Predicting Out-of-Hospital Cardiac Arrest in the General Population Using Electronic Health Records.

BACKGROUND: The majority of out-of-hospital cardiac arrests (OHCAs) occur among individuals in the general population, for whom there is no established strategy to identify risk. In this study, we assess the use of electronic health record (EHR) data to identify OHCA in the general population and define salient factors contributing to OHCA risk. METHODS: The analytical cohort included 2366 individuals with OHCA and 23 660 age- and sex-matched controls receiving health care at the University of Washington. Comorbidities, electrocardiographic measures, vital signs, and medication prescription were abstracted from the EHR. The primary outcome was OHCA. Secondary outcomes included shockable and nonshockable OHCA. Model performance including area under the receiver operating characteristic curve and positive predictive value were assessed and adjusted for observed rate of OHCA across the health system. RESULTS: There were significant differences in demographic characteristics, vital signs, electrocardiographic measures, comorbidities, and medication distribution between individuals with OHCA and controls. In external validation, discrimination in machine learning models (area under the receiver operating characteristic curve 0.80-0.85) was superior to a baseline model with conventional cardiovascular risk factors (area under the receiver operating characteristic curve 0.66). At a specificity threshold of 99%, correcting for baseline OHCA incidence across the health system, positive predictive value was 2.5% to 3.1% in machine learning models compared with 0.8% for the baseline model. Longer corrected QT interval, substance abuse disorder, fluid and electrolyte disorder, alcohol abuse, and higher heart rate were identified as salient predictors of OHCA risk across all machine learning models. Established cardiovascular risk factors retained predictive importance for shockable OHCA, but demographic characteristics (minority race, single marital status) and noncardiovascular comorbidities (substance abuse disorder) also contributed to risk prediction. For nonshockable OHCA, a range of salient predictors, including comorbidities, habits, vital signs, demographic characteristics, and electrocardiographic measures, were identified. CONCLUSIONS: In a population-based case-control study, machine learning models incorporating readily available EHR data showed reasonable discrimination and risk enrichment for OHCA in the general population. Salient factors associated with OCHA risk were myriad across the cardiovascular and noncardiovascular spectrum. Public health and tailored strategies for OHCA prediction and prevention will require incorporation of this complexity.

Presentation and Outcomes of Adults With Overdose-Related Out-of-Hospital Cardiac Arrest.

Importance: Drug overdose (OD) is a public health challenge and an important cause of out-of-hospital cardiac arrest (OHCA). Existing studies evaluating OD-related OHCA (OD-OHCA) either aggregate all drugs or focus on opioids. The epidemiology, presentation, and outcomes of drug-specific OHCA are largely unknown. Objective: To evaluate the temporal pattern, clinical presentation, care, and outcomes of adult patients with OHCA overall and according to the drug-specific profile. Design, Setting, and Participants: This cohort study of adults with OHCA in King County Washington was conducted between January 1, 2015, and December 31, 2021. Etiology of OHCA was determined using emergency medical service, hospital, and medical examiner records. Etiology was classified as non-OD OHCA or OD-OHCA, with drug-specific profiles categorized as (1) opioid without stimulant, (2) stimulant without opioid, (3) opioid and stimulant, or (4) all other nonstimulant, nonopioid drugs. Statistical analysis was performed on July 1, 2023. Exposure: Out-of-hospital cardiac arrest. Main Outcomes and Measures: The primary outcome was survival to hospital discharge. The secondary outcome was survival with favorable functional status defined by Cerebral Performance Category 1 or 2 based on review of the hospital record. Results: In this cohort study, there were 6790 adult patients with emergency medical services-treated OHCA from a US metropolitan system. During the 7-year study period, there were 702 patients with OD-OHCA (median age, 41 years [IQR, 29-53 years]; 64% male [n = 450] and 36% female [n = 252]) and 6088 patients with non-OD OHCA (median age, 66 years [IQR, 56-77 years]; 65% male [n = 3944] and 35% female [n = 2144]). The incidence of OD-OHCA increased from 5.2 (95% CI, 3.8-6.6) per 100 000 person-years in 2015 to 13.0 (95% CI, 10.9-15.1) per 100 000 person-years in 2021 (P < .001 for trend), whereas there was no significant temporal change in the incidence of non-OD OHCA (P = .30). OD-OHCA were more likely to be unwitnessed (66% [460 of 702] vs 41% [2515 of 6088]) and less likely to be shockable (8% [56 of 702] vs 25% [1529 of 6088]) compared with non-OD OHCA. Unadjusted survival was not different (20% [138 of 702] for OD vs 18% [1095 of 6088] for non-OD). When stratified by drug profile, combined opioid-stimulant OHCA demonstrated the greatest relative increase in incidence. Presentation and outcomes differed by drug profile. Patients with stimulant-only OHCA were more likely to have a shockable rhythm (24% [31 of 129]) compared with patients with opioid-only OHCA (4% [11 of 295]) or patients with combined stimulant-opioid OHCA 5% [10 of 205]), and they were more likely to have a witnessed arrest (50% [64 of 129]) compared with patients with OHCA due to other drugs (19% [14 of 73]) or patients with combined stimulant-opioid OHCA (23% [48 of 205]). Patients with a combined opioid-stimulant OHCA had the lowest survival to hospital discharge (10% [21 of 205]) compared with patients with stimulant-only OHCA (22% [29 of 129]) or patients with OHCA due to other drugs (26% [19 of 73]), a difference that persisted after multivariable adjustment. Conclusions and Relevance: In a population-based cohort study, the incidence of OD-OHCA increased significantly from 2015 to 2021, with the greatest increase observed among patients with a combined stimulant-opioid OHCA. Presentation and outcome differed according to the drug-specific profile. The combination of increasing incidence and lower survival among among patients with a opioid-stimulant OHCA supports prevention and treatment initiatives that consider the drug-specific profile.

