Interaction of Cardiovascular Nonmodifiable Risk Factors, Comorbidities and Comedications With Ischemia/Reperfusion Injury and Cardioprotection by Pharmacological Treatments and Ischemic Conditioning.

Preconditioning, postconditioning, and remote conditioning of the myocardium enhance the ability of the heart to withstand a prolonged ischemia/reperfusion insult and the potential to provide novel therapeutic paradigms for cardioprotection. While many signaling pathways leading to endogenous cardioprotection have been elucidated in experimental studies over the past 30 years, no cardioprotective drug is on the market yet for that indication. One likely major reason for this failure to translate cardioprotection into patient benefit is the lack of rigorous and systematic preclinical evaluation of promising cardioprotective therapies prior to their clinical evaluation, since ischemic heart disease in humans is a complex disorder caused by or associated with cardiovascular risk factors and comorbidities. These risk factors and comorbidities induce fundamental alterations in cellular signaling cascades that affect the development of ischemia/reperfusion injury and responses to cardioprotective interventions. Moreover, some of the medications used to treat these comorbidities may impact on cardioprotection by again modifying cellular signaling pathways. The aim of this article is to review the recent evidence that cardiovascular risk factors as well as comorbidities and their medications may modify the response to cardioprotective interventions. We emphasize the critical need for taking into account the presence of cardiovascular risk factors as well as comorbidities and their concomitant medications when designing preclinical studies for the identification and validation of cardioprotective drug targets and clinical studies. This will hopefully maximize the success rate of developing rational approaches to effective cardioprotective therapies for the majority of patients with multiple comorbidities. SIGNIFICANCE STATEMENT: Ischemic heart disease is a major cause of mortality; however, there are still no cardioprotective drugs on the market. Most studies on cardioprotection have been undertaken in animal models of ischemia/reperfusion in the absence of comorbidities; however, ischemic heart disease develops with other systemic disorders (e.g., hypertension, hyperlipidemia, diabetes, atherosclerosis). Here we focus on the preclinical and clinical evidence showing how these comorbidities and their routine medications affect ischemia/reperfusion injury and interfere with cardioprotective strategies.

Valvular Heart Disease: New Concepts in Pathophysiology and Therapeutic Approaches.

This review discusses recent advancements in the field of valvular heart disease. Topics covered include recognition of the impact of atrial fibrillation on development and assessment of valvular disease, strategies for global prevention of rheumatic heart disease, understanding and management of secondary mitral regurgitation, the updated classification of bicuspid aortic valve disease, recognition of heightened cardiovascular risk associated with moderate aortic stenosis, and a growing armamentarium of transcatheter therapies.

Inflammatory risk and cardiovascular events in patients without obstructive coronary artery disease: the ORFAN multicentre, longitudinal cohort study.

BACKGROUND: Coronary computed tomography angiography (CCTA) is the first line investigation for chest pain, and it is used to guide revascularisation. However, the widespread adoption of CCTA has revealed a large group of individuals without obstructive coronary artery disease (CAD), with unclear prognosis and management. Measurement of coronary inflammation from CCTA using the perivascular fat attenuation index (FAI) Score could enable cardiovascular risk prediction and guide the management of individuals without obstructive CAD. The Oxford Risk Factors And Non-invasive imaging (ORFAN) study aimed to evaluate the risk profile and event rates among patients undergoing CCTA as part of routine clinical care in the UK National Health Service (NHS); to test the hypothesis that coronary arterial inflammation drives cardiac mortality or major adverse cardiac events (MACE) in patients with or without CAD; and to externally validate the performance of the previously trained artificial intelligence (AI)-Risk prognostic algorithm and the related AI-Risk classification system in a UK population. METHODS: This multicentre, longitudinal cohort study included 40 091 consecutive patients undergoing clinically indicated CCTA in eight UK hospitals, who were followed up for MACE (ie, myocardial infarction, new onset heart failure, or cardiac death) for a median of 2·7 years (IQR 1·4-5·3). The prognostic value of FAI Score in the presence and absence of obstructive CAD was evaluated in 3393 consecutive patients from the two hospitals with the longest follow-up (7·7 years [6·4-9·1]). An AI-enhanced cardiac risk prediction algorithm, which integrates FAI Score, coronary plaque metrics, and clinical risk factors, was then evaluated in this population. FINDINGS: In the 2·7 year median follow-up period, patients without obstructive CAD (32 533 [81·1%] of 40 091) accounted for 2857 (66·3%) of the 4307 total MACE and 1118 (63·7%) of the 1754 total cardiac deaths in the whole of Cohort A. Increased FAI Score in all the three coronary arteries had an additive impact on the risk for cardiac mortality (hazard ratio [HR] 29·8 [95% CI 13·9-63·9], p<0·001) or MACE (12·6 [8·5-18·6], p<0·001) comparing three vessels with an FAI Score in the top versus bottom quartile for each artery. FAI Score in any coronary artery predicted cardiac mortality and MACE independently from cardiovascular risk factors and the presence or extent of CAD. The AI-Risk classification was positively associated with cardiac mortality (6·75 [5·17-8·82], p<0·001, for very high risk vs low or medium risk) and MACE (4·68 [3·93-5·57], p<0·001 for very high risk vs low or medium risk). Finally, the AI-Risk model was well calibrated against true events. INTERPRETATION: The FAI Score captures inflammatory risk beyond the current clinical risk stratification and CCTA interpretation, particularly among patients without obstructive CAD. The AI-Risk integrates this information in a prognostic algorithm, which could be used as an alternative to traditional risk factor-based risk calculators. FUNDING: British Heart Foundation, NHS-AI award, Innovate UK, National Institute for Health and Care Research, and the Oxford Biomedical Research Centre.

