New Concepts in Sudden Cardiac Arrest to Address an Intractable Epidemic: JACC State-of-the-Art Review

Management of diabetes mellitus patients with sickle cell anemia: Challenges and therapeutic approaches

The coexistence of diabetes mellitus (DM) and sickle cell anemia (SCA) poses significant challenges in clinical management due to the complex interactions and overlapping complications associated with both conditions. Managing diabetes in individuals with SCA requires a comprehensive approach that addresses the unique physiological and pathological aspects of both diseases. This paper reviews the challenges encountered in the management of DM in patients with SCA and explores therapeutic strategies and approaches to optimize patient care. Challenges in the management of DM in individuals with SCA stem from several factors, including the impact of hemoglobin variants on glycemic control assessment, increased susceptibility to infections, altered immune response, and complications associated with both diseases. Moreover, the coexistence of SCA and DM heightens the susceptibility to infections due to compromised immune function, emphasizing the need for vigilant preventive measures, including vaccinations and close monitoring for infectious complications. Close collaboration among healthcare providers specializing in diabetes, hematology, and other relevant fields is crucial for developing comprehensive care plans. Individualized treatment strategies that balance glycemic control, pain management, and preventive care are essential to mitigate complications and optimize the overall health outcomes of patients with both DM and SCA. In conclusion, managing diabetes in the context of SCA necessitates a nuanced and patient-centered approach. By addressing the challenges and employing tailored therapeutic strategies, healthcare providers can improve the quality of life and health outcomes for individuals affected by both conditions.

Recurrence of ventricular fibrillation in out-of-hospital cardiac arrest: Clinical evidence and underlying ionic mechanisms

Defibrillation remains the optimal therapy for terminating ventricular fibrillation (VF) in out-of-hospital cardiac arrest (OHCA) patients, with reported shock success rates of ∼90%. A key persistent challenge, however, is the high rate of VF recurrence (∼50-80%) seen during post-shock cardiopulmonary resuscitation (CPR). Studies have shown that the incidence and time spent in recurrent VF are negatively associated with neurologically-intact survival. Recurrent VF also results in the administration of extra shocks at escalating energy levels, which can cause cardiac dysfunction. Unfortunately, the mechanisms underlying recurrent VF remain poorly understood. In particular, the role of chest-compressions (CC) administered during CPR in mediating recurrent VF remains controversial. In this review, we first summarize the available clinical evidence for refibrillation occurring during CPR in OHCA patients, including the postulated contribution of CC and non-CC related pathways. Next, we examine experimental studies highlighting how CC can re-induce VF via direct mechano-electric feedback. We postulate the ionic mechanisms involved by comparison with similar phenomena seen in commotio cordis. Subsequently, the hypothesized contribution of partial cardiac reperfusion (either as a result of CC or CC independent organized rhythm) in re-initiating VF in a globally ischaemic heart is examined. An overview of the proposed ionic mechanisms contributing to VF recurrence in OHCA during CPR from a cellular level to the whole heart is outlined. Possible therapeutic implications of the proposed mechanistic theories for VF recurrence in OHCA are briefly discussed.

Use of Artificial Intelligence in Improving Outcomes in Heart Disease: A Scientific Statement From the American Heart Association

A major focus of academia, industry, and global governmental agencies is to develop and apply artificial intelligence and other advanced analytical tools to transform health care delivery. The American Heart Association supports the creation of tools and services that would further the science and practice of precision medicine by enabling more precise approaches to cardiovascular and stroke research, prevention, and care of individuals and populations. Nevertheless, several challenges exist, and few artificial intelligence tools have been shown to improve cardiovascular and stroke care sufficiently to be widely adopted. This scientific statement outlines the current state of the art on the use of artificial intelligence algorithms and data science in the diagnosis, classification, and treatment of cardiovascular disease. It also sets out to advance this mission, focusing on how digital tools and, in particular, artificial intelligence may provide clinical and mechanistic insights, address bias in clinical studies, and facilitate education and implementation science to improve cardiovascular and stroke outcomes. Last, a key objective of this scientific statement is to further the field by identifying best practices, gaps, and challenges for interested stakeholders.

The paradigm change from reactive medical services to 3PM in ischemic stroke: a holistic approach utilising tear fluid multi-omics, mitochondria as a vital biosensor and AI-based multi-professional data interpretation

Worldwide stroke is the second leading cause of death and the third leading cause of death and disability combined. The estimated global economic burden by stroke is over US$891 billion per year. Within three decades (1990-2019), the incidence increased by 70%, deaths by 43%, prevalence by 102%, and DALYs by 143%. Of over 100 million people affected by stroke, about 76% are ischemic stroke (IS) patients recorded worldwide. Contextually, ischemic stroke moves into particular focus of multi-professional groups including researchers, healthcare industry, economists, and policy-makers. Risk factors of ischemic stroke demonstrate sufficient space for cost-effective prevention interventions in primary (suboptimal health) and secondary (clinically manifested collateral disorders contributing to stroke risks) care. These risks are interrelated. For example, sedentary lifestyle and toxic environment both cause mitochondrial stress, systemic low-grade inflammation and accelerated ageing; inflammageing is a low-grade inflammation associated with accelerated ageing and poor stroke outcomes. Stress overload, decreased mitochondrial bioenergetics and hypomagnesaemia are associated with systemic vasospasm and ischemic lesions in heart and brain of all age groups including teenagers. Imbalanced dietary patterns poor in folate but rich in red and processed meat, refined grains, and sugary beverages are associated with hyperhomocysteinaemia, systemic inflammation, small vessel disease, and increased IS risks. Ongoing 3PM research towards vulnerable groups in the population promoted by the European Association for Predictive, Preventive and Personalised Medicine (EPMA) demonstrates promising results for the holistic patient-friendly non-invasive approach utilising tear fluid-based health risk assessment, mitochondria as a vital biosensor and AI-based multi-professional data interpretation as reported here by the EPMA expert group. Collected data demonstrate that IS-relevant risks and corresponding molecular pathways are interrelated. For examples, there is an evident overlap between molecular patterns involved in IS and diabetic retinopathy as an early indicator of IS risk in diabetic patients. Just to exemplify some of them such as the 5-aminolevulinic acid/pathway, which are also characteristic for an altered mitophagy patterns, insomnia, stress regulation and modulation of microbiota-gut-brain crosstalk. Further, ceramides are considered mediators of oxidative stress and inflammation in cardiometabolic disease, negatively affecting mitochondrial respiratory chain function and fission/fusion activity, altered sleep-wake behaviour, vascular stiffness and remodelling. Xanthine/pathway regulation is involved in mitochondrial homeostasis and stress-driven anxiety-like behaviour as well as molecular mechanisms of arterial stiffness. In order to assess individual health risks, an application of machine learning (AI tool) is essential for an accurate data interpretation performed by the multiparametric analysis. Aspects presented in the paper include the needs of young populations and elderly, personalised risk assessment in primary and secondary care, cost-efficacy, application of innovative technologies and screening programmes, advanced education measures for professionals and general population-all are essential pillars for the paradigm change from reactive medical services to 3PM in the overall IS management promoted by the EPMA.