Prediction of Sudden Cardiac Death: Looking Beyond Ejection Fraction.

PURPOSE OF REVIEW: Sudden cardiac death (SCD) is a major public health burden accounting for 15-20% of global mortality. Contemporary guidelines for SCD prevention are centered around the presence of low left ventricular ejection fraction, although the majority of SCD accrues in those not meeting contemporary criteria for SCD prevention. The goal of this review is to elaborate on the contemporary landscape of SCD prediction tools and further highlight gaps and opportunities in SCD prediction and prevention. RECENT FINDINGS: There have been considerable advancements in both non-invasive and invasive measures for SCD risk prediction including clinical morbidities, electrocardiographic measures, cardiac imaging (nuclear, magnetic resonance, computed tomography), serum biomarkers, genetics, and invasively assessed electrophysiological characteristics. Novel methodological approaches including application of machine learning, incorporation of competing risk, and use of computational modeling have underscored a future of personalized risk prediction. SCD remains a vital public health challenge. Emerging methods highlight opportunities to improve SCD prediction in the majority of those at risk who do not meet contemporary criteria for SCD prevention therapies. Future efforts will need to focus on easily deployed, multi-parametric risk models that enrich for SCD risk and not for competing mortality.

Academic cardiac electrophysiologists' perspectives on sleep apnea care.

PURPOSE: Obstructive sleep apnea syndrome (OSAS) is an important, modifiable risk factor in the pathophysiology of arrhythmias including atrial fibrillation (AF). The purpose of the study was to evaluate cardiac electrophysiologists' (EPs) perception of OSAS. METHODS: We designed a 27-item online Likert scale-based survey instrument entailing several domains: (1) relevance of OSAS in EP practice, (2) OSAS screening and diagnosis, (3) perception on treatments for OSAS, (4) opinion on the OSAS care model. The survey was distributed to 89 academic EP programs in the USA and Canada. While the survey instrument questions refer to the term sleep apnea (SA), our discussion of the diagnosis, management, and research on the sleep disorder is more accurately described with the term OSAS. RESULTS: A total of 105 cardiac electrophysiologists from 49 institutions responded over a 9-month period. The majority of respondents agreed that sleep apnea (SA) is a major concern in their practice (94%). However, 42% reported insufficient education on SA during training. Many (58%) agreed that they would be comfortable managing SA themselves with proper training and education and 66% agreed cardiac electrophysiologists should become more involved in management. Half of EPs (53%) were not satisfied with the sleep specialist referral process. Additionally, a majority (86%) agreed that trained advanced practice providers should be able to assess and manage SA. Time constraints, lack of knowledge, and the referral process are identified as major barriers to EPs becoming more involved in SA care. CONCLUSIONS: We found that OSAS is widely recognized as a major concern for EP. However, incorporation of OSAS care in training and routine practice lags. Barriers to increased involvement include time constraints and education. This study can serve as an impetus for innovation in the cardiology OSAS care model.