When cardiovascular medicines should be discontinued.

An integral component of the practice of medicine is focused on the initiation of medications, based on clinical practice guidelines and underlying trial evidence, which usually test the addition of novel medications intended for life-long use in short-term clinical trials. Much less attention is given to the question of medication discontinuation, especially after a lengthy period of treatment, during which patients age gets older and diseases may either progress or new diseases may emerge. Given the paucity of data, clinical practice guidelines offer little to no guidance on when and how to deprescribe cardiovascular medications. Such decisions are often left to the discretion of clinicians, who, together with their patients, express concern of potential adverse effects of medication discontinuation. Even in the absence of adverse effects, the continuation of medications without any proven effect may cause harm due to drug-drug interactions, the emergence of polypharmacy, and additional preventable spending to already strained health systems. Herein, several cardiovascular medications or medication classes are discussed that in the opinion of this author group should generally be discontinued, either for the prevention of potential harm, for a lack of benefit, or for the availability of better alternatives.

Management of Atrial Fibrillation Across the Spectrum of Heart Failure With Preserved and Reduced Ejection Fraction.

Atrial fibrillation (AF) is the most common arrhythmia among patients with heart failure (HF), and HF is the most common cause of death for patients presenting with clinical AF. AF is frequently associated with pathological atrial myocardial dysfunction and remodeling, a triad that has been called atrial myopathy. AF can be the cause or consequence of clinical HF, and the directionality varies between individual patients and across the spectrum of HF. Although initial trials suggested no advantage for a systematic rhythm control strategy in HF with reduced ejection fraction, recent data suggest that select patients may benefit from attempts to maintain sinus rhythm with catheter ablation. Preliminary data also show a close relationship among AF, left atrial myopathy, mitral regurgitation, and HF with preserved ejection, with potential clinical benefits to catheter ablation therapy. The modern management of AF in HF also requires consideration of the degree of atrial myopathy and chronicity of AF, in addition to the pathogenesis and phenotype of the underlying left ventricular HF. In this review, we summarize the contemporary management of AF and provide practical guidance and areas in need of future investigation.

Atrial Fibrillation and Dementia: A Report From the AF-SCREEN International Collaboration.

Growing evidence suggests a consistent association between atrial fibrillation (AF) and cognitive impairment and dementia that is independent of clinical stroke. This report from the AF-SCREEN International Collaboration summarizes the evidence linking AF to cognitive impairment and dementia. It provides guidance on the investigation and management of dementia in patients with AF on the basis of best available evidence. The document also addresses suspected pathophysiologic mechanisms and identifies knowledge gaps for future research. Whereas AF and dementia share numerous risk factors, the association appears to be independent of these variables. Nevertheless, the evidence remains inconclusive regarding a direct causal effect. Several pathophysiologic mechanisms have been proposed, some of which are potentially amenable to early intervention, including cerebral microinfarction, AF-related cerebral hypoperfusion, inflammation, microhemorrhage, brain atrophy, and systemic atherosclerotic vascular disease. The mitigating role of oral anticoagulation in specific subgroups (eg, low stroke risk, short duration or silent AF, after successful AF ablation, or atrial cardiopathy) and the effect of rhythm versus rate control strategies remain unknown. Likewise, screening for AF (in cognitively normal or cognitively impaired patients) and screening for cognitive impairment in patients with AF are debated. The pathophysiology of dementia and therapeutic strategies to reduce cognitive impairment warrant further investigation in individuals with AF. Cognition should be evaluated in future AF studies and integrated with patient-specific outcome priorities and patient preferences. Further large-scale prospective studies and randomized trials are needed to establish whether AF is a risk factor for cognitive impairment, to investigate strategies to prevent dementia, and to determine whether screening for unknown AF followed by targeted therapy might prevent or reduce cognitive impairment and dementia.

