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
BackgroundPoststroke 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 eigh...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2022-04-01
|
| Series: | Frontiers in Cardiovascular Medicine |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fcvm.2022.888240/full |
| _version_ | 1818009435280244736 |
|---|---|
| author | Cheng-Yang Hsieh Cheng-Yang Hsieh Hsuan-Min Kao Kuan-Lin Sung Luciano A. Sposato Luciano A. Sposato Luciano A. Sposato Luciano A. Sposato Luciano A. Sposato Sheng-Feng Sung Sheng-Feng Sung Swu-Jane Lin |
| author_facet | Cheng-Yang Hsieh Cheng-Yang Hsieh Hsuan-Min Kao Kuan-Lin Sung Luciano A. Sposato Luciano A. Sposato Luciano A. Sposato Luciano A. Sposato Luciano A. Sposato Sheng-Feng Sung Sheng-Feng Sung Swu-Jane Lin |
| author_sort | Cheng-Yang Hsieh |
| collection | DOAJ |
| description | BackgroundPoststroke 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).MethodsWe 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, C2HEST, CHADS2, CHA2DS2-VASc, CHASE-LESS, HATCH, HAVOC, and Re-CHARGE-AF scores, were compared using the C-index, net reclassification improvement, integrated discrimination improvement, calibration curve, and decision curve analysis.ResultsThe 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.ConclusionThe 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. |
| first_indexed | 2024-04-14T05:41:53Z |
| format | Article |
| id | doaj.art-2b608169ba9d4afcb2283d7d1c4e2b79 |
| institution | Directory Open Access Journal |
| issn | 2297-055X |
| language | English |
| last_indexed | 2024-04-14T05:41:53Z |
| publishDate | 2022-04-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Cardiovascular Medicine |
| spelling | doaj.art-2b608169ba9d4afcb2283d7d1c4e2b792022-12-22T02:09:24ZengFrontiers Media S.A.Frontiers in Cardiovascular Medicine2297-055X2022-04-01910.3389/fcvm.2022.888240888240Validation 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 StudyCheng-Yang Hsieh0Cheng-Yang Hsieh1Hsuan-Min Kao2Kuan-Lin Sung3Luciano A. Sposato4Luciano A. Sposato5Luciano A. Sposato6Luciano A. Sposato7Luciano A. Sposato8Sheng-Feng Sung9Sheng-Feng Sung10Swu-Jane Lin11Department of Neurology, Tainan Sin Lau Hospital, Tainan City, TaiwanSchool of Pharmacy, Institute of Clinical Pharmacy and Pharmaceutical Sciences, College of Medicine, National Cheng Kung University, Tainan City, TaiwanDivision of Geriatrics, Department of Internal Medicine, Ditmanson Medical Foundation Chia-Yi Christian Hospital, Chiayi City, TaiwanSchool of Medicine, National Taiwan University, Taipei City, TaiwanDepartment of Clinical Neurological Sciences, Schulich School of Medicine and Dentistry, Western University, London, ON, CanadaHeart & Brain Laboratory, Western University, London, ON, CanadaDepartment of Epidemiology and Biostatistics and Anatomy and Cell Biology, Schulich School of Medicine and Dentistry, Western University, London, ON, CanadaRobarts Research Institute, Western University, London, ON, CanadaLawson Health Research Institute, London, ON, Canada0Division of Neurology, Department of Internal Medicine, Ditmanson Medical Foundation Chia-Yi Christian Hospital, Chiayi City, Taiwan1Department of Nursing, Min-Hwei Junior College of Health Care Management, Tainan City, Taiwan2Department of Pharmacy Systems, Outcomes and Policy, College of Pharmacy, University of Illinois at Chicago, Chicago, IL, United StatesBackgroundPoststroke 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).MethodsWe 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, C2HEST, CHADS2, CHA2DS2-VASc, CHASE-LESS, HATCH, HAVOC, and Re-CHARGE-AF scores, were compared using the C-index, net reclassification improvement, integrated discrimination improvement, calibration curve, and decision curve analysis.ResultsThe 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.ConclusionThe 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.https://www.frontiersin.org/articles/10.3389/fcvm.2022.888240/fullatrial fibrillationexternal validationischemic strokepredictionrisk score |
| spellingShingle | Cheng-Yang Hsieh Cheng-Yang Hsieh Hsuan-Min Kao Kuan-Lin Sung Luciano A. Sposato Luciano A. Sposato Luciano A. Sposato Luciano A. Sposato Luciano A. Sposato Sheng-Feng Sung Sheng-Feng Sung Swu-Jane Lin 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 Frontiers in Cardiovascular Medicine atrial fibrillation external validation ischemic stroke prediction risk score |
| title | 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 |
| title_full | 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 |
| title_fullStr | 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 |
| title_full_unstemmed | 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 |
| title_short | 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 |
| title_sort | 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 |
| topic | atrial fibrillation external validation ischemic stroke prediction risk score |
| url | https://www.frontiersin.org/articles/10.3389/fcvm.2022.888240/full |
| work_keys_str_mv | AT chengyanghsieh validationofriskscoresforpredictingatrialfibrillationdetectedafterstrokebasedonanelectronicmedicalrecordalgorithmaregistryclaimselectronicmedicalrecordlinkeddatastudy AT chengyanghsieh validationofriskscoresforpredictingatrialfibrillationdetectedafterstrokebasedonanelectronicmedicalrecordalgorithmaregistryclaimselectronicmedicalrecordlinkeddatastudy AT hsuanminkao validationofriskscoresforpredictingatrialfibrillationdetectedafterstrokebasedonanelectronicmedicalrecordalgorithmaregistryclaimselectronicmedicalrecordlinkeddatastudy AT kuanlinsung validationofriskscoresforpredictingatrialfibrillationdetectedafterstrokebasedonanelectronicmedicalrecordalgorithmaregistryclaimselectronicmedicalrecordlinkeddatastudy AT lucianoasposato validationofriskscoresforpredictingatrialfibrillationdetectedafterstrokebasedonanelectronicmedicalrecordalgorithmaregistryclaimselectronicmedicalrecordlinkeddatastudy AT lucianoasposato validationofriskscoresforpredictingatrialfibrillationdetectedafterstrokebasedonanelectronicmedicalrecordalgorithmaregistryclaimselectronicmedicalrecordlinkeddatastudy AT lucianoasposato validationofriskscoresforpredictingatrialfibrillationdetectedafterstrokebasedonanelectronicmedicalrecordalgorithmaregistryclaimselectronicmedicalrecordlinkeddatastudy AT lucianoasposato validationofriskscoresforpredictingatrialfibrillationdetectedafterstrokebasedonanelectronicmedicalrecordalgorithmaregistryclaimselectronicmedicalrecordlinkeddatastudy AT lucianoasposato validationofriskscoresforpredictingatrialfibrillationdetectedafterstrokebasedonanelectronicmedicalrecordalgorithmaregistryclaimselectronicmedicalrecordlinkeddatastudy AT shengfengsung validationofriskscoresforpredictingatrialfibrillationdetectedafterstrokebasedonanelectronicmedicalrecordalgorithmaregistryclaimselectronicmedicalrecordlinkeddatastudy AT shengfengsung validationofriskscoresforpredictingatrialfibrillationdetectedafterstrokebasedonanelectronicmedicalrecordalgorithmaregistryclaimselectronicmedicalrecordlinkeddatastudy AT swujanelin validationofriskscoresforpredictingatrialfibrillationdetectedafterstrokebasedonanelectronicmedicalrecordalgorithmaregistryclaimselectronicmedicalrecordlinkeddatastudy |