Predictors and Outcomes of Sudden Cardiac Arrest in Heart Failure With Preserved Ejection Fraction: A Nationwide Inpatient Sample Analysis

Sudden cardiac arrest (SCA) is the leading cause of cardiovascular mortality in heart failure with preserved ejection fraction (HFpEF), contributing to around 25% of deaths observed in pivotal HFpEF trials. However, predictors and outcomes of in-hospital SCA in HFpEF have not been well characterized. We queried the Nationwide Inpatient Sample (2016 to 2017) to identify adult hospitalizations with a diagnosis of HFpEF. Patients with acute or chronic conditions associated with SCA (e.g., acute myocardial infarction, acute pulmonary embolism, sarcoidosis) were excluded. We ascertained whether SCA occurred during these hospitalizations, identified predictors of SCA using multivariate logistic regression, and determined outcomes of SCA in HFpEF. Of 2,909,134 hospitalizations, SCA occurred in 1.48% (43,105). The mean age of the SCA group was 72.3 ± 12.4 years, 55.8% were women, and 66.4% were White. Presence of third-degree atrioventricular block (odds ratio [OR] 5.95, 95% confidence interval [CI] 5.31 to 6.67), left bundle branch block (OR 1.96, 95% CI 1.72 to 2.25), and liver disease (OR 1.87, 95% CI 1.73 to 2.02) were the leading predictors of SCA in HFpEF. After excluding patients with do-not-resuscitate status, the SCA group versus those without SCA had higher mortality (25.9% vs 1.6%), major bleeding complications (4.1% vs 1.7%), increased use of percutaneous coronary intervention (2.5% vs 0.7%), and mechanical circulatory assist device (1.2% vs 0.1%). These observational inpatient data suggest identifiable risk factors for SCA in HFpEF including cardiac arrhythmias. Further research is warranted to identify the best tools to risk-stratify patients with HFpEF to implement targeted SCA prevention strategies.

Harnessing the therapeutic potential of mesenchymal stem cell-derived exosomes in cardiac arrest: Current advances and future perspectives

BACKGROUND

Cardiac arrest (CA), characterized by sudden onset and high mortality rates, is one of the leading causes of death globally, with a survival rate of approximately 6-24%. Studies suggest that the restoration of spontaneous circulation (ROSC) hardly improved the mortality rate and prognosis of patients diagnosed with CA, largely due to ischemia-reperfusion injury.

MAIN BODY

Mesenchymal stem cells (MSCs) exhibit self-renewal and strong potential for multilineage differentiation. Their effects are largely mediated by extracellular vesicles (EVs). Exosomes are the most extensively studied subgroup of EVs. EVs mainly mediate intercellular communication by transferring vesicular proteins, lipids, nucleic acids, and other substances to regulate multiple processes, such as cytokine production, cell proliferation, apoptosis, and metabolism. Thus, exosomes exhibit significant potential for therapeutic application in wound repair, tissue reconstruction, inflammatory reaction, and ischemic diseases.

CONCLUSION

Based on similar pathological mechanisms underlying post-cardiac arrest syndrome involving various tissues and organs in many diseases, the review summarizes the therapeutic effects of MSC-derived exosomes and explores the prospects for their application in the treatment of CA.

Dynamic changes in nocturnal heart rate predict short-term cardiovascular events in patients using the wearable cardioverter-defibrillator: from the WEARIT-France cohort study

AIMS

While elevated resting heart rate measured at a single point of time has been associated with cardiovascular outcomes, utility of continuous monitoring of nocturnal heart rate (NHR) has never been evaluated. We hypothesized that dynamic NHR changes may predict, at short term, impending cardiovascular events in patients equipped with a wearable cardioverter-defibrillator (WCD).

METHODS AND RESULTS

The WEARIT-France prospective cohort study enrolled heart failure patients with WCD between 2014 and 2018. Night-time was defined as midnight to 7 a.m. NHR initial trajectories were classified into four categories based on mean NHR in the first week (High/Low) and NHR evolution over the second week (Up/Down) of WCD use. The primary endpoint was a composite of cardiovascular death and heart failure hospitalization. A total of 1013 [61 (interquartile range, IQR 53-68) years, 16% women, left ventricular ejection fraction 26% (IQR 22-30)] were included. During a median WCD wear duration of 68 (IQR 44-90) days, 58 patients (6%) experienced 69 events. After considering potential confounders, High-Up NHR trajectory was significantly associated with the primary endpoint compared to Low-Down [adjusted hazard ratio (HR) 6.08, 95% confidence interval (CI) 2.56-14.45, P < 0.001]. Additionally, a rise of >5 bpm in weekly average NHR from the preceding week was associated with 2.5 higher composite event risk (HR 2.51, 95% CI 1.22-5.18, P = 0.012) as well as total mortality (HR 11.21, 95% CI 3.55-35.37, P < 0.001) and cardiovascular hospitalization (HR 2.70, 95% CI 1.51-4.82, P < 0.001).

