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Ashburner JM, Chang Y, Porneala B, Singh SD, Yechoor N, Rosand JM, Singer DE, Anderson CD, Atlas SJ. Predicting post-stroke cognitive impairment using electronic health record data. Int J Stroke 2024:17474930241246156. [PMID: 38546170 DOI: 10.1177/17474930241246156] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/09/2024]
Abstract
BACKGROUND Secondary prevention interventions to reduce post-stroke cognitive impairment (PSCI) can be aided by the early identification of high-risk individuals who would benefit from risk factor modification. AIMS To develop and evaluate a predictive model to identify patients at increased risk of PSCI over 5 years using data easily accessible from electronic health records. METHODS Cohort study that included primary care patients from two academic medical centers. Patients were aged 45 years or older, without prior stroke or prevalent cognitive impairment, with primary care visits and an incident ischemic stroke between 2003 and 2016 (development/internal validation cohort) or 2010 and 2022 (external validation cohort). Predictors of PSCI were ascertained from the electronic health record. The outcome was incident dementia/cognitive impairment within 5 years and beginning 3 months following stroke, ascertained using International Classification of Diseases, Ninth/Tenth Revision (ICD-9/10) codes. For model variable selection, we considered potential predictors of PSCI and constructed 400 bootstrap samples with two-thirds of the model derivation sample. We ran 10-fold cross-validated Cox proportional hazards models using a least absolute shrinkage and selection operator (LASSO) penalty. Variables selected in >25% of samples were included. RESULTS The analysis included 332 incident diagnoses of PSCI in the development cohort (n = 3741), and 161 and 128 incident diagnoses in the internal (n = 1925) and external (n = 2237) validation cohorts, respectively. The C-statistic for predicting PSCI was 0.731 (95% confidence interval (CI): 0.694-0.768) in the internal validation cohort, and 0.724 (95% CI: 0.681-0.766) in the external validation cohort. A risk score based on the beta coefficients of predictors from the development cohort stratified patients into low (0-7 points), intermediate (8-11 points), and high (12-23 points) risk groups. The hazard ratios (HRs) for incident PSCI were significantly different by risk categories in internal (high, HR: 6.2, 95% CI: 4.1-9.3; Intermediate, HR: 2.7, 95% CI: 1.8-4.1) and external (high, HR: 6.1, 95% CI: 3.9-9.6; Intermediate, HR: 2.8, 95% CI: 1.9-4.3) validation cohorts. CONCLUSION Five-year risk of PSCI can be accurately predicted using routinely collected data. Model output can be used to risk stratify and identify individuals at increased risk for PSCI for preventive efforts. DATA ACCESS STATEMENT Mass General Brigham data contain protected health information and cannot be shared publicly. The data processing scripts used to perform analyses will be made available to interested researchers upon reasonable request to the corresponding author.
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Affiliation(s)
- Jeffrey M Ashburner
- Division of General Internal Medicine, Massachusetts General Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Yuchiao Chang
- Division of General Internal Medicine, Massachusetts General Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Bianca Porneala
- Division of General Internal Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Sanjula D Singh
- McCance Center for Brain Health, Massachusetts General Hospital, Boston, MA, USA
| | - Nirupama Yechoor
- McCance Center for Brain Health, Massachusetts General Hospital, Boston, MA, USA
| | - Jonathan M Rosand
- McCance Center for Brain Health, Massachusetts General Hospital, Boston, MA, USA
| | - Daniel E Singer
- Division of General Internal Medicine, Massachusetts General Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Christopher D Anderson
- McCance Center for Brain Health, Massachusetts General Hospital, Boston, MA, USA
- Department of Neurology, Brigham and Women's Hospital, Boston, MA, USA
| | - Steven J Atlas
- Division of General Internal Medicine, Massachusetts General Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
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Ashburner JM, Chang Y, Porneala B, Singh SD, Yechoor N, Rosand JM, Singer DE, Anderson CD, Atlas SJ. Predicting post-stroke cognitive impairment using electronic health record data. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.02.02.24302240. [PMID: 38352557 PMCID: PMC10863024 DOI: 10.1101/2024.02.02.24302240] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/19/2024]
Abstract
