Atrial Fibrillation Occurring During Acute Hospitalization: A Scientific Statement From the American Heart Association

Predicting stroke risk after sepsis hospitalization with new-onset atrial fibrillation

BACKGROUND

New-onset atrial fibrillation (AF) during sepsis is common, but models designed to stratify stroke risk excluded patients with secondary AF. We assessed the predictive validity of CHA2DS2VASc scores among patients with new-onset AF during sepsis and developed a novel stroke prediction model incorporating presepsis and intrasepsis characteristics.

METHODS

We included patients ≥40 years old who survived hospitalizations with sepsis and new-onset AF across 21 Kaiser Permanente Northern California hospitals from January 1, 2011 to September 30, 2017. We calculated the area under the receiver operating curve (AUC) for CHA2DS2VASc scores to predict stroke or transient ischemic attack (TIA) within 1 year after a hospitalization with new-onset AF during sepsis using Fine-Gray models with death as competing risk. We similarly derived and validated a novel model using presepsis and intrasepsis characteristics associated with 1-year stroke/TIA risk.

RESULTS

Among 82,748 adults hospitalized with sepsis, 3992 with new-onset AF (median age: 80 years, median CHA2DS2VASc of 4) survived to discharge, among whom 70 (2.1%) experienced stroke or TIA outcome and 1393 (41.0%) died within 1 year of sepsis. The CHA2DS2VASc score was not predictive of stroke risk after sepsis (AUC: 0.50, 95% confidence interval [CI]: 0.48-0.52). A newly derived model among 2555 (64%) patients in the derivation set and 1437 (36%) in the validation set included 13 variables and produced an AUC of 0.61 (0.49-0.73) in derivation and 0.54 (0.43-0.65) in validation.

CONCLUSION

Current models do not accurately stratify risk of stroke following new-onset AF secondary to sepsis. New tools are required to guide anticoagulation decisions following new-onset AF in sepsis.

Drug-induced AF: Arrhythmogenic Mechanisms and Management Strategies

AF is a prevalent condition that is associated with various modifiable and unmodifiable risk factors. Drug-induced AF, despite being commonly under-recognised, can be relatively easy to manage. Numerous cardiovascular and non-cardiovascular agents, including catecholaminergic agents, adenosine, anti-tumour agents and others, have been reported to induce AF. However, the mechanisms underlying drug-induced AF are diverse and not fully understood. The complexity of clinical scenarios and insufficient knowledge regarding drug-induced AF have rendered the management of this condition complicated, and current treatment guidelines follow those for other types of AF. Here, we present a review of the epidemiology of drug-induced AF and highlight a range of drugs that can induce or exacerbate AF, along with their molecular and electrophysiological mechanisms. Given the inadequate evidence and lack of attention, further research is crucial to underscore the clinical significance of drug-induced AF, clarify the underlying mechanisms and develop effective treatment strategies for the condition.

Analyzing the impact of sleep duration on atrial fibrillation risk: a comprehensive systematic review and meta-analysis

BACKGROUND

Recent studies have suggested an association between sleep duration and cardiovascular diseases; however, the link to AF is inconclusive. This study aimed to explore the relationship between sleep duration and AF by conducting a systematic review and meta-analysis of primary studies to provide evidence of the link between insufficient sleep and AF.

METHODS

A review of the existing literature was conducted to identify the primary studies that examined the association between AF and sleep duration. The inquiry spanned databases, including PubMed, Embase, MEDLINE, and Google Scholar, from their inception through October 2023.

RESULTS

Meta-analysis revealed a statistically significant association (p < 0.01) between shorter sleep duration and the incidence of AF (hazard ratio (HR), 1.18; 95% CI, 1.03-1.34; I2 = 89%). Conversely, longer sleep duration did not exhibit a statistically significant association with the incidence of AF (HR, 1.03; 95% CI, 0.92-1.14; I2 = 66%, p = 0.02). The sensitivity analysis demonstrated reduced heterogeneity after excluding specific studies.

CONCLUSION

Insufficient sleep duration was associated with an increased risk of AF, whereas a longer sleep duration did not show a significant correlation. Standardized methods for sleep assessment and AF diagnosis as well as adjustments for confounding factors are suggested for future studies to improve the clarity and understanding of these associations.

