부산대학교 도서관

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. Keywords: Atrial fibrillation, Manual detection, Automated detection, Stroke, SRAclinic
Author(s): Andreas Rogalewski (corresponding author) [a]; Jorge Plümer [a]; Tobias Feldmann [b]; Christian Oelschläger [a]; Isabell Greeve [a]; Alkisti Kitsiou [a]; Peter D. Schellinger [c]; Carsten Walter Israel [b]; Wolf-Rüdiger [...]