Artificial intelligence-guided screening for atrial fibrillation using electrocardiogram during sinus rhythm: a prospective non-randomised interventional trial.

BACKGROUND: Previous atrial fibrillation screening trials have highlighted the need for more targeted approaches. We did a pragmatic study to evaluate the effectiveness of an artificial intelligence (AI) algorithm-guided targeted screening approach for identifying previously unrecognised atrial fibrillation. METHODS: For this non-randomised interventional trial, we prospectively recruited patients with stroke risk factors but with no known atrial fibrillation who had an electrocardiogram (ECG) done in routine practice. Participants wore a continuous ambulatory heart rhythm monitor for up to 30 days, with the data transmitted in near real time through a cellular connection. The AI algorithm was applied to the ECGs to divide patients into high-risk or low-risk groups. The primary outcome was newly diagnosed atrial fibrillation. In a secondary analysis, trial participants were propensity-score matched (1:1) to individuals from the eligible but unenrolled population who served as real-world controls. This study is registered with ClinicalTrials.gov, NCT04208971. FINDINGS: 1003 patients with a mean age of 74 years (SD 8·8) from 40 US states completed the study. Over a mean 22·3 days of continuous monitoring, atrial fibrillation was detected in six (1·6%) of 370 patients with low risk and 48 (7·6%) of 633 with high risk (odds ratio 4·98, 95% CI 2·11-11·75, p=0·0002). Compared with usual care, AI-guided screening was associated with increased detection of atrial fibrillation (high-risk group: 3·6% [95% CI 2·3-5·4] with usual care vs 10·6% [8·3-13·2] with AI-guided screening, p<0·0001; low-risk group: 0·9% vs 2·4%, p=0·12) over a median follow-up of 9·9 months (IQR 7·1-11·0). INTERPRETATION: An AI-guided targeted screening approach that leverages existing clinical data increased the yield for atrial fibrillation detection and could improve the effectiveness of atrial fibrillation screening. FUNDING: Mayo Clinic Robert D and Patricia E Kern Center for the Science of Health Care Delivery.

Physicians' perspectives on percutaneous left atrial appendage occlusion for patients with atrial fibrillation.

BACKGROUND: There has been an increasing uptake of transcatheter left atrial appendage occlusion (LAAO) for stroke reduction in atrial fibrillation. OBJECTIVES: To investigate the perceptions and approaches among a nationally representative sample of physicians. METHODS: Using the American Medical Association Physician Masterfile, we selected a random sample of 500 physicians from each of the specialties: general cardiologists, interventional cardiologists, electrophysiologists, and vascular neurologists. The participants received the survey by mail up to three times from November 9, 2021 to January 14, 2022. In addition to the questions about experiences, perceptions, and approaches, physicians were randomly assigned to 1 of the 4 versions of a patient vignette: white man, white woman, black man, and black woman, to investigate potential bias in decision-making. RESULTS: The top three reasons for considering LAAO were: a history of intracranial bleeding (94.3%), a history of major extracranial bleeding (91.8%), and gastrointestinal lesions (59.0%), whereas the top three reasons for withholding LAAO were: other indications for long-term oral anticoagulation (87.7%), a low bleeding risk (77.0%), and a low stroke risk (65.6%). For the reasons limiting recommendations for LAAO, 59.8% mentioned procedural risks, 42.6% mentioned "limiting efficacy data comparing LAAO to NOAC" and 32.8% mentioned "limited safety data comparing LAAO to NOAC." There was no difference in physicians' decision-making by patients' race, gender, or the concordance between patients' and physicians' race or gender. CONCLUSIONS: In the first U.S. national physician survey of LAAO, individual physicians' perspectives varied greatly, which provided information that will help customize future educational activities for different audiences. CONDENSED ABSTRACT: Although diverse practice patterns of LAAO have been documented, little is known about the reasoning or perceptions that drive these variations. Unlike prior surveys that were directed to Centers that performed LAAO, the current survey obtained insights from individual physicians, not only those who perform the procedures (interventional cardiologists and electrophysiologists) but also those who are closely involved in the decision-making and referral process (general cardiologists and vascular neurologists). The findings identify key evidence gaps and help prioritize future studies to establish a consistent and evidence-based best practice for AF stroke prevention.

