Paroxysmal Atrial Fibrillation in Cryptogenic Stroke: an Overlooked Explanation?

The association between systemic inflammation markers and paroxysmal atrial fibrillation

BACKGROUND

Systemic inflammation markers have recently been identified as being associated with cardiac disorders. However, limited research has been conducted to estimate the pre-diagnostic associations between these markers and paroxysmal atrial fibrillation (PAF). Our aim is to identify potential biomarkers for early detection of PAF.

METHODS

91 participants in the PAF group and 97 participants in the non-PAF group were included in this study. We investigated the correlations between three systemic inflammation markers, namely the systemic immune inflammation index (SII), system inflammation response index (SIRI), and aggregate index of systemic inflammation (AISI), and PAF.

RESULTS

The proportion of patients with PAF gradually increased with increasing logSII, logSIRI, and logAISI tertiles. Compared to those in the lowest tertiles, the PAF risks in the highest logSII and logSIRI tertiles were 3.2-fold and 2.9-fold, respectively. Conversely, there was no significant correlation observed between logAISI and PAF risk within the highest tertile of logAISI. The restricted cubic splines (RCS) analysis revealed a non-linear relationship between the elevation of systemic inflammation markers and PAF risk. Specifically, the incidence of PAF is respectively increased by 56%, 95%, and 150% for each standard deviation increase in these variables. The ROC curve analysis of logSII, logSIRI and logAISI showed that they had AUC of 0.6, 0.7 and 0.6, respectively. It also demonstrated favorable sensitivity and specificity of these systemic inflammation markers in detecting the presence of PAF.

CONCLUSIONS

In conclusion, our study reveals significant positive correlations between SII, SIRI, and AISI with the incidence of PAF.

Paroxysmal atrial fibrillation and hemochromatosis: a narrative review

Paroxysmal atrial fibrillation (PAF) and hemochromatosis have a complex relationship. This review explores its mechanisms, prevalence, correlations, and clinical manifestations. Hereditary hemochromatosis (HH) involves iron overload due to HFE protein mutations, while atrial fibrillation (AF) is characterized by irregular heart rhythms. Iron overload in hemochromatosis can promote cardiac arrhythmias. AF is prevalent in developed countries and may be linked to cryptogenic strokes. Genetic variations and demographic factors influence the occurrence of both conditions. HH affects multiple organ systems, including the heart, while AF causes palpitations and reduced exercise tolerance. Diagnosis involves iron markers, genotypic testing, and electrocardiogram (ECG) findings. Treatment strategies focus on reducing iron levels in hemochromatosis and managing AF through antithrombotic therapy and rhythm control. Untreated hemochromatosis carries a higher risk of complications, and PAF is associated with increased cardiovascular-related mortality. For better understanding of the mechanisms and to improve management, additional studies are required. Tailored approaches and combined treatments may enhance patient outcomes.

Datawarehouse-enabled quality control of atrial fibrillation detection in the stroke unit setting

Objective

(1) To assess the accuracy of a standard operating procedure (SOP) regarding the utilization of atrial fibrillation (AF) alarms in everyday clinical practice, and (2) to evaluate the performance of automated continuous surveillance for atrial fibrillation (AF) in hospitalized acute stroke patients.

Design

Retrospective cohort study.

Setting

Two stroke units from two tertiary care hospitals in Berlin, Germany.

Participants

We identified 635 patients with ischemic stroke diagnosis for the time period between 01. January and 30. September 2021 of which 176 patients had recorded AF alarms during monitoring. Of those, 115 patients were randomly selected for evaluation. After excluding 6 patients with hemorrhagic stroke in their records, 109 patients (mean age: 79.1 years, median NIHSS at admission: 6, 57% female) remained for analysis.

Intervention

Using a clinical data warehouse for comprehensive data storage we retrospectively downloaded and visualized ECG data segments of 65 s duration around the automated AF alarms. We restricted the maximum number of ECG segments to ten per patient. Each ECG segment plot was uploaded into a REDCap database and categorized as either AF, non-AF or artifact by manual review. Atrial flutter was subsumed as AF. These classifications were then matched with 1) medical history and known diseases before stroke, 2) discharge diagnosis, and 3) recommended treatment plan in the medical history using electronic health records.