Importance Secondary prevention interventions to reduce post-stroke cognitive impairment (PSCI) can be aided by the early identification of high-risk individuals who would benefit from risk factor modification. Objective To develop and evaluate a predictive model to identify patients at increased risk of PSCI over 5 years using data easily accessible from electronic health records. Design Cohort study with patients enrolled between 2003-2016 with follow-up through 2022. Setting Primary care practices affiliated with two academic medical centers. Participants Individuals 45 years or older, without prior stroke or prevalent cognitive impairment, with primary care visits and an incident ischemic stroke between 2003-2016 (development/internal validation cohort) or 2010-2022 (external validation cohort). Exposures Predictors of PSCI were ascertained from the electronic health record. Main Outcome The outcome was incident dementia/cognitive impairment within 5 years and beginning 3 months following stroke, ascertained using ICD-9/10 codes. For model variable selection, we considered potential predictors of PSCI and constructed 400 bootstrap samples with two-thirds of the model derivation sample. We ran 10-fold cross-validated Cox proportional hazards models using a least absolute shrinkage and selection operator (LASSO) penalty. Variables selected in >25% of samples were included. Results The analysis included 332 incident diagnoses of PSCI in the development cohort (n=3,741), and 161 and 128 incident diagnoses in the internal (n=1,925) and external (n=2,237) validation cohorts. The c-statistic for predicting PSCI was 0.731 (95% CI: 0.694-0.768) in the internal validation cohort, and 0.724 (95% CI: 0.681-0.766) in the external validation cohort. A risk score based on the beta coefficients of predictors from the development cohort stratified patients into low (0-7 points), intermediate (8-11 points), and high (12-35 points) risk groups. The hazard ratios for incident PSCI were significantly different by risk categories in internal (High, HR: 6.2, 95% CI 4.1-9.3; Intermediate, HR 2.7, 95% CI: 1.8-4.1) and external (High, HR: 6.1, 95% CI: 3.9-9.6; Intermediate, HR 2.8, 95% CI: 1.9-4.3) validation cohorts. Conclusions and Relevance Five-year risk of PSCI can be accurately predicted using routinely collected data. Model output can be used to risk stratify and identify individuals at increased risk for PSCI for preventive efforts.
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Affiliation(s)
- Jeffrey M. Ashburner
- Division of General Internal Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, USA
| | - Yuchiao Chang
- Division of General Internal Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, USA
| | - Bianca Porneala
- Division of General Internal Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Sanjula D. Singh
- McCance Center for Brain Health, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Nirupama Yechoor
- McCance Center for Brain Health, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Jonathan M. Rosand
- McCance Center for Brain Health, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Daniel E. Singer
- Division of General Internal Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, USA
| | - Christopher D. Anderson
- McCance Center for Brain Health and Department of Neurology, Brigham and Women’s Hospital, Boston, Massachusetts, USA
| | - Steven J. Atlas
- Division of General Internal Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, USA
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3
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Palaiodimou L, Theodorou A, Triantafyllou S, Dilaveris P, Flevari P, Giannopoulos G, Kossyvakis C, Adreanides E, Tympas K, Nikolopoulos P, Zompola C, Bakola E, Chondrogianni M, Magiorkinis G, Deftereos S, Giannopoulos S, Tsioufis K, Filippatos G, Tsivgoulis G. Performance of Different Risk Scores for the Detection of Atrial Fibrillation Among Patients With Cryptogenic Stroke. Stroke 2024; 55:454-462. [PMID: 38174570 DOI: 10.1161/strokeaha.123.044961] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2023] [Accepted: 12/05/2023] [Indexed: 01/05/2024]
Abstract
BACKGROUND Atrial fibrillation (AF) is a frequent underlying cause of cryptogenic stroke (CS) and its detection can be increased using implantable cardiac monitoring (ICM). We sought to evaluate different risk scores and assess their diagnostic ability in identifying patients with CS with underlying AF on ICM. METHODS Patients with CS, being admitted to a single tertiary stroke center between 2017 and 2022 and receiving ICM, were prospectively evaluated. The CHA2DS2-VASc, HAVOC, Brown ESUS-AF, and C2HEST scores were calculated at baseline. The primary outcome of interest was the detection of AF, which was defined as at least 1 AF episode on ICM lasting for 2 consecutive minutes or more. The diagnostic accuracy measures and the net reclassification improvement were calculated for the 4 risk scores. Stroke recurrence was evaluated