Heart rate and death and hospitalization for heart failure in patients with persistent or permanent atrial fibrillation: Insights from the ARISTOTLE trial.

Rate control is fundamental in the treatment of patients with atrial fibrillation (AF). The independent association of heart rate with outcomes and range of heart rate associated with best outcomes remains uncertain. We assessed the relationship between heart rate and clinical outcomes in patients with persistent or permanent AF enrolled in the randomized, double-blind ARISTOTLE trial. In patients with persistent or permanent AF, a faster heart rate is associated with a modest, but statistically significant increase in death and heart failure hospitalizations. TRIAL REGISTRATION: ClinicalTrials.gov (NCT00412984).

Associations of Atrial Fibrillation After Noncardiac Surgery With Stroke, Subsequent Arrhythmia, and Death : A Cohort Study.

BACKGROUND: Postoperative atrial fibrillation (AF) after noncardiac surgery confers increased risks for ischemic stroke and transient ischemic attack (TIA). How outcomes for postoperative AF after noncardiac surgery compare with those for AF occurring outside of the operative setting is unknown. OBJECTIVE: To compare the risks for ischemic stroke or TIA and other outcomes in patients with postoperative AF versus those with incident AF not associated with surgery. DESIGN: Cohort study. SETTING: Olmsted County, Minnesota. PARTICIPANTS: Patients with incident AF between 2000 and 2013. MEASUREMENTS: Patients were categorized as having AF occurring within 30 days of a noncardiac surgery (postoperative AF) or having AF unrelated to surgery (nonoperative AF). RESULTS: Of 4231 patients with incident AF, 550 (13%) had postoperative AF as their first-ever documented AF presentation. Over a mean follow-up of 6.3 years, 486 patients had an ischemic stroke or TIA and 2462 had subsequent AF; a total of 2565 deaths occurred. The risk for stroke or TIA was similar between those with postoperative AF and nonoperative AF (absolute risk difference [ARD] at 5 years, 0.1% [95% CI, -2.9% to 3.1%]; hazard ratio [HR], 1.01 [CI, 0.77 to 1.32]). A lower risk for subsequent AF was seen for patients with postoperative AF (ARD at 5 years, -13.4% [CI, -17.8% to -9.0%]; HR, 0.68 [CI, 0.60 to 0.77]). Finally, no difference was seen for cardiovascular death or all-cause death between patients with postoperative AF and nonoperative AF. LIMITATION: The population consisted predominantly of White patients; caution should be used when extrapolating the results to more racially diverse populations. CONCLUSION: Postoperative AF after noncardiac surgery is associated with similar risk for thromboembolism compared with nonoperative AF. Our findings have potentially important implications for the early postsurgical and subsequent management of postoperative AF. PRIMARY FUNDING SOURCE: National Institute on Aging.

Biomarkers and heart failure events in patients with atrial fibrillation in the ARISTOTLE trial evaluated by a multi-state model.

BACKGROUND: Atrial fibrillation (AF) and heart failure (HF) often coexist. We investigated the prognostic impact of biomarkers on the development of HF and death in patients with AF and different left ventricular systolic function considering the influence of competing events. METHODS: The study included 11,818 patients with AF from the ARISTOTLE trial who at entry had information on history of HF, an estimate of left ventricular function and plasma samples for determination of biomarkers representing cardiorenal dysfunction (NT-proBNP, troponin T, cystatin C) and inflammation (GDF-15, IL-6, CRP). Patients were categorized into: (I) HF with reduced ejection fraction (HFrEF, n = 2,048), (II) HF with preserved ejection fraction (HFpEF, n = 2,520), and (III) No HF (n = 7,250). Biomarker associations with HF hospitalization and death were analyzed using a multi-state model accounting also for repeated events. RESULTS: Baseline levels of NT-proBNP, troponin T, cystatin C, GDF-15, IL-6, and CRP were highest in HFrEF and lowest in No HF. During median 1.9 years follow-up, 546 patients were hospitalized at least once for HF and 819 died. Higher levels of all investigated biomarkers were associated with both outcomes (all P< .0001), with highest event rates in HFrEF and lowest in No HF. The associations remained after adjustments and were more pronounced for first than for recurrent events. CONCLUSIONS: In anticoagulated patients with AF, biomarkers indicating cardiorenal dysfunction and inflammation improve the identification of patients at risk of developing HF or worsening of already existing HF. These biomarkers might be useful for targeting novel HF therapies in patients with AF.