Main outcome measures

The primary outcome was the proportion of previously unknown AF diagnoses correctly identified by the monitoring system but missed by the clinical team during hospitalization. Secondary outcomes included the proportion of patients in whom a diagnosis of AF would likely have led to anticoagulant therapy. We also evaluated the accuracy of the automated detection system in terms of its positive predictive value (PPV).

Results

We evaluated a total of 717 ECG alarm segments from 109 patients. In 4 patients (3.7, 95% confidence interval [CI] 1.18-9.68%) physicians had missed AF despite at least one true positive alarm. All four patients did not receive long-term secondary prevention in form of anticoagulant therapy. 427 out of 717 alarms were rated true positives, resulting in a positive predictive value of 0.6 (CI 0.56-0.63) in this cohort.

Conclusion

By connecting a data warehouse, electronic health records and a REDCap survey tool, we introduce a path to assess the monitoring quality of AF in acute stroke patients. We find that implemented standards of procedure to detect AF during stroke unit care are effective but leave room for improvement. Such data warehouse-based concepts may help to adjust internal processes or identify targets of further investigations.

Scanning of paroxysmal atrial fibrillation as an etiological risk factor in patients with acute ischemic stroke: prospective study

BACKGROUND

Prevention of recurrence of stroke depends on recognition of the underlying mechanism of ischemia.

OBJECTIVE

To screen patients who were hospitalized with diagnosis of acute ischemic stroke in terms of atrial fibrillation (AF) with repeated Holter electrocardiography recordings.

DESIGN AND SETTING

Prospective study conducted at Konya Education and Research Hospital, Turkey.

METHODS

Patients with a diagnosis of acute ischemic stroke, without atrial fibrillation on electrocardiography (ECG), were evaluated. Their age, gender, histories of previous ischemic attack, occurrences of paroxysmal atrial fibrillation (PAF) and other risks were assessed during the first week after acute ischemic stroke and one month thereafter. ECG recordings were obtained from 130 patients through 24-hour ambulatory Holter. Patients without PAF attack during the first Holter were re-evaluated.

RESULTS

PAF was detected through the first Holter in 33 (25.4%) out of 130 acute ischemic stroke patients. A second Holter was planned for 97 patients: 53 (54.6%) of them could not attend due to COVID-19 pandemic; while 44 (45.3%) patients had the second Holter and, among these, 4 (9.1%) had PAF. The only parameter associated with PAF was older age. Four (10.8%) of the 37 patients with PAF had also symptomatic carotid stenosis.

CONCLUSIONS

Detecting the presence of PAF by screening patients with no AF in the ECG through Holter ECG examinations is valuable in terms of changing the course of the treatment. It should be kept in mind that the possibility of accompanying PAF cannot be ruled out in the presence of other factors that pose a risk of stroke.

The value of STAF (Score for the Targeting of Atrial Fibrillation) in Patients with Cryptogenic Embolic Stroke

Introduction

The aim of the present study was to predict paroxysmal atrial fibrillation (PAF) in acute ischemic stroke patients with presumed cryptogenic embolic etiology.

Methods

In this retrospective cohort study, demographics, blood tests, data of neuroimaging studies such as non-contrast computed tomography (NCCT), magnetic resonance imaging (MRI), standard 12-lead electrocardigraphy (ECG), 24-hour Holter ECG, echocardiography was collected. The diagnostic work-up to detect atrial fibrillation (AF) was either medical history of the patient or 12-lead ECG or 24-hour Holter ECG or continuous ECG monitoring. Score for the targeting of atrial fibrillation (STAF) was calculated for all patients. Cryptogenic ischemic stroke (CS) patients with and without documented AF were recorded.

Results

Between July 2014 and December 2015, a total of 133 of the 258 patients with CS were included in this study. Overall, 133 patients were enrolled and AF was detected in 30 (22.6%) patients. In univariate analysis gender (p<0.001), age (p=0.001), smoking habit (p=0.004), aortic and mitral valve insufficiency (p=0.014 and p=0.021), left ventricular systolic dysfunction (p=0.04), and left atrial dilatation (p=0.03) were predictors of AF but multivariate analysis showed that only gender and age were independent predictors of AF in patients with presumed cryptogenic ischemic stroke. According to ROC analysis, area under the curve was 70% and the sensitivity and specificity of STAF score of ≥5 was 86% and 71% respectively.

Conclusion

STAF score predicted with fair accuracy, and has a limited use for the risk of PAF in stroke patients.