CONCLUSION

Dynamic monitoring of NHR may allow timely identification of impending cardiovascular events, with the potential for 'pre-emptive' action.

REGISTRATION NUMBER

Clinical Trials.gov Identifier: NCT03319160.

Medicine 2032: The future of cardiovascular disease prevention with machine learning and digital health technology

Machine learning (ML) refers to computational algorithms that iteratively improve their ability to recognize patterns in data. The digitization of our healthcare infrastructure is generating an abundance of data from electronic health records, imaging, wearables, and sensors that can be analyzed by ML algorithms to generate personalized risk assessments and promote guideline-directed medical management. ML's strength in generating insights from complex medical data to guide clinical decisions must be balanced with the potential to adversely affect patient privacy, safety, health equity, and clinical interpretability. This review provides a primer on key advances in ML for cardiovascular disease prevention and how they may impact clinical practice.

Heart Failure Burden by Autopsy, Guideline-Directed Medical Therapy, and ICD Utilization Among Sudden Deaths

BACKGROUND

Studies of heart failure with reduced ejection fraction (HFrEF) and preserved ejection fraction (HFpEF) report high sudden cardiac death (SCD) rates but presume cardiac cause. Underlying causes, guideline-directed medical therapy (GDMT), and implantable cardioverter-defibrillator (ICD) use in community sudden deaths with heart failure (HF) are unknown.

OBJECTIVES

This study aims to assess the burden of HF, GDMT, and ICD use among autopsied sudden deaths in the POST SCD (Postmortem Systematic Investigation of Sudden Cardiac Death) study, a countywide postmortem study of all presumed SCDs.

METHODS

Incident WHO-defined (presumed) SCDs for individuals of ages 18 to 90 years were autopsied via prospective surveillance of consecutive out-of-hospital deaths in San Francisco County from February 1, 2011, to March 1, 2014. Sudden arrhythmic deaths (SADs) had no identifiable nonarrhythmic cause (eg, pulmonary embolism), and are thus considered potentially rescuable with ICD.

RESULTS

Of 525 presumed SCDs, 100 (19%) had HF. There were 85 patients with known HF (31 HFpEF, 54 HFrEF) and 15 with subclinical HF (postmortem evidence of cardiomyopathy and pulmonary edema without HF diagnosis). SADs comprised 56% (293 of 525) of all presumed SCDs, and 69% (69 of 100) of HF SCDs. The rates were similar in HFrEF (40 of 54 [74%]) and HFpEF (19 of 31 [61%], P = 0.45). Four SAD patients (4%) had ICDs, 3 of which experienced device failure. Twenty-eight SCDs had ejection fraction ≤35%: 22 (79%) with arrhythmic and 6 (21%) with noncardiac causes. Of the 22 SAD patients, 8 (36%) had no identifiable barrier to ICD referral. Complete use of GDMT in HFrEF was 6%.

CONCLUSIONS

One in 5 community sudden deaths had HF; two-thirds had autopsy-confirmed arrhythmic causes. ICD prevention criteria captured only 8% (22 of 293) of all SAD cases countywide; GDMT and ICD use remain important targets for HF sudden death prevention.

Factors predicting cardiac arrest in acute coronary syndrome patients under 50: a state-wide angiographic and forensic evaluation of outcomes

BACKGROUND

An uncertain proportion of patients with acute coronary syndrome (ACS) also experience out-of-hospital cardiac arrest (OHCA). Predictors of OHCA in ACS remain unclear and vulnerable to selection bias as pre-hospital deceased patients are usually not included.

METHODS

Data on patients aged 18-50 years from a percutaneous coronary intervention (PCI) and OHCA registry were combined to identify all patients experiencing OHCA due to ACS (not including those managed medically or who proceeded to cardiac surgery). Clinical, angiographic and forensic details were collated. In-hospital and post-discharge outcomes were compared between OHCA survivors and non-OHCA ACS patients.

RESULTS

OHCA occurred in 6.0% of ACS patients transported to hospital and 10.0% of all ACS patients. Clinical predictors were non-diabetic status (p=0.015), non-obesity (p=0.004), ST-elevation myocardial infarction (p<0.0001) and left main (p<0.0002) or left anterior descending (LAD) coronary artery (p<0.0001) as culprit vessel. OHCA patients had poorer in-hospital clinical outcomes, including longer length of stay and higher pre-procedural intubation, cardiogenic shock, major adverse cardiovascular events, bleeding, and mortality (p<0.0001 for all). At 30 days, OHCA survivors had equivalent cardiac function and return to premorbid independence but higher rates of anxiety/depression (p=0.029).

CONCLUSION

OHCA complicates approximately 10% of ACS in the young. Predictors of OHCA are being non-diabetic, non-obese, having a STEMI presentation, and left main or LAD coronary culprit lesion. For OHCA patients surviving to PCI, higher rates of in-hospital complications are observed. Despite this, recovery of pre-morbid physical and cardiac function is equivalent to non-OHCA patients, apart from higher rates of anxiety/depression.