Natural language processing for identification of hypertrophic cardiomyopathy patients from cardiac magnetic resonance reports.

BACKGROUND: Cardiac magnetic resonance (CMR) imaging is important for diagnosis and risk stratification of hypertrophic cardiomyopathy (HCM) patients. However, collection of information from large numbers of CMR reports by manual review is time-consuming, error-prone and costly. Natural language processing (NLP) is an artificial intelligence method for automated extraction of information from narrative text including text in CMR reports in electronic health records (EHR). Our objective was to assess whether NLP can accurately extract diagnosis of HCM from CMR reports. METHODS: An NLP system with two tiers was developed for information extraction from narrative text in CMR reports; the first tier extracted information regarding HCM diagnosis while the second extracted categorical and numeric concepts for HCM classification. We randomly allocated 200 HCM patients with CMR reports from 2004 to 2018 into training (100 patients with 185 CMR reports) and testing sets (100 patients with 206 reports). RESULTS: NLP algorithms demonstrated very high performance compared to manual annotation. The algorithm to extract HCM diagnosis had accuracy of 0.99. The accuracy for categorical concepts included HCM morphologic subtype 0.99, systolic anterior motion of the mitral valve 0.96, mitral regurgitation 0.93, left ventricular (LV) obstruction 0.94, location of obstruction 0.92, apical pouch 0.98, LV delayed enhancement 0.93, left atrial enlargement 0.99 and right atrial enlargement 0.98. Accuracy for numeric concepts included maximal LV wall thickness 0.96, LV mass 0.99, LV mass index 0.98, LV ejection fraction 0.98 and right ventricular ejection fraction 0.99. CONCLUSIONS: NLP identified and classified HCM from CMR narrative text reports with very high performance.

Incidence, Trends, and Outcomes of Type 2 Myocardial Infarction in a Community Cohort.

BACKGROUND: Type 2 myocardial infarction (T2MI) occurs because of an acute imbalance in myocardial oxygen supply and demand in the absence of atherothrombosis. Despite being frequently encountered in clinical practice, the population-based incidence and trends remain unknown, and the long-term outcomes are incompletely characterized. METHODS: We prospectively recruited residents of Olmsted County, Minnesota, who experienced an event associated with a cardiac troponin T >99th percentile of a normal reference population (≥0.01 ng/mL) between January 1, 2003, and December 31, 2012. Events were retrospectively classified into type 1 myocardial infarction (T1MI, atherothombotic event), T2MI, or myocardial injury (troponin rise not meeting criteria for myocardial infarction [MI]) using the universal definition. Outcomes were long-term all-cause and cardiovascular mortality and recurrent MI. T2MI was further subclassified by the inciting event for supply/demand mismatch. RESULTS: A total of 5460 patients had at least one cardiac troponin T ≥0.01 ng/mL; 1365 of these patients were classified as index T1MI (age, 68.5±14.8 years; 63% male) and 1054 were classified as T2MI (age, 73.7±15.8 years; 46% male). The annual incidence of T1MI decreased markedly from 202 to 84 per 100 000 persons between 2003 and 2012 (P<0.001), whereas the incidence of T2MI declined from 130 to 78 per 100 000 persons (P=0.02). In comparison with patients with T1MI, patients with T2MI had higher long-term all-cause mortality after adjustment for age and sex, driven by early and noncardiovascular death. Rates of cardiovascular death were similar after either type of MI (hazard ratio, 0.8 [95% CI, 0.7-1.0], P=0.11). Subclassification of T2MI by cause demonstrated a more favorable prognosis when the principal provoking mechanism was arrhythmia, in comparison with postoperative status, hypotension, anemia, and hypoxia. After index T2MI, the most common MI during follow-up was a recurrent T2MI, whereas the occurrence of a new T1MI was relatively rare (estimated rates at 5 years, 9.7% and 1.7%). CONCLUSIONS: There has been an evolution in the type of MI occurring in the community over a decade, with the incidence of T2MI now being similar to T1MI. Mortality after T2MI is higher and driven by early and noncardiovascular death. The provoking mechanism of supply/demand mismatch affects long-term survival. These findings underscore the healthcare burden of T2MI and provide benchmarks for clinical trial design.

