Validation of Risk Scores for Predicting Atrial Fibrillation Detected After Stroke Based on an Electronic Medical Record Algorithm: A Registry-Claims-Electronic Medical Record Linked Data Study.
| Autor: | Hsieh CY; Department of Neurology, Tainan Sin Lau Hospital, Tainan City, Taiwan.; School of Pharmacy, Institute of Clinical Pharmacy and Pharmaceutical Sciences, College of Medicine, National Cheng Kung University, Tainan City, Taiwan., Kao HM; Division of Geriatrics, Department of Internal Medicine, Ditmanson Medical Foundation Chia-Yi Christian Hospital, Chiayi City, Taiwan., Sung KL; School of Medicine, National Taiwan University, Taipei City, Taiwan., Sposato LA; Department of Clinical Neurological Sciences, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada.; Heart & Brain Laboratory, Western University, London, ON, Canada.; Department of Epidemiology and Biostatistics and Anatomy and Cell Biology, Schulich School of Medicine and Dentistry, Western University, London, ON, Canada.; Robarts Research Institute, Western University, London, ON, Canada.; Lawson Health Research Institute, London, ON, Canada., Sung SF; Division of Neurology, Department of Internal Medicine, Ditmanson Medical Foundation Chia-Yi Christian Hospital, Chiayi City, Taiwan.; Department of Nursing, Min-Hwei Junior College of Health Care Management, Tainan City, Taiwan., Lin SJ; Department of Pharmacy Systems, Outcomes and Policy, College of Pharmacy, University of Illinois at Chicago, Chicago, IL, United States. |
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| Jazyk: | angličtina |
| Zdroj: | Frontiers in cardiovascular medicine [Front Cardiovasc Med] 2022 Apr 29; Vol. 9, pp. 888240. Date of Electronic Publication: 2022 Apr 29 (Print Publication: 2022). |
| DOI: | 10.3389/fcvm.2022.888240 |
| Abstrakt: | Background: Poststroke atrial fibrillation (AF) screening aids decisions regarding the optimal secondary prevention strategies in patients with acute ischemic stroke (AIS). We used an electronic medical record (EMR) algorithm to identify AF in a cohort of AIS patients, which were used to validate eight risk scores for predicting AF detected after stroke (AFDAS). Methods: We used linked data between a hospital stroke registry and a deidentified database including EMRs and administrative claims data. EMR algorithms were constructed to identify AF using diagnostic and medication codes as well as free clinical text. Based on the optimal EMR algorithm, the incidence rate of AFDAS was estimated. The predictive performance of 8 risk scores including AS5F, C Results: The algorithm that defines AF as any positive mention of AF-related keywords in electrocardiography or echocardiography reports, or presence of diagnostic codes of AF was used to identify AF. Among the 5,412 AIS patients without known AF at stroke admission, the incidence rate of AFDAS was 84.5 per 1,000 person-year. The CHASE-LESS and AS5F scores were well calibrated and showed comparable C-indices (0.741 versus 0.730, p = 0.223), which were significantly higher than the other risk scores. Conclusion: The CHASE-LESS and AS5F scores demonstrated adequate discrimination and calibration for predicting AFDAS. Both simple risk scores may help select patients for intensive AF monitoring. Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. (Copyright © 2022 Hsieh, Kao, Sung, Sposato, Sung and Lin.) |
| Databáze: | MEDLINE |
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