Competing Risks for Monomorphic versus Non-Monomorphic Ventricular Arrhythmias in Primary Prevention Implantable Cardioverter Defibrillator Recipients: Global Electrical Heterogeneity and Clinical Outcomes (GEHCO) Study.

BACKGROUND AND AIMS: Ablation of monomorphic ventricular tachycardia (MMVT) has been shown to reduce shock frequency and improve survival. We aimed to compare cause-specific risk factors of MMVT and polymorphic ventricular tachycardia (PVT)/ventricular fibrillation (VF) and to develop predictive models. METHODS: The multicenter retrospective cohort study included 2,668 patients (age 63.1±13.0 y; 23% female; 78% white; 43% nonischemic cardiomyopathy, left ventricular ejection fraction 28.2±11.1%). Cox models were adjusted for demographic characteristics, heart failure severity and treatment, device programming, and ECG metrics. Global electrical heterogeneity was measured by spatial QRS-T angle (QRSTa), spatial ventricular gradient elevation (SVGel), azimuth, magnitude (SVGmag), and sum absolute QRST integral (SAIQRST). We compared the out-of-sample performance of the lasso and elastic net for Cox proportional hazards and the Fine-Gray competing risk model. RESULTS: During a median follow-up of 4 years, 359 patients experienced their first sustained MMVT with appropriate ICD therapy, and 129 patients had their first PVT/VF with appropriate ICD shock. The risk of MMVT was associated with wider QRSTa (HR 1.16; 95%CI 1.01-1.34), larger SVGel (HR 1.17; 95%CI 1.05-1.30), and smaller SVGmag (HR 0.74; 95%CI 0.63-0.86) and SAIQRST (HR 0.84; 95%CI 0.71-0.99). The best-performing 3-year competing risk Fine-Gray model for MMVT (ROC(t)AUC 0.728; 95%CI 0.668-0.788) identified high-risk (> 50%) patients with 75% sensitivity, 65% specificity, and PVT/VF prediction model had ROC(t)AUC 0.915 (95%CI 0.868-0.962), both satisfactory calibration. CONCLUSION: We developed and validated models to predict the competing risks of MMVT or PVT/VF that could inform procedural planning and future RCTs of prophylactic VT ablation.

Deep learning evaluation of echocardiograms to identify occult atrial fibrillation.

Atrial fibrillation (AF) often escapes detection, given its frequent paroxysmal and asymptomatic presentation. Deep learning of transthoracic echocardiograms (TTEs), which have structural information, could help identify occult AF. We created a two-stage deep learning algorithm using a video-based convolutional neural network model that (1) distinguished whether TTEs were in sinus rhythm or AF and then (2) predicted which of the TTEs in sinus rhythm were in patients who had experienced AF within 90 days. Our model, trained on 111,319 TTE videos, distinguished TTEs in AF from those in sinus rhythm with high accuracy in a held-out test cohort (AUC 0.96 (0.95-0.96), AUPRC 0.91 (0.90-0.92)). Among TTEs in sinus rhythm, the model predicted the presence of concurrent paroxysmal AF (AUC 0.74 (0.71-0.77), AUPRC 0.19 (0.16-0.23)). Model discrimination remained similar in an external cohort of 10,203 TTEs (AUC of 0.69 (0.67-0.70), AUPRC 0.34 (0.31-0.36)). Performance held across patients who were women (AUC 0.76 (0.72-0.81)), older than 65 years (0.73 (0.69-0.76)), or had a CHA2DS2VASc ≥2 (0.73 (0.79-0.77)). The model performed better than using clinical risk factors (AUC 0.64 (0.62-0.67)), TTE measurements (0.64 (0.62-0.67)), left atrial size (0.63 (0.62-0.64)), or CHA2DS2VASc (0.61 (0.60-0.62)). An ensemble model in a cohort subset combining the TTE model with an electrocardiogram (ECGs) deep learning model performed better than using the ECG model alone (AUC 0.81 vs. 0.79, p = 0.01). Deep learning using TTEs can predict patients with active or occult AF and could be used for opportunistic AF screening that could lead to earlier treatment.

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.

Electrocardiographic deep learning for predicting post-procedural mortality: a model development and validation study.