Patients With Atrial Fibrillation Taking Nonsteroidal Anti-Inflammatory Drugs and Oral Anticoagulants in the ARISTOTLE Trial.

BACKGROUND: The use of nonsteroidal anti-inflammatory drugs (NSAIDs) with oral anticoagulants has been associated with an increased risk of bleeding. We investigated the risk of bleeding and major cardiovascular outcomes in patients with atrial fibrillation taking NSAIDs and apixaban or warfarin. METHODS: The ARISTOTLE trial (Apixaban for Reduction in Stroke and Other Thromboembolic Events in Atrial Fibrillation; n=18 201) compared apixaban with warfarin in patients with atrial fibrillation at an increased risk of stroke. Patients in ARISTOTLE without severe renal (creatine clearance ≤30 mL/min) or liver disease were included in this analysis (n=17 423). NSAID use at baseline, NSAID use during the trial (incident NSAID use), and never users were described. The primary outcome was major bleeding. Secondary outcomes included clinically relevant nonmajor bleeding, gastrointestinal bleeding, heart failure hospitalization, stroke or systemic embolism, and all-cause mortality. NSAID use during the trial, and the interaction between randomized treatment, was analyzed using time-dependent Cox proportional hazards models. RESULTS: Those with baseline NSAID use (n=832 [4.8%]), incident NSAID use (n=2185 [13.2%]), and never users were similar in median age (age [25th, 75th]; 70 [64, 77] versus 70 [63, 75] versus 70 [62, 76]). Those with NSAID use at baseline and incident NSAID use were more likely to have a history of bleeding than never users (24.5% versus 21.0% versus 15.6%, respectively). During a median follow-up (25th, 75th) of 1.8 (1.4, 2.3) years and when excluding those taking NSAID at baseline, we found that incident NSAID use was associated with an increased risk of major bleeding (hazard ratio [HR], 1.61 [95% CI, 1.11-2.33]) and clinically relevant nonmajor bleeding (HR, 1.70 [95% CI, 1.16-2.48]), but not gastrointestinal bleeding. No significant interaction was observed between NSAID use and randomized treatment for any outcome. CONCLUSIONS: A substantial number of patients in the ARISTOTLE trial took NSAIDs. Incident NSAID use was associated with major and clinically relevant nonmajor bleeding, but not with gastrointestinal bleeding. The safety and efficacy of apixaban versus warfarin appeared not significantly to be altered by NSAID use. This study warrants more investigation of the effect of NSAIDs on the outcomes of patients treated with apixaban. CLINICAL TRIAL REGISTRATION: URL: https://www.clinicaltrials.gov. Unique identifier: NCT00412984.

Batch enrollment for an artificial intelligence-guided intervention to lower neurologic events in patients with undiagnosed atrial fibrillation: rationale and design of a digital clinical trial.

BACKGROUND: Clinical trials are a fundamental tool to evaluate medical interventions but are time-consuming and resource-intensive. OBJECTIVES: To build infrastructure for digital trials to improve efficiency and generalizability and test it using a study to validate an artificial intelligence algorithm to detect atrial fibrillation (AF). DESIGN: We will prospectively enroll 1,000 patients who underwent an electrocardiogram for any clinical reason in routine practice, do not have a previous diagnosis of AF or atrial flutter and would be eligible for anticoagulation if AF is detected. Eligible patients will be identified using digital phenotyping algorithms, including natural language processing that runs on the electronic health records. Study invitations will be sent in batches via patient portal or letter, which will direct patients to a website to verify eligibility, learn about the study (including video-based informed consent), and consent electronically. The method aims to enroll participants representative of the general patient population, rather than a convenience sample of patients presenting to clinic. A device will be mailed to patients to continuously monitor for up to 30 days. The primary outcome is AF diagnosis and burden; secondary outcomes include patients' experience with the trial conduct methods and the monitoring device. The enrollment, intervention, and follow-up will be conducted remotely, ie, a patient-centered site-less trial. SUMMARY: This is among the first wave of trials to adopt digital technologies, artificial intelligence, and other pragmatic features to create efficiencies, which will pave the way for future trials in a broad range of disease and treatment areas. Clinicaltrials.gov: NCT04208971.

Use of the HAVOC Score to Identify Patients at Highest Risk of Developing Atrial Fibrillation.