as a secondary outcome. RESULTS A total of 250 patients with CS were included, and AF was detected by ICM in 20.4% (n=51) during a median monitoring period of 16 months. Patients with CS with AF detection were older compared with the rest (P=0.045). The median HAVOC, Brown ESUS-AF, and C2HEST scores were higher among the patients with AF compared with the patients without AF (all P<0.05), while the median CHA2DS2-VASc score was similar between the 2 groups. The corresponding C statistics for CHA2DS2-VASc, HAVOC, Brown ESUS-AF, and C2HEST for AF prediction were 0.576 (95% CI, 0.482-0.670), 0.612 (95% CI, 0.523-0.700), 0.666 (95% CI, 0.587-0.746), and 0.770 (95% CI, 0.699-0.839). The C2HEST score presented the highest diagnostic performance based on C statistics (P<0.05 after correction for multiple comparisons) and provided significant improvement in net reclassification for AF detection (>70%) compared with the other risk scores. Finally, stroke recurrence was documented in 5.6% of the study population, with no difference regarding the 4 risk scores between patients with and without recurrent stroke. CONCLUSIONS The C2HEST score was superior to the CHA2DS2-VASc, HAVOC, and Brown ESUS-AF scores for discriminating patients with CS with underlying AF using ICM.
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Affiliation(s)
- Lina Palaiodimou
- Second Department of Neurology (L.P., A.T., S.T., C.Z., E.B., M.C., S.G., G.T.), Attikon University Hospital, School of Medicine, National and Kapodistrian University of Athens, Greece
| | - Aikaterini Theodorou
- Second Department of Neurology (L.P., A.T., S.T., C.Z., E.B., M.C., S.G., G.T.), Attikon University Hospital, School of Medicine, National and Kapodistrian University of Athens, Greece
| | - Sokratis Triantafyllou
- Second Department of Neurology (L.P., A.T., S.T., C.Z., E.B., M.C., S.G., G.T.), Attikon University Hospital, School of Medicine, National and Kapodistrian University of Athens, Greece
| | - Polychronis Dilaveris
- First Department of Cardiology, Hippokration Hospital, Athens Medical School (P.D., K. Tsioufis), National and Kapodistrian University of Athens, Greece
| | - Panagiota Flevari
- Second Department of Cardiology (P.F., K. Tympas, P.N., S.D., G.F.), Attikon University Hospital, School of Medicine, National and Kapodistrian University of Athens, Greece
| | | | - Charalampos Kossyvakis
- Department of Cardiology, General Hospital of Athens "Georgios Gennimatas," Greece (C.K.)
| | - Elias Adreanides
- Department of Cardiology, Medical Institution Military Shareholder Fund, Athens, Greece (E.A.)
| | - Konstantinos Tympas
- Second Department of Cardiology (P.F., K. Tympas, P.N., S.D., G.F.), Attikon University Hospital, School of Medicine, National and Kapodistrian University of Athens, Greece
| | - Petros Nikolopoulos
- Second Department of Cardiology (P.F., K. Tympas, P.N., S.D., G.F.), Attikon University Hospital, School of Medicine, National and Kapodistrian University of Athens, Greece
| | - Christina Zompola
- Second Department of Neurology (L.P., A.T., S.T., C.Z., E.B., M.C., S.G., G.T.), Attikon University Hospital, School of Medicine, National and Kapodistrian University of Athens, Greece
| | - Eleni Bakola
- Second Department of Neurology (L.P., A.T., S.T., C.Z., E.B., M.C., S.G., G.T.), Attikon University Hospital, School of Medicine, National and Kapodistrian University of Athens, Greece
| | - Maria Chondrogianni
- Second Department of Neurology (L.P., A.T., S.T., C.Z., E.B., M.C., S.G., G.T.), Attikon University Hospital, School of Medicine, National and Kapodistrian University of Athens, Greece
| | - Gkikas Magiorkinis
- Department of Hygiene, Epidemiology and Medical Statistics, Medical School (G.M.), National and Kapodistrian University of Athens, Greece
| | - Spyridon Deftereos
- Second Department of Cardiology (P.F., K. Tympas, P.N., S.D., G.F.), Attikon University Hospital, School of Medicine, National and Kapodistrian University of Athens, Greece
| | - Sotirios Giannopoulos
- Second Department of Neurology (L.P., A.T., S.T., C.Z., E.B., M.C., S.G., G.T.), Attikon University Hospital, School of Medicine, National and Kapodistrian University of Athens, Greece
| | - Konstantinos Tsioufis
- First Department of Cardiology, Hippokration Hospital, Athens Medical School (P.D., K. Tsioufis), National and Kapodistrian University of Athens, Greece
| | - Gerasimos Filippatos
- Second Department of Cardiology (P.F., K. Tympas, P.N., S.D., G.F.), Attikon University Hospital, School of Medicine, National and Kapodistrian University of Athens, Greece
| | - Georgios Tsivgoulis
- Second Department of Neurology (L.P., A.T., S.T., C.Z., E.B., M.C., S.G., G.T.), Attikon University Hospital, School of Medicine, National and Kapodistrian University of Athens, Greece
- Department of Neurology, University of Tennessee Health Science Center, Memphis (G.T.)