BACKGROUND: Preoperative risk assessments used in clinical practice are insufficient in their ability to identify risk for postoperative mortality. Deep-learning analysis of electrocardiography can identify hidden risk markers that can help to prognosticate postoperative mortality. We aimed to develop a prognostic model that accurately predicts postoperative mortality in patients undergoing medical procedures and who had received preoperative electrocardiographic diagnostic testing. METHODS: In a derivation cohort of preoperative patients with available electrocardiograms (ECGs) from Cedars-Sinai Medical Center (Los Angeles, CA, USA) between Jan 1, 2015 and Dec 31, 2019, a deep-learning algorithm was developed to leverage waveform signals to discriminate postoperative mortality. We randomly split patients (8:1:1) into subsets for training, internal validation, and final algorithm test analyses. Model performance was assessed using area under the receiver operating characteristic curve (AUC) values in the hold-out test dataset and in two external hospital cohorts and compared with the established Revised Cardiac Risk Index (RCRI) score. The primary outcome was post-procedural mortality across three health-care systems. FINDINGS: 45 969 patients had a complete ECG waveform image available for at least one 12-lead ECG performed within the 30 days before the procedure date (59 975 inpatient procedures and 112 794 ECGs): 36 839 patients in the training dataset, 4549 in the internal validation dataset, and 4581 in the internal test dataset. In the held-out internal test cohort, the algorithm discriminates mortality with an AUC value of 0·83 (95% CI 0·79-0·87), surpassing the discrimination of the RCRI score with an AUC of 0·67 (0·61-0·72). The algorithm similarly discriminated risk for mortality in two independent US health-care systems, with AUCs of 0·79 (0·75-0·83) and 0·75 (0·74-0·76), respectively. Patients determined to be high risk by the deep-learning model had an unadjusted odds ratio (OR) of 8·83 (5·57-13·20) for postoperative mortality compared with an unadjusted OR of 2·08 (0·77-3·50) for postoperative mortality for RCRI scores of more than 2. The deep-learning algorithm performed similarly for patients undergoing cardiac surgery (AUC 0·85 [0·77-0·92]), non-cardiac surgery (AUC 0·83 [0·79-0·88]), and catheterisation or endoscopy suite procedures (AUC 0·76 [0·72-0·81]). INTERPRETATION: A deep-learning algorithm interpreting preoperative ECGs can improve discrimination of postoperative mortality. The deep-learning algorithm worked equally well for risk stratification of cardiac surgeries, non-cardiac surgeries, and catheterisation laboratory procedures, and was validated in three independent health-care systems. This algorithm can provide additional information to clinicians making the decision to perform medical procedures and stratify the risk of future complications. FUNDING: National Heart, Lung, and Blood Institute.

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.

Risk Factors for Sudden Cardiac Arrest Among Hispanic or Latino Adults in Southern California: Ventura PRESTO and HCHS/SOL.

Background Out-of-hospital sudden cardiac arrest (SCA) is a leading cause of mortality, making prevention of SCA a public health priority. No studies have evaluated predictors of SCA risk among Hispanic or Latino individuals in the United States. Methods and Results In this case-control study, adult SCA cases ages 18-85 (n=1,468) were ascertained in the ongoing Ventura Prediction of Sudden Death in Multi-Ethnic Communities (PRESTO) study (2015-2021) in Ventura County, California. Control subjects were selected from 3033 Hispanic or Latino participants who completed Visit 2 examinations (2014-2017) at the San Diego site of the HCHS/SOL (Hispanic Community Health Survey/Study of Latinos). We used logistic regression to evaluate the association of clinical factors with SCA. Among Hispanic or Latino SCA cases (n=295) and frequency-matched HCHS/SOL controls (n=590) (70.2% men with mean age 63.4 and 61.2 years, respectively), the following clinical variables were associated with SCA in models adjusted for age, sex, and other clinical variables: chronic kidney disease (odds ratio [OR], 7.3 [95% CI, 3.8-14.3]), heavy drinking (OR, 4.5 [95% CI, 2.3-9.0]), stroke (OR, 3.1 [95% CI, 1.2-8.0]), atrial fibrillation (OR, 3.7 [95% CI, 1.7-7.9]), coronary artery disease (OR, 2.9 [95% CI, 1.5-5.9]), heart failure (OR, 2.5 [95% CI, 1.2-5.1]), and diabetes (OR, 1.5 [95% CI, 1.0-2.3]). Conclusions In this first population-based study, to our knowledge, of SCA risk predictors among Hispanic or Latino adults, chronic kidney disease was the strongest risk factor for SCA, and established cardiovascular disease was also important. Early identification and management of chronic kidney disease may reduce SCA risk among Hispanic or Latino individuals, in addition to prevention and treatment of cardiovascular disease.

Risk of Arrhythmic Death in Patients With Nonischemic Cardiomyopathy: JACC Review Topic of the Week.

