030, respectively, high values abnormal), and P-wave heterogeneity (PWH: 20.0 ± 9.44, 19.7 ± 8.87, 17.9 ± 9.78, 15.4 ± 4.60, P = 0.019, respectively, high values abnormal) differed significantly between the groups with different stages of left ventricular fibrosis. After adjustment with heart weight, T_NonDipolarABS [standardized ß (sß) = 0.131, P = 0.014], PWAd (sß = -0.161, P = 0.003), P_NonDipolarABS (sß = 0.174, P = 0.001), and PWH (sß = 0.128, P = 0.015) retained independent association, and TWAd (sß = -0.091, P = 0.074) and TMD (sß = 0.097, P = 0.063) tended to retain their association with the degree of myocardial fibrosis. CONCLUSION: Our findings suggest that abnormal values of T- and P-wave morphology are associated with arrhythmic substrate represented by ventricular fibrosis partly explaining the mechanism behind their prognostic significance.

Sudden cardiac death after alcohol intake: classification and autopsy findings.

Alcohol is known to have an immediate effect on cardiac rhythm, and previous studies have found that a notable proportion of sudden cardiac deaths (SCD) occur after alcohol intake. The objective of the present study was to investigate the association between the timing of alcohol intake and SCD. Our study population is drawn from the Fingesture study, which includes 5869 consecutive SCD cases from Northern Finland who underwent medicolegal autopsy 1998-2017. Toxicological analysis was performed if there was any suspicion of toxic exposure, or if there was no obvious immediate cause of SCD at autopsy. We found that 1563 (27%) of all SCD victims had alcohol in blood or urine at autopsy (mean age (61 ± 10 years, 88% male). Eighty-six percent of alcohol-related SCD victims had higher urine alcohol concentration than blood alcohol concentration, referring to the late-stage inebriation. These results suggest that the majority of alcohol-related SCDs occur at the late stage of inebriation.

Survivors of Sudden Cardiac Arrest Presenting With Pulseless Electrical Activity: Clinical Substrate, Triggers, Long-Term Prognosis.

BACKGROUND: The proportion of sudden cardiac arrest (SCA) presenting as pulseless electrical activity (PEA) is rising, and survival remains low. The pathophysiology of PEA-SCA is poorly understood, and current clinical practice lacks specific options for the management of survivors. OBJECTIVES: In this study, the authors sought to investigate clinical profile, triggers, and long-term prognosis in survivors of SCA presenting with PEA. METHODS: The community-based Oregon SUDS (Sudden Unexpected Death Study) (since 2002) and Ventura PRESTO (Prediction of Sudden Death in Multi-ethnic Communities) (since 2015) studies prospectively ascertain all out-of-hospital SCAs of likely cardiac etiology. Lifetime clinical history and detailed evaluation of SCA events is available. We evaluated all SCA survivors with PEA as the presenting rhythm. RESULTS: The study population included 201 PEA-SCA survivors. Of these, 97 could be contacted for access to their clinical records. Among the latter, the mean age was 67 ± 17 years and 58 (60%) were male. After in-hospital examinations, 29 events (30%) were associated with acute myocardial infarction, and 5 (5%) had bradyarrhythmias. Among the remaining 63 patients (65%), specific triggers remained undetermined, although 31 (49%) had a previous history of heart failure. Of the 201 overall survivors, 91 (45%) were deceased after a mean follow-up of 4.2 ± 4.0 years. Survivors under the age of 40 years had an excellent long-term prognosis. CONCLUSIONS: Survivors of PEA-SCA are a heterogeneous group with high prevalence of multiple comorbidities, especially heart failure. Surprisingly good long-term survival was observed in young individuals. Acute myocardial infarction as the precipitating event was common, but triggers remained undetermined in the majority. Provision of individualized care to PEA survivors requires a renewed investigative focus on PEA-SCA.

Plaque histology and myocardial disease in sudden coronary death: the Fingesture study.

AIMS: At least 50% of deaths due to coronary artery disease (CAD) are sudden cardiac deaths (SCDs), but the role of acute plaque complications on the incidence of sudden death in CAD is somewhat unclear. The present study aimed to investigate plaque histology and concomitant myocardial disease in sudden coronary death. METHODS AND RESULTS: The study population is derived from the Fingesture study, which has collected data from 5869 consecutive autopsy-verified SCD victims in Northern Finland (population ≈600 000) between 1998 and 2017. In this substudy, histological examination of culprit lesions was performed in 600 SCD victims whose death was due to CAD. Determination of the cause of death was based on the combination of medical records, police reports, and autopsy data. Plaque histology was classified as either (i) plaque rupture or erosion, (ii) intraplaque haemorrhage, or (iii) stable plaque. The mean age of the study subjects was 64.9 ± 11.2 years, and 82% were male. Twenty-four per cent had plaque rupture or plaque erosion, 24% had an intraplaque haemorrhage, and 52% had a stable plaque. Myocardial hypertrophy was present in 78% and myocardial fibrosis in 93% of victims. The presence of myocardial hypertrophy or fibrosis was not associated with specific plaque histology. CONCLUSION: Less than half of sudden deaths due to CAD had evidence of acute plaque complication, an observation which is contrary to historical perceptions. The prevalence of concomitant myocardial disease was high and independent of associated plaque morphology.