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Sagris D, Harrison SL, Buckley BJR, Ntaios G, Lip GYH. Long-Term Cardiac Monitoring After Embolic Stroke of Undetermined Source: Search Longer, Look Harder. Am J Med 2022; 135:e311-e317. [PMID: 35580719 DOI: 10.1016/j.amjmed.2022.04.030] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/24/2022] [Revised: 04/08/2022] [Accepted: 04/08/2022] [Indexed: 11/29/2022]
Abstract
Embolic stroke of undetermined source (ESUS) represents a heterogeneous subgroup of patients with cryptogenic stroke, in which despite an extensive diagnostic workup the cause of stroke remains uncertain. Identifying covert atrial fibrillation among patients with ESUS remains challenging. The increasing use of cardiac implanted electronic devices (CIED), such as pacemakers, implantable defibrillators, and implantable loop recorders (ILR), has provided important information on the burden of subclinical atrial fibrillation. Accumulating evidence indicate that long-term continuous monitoring, especially in selected patients with ESUS, significantly increases the possibility of atrial fibrillation detection, suggesting it may be a cost-effective tool in secondary stroke prevention. This review summarizes available evidence related to the use of long-term cardiac monitoring and the use of implantable cardiac monitoring devices in patients with ESUS.
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Affiliation(s)
- Dimitrios Sagris
- Liverpool Centre for Cardiovascular Science, University of Liverpool and Liverpool Heart & Chest Hospital, Liverpool, UK; Department of Internal Medicine, School of Health Sciences, Faculty of Medicine, University of Thessaly, Larissa, Greece
| | - Stephanie L Harrison
- Liverpool Centre for Cardiovascular Science, University of Liverpool and Liverpool Heart & Chest Hospital, Liverpool, UK; Department of Cardiovascular and Metabolic Medicine, Institute of Life Course and Medical Sciences, University of Liverpool, Liverpool, UK
| | - Benjamin J R Buckley
- Liverpool Centre for Cardiovascular Science, University of Liverpool and Liverpool Heart & Chest Hospital, Liverpool, UK; Department of Cardiovascular and Metabolic Medicine, Institute of Life Course and Medical Sciences, University of Liverpool, Liverpool, UK
| | - George Ntaios
- Department of Internal Medicine, School of Health Sciences, Faculty of Medicine, University of Thessaly, Larissa, Greece
| | - Gregory Y H Lip
- Liverpool Centre for Cardiovascular Science, University of Liverpool and Liverpool Heart & Chest Hospital, Liverpool, UK; Department of Cardiovascular and Metabolic Medicine, Institute of Life Course and Medical Sciences, University of Liverpool, Liverpool, UK; Department of Clinical Medicine, Aalborg University, Aalborg, Denmark.