Nonischemic cardiomyopathy (NICM) is common and patients are at significant risk for early mortality secondary to ventricular arrhythmias. Current guidelines recommend implantable cardioverter-defibrillator (ICD) therapy to decrease sudden cardiac death (SCD) in patients with heart failure and reduced left ventricular ejection fraction. However, in randomized clinical trials comprised solely of patients with NICM, primary prevention ICDs did not confer significant mortality benefit. Moreover, left ventricular ejection fraction has limited sensitivity and specificity for predicting SCD. Therefore, precise risk stratification algorithms are needed to define those at the highest risk of SCD. This review examines mechanisms of sudden arrhythmic death in patients with NICM, discusses the role of ICD therapy and treatment of heart failure for prevention of SCD in patients with NICM, examines the role of cardiac magnetic resonance imaging and computational modeling for SCD risk stratification, and proposes new strategies to guide future clinical trials on SCD risk assessment in patients with NICM.

Warning symptoms associated with imminent sudden cardiac arrest: a population-based case-control study with external validation.

BACKGROUND: Sudden cardiac arrest is a global public health problem with a mortality rate of more than 90%. Prearrest warning symptoms could be harnessed using digital technology to potentially improve survival outcomes. We aimed to estimate the strength of association between symptoms and imminent sudden cardiac arrest. METHODS: We conducted a case-control study of individuals with sudden cardiac arrest and participants without sudden cardiac arrest who had similar symptoms identified from two US community-based studies of patients with sudden cardiac arrest in California state, USA (discovery population; the Ventura Prediction of Sudden Death in Multi-Ethnic Communities [PRESTO] study), and Oregon state, USA (replication population; the Oregon Sudden Unexpected Death Study [SUDS]). Participant data were obtained from emergency medical services reports for people aged 18-85 years with witnessed sudden cardiac arrest (between Feb 1, 2015, and Jan 31, 2021) and an inclusion symptom. Data were also obtained from corresponding control populations without sudden cardiac arrest who were attended by emergency medical services for similar symptoms (between Jan 1 and Dec 31, 2019). We evaluated the association of symptoms with sudden cardiac arrest in the discovery population and validated our results in the replication population by use of logistic regression models. FINDINGS: We identified 1672 individuals with sudden cardiac arrest from the PRESTO study, of whom 411 patients (mean age 65·7 [SD 12·4] years; 125 women and 286 men) were included in the analysis for the discovery population. From a total of 76 734 calls to emergency medical services, 1171 patients (mean age 61·8 [SD 17·3] years; 643 women, 514 men, and 14 participants without data for sex) were included in the control group. Patients with sudden cardiac arrest were more likely to have dyspnoea (168 [41%] of 411 vs 262 [22%] of 1171; p<0·0001), chest pain (136 [33%] vs 296 [25%]; p=0·0022), diaphoresis (50 [12%] vs 90 [8%]; p=0·0059), and seizure-like activity (43 [11%] vs 77 [7%], p=0·011). Symptom frequencies and patterns differed significantly by sex. Among men, chest pain (odds ratio [OR] 2·2, 95% CI 1·6-3·0), dyspnoea (2·2, 1·6-3·0), and diaphoresis (1·7, 1·1-2·7) were significantly associated with sudden cardiac arrest, whereas among women, only dyspnoea was significantly associated with sudden cardiac arrest (2·9, 1·9-4·3). 427 patients with sudden cardiac arrest (mean age 62·2 [SD 13·5]; 122 women and 305 men) were included in the analysis for the replication population and 1238 patients (mean age 59·3 [16·5] years; 689 women, 548 men, and one participant missing data for sex) were included in the control group. Findings were mostly consistent in the replication population; however, notable differences included that, among men, diaphoresis was not associated with sudden cardiac arrest and chest pain was associated with sudden cardiac arrest only in the sex-stratified multivariable analysis. INTERPRETATION: The prevalence of warning symptoms was sex-specific and differed significantly between patients with sudden cardiac arrest and controls. Warning symptoms hold promise for prediction of imminent sudden cardiac arrest but might need to be augmented with additional features to maximise predictive power. FUNDING: US National Heart Lung and Blood Institute.

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.

Sudden cardiac arrest during the COVID-19 pandemic: A two-year prospective evaluation in a North American community.