BACKGROUND: Recent studies using insertable cardiac monitors (ICMs) show a high incidence of atrial fibrillation (AF). Further identifying subsets of patients who could benefit most from ICMs is desirable. We evaluated whether the HAVOC risk score which predicts AF in patients with cryptogenic stroke also predicts AF detection by ICMs in those without recent stroke. METHODS: Participants were included from the prospective, industry-sponsored REVEAL AF study assessing AF incidence in patients with CHADS2 scores ≥3 or =2 with 1 or more additional AF risk factors, who had ICM data and were not receiving anti-arrhythmic drugs. Ischemic stroke occurring less than 1 year prior to enrollment or documented AF were exclusion criteria. AF was defined as an adjudicated ICM-detected episode ≥6 min in duration. HAVOC scores were calculated by assigning 4 points for congestive heart failure, 2 points for each of hypertension, age ≥75 years, valvular disease, and coronary artery disease, and 1 point for each of peripheral vascular disease and obesity (body mass index >30). Scores classified risk as low (0-4), intermediate (5-9), or high (10-14); corresponding AF detection rates were compared using the log-rank test. AF incidence rates in patients with and without a history of remote stroke at baseline were also compared. RESULTS: Among 391 participants, the mean age was 71.5 ± 9.8 years and 186 (47.6%) were women. In total, 130 (33.2%) developed AF over 18 months. Stratification by HAVOC risk score was: 95 (24%) low, 241 (62%) intermediate, and 55 (14%) high. At 18 months, AF incidence in patients with low HAVOC scores (19.5%) was lower than in those with intermediate (32.1%) or high (34.2%) scores. AF incidence was similar among those with (n = 78) versus without (n = 313) remote stroke (27.3% vs. 29.8%; median time from stroke to ICM insertion was 4.2 [2.2-8.2] years). CONCLUSIONS: The HAVOC risk score identified a subset of individuals at greatest risk of developing AF, while AF incidence rates were similar among those with and without remote stroke. The use of the HAVOC score could help identify those at greatest likelihood of manifesting AF during long-term monitoring.

An under-recognized phenomenon: Myocardial volume change during the cardiac cycle.

BACKGROUND: Myocardial volume is assumed to be constant over the cardiac cycle in the echocardiographic models used by professional guidelines, despite evidence that suggests otherwise. The aim of this paper is to use literature-derived myocardial strain values from healthy patients to determine if myocardial volume changes during the cardiac cycle. METHODS: A systematic review for studies with longitudinal, radial, and circumferential strain from echocardiography in healthy volunteers ultimately yielded 16 studies, corresponding to 2917 patients. Myocardial volume in systole (MVs) and diastole (MVd) was used to calculate MVs/MVd for each study by applying this published strain data to three models: the standard ellipsoid geometric model, a thin-apex geometric model, and a strain-volume ratio. RESULTS: MVs/MVd<1 in 14 of the 16 studies, when computed using these three models. A sensitivity analysis of the two geometric models was performed by varying the dimensions of the ellipsoid and calculating MVs/MVd. This demonstrated little variability in MVs/MVd, suggesting that strain values were the primary determinant of MVs/MVd rather than the geometric model used. Another sensitivity analysis using the 97.5th percentile of each orthogonal strain demonstrated that even with extreme values, in the largest two studies of healthy populations, the calculated MVs/MVd was <1. CONCLUSIONS: Healthy human myocardium appears to decrease in volume during systole. This is seen in MRI studies and is clinically relevant, but this study demonstrates that this characteristic was also present but unrecognized in the existing echocardiography literature.

Novel approaches to the management of chronic systolic heart failure: future directions and unanswered questions.

Despite improvements in outcomes in the last few decades for heart failure (HF) with reduced ejection fraction (HFrEF), there still remains a need for novel therapies as many patients incompletely recover with existing therapies and progress to advanced HF. In this review, we will discuss recent advances in the management of HFrEF with a focus on upcoming therapies that hold the greatest promise for clinical use. We will discuss novel pharmacological therapies and areas of uncertainty with existing therapies. We will also discuss the potential utility and controversy surrounding novel interventions for HF such as percutaneous mitral valve repair, atrial fibrillation ablation, and other emerging interventions with positive signals for benefit in HFrEF. Finally, we will summarize the current state of stem cell and gene therapy for HFrEF and future directions.

An artificial intelligence-enabled ECG algorithm for the identification of patients with atrial fibrillation during sinus rhythm: a retrospective analysis of outcome prediction.