Polygenic Risk Score Predicts Sudden Death in Patients With Coronary Disease and Preserved Systolic Function.

BACKGROUND: A familial predisposition to sudden and/or arrhythmic death (SAD) in the setting of coronary artery disease (CAD) exists; however, the genetic basis is poorly understood. OBJECTIVES: The purpose of this study was to determine whether a genome-wide polygenic score for coronary artery disease (GPSCAD) might have utility in SAD risk stratification in CAD patients without severe systolic dysfunction. METHODS: A previously validated GPSCAD was generated utilizing genome-wide genotyping in 4,698 PRE-DETERMINE participants of European ancestry with CAD and left ventricular ejection fraction >30%-35%. The population was dichotomized according to top GPSCAD decile as defined by the general population, and absolute, proportional, and relative risks for SAD and non-SAD were estimated using competing risk analyses. RESULTS: Over a median follow-up of 8.0 years, participants in the top GPSCAD decile were at elevated absolute SAD risk (8.0%; 95% CI: 5.1%-12.4% vs 4.8%; 95% CI: 3.3%-7.0%; P = 0.005) and proportional SAD risk (29% vs 16%; P = 0.0003) compared with the remainder. After controlling for left ventricular ejection fraction, clinical factors, and electrocardiogram parameters, the top GPSCAD decile was associated with SAD (subdistribution HR: 1.77; 95% CI: 1.23-2.54; P = 0.002) but not non-SAD (subdistribution HR: 1.00; 95% CI: 0.80-1.25; P = 0.98) (P for Δ = 0.003). The addition of the top GPSCAD decile to the multivariable model significantly improved net reclassification indexes (NRIs) (continuous NRI: 14.0%; P = 0.024; and categorical NRI: 6.6%; P = 0.005) but not the C-index (difference in C-index: 0.007; P = 0.143). CONCLUSIONS: Among CAD patients without severe systolic dysfunction, high GPSCAD specifically predicted SAD and enriched for both absolute and proportional SAD risk, identifying a population who might benefit from defibrillator therapy.

Identifying risk of adverse outcomes in COVID-19 patients via artificial intelligence-powered analysis of 12-lead intake electrocardiogram.

BACKGROUND: Adverse events in COVID-19 are difficult to predict. Risk stratification is encumbered by the need to protect healthcare workers. We hypothesize that artificial intelligence (AI) can help identify subtle signs of myocardial involvement in the 12-lead electrocardiogram (ECG), which could help predict complications. OBJECTIVE: Use intake ECGs from COVID-19 patients to train AI models to predict risk of mortality or major adverse cardiovascular events (MACE). METHODS: We studied intake ECGs from 1448 COVID-19 patients (60.5% male, aged 63.4 ± 16.9 years). Records were labeled by mortality (death vs discharge) or MACE (no events vs arrhythmic, heart failure [HF], or thromboembolic [TE] events), then used to train AI models; these were compared to conventional regression models developed using demographic and comorbidity data. RESULTS: A total of 245 (17.7%) patients died (67.3% male, aged 74.5 ± 14.4 years); 352 (24.4%) experienced at least 1 MACE (119 arrhythmic, 107 HF, 130 TE). AI models predicted mortality and MACE with area under the curve (AUC) values of 0.60 ± 0.05 and 0.55 ± 0.07, respectively; these were comparable to AUC values for conventional models (0.73 ± 0.07 and 0.65 ± 0.10). There were no prominent temporal trends in mortality rate or MACE incidence in our cohort; holdout testing with data from after a cutoff date (June 9, 2020) did not degrade model performance. CONCLUSION: Using intake ECGs alone, our AI models had limited ability to predict hospitalized COVID-19 patients' risk of mortality or MACE. Our models' accuracy was comparable to that of conventional models built using more in-depth information, but translation to clinical use would require higher sensitivity and positive predictive value. In the future, we hope that mixed-input AI models utilizing both ECG and clinical data may be developed to enhance predictive accuracy.