BACKGROUND: Early during the coronavirus disease 2019 (COVID-19) pandemic, higher sudden cardiac arrest (SCA) incidence and lower survival rates were reported. However, ongoing effects on SCA during the evolving pandemic have not been evaluated. OBJECTIVE: The purpose of this study was to assess the impact of COVID-19 on SCA during 2 years of the pandemic. METHODS: In a prospective study of Ventura County, California (2020 population 843,843; 44.1% Hispanic), we compared SCA incidence and outcomes during the first 2 years of the COVID-19 pandemic to the prior 4 years. RESULTS: Of 2222 out-of-hospital SCA cases identified, 907 occurred during the pandemic (March 2020 to February 2022) and 1315 occurred prepandemic (March 2016 to February 2020). Overall age-standardized annual SCA incidence increased from 39 per 100,000 (95% confidence [CI] 37-41) prepandemic to 54 per 100,000 (95% CI 50-57; P <.001) during the pandemic. Among Hispanics, incidence increased by 77%, from 38 per 100,000 (95% CI 34-43) to 68 per 100,000 (95% CI 60-76; P <.001). Among non-Hispanics, incidence increased by 26%, from 39 per 100,000 (95% CI 37-42; P <.001) to 50 per 100,000 (95% CI 46-54). SCA incidence rates closely tracked COVID-19 infection rates. During the pandemic, SCA survival was significantly reduced (15% to 10%; P <.001), and Hispanics were less likely than non-Hispanics to receive bystander cardiopulmonary resuscitation (45% vs 55%; P = .005) and to present with shockable rhythm (15% vs 24%; P = .003). CONCLUSION: Overall SCA rates remained consistently higher and survival outcomes consistently lower, with exaggerated effects during COVID infection peaks. This longer evaluation uncovered higher increases in SCA incidence among Hispanics, with worse resuscitation profiles. Potential ethnicity-specific barriers to acute SCA care warrant urgent evaluation and intervention.

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.

High-Throughput Assessment of Real-World Medication Effects on QT Interval Prolongation: Observational Study.

BACKGROUND: Drug-induced prolongation of the corrected QT interval (QTc) increases the risk for Torsades de Pointes (TdP) and sudden cardiac death. Medication effects on the QTc have been studied in controlled settings but may not be well evaluated in real-world settings where medication effects may be modulated by patient demographics and comorbidities as well as the usage of other concomitant medications. OBJECTIVE: We demonstrate a new, high-throughput method leveraging electronic health records (EHRs) and the Surescripts pharmacy database to monitor real-world QTc-prolonging medication and potential interacting effects from demographics and comorbidities. METHODS: We included all outpatient electrocardiograms (ECGs) from September 2008 to December 2019 at a large academic medical system, which were in sinus rhythm with a heart rate of 40-100 beats per minute, QRS duration of <120 milliseconds, and QTc of 300-700 milliseconds, determined using the Bazett formula. We used prescription information from the Surescripts pharmacy database and EHR medication lists to classify whether a patient was on a medication during an ECG. Negative control ECGs were obtained from patients not currently on the medication but who had been or would be on that medication within 1 year. We calculated the difference in mean QTc between ECGs of patients who are on and those who are off a medication and made comparisons to known medication TdP risks per the CredibleMeds.org database. Using linear regression analysis, we studied the interaction of patient-level demographics or comorbidities on medication-related QTc prolongation. RESULTS: We analyzed the effects of 272 medications on 310,335 ECGs from 159,397 individuals. Medications associated with the greatest QTc prolongation were dofetilide (mean QTc difference 21.52, 95% CI 10.58-32.70 milliseconds), mexiletine (mean QTc difference 18.56, 95% CI 7.70-29.27 milliseconds), amiodarone (mean QTc difference 14.96, 95% CI 13.52-16.33 milliseconds), rifaximin (mean QTc difference 14.50, 95% CI 12.12-17.13 milliseconds), and sotalol (mean QTc difference 10.73, 95% CI 7.09-14.37 milliseconds). Several top QT prolonging medications such as rifaximin, lactulose, cinacalcet, and lenalidomide were not previously known but have plausible mechanistic explanations. Significant interactions were observed between demographics or comorbidities and QTc prolongation with many medications, such as coronary disease and amiodarone. CONCLUSIONS: We demonstrate a new, high-throughput technique for monitoring real-world effects of QTc-prolonging medications from readily accessible clinical data. Using this approach, we confirmed known medications for QTc prolongation and identified potential new associations and demographic or comorbidity interactions that could supplement findings in curated databases. Our single-center results would benefit from additional verification in future multisite studies that incorporate larger numbers of patients and ECGs along with more precise medication adherence and comorbidity data.