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5
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Ashburner JM, Chang Y, Wang X, Khurshid S, Anderson CD, Dahal K, Weisenfeld D, Cai T, Liao KP, Wagholikar KB, Murphy SN, Atlas SJ, Lubitz SA, Singer DE. Natural Language Processing to Improve Prediction of Incident Atrial Fibrillation Using Electronic Health Records. J Am Heart Assoc 2022; 11:e026014. [PMID: 35904194 PMCID: PMC9375475 DOI: 10.1161/jaha.122.026014] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Background Models predicting atrial fibrillation (AF) risk, such as Cohorts for Heart and Aging Research in Genomic Epidemiology AF (CHARGE-AF), have not performed as well in electronic health records. Natural language processing (NLP) may improve models by using narrative electronic health record text. Methods and Results From a primary care network, we included patients aged ≥65 years with visits between 2003 and 2013 in development (n=32 960) and internal validation cohorts (n=13 992). An external validation cohort from a separate network from 2015 to 2020 included 39 051 patients. Model features were defined using electronic health record codified data and narrative data with NLP. We developed 2 models to predict 5-year AF incidence using (1) codified+NLP data and (2) codified data only and evaluated model performance. The analysis included 2839 incident AF cases in the development cohort and 1057 and 2226 cases in internal and external validation cohorts, respectively. The C-statistic was greater (P<0.001) in codified+NLP model (0.744 [95% CI, 0.735-0.753]) compared with codified-only (0.730 [95% CI, 0.720-0.739]) in the development cohort. In internal validation, the C-statistic of codified+NLP was modestly higher (0.735 [95% CI, 0.720-0.749]) compared with codified-only (0.729 [95% CI, 0.715-0.744]; P=0.06) and CHARGE-AF (0.717 [95% CI, 0.703-0.731]; P=0.002). Codified+NLP and codified-only were well calibrated, whereas CHARGE-AF underestimated AF risk. In external validation, the C-statistic of codified+NLP (0.750 [95% CI, 0.740-0.760]) remained higher (P<0.001) than codified-only (0.738 [95% CI, 0.727-0.748]) and CHARGE-AF (0.735 [95% CI, 0.725-0.746]). Conclusions Estimation of 5-year risk of AF can be modestly improved using NLP to incorporate narrative electronic health record data.
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Affiliation(s)
- Jeffrey M Ashburner
- Division of General Internal Medicine Massachusetts General Hospital Boston MA.,Harvard Medical School Boston MA
| | - Yuchiao Chang
- Division of General Internal Medicine Massachusetts General Hospital Boston MA.,Harvard Medical School Boston MA
| | - Xin Wang
- Cardiovascular Research Center Massachusetts General Hospital Boston MA
| | - Shaan Khurshid
- Cardiovascular Research Center Massachusetts General Hospital Boston MA.,Division of Cardiology Massachusetts General Hospital Boston MA
| | | | - Kumar Dahal
- Department of Rheumatology, Inflammation, and Immunity Brigham and Women's Hospital Boston MA
| | - Dana Weisenfeld
- Department of Rheumatology, Inflammation, and Immunity Brigham and Women's Hospital Boston MA
| | - Tianrun Cai
- Harvard Medical School Boston MA.,Department of Rheumatology, Inflammation, and Immunity Brigham and Women's Hospital Boston MA
| | - Katherine P Liao
- Harvard Medical School Boston MA.,Department of Rheumatology, Inflammation, and Immunity Brigham and Women's Hospital Boston MA
| | - Kavishwar B Wagholikar
- Harvard Medical School Boston MA.,Laboratory of Computer Science Massachusetts General Hospital Boston MA
| | - Shawn N Murphy
- Harvard Medical School Boston MA.,Research Information Science and Computing Mass General Brigham Somerville MA
| | - Steven J Atlas
- Division of General Internal Medicine Massachusetts General Hospital Boston MA.,Harvard Medical School Boston MA
| | - Steven A Lubitz
- Cardiovascular Research Center Massachusetts General Hospital Boston MA.,Cardiac Arrhythmia Service Massachusetts General Hospital Boston MA
| | - Daniel E Singer
- Division of General Internal Medicine Massachusetts General Hospital Boston MA.,Harvard Medical School Boston MA
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6
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Sung SF, Sung KL, Pan RC, Lee PJ, Hu YH. Automated risk assessment of newly detected atrial fibrillation poststroke from electronic health record data using machine learning and natural language processing. Front Cardiovasc Med 2022; 9:941237. [PMID: 35966534 PMCID: PMC9372298 DOI: 10.3389/fcvm.2022.941237] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2022] [Accepted: 07/11/2022] [Indexed: 11/13/2022] Open
Abstract