Catheter ablation of atrial fibrillation results in significant QTc prolongation in the postoperative period.

BACKGROUND: The corrected QT interval (QTc) is a measure of ventricular repolarization time, and a prolonged QTc increases risk for malignant ventricular arrhythmias. Pulmonary vein isolation (PVI) may increase QTc but its effects have not been well studied. OBJECTIVE: Determine the incidence, risk factors, and outcomes of patients presenting for PVI in sinus and atrial fibrillation with postoperative QTc prolongation in a large cohort. METHODS: We performed a single-center retrospective study of consecutive atrial fibrillation ablations. QTc durations using Bazett correction were obtained from electrocardiograms at different postoperative intervals and compared to preoperative QTc. We studied clinical outcomes including clinically significant ventricular arrhythmia and death. A multivariable model was used to identify factors associated with clinically significant QTc prolongation, defined as ΔQTc ≥60 ms or new QTc duration ≥500 ms. RESULTS: A total of 352 PVIs were included in this study. We observed a statistically significant increase in mean QTc compared to baseline (446.3 ± 37.8 ms) on postoperative day (POD)0 (471.7 ± 38.2 ms, P < .001) and at POD1 (456.5 ± 35.0 ms, P < .001). There was no significant difference at 1 month (452.4 ± 33.5 ms, P = .39) and 3 months (447.3 ± 40.0 ms, P = .78). Sixty-six patients (19.2%) developed ΔQTc ≥60 ms or QTc ≥500 ms on POD0, with 4.1% persisting past 90 days. Female sex (odds ratio [OR] = 1.82, 95% confidence interval [CI] =1.01-3.29, P = .047) and history of coronary artery disease (OR = 2.16, 95% CI = 1.03-4.55, P = .042) were independently predictive of QTc prolongation ≥500 ms or ΔQTc ≥60 ms. There were no episodes of clinically significant ventricular arrhythmia or death attributable to arrhythmia. CONCLUSION: QTc duration increased significantly immediately post-PVI and returned to baseline by 1 month. PVI did not provoke significant ventricular arrhythmias in our cohort.

The Influence of Obesity on the Association of Obstructive Sleep Apnea and Atrial Fibrillation.

BACKGROUND AND OBJECTIVE: The association between obstructive sleep apnea (OSA) and atrial fibrillation (AF) has been closely studied. However, obesity is a powerful confounder in the causal relationship between OSA and cardiovascular disease. The contribution of obesity in the relationship between OSA and AF remains unclear. METHODS: We recruited 457 consecutive patients equally with and without AF who underwent clinically indicated diagnostic polysomnography at a single academic sleep center. Multivariable logistic regression adjusting for age, sex, hypertension, and heart failure was performed to study the independent association between OSA and AF stratified by obesity. RESULTS: A total of 457 patients (male: 56.2%, mean age 63.1 ± 13.3 years) was included. OSA prevalence was similar between those with and without AF (52.6% vs. 47.4%, respectively; p = 0.24). In multivariable analysis, no association was found between AF and OSA regardless of obesity status. When severe OSA (vs. non-severe OSA) was modeled as a dependent variable, AF was associated with a higher likelihood of severe OSA in non-obese patients [odds ratio (OR): 2.29, 95% confidence interval (CI): 1.23-4.35, p = 0.01], but not in obese patients (OR: 0.95, 95% CI: 0.48-1.90, p = 0.89). CONCLUSION: The association of OSA with AF was present only in the non-obese and was limited to severe OSA patients. In contrast, no association was found in obese patients. The association between OSA and AF is partly dependent on the body habitus.

Incidence, Mechanism, and Outcomes of On-Plane Versus Off-Plane Cardiac Arrest in Air Travelers.