A Powerful Paradigm for Cardiovascular Risk Stratification Using Multiclass, Multi-Label, and Ensemble-Based Machine Learning Paradigms: A Narrative Review

Background and Motivation: Cardiovascular disease (CVD) causes the highest mortality globally. With escalating healthcare costs, early non-invasive CVD risk assessment is vital. Conventional methods have shown poor performance compared to more recent and fast-evolving Artificial Intelligence (AI) methods. The proposed study reviews the three most recent paradigms for CVD risk assessment, namely multiclass, multi-label, and ensemble-based methods in (i) office-based and (ii) stress-test laboratories. Methods: A total of 265 CVD-based studies were selected using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) model. Due to its popularity and recent development, the study analyzed the above three paradigms using machine learning (ML) frameworks. We review comprehensively these three methods using attributes, such as architecture, applications, pro-and-cons, scientific validation, clinical evaluation, and AI risk-of-bias (RoB) in the CVD framework. These ML techniques were then extended under mobile and cloud-based infrastructure. Findings: Most popular biomarkers used were office-based, laboratory-based, image-based phenotypes, and medication usage. Surrogate carotid scanning for coronary artery risk prediction had shown promising results. Ground truth (GT) selection for AI-based training along with scientific and clinical validation is very important for CVD stratification to avoid RoB. It was observed that the most popular classification paradigm is multiclass followed by the ensemble, and multi-label. The use of deep learning techniques in CVD risk stratification is in a very early stage of development. Mobile and cloud-based AI technologies are more likely to be the future. Conclusions: AI-based methods for CVD risk assessment are most promising and successful. Choice of GT is most vital in AI-based models to prevent the RoB. The amalgamation of image-based strategies with conventional risk factors provides the highest stability when using the three CVD paradigms in non-cloud and cloud-based frameworks.

Epinephrine versus norepinephrine in cardiac arrest patients with post-resuscitation shock

PURPOSE

Whether epinephrine or norepinephrine is preferable as the continuous intravenous vasopressor used to treat post-resuscitation shock is unclear. We assessed outcomes of patients with post-resuscitation shock after out-of-hospital cardiac arrest according to whether the continuous intravenous vasopressor used was epinephrine or norepinephrine.

METHODS

We conducted an observational multicenter study of consecutive patients managed in 2011-2018 for post-resuscitation shock. The primary outcome was all-cause hospital mortality, and secondary outcomes were cardiovascular hospital mortality and unfavorable neurological outcome (Cerebral Performance Category 3-5). A multivariate regression analysis and a propensity score analysis were performed, as well as several sensitivity analyses.

RESULTS

Of the 766 patients included in five hospitals, 285 (37%) received epinephrine and 481 (63%) norepinephrine. All-cause hospital mortality was significantly higher in the epinephrine group (OR 2.6; 95%CI 1.4-4.7; P = 0.002). Cardiovascular hospital mortality was also higher with epinephrine (aOR 5.5; 95%CI 3.0-10.3; P < 0.001), as was the proportion of patients with CPC of 3-5 at hospital discharge. Sensitivity analyses produced consistent results. The analysis involving adjustment on a propensity score to control for confounders showed similar findings (aOR 2.1; 95%CI 1.1-4.0; P = 0.02).

CONCLUSION

Among patients with post-resuscitation shock after out-of-hospital cardiac arrest, use of epinephrine was associated with higher all-cause and cardiovascular-specific mortality, compared with norepinephrine infusion. Until additional data become available, intensivists may want to choose norepinephrine rather than epinephrine for the treatment of post-resuscitation shock after OHCA.

Prediction of sudden cardiac arrest in the general population: Review of traditional and emerging risk factors

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

Organized Structure of Ventricular Fibrillation during Prolonged Heart Perfusion in Dogs

The aim of the study was to identify the organized ventricular fibrillation (VF) activity in the dog heart and characterize its quantitative parameters during prolonged heart perfusion.

Predictors and outcomes of cardiac arrest in the emergency department and in-patient settings in the United States (2016-2018)

BACKGROUND

Outcomes of cardiac arrest (CA) remain dismal despite therapeutic advances. Literature is limited regarding outcomes of CA in emergency departments (ED).

OBJECTIVE

To study the possible causes, predictors, and outcomes of CA in ED and in-patient settings throughout the United States (US).

METHODS

Data from the US national emergency department sample (NEDS) was analyzed for the episodes of CA for 2016-2018. In-hospital CA was divided into in-patient (IPCA) and in the ED (EDCA). Only patients who had cardiopulmonary resuscitation (CPR) within the hospital were included in the study (out-of-hospital were excluded).

RESULTS

A total of 1,068,847 CA (mean age 63.7 ± 19.4 years, 24%females), of whom 325,062 (30.4%) EDCA and 177,104 (16.6%) IPCA were included in the study. Patients without CPR, 743,785 (69.6%), were excluded. Survival was higher among IPCA 55,821 (31.6%) than the EDCA 32,516 (10%). IPCA encounters had multifactorial associated etiologies including respiratory failure (73%), acidosis (38.7%) sepsis (36.8%) and ST-elevated myocardial infarction (STEMI) (7.3%). Majority of ED arrests (67.1%) had no possible identifiable cause. The predominant known causes include intoxication (7.5%), trauma (6.4%), respiratory failure (5%), and STEMI (2.7%). Cardiovascular interventions had significant survival benefits in IPCA on univariate logistic regression after coarsened exact matching for comorbidities. IPCA had higher intervention rates than EDCA. For all live discharges, a total of 40% of patients were discharged to hospice.

CONCLUSION

Survival remains dismal among CA patients especially those occurring in the ED. Given that there are considerable variations in the etiology between the two studied cohorts, more research is required to improve the understanding of these factors, which may improve survival outcomes.