Outcomes of mechanical thrombectomy in acute stroke patients with atrial fibrillation detected after stroke versus known atrial fibrillation

We aim to compare the outcomes in patients with atrial fibrillation detected after stroke (AFDAS) and their counterparts with known AF (KAF) presenting with large vessel occlusion (LVO) treated with mechanical thrombectomy (MT). This observational, prospective study included consecutive patients with acute LVO ischemic stroke of the anterior circulation with AFDAS, KAF and without AF. The primary study outcome was functional independence at 90 days after stroke. The secondary study outcomes were variation of the NIHSS score at 24 h, rate of successful reperfusion, death at 90 days and rate of immediate complications post-procedure. Overall, our cohort included 518 patients with acute ischemic stroke and LVO treated with MT, with 289 (56.8%) without a diagnosis of AF; 107 (21%) with AFDAS; 122 (22.2%) with KAF. There was no significant difference in terms of functional independence at 90 days after stroke between the three groups. Regarding the secondary study outcome, the rate of symptomatic intracranial haemorrhage (sICH) and/or parenchymal hematoma (PH) were significantly higher in the group of patients without AF (respectively, P = 0.030 and  < 0.010). Logistic regression analysis showed that the subtypes of AF were not statistically significantly associated with functional independence at 90 days after stroke and with the likelihood of any ICH. Our results suggest that the subtypes of AF are not associated with clinical and safety outcomes of MT in patients with acute stroke and LVO. Further studies are needed to confirm our findings.

2023 ACC/AHA/ACCP/HRS Guideline for the Diagnosis and Management of Atrial Fibrillation: A Report of the American College of Cardiology/American Heart Association Joint Committee on Clinical Practice Guidelines

AIM

The "2023 ACC/AHA/ACCP/HRS Guideline for the Diagnosis and Management of Atrial Fibrillation" provides recommendations to guide clinicians in the treatment of patients with atrial fibrillation.

METHODS

A comprehensive literature search was conducted from May 12, 2022, to November 3, 2022, encompassing studies, reviews, and other evidence conducted on human subjects that were published in English from PubMed, EMBASE, the Cochrane Library, the Agency for Healthcare Research and Quality, and other selected databases relevant to this guideline. Additional relevant studies, published through November 2022, during the guideline writing process, were also considered by the writing committee and added to the evidence tables, where appropriate.

STRUCTURE

Atrial fibrillation is the most sustained common arrhythmia, and its incidence and prevalence are increasing in the United States and globally. Recommendations from the "2014 AHA/ACC/HRS Guideline for the Management of Patients With Atrial Fibrillation" and the "2019 AHA/ACC/HRS Focused Update of the 2014 AHA/ACC/HRS Guideline for the Management of Patients With Atrial Fibrillation" have been updated with new evidence to guide clinicians. In addition, new recommendations addressing atrial fibrillation and thromboembolic risk assessment, anticoagulation, left atrial appendage occlusion, atrial fibrillation catheter or surgical ablation, and risk factor modification and atrial fibrillation prevention have been developed.

2023 ACC/AHA/ACCP/HRS Guideline for the Diagnosis and Management of Atrial Fibrillation: A Report of the American College of Cardiology/American Heart Association Joint Committee on Clinical Practice Guidelines

AIM

The "2023 ACC/AHA/ACCP/HRS Guideline for the Diagnosis and Management of Patients With Atrial Fibrillation" provides recommendations to guide clinicians in the treatment of patients with atrial fibrillation.

METHODS

A comprehensive literature search was conducted from May 12, 2022, to November 3, 2022, encompassing studies, reviews, and other evidence conducted on human subjects that were published in English from PubMed, EMBASE, the Cochrane Library, the Agency for Healthcare Research and Quality, and other selected databases relevant to this guideline. Additional relevant studies, published through November 2022, during the guideline writing process, were also considered by the writing committee and added to the evidence tables, where appropriate.

STRUCTURE

Atrial fibrillation is the most sustained common arrhythmia, and its incidence and prevalence are increasing in the United States and globally. Recommendations from the "2014 AHA/ACC/HRS Guideline for the Management of Patients With Atrial Fibrillation" and the "2019 AHA/ACC/HRS Focused Update of the 2014 AHA/ACC/HRS Guideline for the Management of Patients With Atrial Fibrillation" have been updated with new evidence to guide clinicians. In addition, new recommendations addressing atrial fibrillation and thromboembolic risk assessment, anticoagulation, left atrial appendage occlusion, atrial fibrillation catheter or surgical ablation, and risk factor modification and atrial fibrillation prevention have been developed.

Exploring the Applicability of Pre-Anesthetic Cardiac POCUS in Unexpected Conditions: Could it be Helpful?

Formal preoperative echocardiography has traditionally been recommended when there is substantial cardiovascular disease without recent follow up, unexplained dyspnea, a functional class less than 4 METS or a Duke Activity Status Index less than 34. However, it is important to note that certain patients may present with a variety of cardiac abnormalities due to their preexisting condition or multiple treatments, and these individuals warrant consideration. The objective of pre-anesthetic cardiac POCUS is to provide clinical information in a timely manner. Although it does not aim to replace conventional echocardiography, cardiac POCUS can undoubtedly assist anesthesia practitioners in identifying asymptomatic and potentially hazardous conditions, allowing for more accurate risk allocation and individualized patient care.