Background Air travel affords an opportunity to evaluate resuscitation performance and outcome in a setting where automated external defibrillators (AEDs) are readily available. Methods and Results The study cohort included people aged ≥18 years with out of hospital cardiac arrest (OHCA) traveling through Seattle-Tacoma International Airport between January 1, 2004 and December 31, 2019 treated by emergency medical services (EMS). The primary outcomes were pre-EMS therapies (cardiopulmonary resuscitation, application of AED), return of spontaneous circulation, and survival to hospital discharge. Over the 16-year study period, there were 143 OHCA occurring before EMS arrival, 34 (24%) on-plane and 109 (76%) off-plane. Cardiac etiology (81%) was the most common mechanism of arrest. The majority of arrests were bystander-witnessed and presented with a shockable rhythm; these characteristics were more common in off-plane OHCA compared with on-plane (witnessed: 89% versus 74% and shockable: 72% versus 50%). Pre-EMS therapies including cardiopulmonary resuscitation and AED application were common regardless of arrest location. Compared with on-plane OHCA, off-plane OHCA was associated with greater rates of return of spontaneous circulation (68% versus 44%) and 3-fold higher rate of survival to hospital discharge (44% versus 15%). All survivors of on-plane OHCA had AED application with defibrillation before EMS arrival. Conclusions When applied to air travel volumes, we estimate 350 air travel-associated OHCA occur in the United States and 2000 OHCA worldwide each year, nearly a quarter of which happen on-plane. These events are survivable when early arrest interventions including rapid arrest recognition, AED application, and CPR are deployed.

Diabetes and Risk of Sudden Death in Coronary Artery Disease Patients Without Severe Systolic Dysfunction.

OBJECTIVES: This study sought to determine the absolute and relative associations of diabetes mellitus (DM) and hemoglobin A1c (HbA1c) with sudden and/or arrhythmic death (SAD) versus other modes of death in patients with coronary artery disease (CAD) who do not qualify for implantable cardioverter-defibrillators. BACKGROUND: Patients with CAD and DM are at elevated risk for SAD; however, it is unclear whether these patients would benefit from implantable cardioverter-defibrillators given competing causes of death and/or whether HbA1c might augment SAD risk stratification. METHODS: In the PRE-DETERMINE study of 5,764 patients with CAD with left ventricular ejection fraction (LVEF) of >30% to 35%, competing risk analyses were used to compare the absolute and relative risks of SAD versus non-SAD by DM status and HbA1c level and to identify risk factors for SAD among 1,782 patients with DM. RESULTS: Over a median follow-up of 6.8 years, DM and HbA1c were significantly associated with SAD and non-SAD (P < 0.05 for all comparisons); however, the cumulative incidence of non-SAD (19.2%; 95% CI: 17.3%-21.2%) was almost 4 times higher than SAD (4.8%; 95% CI: 3.8%-5.9%) in DM patients. A similar pattern of absolute risk was observed across categories of HbA1c. In analyses limited to patients with DM, HbA1c was not associated with SAD, whereas low LVEF, atrial fibrillation, and electrocardiogram measurements were associated with higher SAD risk. CONCLUSIONS: In patients with CAD and LVEF of >30% to 35%, patients with DM and/or elevated HbA1c are at much higher absolute risk of dying from non-SAD than SAD. Clinical risk markers, and not HbA1c, were associated with SAD risk in patients with DM. (PRE-DETERMINE: Biologic Markers and MRI SCD Cohort Study; NCT01114269).

Autonomic modulation and cardiac arrhythmias: old insights and novel strategies.

The autonomic nervous system (ANS) plays a critical role in both health and states of cardiovascular disease. There has been a long-recognized role of the ANS in the pathogenesis of both atrial and ventricular arrhythmias (VAs). This historical understanding has been expanded in the context of evolving insights into the anatomy and physiology of the ANS, including dysfunction of the ANS in cardiovascular disease such as heart failure and myocardial infarction. An expanding armamentarium of therapeutic strategies-both invasive and non-invasive-have brought the potential of ANS modulation to contemporary clinical practice. Here, we summarize the integrative neuro-cardiac anatomy underlying the ANS, review the physiological rationale for autonomic modulation in atrial and VAs, highlight strategies for autonomic modulation, and finally frame future challenges and opportunities for ANS therapeutics.

Association between functional status at hospital discharge and long-term survival after out-of-hospital-cardiac-arrest.