Whole-heart ventricular arrhythmia modeling moving forward: Mechanistic insights and translational applications

Ventricular arrhythmias are the primary cause of sudden cardiac death and one of the leading causes of mortality worldwide. Whole-heart computational modeling offers a unique approach for studying ventricular arrhythmias, offering vast potential for developing both a mechanistic understanding of ventricular arrhythmias and clinical applications for treatment. In this review, the fundamentals of whole-heart ventricular modeling and current methods of personalizing models using clinical data are presented. From this foundation, the authors summarize recent advances in whole-heart ventricular arrhythmia modeling. Efforts in gaining mechanistic insights into ventricular arrhythmias are discussed, in addition to other applications of models such as the assessment of novel therapeutics. The review emphasizes the unique benefits of computational modeling that allow for insights that are not obtainable by contemporary experimental or clinical means. Additionally, the clinical impact of modeling is explored, demonstrating how patient care is influenced by the information gained from ventricular arrhythmia models. The authors conclude with future perspectives about the direction of whole-heart ventricular arrhythmia modeling, outlining how advances in neural network methodologies hold the potential to reduce computational expense and permit for efficient whole-heart modeling.

Cardiovascular mortality following liver transplantation: predictors and temporal trends over 30 years

AIMS

There has been significant evolution in operative and post-transplant therapies following liver transplantation (LT). We sought to study their impact on cardiovascular (CV) mortality, particularly in the longer term.

METHODS AND RESULTS

A retrospective cohort study was conducted of all adult LTs in Australia and New Zealand across three 11-year eras from 1985 to assess prevalence, modes, and predictors of early (≤30 days) and late (>30 days) CV mortality. A total of 4265 patients were followed-up for 37 409 person-years. Overall, 1328 patients died, and CV mortality accounted for 228 (17.2%) deaths. Both early and late CV mortality fell significantly across the eras (P < 0.001). However, CV aetiologies were consistently the leading cause of early mortality and accounted for ∼40% of early deaths in the contemporary era. Cardiovascular deaths occurred significantly later than non-cardiac aetiologies (8.8 vs. 5.2 years, P < 0.001). On multivariable Cox regression, coronary artery disease [hazard ratio (HR) 4.6, 95% confidence interval (CI) 1.2-21.6; P = 0.04] and era of transplantation (HR 0.44; 95% CI 0.28-0.70; P = 0.01) were predictors of early CV mortality, while advancing age (HR 1.05, 95% CI 1.02-1.10; P = 0.005) was an independent predictors of late CV mortality. Most common modes of CV death were cardiac arrest, cerebrovascular events, and myocardial infarction.

CONCLUSION

Despite reductions in CV mortality post-LT over 30 years, they still account for a substantial proportion of early and late deaths. The late occurrence of CV deaths highlights the importance of longitudinal follow-up to study the efficacy of targeted risk-reduction strategies in this unique patient population.

Factors Predisposing to Survival After Resuscitation for Sudden Cardiac Arrest

BACKGROUND

In the POST SCD study, the authors autopsied all World Health Organization (WHO)-defined sudden cardiac deaths (SCDs) and found that only 56% had an arrhythmic cause; resuscitated sudden cardiac arrests (SCAs) were excluded because they did not die suddenly. They hypothesized that causes underlying resuscitated SCAs would be similarly heterogeneous.

OBJECTIVES

The aim of this study was to determine the causes and outcomes of resuscitated SCAs.

METHODS

The authors identified all out-of-hospital cardiac arrests (OHCAs) from February 1, 2011, to January 1, 2015, of patients aged 18 to 90 years in San Francisco County. Resuscitated SCAs were OHCAs surviving to hospitalization and meeting WHO criteria for suddenness. Underlying cause was determined by comprehensive record review.

RESULTS

The authors identified 734 OHCAs over 48 months; 239 met SCA criteria, 133 (55.6%) were resuscitated to hospitalization, and 47 (19.7%) survived to discharge. Arrhythmic causes accounted for significantly more resuscitated SCAs overall (92 of 133, 69.1%), particularly among survivors (43 of 47, 91.5%), than WHO-defined SCDs in POST SCD (293 of 525, 55.8%; p < 0.004 for both). Among resuscitated SCAs, arrhythmic cause, ventricular tachycardia/fibrillation initial rhythm, and white race were independent predictors of survival. None of the resuscitated SCAs due to neurologic causes survived.

CONCLUSIONS

In this 4-year countywide study of OHCAs, only one-third were sudden, of which one-half were resuscitated to hospitalization and 1 in 5 survived to discharge. Arrhythmic cause predicted survival and nearly one-half of nonsurvivors had nonarrhythmic causes, suggesting that SCA survivors are not equivalent to SCDs. Early identification of nonarrhythmic SCAs, such as neurologic emergencies, may be a target to improve OHCA survival.

Surviving sudden cardiac arrest-successes, challenges, and opportunities

Sudden cardiac arrest (SCA) is the most common cause of death in the world. This manuscript highlights the various challenges in prevention and early management of SCA and also discusses the current state of SCA awareness. The manuscript also outlines the various national and international initiatives in improving SCA awareness and their impact on improving outcomes in SCA. Various campaigns have strived for widespread dissemination of cardiopulmonary resuscitation training and advocated for broader public access defibrillator availability. Finally, the manuscript describes future directions including harnessing technology with voice command and artificial intelligence to allow lay person deliver effective CPR, to improve EMS response times, and to allow wider CPR knowledge dissemination in schools and places of employment. Future research should be focused on optimizing SCA outcomes among vulnerable populations and minorities. Advancements in resuscitation science and use of big data for improvement of EMS services will improve outcomes in SCA.

Metformin prevents brain injury after cardiopulmonary resuscitation by inhibiting the endoplasmic reticulum stress response and activating AMPK-mediated autophagy

BACKGROUND AND AIMS

The neurological damage caused by cardiac arrest (CA) can seriously affect quality of life. We investigated the effect of metformin pretreatment on brain injury and survival in a rat CA/cardiopulmonary resuscitation (CPR) model.