Forecasting imminent atrial fibrillation in long-term electrocardiogram recordings

BACKGROUND

Despite the morbidity associated with acute atrial fibrillation (AF), no models currently exist to forecast its imminent onset. We sought to evaluate the ability of deep learning to forecast the imminent onset of AF with sufficient lead time, which has important implications for inpatient care.

METHODS

We utilized the Physiobank Long-Term AF Database, which contains 24-h, labeled ECG recordings from patients with a history of AF. AF episodes were defined as ≥5 min of sustained AF. Three deep learning models incorporating convolutional and transformer layers were created for forecasting, with two models focusing on the predictive nature of sinus rhythm segments and AF epochs separately preceding an AF episode, and one model utilizing all preceding waveform as input. Cross-validated performance was evaluated using area under time-dependent receiver operating characteristic curves (AUC(t)) at 7.5-, 15-, 30-, and 60-min lead times, precision-recall curves, and imminent AF risk trajectories.

RESULTS

There were 367 AF episodes from 84 ECG recordings. All models showed average risk trajectory divergence of those with an AF episode from those without ∼15 min before the episode. Highest AUC was associated with the sinus rhythm model [AUC = 0.74; 7.5-min lead time], though the model using all preceding waveform data had similar performance and higher AUCs at longer lead times.

CONCLUSIONS

In this proof-of-concept study, we demonstrated the potential utility of neural networks to forecast the onset of AF in long-term ECG recordings with a clinically relevant lead time. External validation in larger cohorts is required before deploying these models clinically.

Association between the neutrophil-to-lymphocyte ratio and risk of in-hospital heart failure and arrhythmia in patients with acute myocardial infarction

Background

This study was to probe into the relationship between the neutrophil-to-lymphocyte ratio (NLR) and both in-hospital and long-term heart failure risk in patients with acute myocardial infarction (AMI).

Methods

990 patients with AMI, including 386 with non-ST-segment elevation myocardial infarction (NSTEMI) and 604 with segment elevation myocardial infarction (STEMI) were recruited between January 2019 and March 2022. The in-hospital acute heart failure (AHF) and arrhythmia events were recorded.

Results

The NLR was significantly greater in the AHF group in STEMI and NSTEMI patients, with a higher frequency of arrhythmia in comparison to the non-AHF group. A high NLR was related to a high level of myocardial injury markers, accompanied with more AHF and arrhythmia events. Multivariate logistic regression analyses revealed that high NLR is independently linked with increased in-hospital AHF and arrhythmia risk. Receiver operating characteristic curve analyses revealed that the prognostic value of NLR for in-hospital AHF was 0.704 in STEMI patients and 0.766 in NSTEMI patients. However, during a median follow-up of 28 months with 32 heart failure patients, there was no significant difference between the low NLR group (n = 18) and the high NLR group (n = 14). Further analysis showed that the two groups did not significantly differ in the occurrence of heart failure within 12 months of discharge.

Conclusion

Our results indicate that NLR is an independent risk factor of in-hospital AHF in AMI patients. However, NLR has no value in predicting long-term heart failure.

Clinical Profiling and Biomarkers for Post-Operative Atrial Fibrillation Prediction in Patients Undergoing Cardiac Surgery

Post-operative atrial fibrillation (POAF) is the most common arrhythmia in the post-operative period after cardiac surgery. We aim to investigate the main clinical, local, and/or peripheral biochemical and molecular predictors for POAF in patients undergoing coronary and/or valve surgery. Between August 2020 and September 2022, consecutive patients undergoing cardiac surgery without previous history of AF were studied. Clinical variables, plasma, and biological tissues (epicardial and subcutaneous fat) were obtained before surgery. Pre-operative markers associated with inflammation, adiposity, atrial stretch, and fibrosis were analyzed on peripheral and local samples with multiplex assay and real-time PCR. Univariate and multivariate logistic regression analyses were performed in order to identify the main predictors for POAF. Patients were followed-up until hospital discharge. Out of 123 consecutive patients without prior AF, 43 (34.9%) developed POAF during hospitalization. The main predictors were cardiopulmonary bypass time (odds ratio (OR) 1.008 (95% confidence interval (CI), 1.002-1.013), p = 0.005), and plasma pre-operative orosomucoid levels (OR 1.008 (1.206-5.761). After studying differences regarding sex, orosomucoid was the best predictor for POAF in women (OR 2.639 (95% CI, 1.455-4.788), p = 0.027) but not in men. The results support the pre-operative inflammation pathway as a factor involved in the risk of POAF, mainly in women.