Electrocardiographic RR Interval Dynamic Analysis to Identify Acute Stroke Patients at High Risk for Atrial Fibrillation Episodes During Stroke Unit Admission.

Patients at short-term risk of paroxysmal atrial fibrillation (PAF) often exhibit increased RR interval variability during sinus rhythm. We studied if RR dynamic analysis, applied in the first hours after stroke unit (SU) admission, identified acute ischemic stroke patients at higher risk for subsequent PAF episodes detected within the SU hospitalization. Acute ischemic stroke patients underwent continuous cardiac monitoring (CCM) using standard bedside monitors immediately after SU admission. The CCM tracks from the first 48 h were analyzed using a telemedicine service (SRA clinic, Apoplex Medical, Germany). Based on the RR dynamics, the stroke risk analysis (SRA) algorithm stratified the risk for PAF as follows: low risk for PAF, high risk for PAF, presence of manifest AF. The subsequent presence/absence of PAF during the whole SU hospitalization was ruled out using all available CCMs, standard ECGs, or 24-h Holter ECGs. Two hundred patients (40% females, mean age 71 ± 16 years) were included. According to the initial SRA analysis, 111 patients (56%) were considered as low risk for PAF, 52 (26%) as high risk while 37 patients (18%) had manifest AF. A low-risk level SRA was associated with a reduced probability for subsequent PAF detection (1/111, 0.9%, 95% CI 0-4.3%) while a high-risk level SRA predicted an increased probability (20/52, 38.5% (95% CI 25-52%). RR dynamic analysis performed in the first hours after ischemic stroke may stratify patients into categories at low or high risk for forthcoming paroxysmal AF episodes detected within the SU hospitalization.

Improved detection of paroxysmal atrial fibrillation utilizing a software-assisted electrocardiogram approach.

BACKGROUND: Automated complexity-based statistical stroke risk analysis (SRA) of electrocardiogram (ECG) recordings can be used to estimate the risk of paroxysmal atrial fibrillation (pAF). We investigated whether this method could improve the reliability of detection of patients at risk for pAF. METHODS AND RESULTS: Data from 12-lead ECGs, 24-h Holter ECGs, and SRA based on separate 1-hour Holter ECG snips were collected from three groups: 70 patients with a history of pAF but who showed no AF episode in the 12-lead ECG at study entry; 19 patients with chronic AF (at study entry); and 100 young healthy individuals. AF episodes were detected by Holter ECG in 19 of the 70 non-chronic AF patients (27.1% overall, 18.6% in the first hour), and 37 of these 70 patients were classified as at risk for pAF by SRA (representing a sensitivity of 52.9% based on the first hour of analyzed recording). Fifty-four of the 70 patients also showed a sinus rhythm in the first hour. SRA detected pAF risk in 23 of these 54 patients (representing a sensitivity of 42.6%). The Holter data showed at least 1 AF episode and at least 1 hour of sinus rhythm in nine of the patients with pAF. For these patients, SRA classified 77.8% as being at risk in the first hour after the end of the AF episode, and 71.4% and 42.9% as being at risk in the second and third hours, respectively. SRA detected almost all cardiologist-confirmed AF episodes that had been recorded in 1-hour ECG snips (sensitivity, 99.2%; specificity, 99.2%). CONCLUSIONS: This outpatient study confirms previous findings that routine use of SRA could improve AF detection rates and thus may shorten the time between AF onset and initiation of prevention measures for patients at high risk for stroke.

Continuous stroke unit electrocardiographic monitoring versus 24-hour Holter electrocardiography for detection of paroxysmal atrial fibrillation after stroke.

BACKGROUND AND PURPOSE: Cardioembolism in paroxysmal atrial fibrillation (pxAF) is a frequent cause of ischemic stroke. Sensitive detection of pxAF after stroke is crucial for adequate secondary stroke prevention; the optimal diagnostic modality to detect pxAF on stroke units is unknown. We compared 24-hour Holter electrocardiography (ECG) with continuous stroke unit ECG monitoring (CEM) for pxAF detection. METHODS: Patients with acute ischemic stroke or transient ischemic attack were prospectively enrolled. After a 12-channel ECG on admission, all patients received 24-hour Holter ECG and CEM. Additionally, ECG monitoring data underwent automated analysis using dedicated software to identify pxAF. Patients with a history of atrial fibrillation or with atrial fibrillation on the admission ECG were excluded. RESULTS: Four hundred ninety-six patients (median age, 69 years; 61.5% male) fulfilled all inclusion criteria (ischemic stroke: 80.4%; transient ischemic attack: 19.6%). Median stroke unit stay lasted 88.8 hours (interquartile range, 65.0-122.0). ECG data for automated CEM analysis were available for a median time of 64.0 hours (43.0-89.8). Paroxysmal AF was documented in 41 of 496 patients (8.3%). Of these, Holter detected pxAF in 34.1%; CEM in 65.9%; and automated CEM in 92.7%. CEM and automated CEM detected significantly more patients with pxAF than Holter (P<0.001), and automated CEM detected more patients than CEM (P<0.001). CONCLUSIONS: Automated analysis of CEM improves pxAF detection in patients with stroke on stroke units compared with 24-hour Holter ECG. The comparative usefulness of prolonged or repetitive Holter ECG recordings requires further evaluation.