BackgroundTimely detection of atrial fibrillation (AF) after stroke is highly clinically relevant, aiding decisions on the optimal strategies for secondary prevention of stroke. In the context of limited medical resources, it is crucial to set the right priorities of extended heart rhythm monitoring by stratifying patients into different risk groups likely to have newly detected AF (NDAF). This study aimed to develop an electronic health record (EHR)-based machine learning model to assess the risk of NDAF in an early stage after stroke.MethodsLinked data between a hospital stroke registry and a deidentified research-based database including EHRs and administrative claims data was used. Demographic features, physiological measurements, routine laboratory results, and clinical free text were extracted from EHRs. The extreme gradient boosting algorithm was used to build the prediction model. The prediction performance was evaluated by the C-index and was compared to that of the AS5F and CHASE-LESS scores.ResultsThe study population consisted of a training set of 4,064 and a temporal test set of 1,492 patients. During a median follow-up of 10.2 months, the incidence rate of NDAF was 87.0 per 1,000 person-year in the test set. On the test set, the model based on both structured and unstructured data achieved a C-index of 0.840, which was significantly higher than those of the AS5F (0.779, p = 0.023) and CHASE-LESS (0.768, p = 0.005) scores.ConclusionsIt is feasible to build a machine learning model to assess the risk of NDAF based on EHR data available at the time of hospital admission. Inclusion of information derived from clinical free text can significantly improve the model performance and may outperform risk scores developed using traditional statistical methods. Further studies are needed to assess the clinical usefulness of the prediction model.
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Affiliation(s)
- Sheng-Feng Sung
- Division of Neurology, Department of Internal Medicine, Ditmanson Medical Foundation Chiayi Christian Hospital, Chiayi City, Taiwan
- Department of Nursing, Min-Hwei Junior College of Health Care Management, Tainan, Taiwan
| | - Kuan-Lin Sung
- School of Medicine, National Taiwan University, Taipei, Taiwan
| | - Ru-Chiou Pan
- Clinical Data Center, Department of Medical Research, Ditmanson Medical Foundation Chiayi Christian Hospital, Chiayi City, Taiwan
| | - Pei-Ju Lee
- Department of Information Management and Institute of Healthcare Information Management, National Chung Cheng University, Chiayi County, Taiwan
- *Correspondence: Pei-Ju Lee
| | - Ya-Han Hu
- Department of Information Management, National Central University, Taoyuan, Taiwan
- Ya-Han Hu
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7
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Lee JD, Kuo YW, Lee CP, Huang YC, Lee M, Lee TH. Development and Validation of a Novel Score for Predicting Paroxysmal Atrial Fibrillation in Acute Ischemic Stroke. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph19127277. [PMID: 35742524 PMCID: PMC9223581 DOI: 10.3390/ijerph19127277] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/20/2022] [Revised: 06/09/2022] [Accepted: 06/11/2022] [Indexed: 12/03/2022]
Abstract
Atrial fibrillation (AF)—whether paroxysmal or sustained—increases the risk of stroke. We developed and validated a risk score for identifying patients at risk of paroxysmal atrial fibrillation (pAF) after acute ischemic stroke (AIS). A total of 6033 patients with AIS who received 24 h Holter monitoring were identified in the Chang Gung Research Database. Among the identified patients, 5290 with pAF and without AF were included in the multivariable logistic regression analysis to develop the pAF prediction model. The ABCD-SD score (Age, Systolic Blood pressure, Coronary artery disease, Dyslipidemia, and Standard Deviation of heart rate) comprises age (+2 points for every 10 years), systolic blood pressure (−1 point for every 20 mmHg), coronary artery disease (+2 points), dyslipidemia (−2 points), and standard deviation of heart rate (+2 points for every 3 beats per minute). Overall, 5.2% (274/5290) of patients had pAF. The pAF risk ranged from 0.8% (ABCD-SD score ≤ 7) to 18.3% (ABCD-SD score ≥ 15). The model achieved an area under the receiver operating characteristic curve (AUROCC) of 0.767 in the model development group. The ABCD-SD score could aid clinicians in identifying patients with AIS at risk of pAF for advanced cardiac monitoring.
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Affiliation(s)
- Jiann-Der Lee
- Department of Neurology, Chiayi Chang Gung Memorial Hospital, No. 6, West Sec., Jiapu Road, Puzi City 613, Taiwan; (J.-D.L.); (Y.-C.H.); (M.L.)
