Cheng-Yang Hsieh1, Cheng-Han Lee2, Darren Philbert Wu3, Sheng-Feng Sung4. 1. Department of Neurology, Tainan Sin Lau Hospital, Tainan, Taiwan. 2. Division of Cardiology, Department of Internal Medicine, National Cheng Kung University Hospital and College of Medicine, Tainan, Taiwan. 3. Division of Neurology, Department of Internal Medicine, Ditmanson Medical Foundation Chiayi Christian Hospital, Chiayi City, Taiwan. 4. Division of Neurology, Department of Internal Medicine, Ditmanson Medical Foundation Chiayi Christian Hospital, Chiayi City, Taiwan. Electronic address: sfsung@cych.org.tw.
Abstract
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.
BACKGROUND AND AIMS: Early detection of atrial fibrillation after stroke is important for secondary prevention in strokepatients 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 strokepatients 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.
Authors: Jiann-Der Lee; Ya-Wen Kuo; Chuan-Pin Lee; Yen-Chu Huang; Meng Lee; Tsong-Hai Lee Journal: Int J Environ Res Public Health Date: 2022-06-14 Impact factor: 4.614
Authors: Cheng-Yang Hsieh; Hsuan-Min Kao; Kuan-Lin Sung; Luciano A Sposato; Sheng-Feng Sung; Swu-Jane Lin Journal: Front Cardiovasc Med Date: 2022-04-29