Stroke Risk Analysis, a System With a High Detection Rate of Atrial Fibrillation in Stroke and Transient Ischemic Attack

Is Stroke risk analysis (SRA) a reliable method for predicting atrial fibrillation? A systematic review

INTRODUCTION

Atrial fibrillation is responsible for a considerable number of cases of cardioembolism, accounting for 17% to 30% of the etiologies of all strokes. The software known as Stroke Risk Analysis (SRA) detects patients at high risk of paroxysmal atrial fibrillation by analyzing a continuous electrocardiogram recorded over different periods of time.

OBJECTIVES

This article aims to carry out a systematic review investigating the effectiveness of the SRA method in predicting the risk of stroke patients having paroxysmal atrial fibrillation as the cause of the event.

METHODS

The methods correspond to the format of the International Prospective Register of Systematic Reviews Protocol, according to CRD Identification Code: CRD42021253974. A systematic search was carried out in BMJB, PubMed/MEDLINE, Science Direct and LILACS. Six cohort studies met the inclusion criteria, representing a total of 2,088 participants with stroke, and compared the detection of patients with paroxysmal atrial fibrillation on the continuous recording electrocardiogram with a time variation of 1 to 48h with the use of SRA.

RESULTS

Studies have shown that SRA has a high negative predictive value (between 96 and 99.1%) and can contribute to the selection of patients at high risk of paroxysmal atrial fibrillation to be referred for implantable cardiac monitoring to continue the investigation.

CONCLUSIONS

A sequential combination of SRA with implantable cardiac monitoring is a promising strategy for detecting undiagnosed paroxysmal atrial fibrillation. Thus, the SRA can act as a cost-effective pre-selection tool to identify patients at higher risk of having paroxysmal atrial fibrillation as a possible cause of stroke and who may benefit from implantable cardiac monitoring. However, the lack of randomized studies is a limitation that must be considered.

Rate of atrial fibrillation by Holter-Stroke Risk Analysis in undetermined TIA/rapidly improving stroke symptoms patients

Introduction

Holter-SRA (Stroke Risk Analysis) is an automated analysis of ECG monitoring for Atrial Fibrillation (AF) detection. The aim of this study was to evaluate the rate of AF in undetermined TIA/Rapidly improving stroke symptoms (RISS) patients.

Methods

Prospective study of undetermined TIA/RISS patients who presented to the emergency department. Early vascular studies (angio CT, transthoracic echocardiography and ECG) were performed in emergency department. The Holter-SRA device was placed for 2 h and the patients were classified into: confirmed AF, high risk of AF or low risk of AF. Prolonged ambulatory monitoring (7 days) was carried out every month for patients with a high-risk pattern. The results were evaluated until definitive detection of AF or low-risk pattern. The endpoints were rate of AF and vascular recurrence at 90 days.

Results

Over a period of 24 months, 83 undetermined TIA/RISS patients were enrolled. The mean age was 70 ± 10 years and 61% were men. The median ABCD2 score was 4 points (1-7). After 2 h of monitoring in the emergency department, AF was detected in one patient (1.2%), 51 patients with a low-risk pattern and 31 patients (37.3%) showed a high-risk pattern of AF. During the ambulatory monitoring, of the 31 patients high risk pattern patients, AF was diagnosed to 17 cases and of the 51 patients with a low-risk pattern, one case experienced a recurrent vascular due to undetected AF (1.9% false negative). Three patients (3.6%) suffered a vascular recurrence within the first 90 days, before AF diagnosis.

Conclusions

In our study, AF was detected in 22.9% of the 83 patients with indeterminate TIA/RISS. Holter-SRA has allowed us to increase the detection of AF, especially those patients with a high-risk pattern in the first 3 months.

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.

The significance of Holter electrocardiography in the etiological evaluation of transient ischemic stroke

BACKGROUND:

Transient ischemic attack (TIA) is a common neurovascular disorder associated with a higher risk of stroke within the first 24 h after the first event. Acute cerebral and arterial neuroimaging combined with long-term electrocardiography (ECG) monitoring have been proven to be useful in determining etiology. Cardio-embolism constitutes 20%–26% etiology of TIAs most of them with atrial fibrillation (AF). Investigation of AF after TIA is very important because oral anticoagulants can reduce the risk of subsequent stroke by two thirds.

MATERIALS AND METHODS:

The present study included 45 patients suffering from TIA with undetermined source according to the Trial of Org 10172 in Acute Stroke Treatment criteria; the control group (n = 45) was selected from the patients admitted to cardiology outpatient clinic with nonspecific complaints without cerebrovascular and/or cardiovascular disease. All patients underwent echocardiography and 24 h Holter ECG monitoring (HM).

RESULTS:

There was no significant difference between the patient group and the control group in terms of age and gender. Cholesterol, low-density lipoprotein and urea levels, left atrium diameters and the incidence of hypertension, coronary artery diseases, and AF were significantly higher in TIA group (P < 0.05). In the results of HM, there were six patients with AF in the study group, and in the control group, there was no patients with AF (P = 0.03).

DISCUSSION AND CONCLUSION:

In acute phase of TIA, 24 h HM is important for determining the etiology and selecting an appropriate treatment that can protect patients from subsequent strokes.

Detection of Atrial Fibrillation on Stroke Units: Comparison of Manual versus Automatic Analysis of Continuous Telemetry

BACKGROUND

Detection of atrial fibrillation (AF) is one of the primary diagnostic goals for patients on a stroke unit. Physician-based manual analysis of continuous ECG monitoring is regarded as the gold standard for AF detection but requires considerable resources. Recently, automated computer-based analysis of RR intervals was established to simplify AF detection. The present prospective study analyzes both methods head to head regarding AF detection specificity, sensitivity, and overall effectiveness.

METHODS

Consecutive stroke patients without history of AF or proof of AF in the admission ECG were enrolled over the period of 7 months. All patients received continuous ECG telemetry during the complete stay on the stroke unit. All ECGs underwent automated analysis by a commercially available program. Blinded to these results, all ECG tracings were also assessed manually. Sensitivity, specificity, time consumption, costs per day, and cost-effectiveness were compared.

RESULTS

216 consecutive patients were enrolled (70.7 ± 14.1 years, 56% male) and 555 analysis days compared. AF was detected by manual ECG analysis on 37 days (6.7%) and automatically on 57 days (10.3%). Specificity of the automated algorithm was 94.6% and sensitivity 78.4% (28 [5.0%] false positive and 8 [1.4%] false negative). Patients with AF were older and had more often arterial hypertension, higher NIHSS at admission, more often left atrial dilatation, and a higher CHA2DS2-VASc score. Automation significantly reduced human resources but was more expensive compared to manual analysis alone.

CONCLUSION

Automatic AF detection is highly specific, but sensitivity is relatively low. Results of this study suggest that automated computer-based AF detection should be rather complementary to manual ECG analysis than replacing it.