BACKGROUND: Atrial fibrillation is frequently asymptomatic and thus underdetected but is associated with stroke, heart failure, and death. Existing screening methods require prolonged monitoring and are limited by cost and low yield. We aimed to develop a rapid, inexpensive, point-of-care means of identifying patients with atrial fibrillation using machine learning. METHODS: We developed an artificial intelligence (AI)-enabled electrocardiograph (ECG) using a convolutional neural network to detect the electrocardiographic signature of atrial fibrillation present during normal sinus rhythm using standard 10-second, 12-lead ECGs. We included all patients aged 18 years or older with at least one digital, normal sinus rhythm, standard 10-second, 12-lead ECG acquired in the supine position at the Mayo Clinic ECG laboratory between Dec 31, 1993, and July 21, 2017, with rhythm labels validated by trained personnel under cardiologist supervision. We classified patients with at least one ECG with a rhythm of atrial fibrillation or atrial flutter as positive for atrial fibrillation. We allocated ECGs to the training, internal validation, and testing datasets in a 7:1:2 ratio. We calculated the area under the curve (AUC) of the receiver operatoring characteristic curve for the internal validation dataset to select a probability threshold, which we applied to the testing dataset. We evaluated model performance on the testing dataset by calculating the AUC and the accuracy, sensitivity, specificity, and F1 score with two-sided 95% CIs. FINDINGS: We included 180 922 patients with 649 931 normal sinus rhythm ECGs for analysis: 454 789 ECGs recorded from 126 526 patients in the training dataset, 64 340 ECGs from 18 116 patients in the internal validation dataset, and 130 802 ECGs from 36 280 patients in the testing dataset. 3051 (8·4%) patients in the testing dataset had verified atrial fibrillation before the normal sinus rhythm ECG tested by the model. A single AI-enabled ECG identified atrial fibrillation with an AUC of 0·87 (95% CI 0·86-0·88), sensitivity of 79·0% (77·5-80·4), specificity of 79·5% (79·0-79·9), F1 score of 39·2% (38·1-40·3), and overall accuracy of 79·4% (79·0-79·9). Including all ECGs acquired during the first month of each patient's window of interest (ie, the study start date or 31 days before the first recorded atrial fibrillation ECG) increased the AUC to 0·90 (0·90-0·91), sensitivity to 82·3% (80·9-83·6), specificity to 83·4% (83·0-83·8), F1 score to 45·4% (44·2-46·5), and overall accuracy to 83·3% (83·0-83·7). INTERPRETATION: An AI-enabled ECG acquired during normal sinus rhythm permits identification at point of care of individuals with atrial fibrillation. FUNDING: None.

Rhythm monitoring strategies in patients at high risk for atrial fibrillation and stroke: A comparative analysis from the REVEAL AF study.

BACKGROUND: Reducing atrial fibrillation (AF)-related stroke requires timely AF diagnosis, but the optimal monitoring strategy is unknown. OBJECTIVE: We used insertable cardiac monitor (ICM) data from the REVEAL AF study to compare AF detection rates by various short-term continuous monitoring (STM) strategies. METHODS AND RESULTS: Patients without known AF, but with CHADS2 scores ≥3 (or = 2 with ≥1 additional AF risk factor) received an ICM for AF detection. One-time STM strategies were assessed by computing AF incidence at 1, 2, 7, 14, and 30 days post-ICM insertion. Repeated STM strategies (quarterly 24-hour, 48-hour, 7-day, or monthly 24-hour monitoring) were modeled by randomly selecting day(s) within a 30-day window around each nominal evaluation date over a 1-year period (simulated 10,000 times). Endpoints included AF ≥6 minutes, AF ≥1 hour, and daily AF burden ≥1 and ≥ 5.5 hours. The impact of compliance on AF detection was evaluated using daily compliance rates of 85%, 75%, 65% and 55% during follow-up months 1-3, 4-6, 7-9, and 9-12, respectively. Based on data from 385 patients (71.5 ±â€¯9.9 years; CHADS2 score 3.0 ±â€¯1.0) the incidence of AF ≥6 minutes via ICM at 12 months was 27.1% (95% CI, 22.5-31.5%). This exceeded the range of estimated rates from all modeled one-time and repeated STM strategies (0.8% for 24-hour Holter monitoring to 10.6% for quarterly 7-day monitoring). Findings were similar for all AF endpoints. Modeled non-compliance reduced AF detection by 4.5% to 22.9%. CONCLUSIONS: Most AF episodes detected via ICMs would go undetected via conventional STM strategies, thus preventing optimal prophylaxis for adverse consequences.