METHODS AND RESULTS

After 14 days of pretreatment with metformin, rats underwent 9 minutes of asphyxia CA/CPR. Survival was evaluated 7 days after restoration of spontaneous circulation; neurological deficit scale (NDS) score was evaluated at days 1, 3, and 7. Proteins related to the endoplasmic reticulum (ER) stress response and autophagy were measured using immunoblotting. Seven-day survival was significantly improved and NDS score was significantly improved in rats pretreated with metformin. Metformin enhanced AMPK-induced autophagy activation. AMPK and autophagy inhibitors removed the metformin neuroprotective effect. Although metformin inhibited the ER stress response, its inhibitory effect was weaker than 4-PBA.

CONCLUSION

In a CA/CPR rat model, 14-day pretreatment with metformin has a neuroprotective effect. This effect is closely related to the activation of AMPK-induced autophagy and inhibition of the ER stress response. Long-term use of metformin can reduce brain damage following CA/CPR.

Machine Learned Cellular Phenotypes in Cardiomyopathy Predict Sudden Death

RATIONALE

Susceptibility to VT/VF (ventricular tachycardia/fibrillation) is difficult to predict in patients with ischemic cardiomyopathy either by clinical tools or by attempting to translate cellular mechanisms to the bedside.

OBJECTIVE

To develop computational phenotypes of patients with ischemic cardiomyopathy, by training then interpreting machine learning of ventricular monophasic action potentials (MAPs) to reveal phenotypes that predict long-term outcomes.

METHODS AND RESULTS

We recorded 5706 ventricular MAPs in 42 patients with coronary artery disease and left ventricular ejection fraction ≤40% during steady-state pacing. Patients were randomly allocated to independent training and testing cohorts in a 70:30 ratio, repeated K=10-fold. Support vector machines and convolutional neural networks were trained to 2 end points: (1) sustained VT/VF or (2) mortality at 3 years. Support vector machines provided superior classification. For patient-level predictions, we computed personalized MAP scores as the proportion of MAP beats predicting each end point. Patient-level predictions in independent test cohorts yielded c-statistics of 0.90 for sustained VT/VF (95% CI, 0.76-1.00) and 0.91 for mortality (95% CI, 0.83-1.00) and were the most significant multivariate predictors. Interpreting trained support vector machine revealed MAP morphologies that, using in silico modeling, revealed higher L-type calcium current or sodium-calcium exchanger as predominant phenotypes for VT/VF.

CONCLUSIONS

Machine learning of action potential recordings in patients revealed novel phenotypes for long-term outcomes in ischemic cardiomyopathy. Such computational phenotypes provide an approach which may reveal cellular mechanisms for clinical outcomes and could be applied to other conditions.

Sudden Cardiac Death in Anabolic-Androgenic Steroid Users: A Literature Review

Background and objectives: Anabolic-androgenic steroids (AASs) are a group of synthetic molecules derived from testosterone and its related precursors. AASs are widely used illicitly by adolescents and athletes, especially by bodybuilders, both for aesthetic uses and as performance enhancers to increase muscle growth and lean body mass. When used illicitly they can damage health and cause disorders affecting several functions. Sudden cardiac death (SCD) is the most common medical cause of death in athletes. SCD in athletes has also been associated with the use of performance-enhancing drugs. This review aimed to focus on deaths related to AAS abuse to investigate the cardiac pathophysiological mechanism that underlies this type of death, which still needs to be fully investigated. Materials and Methods: This review was conducted using PubMed Central and Google Scholar databases, until 21 July 2020, using the following key terms: “((Sudden cardiac death) OR (Sudden death)) AND ((androgenic anabolic steroid) OR (androgenic anabolic steroids) OR (anabolic-androgenic steroids) OR (anabolic-androgenic steroid))”. Thirteen articles met the inclusion and exclusion criteria, for a total of 33 reported cases. Results: Of the 33 cases, 31 (93.9%) were males while only 2 (61%) were females. Mean age was 29.79 and, among sportsmen, the most represented sports activity was bodybuilding. In all cases there was a history of AAS abuse or a physical phenotype suggesting AAS use; the total usage period was unspecified in most cases. In 24 cases the results of the toxicological analysis were reported. The most detected AASs were nandrolone, testosterone, and stanozolol. The most frequently reported macroscopic alterations were cardiomegaly and left ventricular hypertrophy, while the histological alterations were foci of fibrosis and necrosis of the myocardial tissue. Conclusions: Four principal mechanisms responsible for SCD have been proposed in AAS abusers: the atherogenic model, the thrombosis model, the model of vasospasm induced by the release of nitric oxide, and the direct myocardial injury model. Hypertrophy, fibrosis, and necrosis represent a substrate for arrhythmias, especially when combined with exercise. Indeed, AAS use has been shown to change physiological cardiac remodeling of athletes to pathophysiological cardiac hypertrophy with an increased risk of life-threatening arrhythmias.

Integration of novel monitoring devices with machine learning technology for scalable cardiovascular management

Ambulatory monitoring is increasingly important for cardiovascular care but is often limited by the unpredictability of cardiovascular events, the intermittent nature of ambulatory monitors and the variable clinical significance of recorded data in patients. Technological advances in computing have led to the introduction of novel physiological biosignals that can increase the frequency at which abnormalities in cardiovascular parameters can be detected, making expert-level, automated diagnosis a reality. However, use of these biosignals for diagnosis also raises numerous concerns related to accuracy and actionability within clinical guidelines, in addition to medico-legal and ethical issues. Analytical methods such as machine learning can potentially increase the accuracy and improve the actionability of device-based diagnoses. Coupled with interoperability of data to widen access to all stakeholders, seamless connectivity (an internet of things) and maintenance of anonymity, this approach could ultimately facilitate near-real-time diagnosis and therapy. These tools are increasingly recognized by regulatory agencies and professional medical societies, but several technical and ethical issues remain. In this Review, we describe the current state of cardiovascular monitoring along the continuum from biosignal acquisition to the identification of novel biosensors and the development of analytical techniques and ultimately to regulatory and ethical issues. Furthermore, we outline new paradigms for cardiovascular monitoring.

