Background and aims: Early detection of atrial fibrillation after stroke is important for secondary prevention in stroke patients without known atrial fibrillation (AF). We aimed to compare the performance of CHADS2, CHA2DS2-VASc and HATCH scores in predicting AF detected after stroke (AFDAS) and to test whether adding stroke severity to the risk scores improves predictive performance. Methods: Adult patients with first ischemic stroke event but without a prior history of AF were retrieved from a nationwide population-based database. We compared C-statistics of CHADS2, CHA2DS2-VASc and HATCH scores for predicting the occurrence of AFDAS during stroke admission (cohort I) and during follow-up after hospital discharge (cohort II). The added value of stroke severity to prediction models was evaluated using C-statistics, net reclassification improvement, and integrated discrimination improvement. Results: Cohort I comprised 13,878 patients and cohort II comprised 12,567 patients. Among them, 806 (5.8%) and 657 (5.2%) were diagnosed with AF, respectively. The CHADS2 score had the lowest C-statistics (0.558 in cohort I and 0.597 in cohort II), whereas the CHA2DS2-VASc score had comparable C-statistics (0.603 and 0.644) to the HATCH score (0.612 and 0.653) in predicting AFDAS. Adding stroke severity to each of the three risk scores significantly increased the model performance. Conclusions: In stroke patients without known AF, all three risk scores predicted AFDAS during admission and follow-up, but with suboptimal discrimination. Adding stroke severity improved their predictive abilities. These risk scores, when combined with stroke severity, may help prioritize patients for continuous cardiac monitoring in daily practice.
|Number of pages||7|
|Publication status||Published - 2018 May|
All Science Journal Classification (ASJC) codes
- Cardiology and Cardiovascular Medicine