- College of Medicine, Chang Gung University, No. 259, Wenhua 1st Rd., Guishan Dist., Taoyuan 333, Taiwan;
| | - Ya-Wen Kuo
- Department of Neurology, Chiayi Chang Gung Memorial Hospital, No. 6, West Sec., Jiapu Road, Puzi City 613, Taiwan; (J.-D.L.); (Y.-C.H.); (M.L.)
- Department of Nursing, College of Nursing, Chang Gung University of Science and Technology, No. 2, Sec. W., Jiapu Rd., Puzi City 613, Taiwan
- Correspondence:
| | - Chuan-Pin Lee
- Health Information and Epidemiology Laboratory, Chang Gung Memorial Hospital, Chiayi 613, Taiwan;
| | - Yen-Chu Huang
- Department of Neurology, Chiayi Chang Gung Memorial Hospital, No. 6, West Sec., Jiapu Road, Puzi City 613, Taiwan; (J.-D.L.); (Y.-C.H.); (M.L.)
- College of Medicine, Chang Gung University, No. 259, Wenhua 1st Rd., Guishan Dist., Taoyuan 333, Taiwan;
| | - Meng Lee
- Department of Neurology, Chiayi Chang Gung Memorial Hospital, No. 6, West Sec., Jiapu Road, Puzi City 613, Taiwan; (J.-D.L.); (Y.-C.H.); (M.L.)
- College of Medicine, Chang Gung University, No. 259, Wenhua 1st Rd., Guishan Dist., Taoyuan 333, Taiwan;
| | - Tsong-Hai Lee
- College of Medicine, Chang Gung University, No. 259, Wenhua 1st Rd., Guishan Dist., Taoyuan 333, Taiwan;
- Department of Neurology, Linkou Chang Gung Memorial Hospital, Taoyuan 333, Taiwan
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8
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Hsieh CY, Kao HM, Sung KL, Sposato LA, Sung SF, Lin SJ. 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. Front Cardiovasc Med 2022; 9:888240. [PMID: 35571191 PMCID: PMC9098928 DOI: 10.3389/fcvm.2022.888240] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2022] [Accepted: 04/11/2022] [Indexed: 11/30/2022] Open
Abstract
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, 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. 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.
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Affiliation(s)
- Cheng-Yang Hsieh
- 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
| | - Hsuan-Min Kao
- Division of Geriatrics, Department of Internal Medicine, Ditmanson Medical Foundation Chia-Yi Christian Hospital, Chiayi City, Taiwan
| | - Kuan-Lin Sung
- School of Medicine, National Taiwan University, Taipei City, Taiwan
| | - Luciano A. Sposato
- 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
| | - Sheng-Feng Sung
- 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
- *Correspondence: Sheng-Feng Sung, ;
| | - Swu-Jane Lin
- Department of Pharmacy Systems, Outcomes and Policy, College of Pharmacy, University of Illinois at Chicago, Chicago, IL, United States
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9
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Khurshid S, Reeder C, Harrington LX, Singh P, Sarma G, Friedman SF, Di Achille P, Diamant N, Cunningham JW, Turner AC, Lau ES, Haimovich JS, Al-Alusi MA, Wang X, Klarqvist MDR, Ashburner JM, Diedrich C, Ghadessi M, Mielke J, Eilken HM, McElhinney A, Derix A, Atlas SJ, Ellinor PT, Philippakis AA, Anderson CD, Ho JE, Batra P, Lubitz SA. Cohort design and natural language processing to reduce bias in electronic health records research. NPJ Digit Med 2022; 5:47. [PMID: 35396454 PMCID: PMC8993873 DOI: 10.1038/s41746-022-00590-0] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2021] [Accepted: 03/09/2022] [Indexed: 01/04/2023] Open
Abstract
Electronic health record (EHR) datasets are statistically powerful but are subject to ascertainment bias and missingness. Using the Mass General Brigham multi-institutional EHR, we approximated a community-based cohort by sampling patients receiving longitudinal primary care between 2001-2018 (Community Care Cohort Project [C3PO], n = 520,868). We utilized natural language processing (NLP) to recover vital signs from unstructured notes. We assessed the validity of C3PO by deploying established risk models for myocardial infarction/stroke and atrial fibrillation. We then compared C3PO to Convenience Samples including all individuals from the same EHR with complete data, but without a longitudinal primary care requirement. NLP reduced the missingness of vital signs by 31%. NLP-recovered vital signs were highly correlated with values derived from structured fields (Pearson r range 0.95-0.99). Atrial fibrillation and myocardial infarction/stroke incidence were lower and risk models were better calibrated in C3PO as opposed to the Convenience Samples (calibration error range for myocardial infarction/stroke: 0.012-0.030 in C3PO vs. 0.028-0.046 in Convenience Samples; calibration error for atrial fibrillation 0.028 in C3PO vs. 0.036 in Convenience Samples). Sampling patients receiving regular primary care and using NLP to recover missing data may reduce bias and maximize generalizability of EHR research.