Advances in cardiovascular monitoring technologies have resulted in an influx of consumer-targeted wearable sensors that have the potential to detect numerous heart conditions. In this Review, Krittanawong and colleagues describe processes involved in biosignal acquisition and analysis of cardiovascular monitors, as well as their associated ethical, regulatory and legal challenges.

Advances in the use of cardiovascular monitoring technologies, such as the development of novel portable sensors and machine learning algorithms that can provide near-real-time diagnosis, have the potential to provide personalized care.

Wearable sensor technologies can detect numerous biosignals, such as cardiac output, blood-pressure levels and heart rhythm, and can integrate multiple modalities.

The use of novel biosignals for diagnosis raises concerns regarding accuracy and actionability within clinical guidelines, in addition to medical, legal and ethical issues.

Machine learning-based interpretation of biosensor data can facilitate rapid evaluation of the haemodynamic consequences of heart failure or arrhythmias, but is limited by the presence of noise and training data that might not be representative of the real-world clinical setting.

The use of data derived from cardiovascular monitoring devices is associated with numerous challenges, such as data security, accessibility and ownership, in addition to other ethical and regulatory concerns.

Effect of Optimized Versus Guidelines-Based Automated External Defibrillator Placement on Out-of-Hospital Cardiac Arrest Coverage: An In Silico Trial

Background

Mathematical optimization of automated external defibrillator (AED) placement may improve AED accessibility and out‐of‐hospital cardiac arrest (OHCA) outcomes compared with American Heart Association (AHA) and European Resuscitation Council (ERC) placement guidelines. We conducted an in silico trial (simulated prospective cohort study) comparing mathematically optimized placements with placements derived from current AHA and ERC guidelines, which recommend placement in locations where OHCAs are usually witnessed.

Methods and Results

We identified all public OHCAs of presumed cardiac cause from 2008 to 2016 in Copenhagen, Denmark. For the control, we computationally simulated placing 24/7‐accessible AEDs at every unique, public, witnessed OHCA location at monthly intervals over the study period. The intervention consisted of an equal number of simulated AEDs placements, deployed monthly, at mathematically optimized locations, using a model that analyzed historical OHCAs before that month. For each approach, we calculated the number of OHCAs in the study period that occurred within a 100‐m route distance based on Copenhagen’s road network of an available AED after it was placed (“OHCA coverage”). Estimated impact on bystander defibrillation and 30‐day survival was calculated by multivariate logistic regression. The control scenario involved 393 AEDs at historical, public, witnessed OHCA locations, covering 15.8% of the 653 public OHCAs from 2008 to 2016. The optimized locations provided significantly higher coverage (24.2%; P<0.001). Estimated bystander defibrillation and 30‐day survival rates increased from 15.6% to 18.2% (P<0.05) and from 32.6% to 34.0% (P<0.05), respectively. As a baseline, the 1573 real AEDs in Copenhagen covered 14.4% of the OHCAs.

Conclusions

Mathematical optimization can significantly improve OHCA coverage and estimated clinical outcomes compared with a guidelines‐based approach to AED placement.

Exploring the Rescue Strategy for Cardiac Arrest in Makeshift (FangCang) Hospital Workers during the Pneumonia Outbreak Associated with COVID-19

Background:

Beginning in Dec 2019, a novel coronavirus, designated SARS-CoV-2, has caused an international outbreak of respiratory illness termed COVID-19. The workers in the FangCang hospital have to work for more than 8 h and the work is high strength. Furthermore, to protect health and prevent serious cross-infection, they need to wear isolation equipment when working in FangCang hospital. These characteristics increase the risk of cardiac arrest (CA), which seriously endangers the lives of workers.

Methods:

We participated in the rescue of the patient and workers at first-line in FangCang hospital, and summarized the rescue strategies for workers rescuing.

Results:

Workers with CA were rescued in time according our guideline and showed zero dead in FangCang hospital.

Conclusion:

This study establishes the strategy for the CA of workers including the establishment of an in-FangCang resuscitation team, the establishment of a dedicated rescue room, and the formulation of rescue measures and procedures for CA of workers in the FangCang hospital. Therefore, we aimed to provide a strategy for the rescue of workers with CA in the FangCang hospital and share the success in rescuing with the world.

Prognostic Value of Late Gadolinium Enhancement for the Prediction of Cardiovascular Outcomes in Dilated Cardiomyopathy: An International, Multi-Institutional Study of the MINICOR Group

BACKGROUND

Dilated cardiomyopathy is associated with increased risk of major cardiovascular events. Late gadolinium enhancement (LGE) cardiac magnetic resonance imaging is a unique tissue-based marker that, in single-center studies, suggests strong prognostic value. We retrospectively studied associations between LGE presence and adverse cardiovascular events in patients with dilated cardiomyopathy in a multicenter setting as part of an emerging global consortium (MINICOR [Multi-Modal International Cardiovascular Outcomes Registry]).

METHODS

Consecutive patients with dilated cardiomyopathy referred for cardiac magnetic resonance (2000-2017) at 12 institutions in 4 countries were studied. Using multivariable Cox proportional hazard and semiparametric Fine and Gray models, we evaluated the association between LGE and the composite primary end point of all-cause mortality, heart transplantation, or left ventricular assist device implant and a secondary arrhythmic end point of sudden cardiac death or appropriate implantable cardioverter-defibrillator shock.

RESULTS

We studied 1672 patients, mean age 56±14 years (29% female), left ventricular ejection fraction 33±11%, and 25% having New York Heart Association class III to IV; 650 patients (39%) had LGE. During 2.3 years (interquartile range, 1.0-4.3) follow-up, 160 patients experienced the primary end point, and 88 experienced the arrhythmic end point. In multivariable analyses, LGE was associated with 1.5-fold (hazard ratio, 1.45 [95% CI, 1.03-2.04]) risk of the primary end point and 1.8-fold (hazard ratio, 1.82 [95% CI, 1.20-3.06]) risk of the arrhythmic end point. Primary end point risk was increased in patients with multiple LGE patterns, although arrhythmic risk was higher among patients receiving primary prevention implantable cardioverter-defibrillator and widening QRS.