BACKGROUND: Out-of-hospital cardiac arrest (OHCA) causes brain injury. Functional status of survivors at hospital discharge is a core resuscitation measure, frequently using the Cerebral Performance Category (CPC) or modified Rankin Scale (mRS). Which scale better predicts long-term survival following OHCA is not known. METHODS: We evaluated long-term survival after hospital discharge in a retrospective cohort of persons resuscitated from OHCA in King County, WA from 2007 to 2015. Patients were independently assessed at discharge using both scales, leveraging the regional quality improvement registry, which records the 5-level CPC, and concurrent research studies involving the Resuscitation Outcomes Consortium, which used the 7-level mRS, taken from information in the hospital record. The risk of mortality associated with CPC and mRS categories was estimated using Kaplan-Meier survival analysis and Cox proportional hazards regression. RESULTS: Among 878 eligible patients discharged alive, there were 358 deaths during 9118.5 person-years of follow-up. Overall 1, 5 and 10-year survival was 84.4%, 68.5%, and 53.7% and varied according to CPC and mRS (p < 0.01 per Kaplan-Meier). Compared to CPC-1, hazard ratio (HR) increased incrementally for CPC-2 = 1.33 (1.03-1.73), CPC-3 = 1.90 (1.37-2.65), and CPC-4 = 8.25 (5.63-12.10). Compared to mRS = 0, HR for mRS-1 = 1.02 (0.66-1.58), mRS-2 = 1.52 (1.00-2.32), mRS-3 = 1.41 (0.92-2.14), mRS-4 = 2.00 (1.37-2.97), and mRS-5 = 4.90 (3.23-7.44). CONCLUSION: In OHCA survivors, CPC and mRS scales both predicted long-term survival. However mRS 0-1 and 2-3 groups did not have distinct prognoses, suggesting that a consolidated mRS score may simplify capture of relevant prognostic information for survival predictions.

Admission respiratory status predicts mortality in COVID-19.

COVID-19 has significant case fatality. Glucocorticoids are the only treatment shown to improve survival, but only among patients requiring supplemental oxygen. WHO advises patients to seek medical care for "trouble breathing," but hypoxemic patients frequently have no respiratory symptoms. Our cohort study of hospitalized COVID-19 patients shows that respiratory symptoms are uncommon and not associated with mortality. By contrast, objective signs of respiratory compromise-oxygen saturation and respiratory rate-are associated with markedly elevated mortality. Our findings support expanding guidelines to include at-home assessment of oxygen saturation and respiratory rate in order to expedite life-saving treatments patients to high-risk COVID-19 patients.

Implementation of a fully remote randomized clinical trial with cardiac monitoring.

Background: The coronavirus disease 2019 (COVID-19) pandemic has challenged researchers performing clinical trials to develop innovative approaches to mitigate infectious risk while maintaining rigorous safety monitoring. Methods: In this report we describe the implementation of a novel exclusively remote randomized clinical trial (ClinicalTrials.gov NCT04354428) of hydroxychloroquine and azithromycin for the treatment of the SARS-CoV-2-mediated COVID-19 disease which included cardiovascular safety monitoring. All study activities were conducted remotely. Self-collected vital signs (temperature, respiratory rate, heart rate, and oxygen saturation) and electrocardiographic (ECG) measurements were transmitted digitally to investigators while mid-nasal swabs for SARS-CoV-2 testing were shipped. ECG collection relied on a consumer device (KardiaMobile 6L, AliveCor Inc.) that recorded and transmitted six-lead ECGs via participants' internet-enabled devices to a central core laboratory, which measured and reported QTc intervals that were then used to monitor safety. Results: Two hundred and thirty-one participants uploaded 3245 ECGs. Mean daily adherence to the ECG protocol was 85.2% and was similar to the survey and mid-nasal swab elements of the study. Adherence rates did not differ by age or sex assigned at birth and were high across all reported race and ethnicities. QTc prolongation meeting criteria for an adverse event occurred in 28 (12.1%) participants, with 2 occurring in the placebo group, 19 in the hydroxychloroquine group, and 7 in the hydroxychloroquine + azithromycin group. Conclusions: Our report demonstrates that digital health technologies can be leveraged to conduct rigorous, safe, and entirely remote clinical trials.