Cheng-Yang Hsieh1, Cheng-Han Lee2, Sheng-Feng Sung3. 1. Department of Neurology, Tainan Sin Lau Hospital, Tainan, Taiwan; School of Pharmacy, Institute of Clinical Pharmacy and Pharmaceutical Sciences, College of Medicine, National Cheng Kung University, 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; Department of Information Management and Institute of Healthcare Information Management, National Chung Cheng University, Chiayi County, Taiwan. Electronic address: sfusng@cych.org.tw.
Abstract
BACKGROUND AND AIMS: Prompt detection of atrial fibrillation (AF) is essential for optimal secondary stroke prevention, but routine long-term cardiac monitoring of all ischemic stroke patients is neither practical nor affordable. We aimed to develop and validate a risk score to identify patients at risk for newly diagnosed AF (NDAF) after ischemic stroke. METHODS: Information on adult patients hospitalized for ischemic stroke without known AF was retrieved from a nationwide database. Primary outcome was NDAF within one year following index stroke. A stepwise Cox model was used to screen for predictors. Beta coefficients for the independent predictors were converted to integer points, which were summed to create a risk score. RESULTS: We identified 4 positive predictors and 3 negative predictors. The CHASE-LESS score (Coronary, Heart failure, Age, stroke SEverity, - LipidEmia, Sugar, prior Stroke) comprises coronary artery disease (1 point), congestive heart failure (1 point), age (1 point for every 10 years), stroke severity (National Institutes of Health Stroke Scale; 1 point for 6-13 and 4 points for ≥14), hyperlipidemia (-1 point), diabetes (-1 point), and prior history of stroke or transient ischemic attack (-1 point). Overall, 6.0% (1029/17,076) of patients developed NDAF. The incidence rate ranged from 8/1000 person-years (CHASE-LESS ≤3) to 240/1000 person-years (CHASE-LESS ≥10). The model achieved a c-index of 0.730 in the development cohort and 0.732 in the validation cohort. CONCLUSIONS: The CHASE-LESS score could aid clinicians to identify patients at risk of developing NDAF and help prioritize patients for advanced cardiac monitoring.
BACKGROUND AND AIMS: Prompt detection of atrial fibrillation (AF) is essential for optimal secondary stroke prevention, but routine long-term cardiac monitoring of all ischemic strokepatients is neither practical nor affordable. We aimed to develop and validate a risk score to identify patients at risk for newly diagnosed AF (NDAF) after ischemic stroke. METHODS: Information on adult patients hospitalized for ischemic stroke without known AF was retrieved from a nationwide database. Primary outcome was NDAF within one year following index stroke. A stepwise Cox model was used to screen for predictors. Beta coefficients for the independent predictors were converted to integer points, which were summed to create a risk score. RESULTS: We identified 4 positive predictors and 3 negative predictors. The CHASE-LESS score (Coronary, Heart failure, Age, stroke SEverity, - LipidEmia, Sugar, prior Stroke) comprises coronary artery disease (1 point), congestive heart failure (1 point), age (1 point for every 10 years), stroke severity (National Institutes of Health Stroke Scale; 1 point for 6-13 and 4 points for ≥14), hyperlipidemia (-1 point), diabetes (-1 point), and prior history of stroke or transient ischemic attack (-1 point). Overall, 6.0% (1029/17,076) of patients developed NDAF. The incidence rate ranged from 8/1000 person-years (CHASE-LESS ≤3) to 240/1000 person-years (CHASE-LESS ≥10). The model achieved a c-index of 0.730 in the development cohort and 0.732 in the validation cohort. CONCLUSIONS: The CHASE-LESS score could aid clinicians to identify patients at risk of developing NDAF and help prioritize patients for advanced cardiac monitoring.
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: Hans-Christoph Diener; J Donald Easton; Robert G Hart; Scott Kasner; Hooman Kamel; George Ntaios Journal: Nat Rev Neurol Date: 2022-05-10 Impact factor: 44.711
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