CONCLUSIONS

In this large multinational study of patients with dilated cardiomyopathy, the presence of LGE showed strong prognostic value for identification of high-risk patients. Randomized controlled trials evaluating LGE-based care management strategies are warranted.

Cardiac arrest: prediction models in the early phase of hospitalization

PURPOSE OF REVIEW

There is a need for an early assessment of outcome in patients with return of spontaneous circulation after cardiac arrest. During the last decade, several models were developed in order to identify predictive factors that may facilitate prognostication and stratification of outcome.

RECENT FINDINGS

In addition to prognostication tools that are used in intensive care, at least five scores were recently developed using large datasets, based on simple and immediately available parameters, such as circumstances of arrest and early in-hospital indicators. Regarding neurological outcome, predictive performance of these models is good and even excellent for some of them. These scores perform very well for identifying patients at high-risk of unfavorable outcome. The most important limitation of these scores remains the lack of replication in different communities. In addition, these scores were not developed for individual decision- making, but they could instead be useful for the description and comparison of different cohorts, and also to design trials targeting specific categories of patients regarding outcome. Finally, the recent development of big data allows extension of research in epidemiology of cardiac arrest, including the identification of new prognostic factors and the improvement of prediction according to the profile of populations.

SUMMARY

In addition to the development of artificial intelligence, the prediction approach based on adequate scores will further increase the knowledge in prognostication after cardiac arrest. This strategy may help to develop treatment strategies according to the predicted severity of the outcome.

RESCUE transesophageal echocardiography for monitoring of mechanical chest compressions and guidance for extracorporeal cardiopulmonary resuscitation cannulation in refractory cardiac arrest

There is a growing interest in using point-of-care transesophageal echocardiography (TEE) during cardiac arrest. TEE is effective at identifying the etiology of sudden cardiovascular collapse and guiding management during the resuscitation. In selected patients with refractory cardiac arrest, extracorporeal cardiopulmonary resuscitation (ECPR) can be considered. ECPR requires percutaneous vascular access for the implantation of veno-arterial extracorporeal membrane oxygenation circuit. We present a case of prolonged cardiac arrest in which rescue TEE was pivotal in narrowing the differential diagnosis, monitoring of mechanical chest compression performance, and guiding cannulation for ECPR.

Physical activity is reduced prior to ventricular arrhythmias in patients with a wearable cardioverter defibrillator

INTRODUCTION

The utility of accelerometer-based activity data to identify patients at risk of sustained ventricular tachycardia (VT) or ventricular fibrillation (VF) has not previously been investigated. The aim of the current study was to determine whether physical activity is associated with manifesting spontaneous sustained VT/VF requiring emergent defibrillation in patients with an ejection fraction of ≤35%.

METHODS

Patients consecutively prescribed a wearable cardioverter defibrillator (WCD) from April 2015 to May 2018 were included. Shock data and 4 weeks of physical activity data, beginning with the first week of WCD wear, were analyzed.

RESULTS

Based on the ROC curve outcome generated from 4057 patients, average daily step count during the first week accurately predicted those patients with sustained VT/VF compared to those without (shocked (n = 81) vs nonshocked (n = 3976) area under the curve, c-index = 0.71, 95% CI = 0.65-0.77, P < .001). An average cutoff of 3637 daily steps during week 1 separated the groups. Patients who averaged fewer than 3637 steps per day during the first week of WCD use were 4.3 times more likely to experience a shock than those who walked more than 3637 steps per day (OR = 4.29, 95% CI = 2.58-7.15, P < .001).

DISCUSSION

Average daily step counts are lower in WCD patients who manifest spontaneous VT/VF. Whether these findings represent a causal or correlational relationship, future studies to encourage a minimum daily step count in high-risk patients may impact the incidence of sustained VT/VF.

The cardiac arrest survival score: A predictive algorithm for in-hospital mortality after out-of-hospital cardiac arrest

BACKGROUND

Out-of-hospital cardiac arrest (OHCA) is associated with high mortality. Current methods for predicting mortality post-arrest require data unavailable at the time of initial medical contact. We created and validated a risk prediction model for patients experiencing OHCA who achieved return of spontaneous circulation (ROSC) which relies only on objective information routinely obtained at first medical contact.

METHODS

We performed a retrospective evaluation of 14,892 OHCA patients in a large metropolitan cardiac arrest registry, of which 3952 patients had usable data. This population was divided into a derivation cohort (n = 2,635) and a verification cohort (n = 1,317) in a 2:1 ratio. Backward stepwise logistic regression was used to identify baseline factors independently associated with death after sustained ROSC in the derivation cohort. The cardiac arrest survival score (CASS) was created from the model and its association with in-hospital mortality was examined in both the derivation and verification cohorts.

RESULTS

Baseline characteristics of the derivation and verification cohorts were not different. The final CASS model included age >75 years (odds ratio [OR] = 1.61, confidence interval [CI][1.30-1.99], p < 0.001), unwitnessed arrest (OR = 1.95, CI[1.58-2.40], p < 0.001), home arrest (OR = 1.28, CI[1.07-1.53], p = 0.008), absence of bystander CPR (OR = 1.35, CI[1.12-1.64], p = 0.003), and non-shockable initial rhythm (OR = 3.81, CI[3.19-4.56], p < 0.001). The area under the curve for the model derivation and model verification cohorts were 0.7172 and 0.7081, respectively.

CONCLUSION

CASS accurately predicts mortality in OHCA patients. The model uses only binary, objective clinical data routinely obtained at first medical contact. Early risk stratification may allow identification of more patients in whom timely and aggressive invasive management may improve outcomes.