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Affiliation(s)
- Shaan Khurshid
- Division of Cardiology, Massachusetts General Hospital, Boston, MA, USA
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
- Cardiovascular Disease Initiative, Broad Institute of Harvard and the Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Christopher Reeder
- Data Sciences Platform, Broad Institute of Harvard and the Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Lia X Harrington
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
- Cardiovascular Disease Initiative, Broad Institute of Harvard and the Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Pulkit Singh
- Data Sciences Platform, Broad Institute of Harvard and the Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Gopal Sarma
- Data Sciences Platform, Broad Institute of Harvard and the Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Samuel F Friedman
- Data Sciences Platform, Broad Institute of Harvard and the Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Paolo Di Achille
- Data Sciences Platform, Broad Institute of Harvard and the Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Nathaniel Diamant
- Data Sciences Platform, Broad Institute of Harvard and the Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Jonathan W Cunningham
- Cardiovascular Disease Initiative, Broad Institute of Harvard and the Massachusetts Institute of Technology, Cambridge, MA, USA
- Division of Cardiology, Brigham and Women's Hospital, Boston, MA, USA
| | - Ashby C Turner
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
- Henry and Allison McCance Center for Brain Health, Massachusetts General Hospital, Boston, MA, USA
| | - Emily S Lau
- Division of Cardiology, Massachusetts General Hospital, Boston, MA, USA
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
- Cardiovascular Disease Initiative, Broad Institute of Harvard and the Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Julian S Haimovich
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
- Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Mostafa A Al-Alusi
- Division of Cardiology, Massachusetts General Hospital, Boston, MA, USA
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
| | - Xin Wang
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
- Cardiovascular Disease Initiative, Broad Institute of Harvard and the Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Marcus D R Klarqvist
- Data Sciences Platform, Broad Institute of Harvard and the Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Jeffrey M Ashburner
- Harvard Medical School, Boston, MA, USA
- Division of General Internal Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Christian Diedrich
- Bayer AG, Research and Development, Pharmaceuticals, Leverkusen, Germany
| | - Mercedeh Ghadessi
- Bayer AG, Research and Development, Pharmaceuticals, Leverkusen, Germany
| | - Johanna Mielke
- Bayer AG, Research and Development, Pharmaceuticals, Leverkusen, Germany
| | - Hanna M Eilken
- Bayer AG, Research and Development, Pharmaceuticals, Leverkusen, Germany
| | - Alice McElhinney
- Cardiovascular Disease Initiative, Broad Institute of Harvard and the Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Andrea Derix
- Bayer AG, Research and Development, Pharmaceuticals, Leverkusen, Germany
| | - Steven J Atlas
- Harvard Medical School, Boston, MA, USA
- Division of General Internal Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Patrick T Ellinor
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
- Cardiovascular Disease Initiative, Broad Institute of Harvard and the Massachusetts Institute of Technology, Cambridge, MA, USA
- Demoulas Center for Cardiac Arrhythmias, Massachusetts General Hospital, Boston, MA, USA
| | - Anthony A Philippakis
- Data Sciences Platform, Broad Institute of Harvard and the Massachusetts Institute of Technology, Cambridge, MA, USA
- Eric and Wendy Schmidt Center, Broad Institute of Harvard and the Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Christopher D Anderson
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
- Henry and Allison McCance Center for Brain Health, Massachusetts General Hospital, Boston, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Department of Neurology, Brigham and Women's Hospital, Boston, MA, USA
| | - Jennifer E Ho
- Division of Cardiology, Massachusetts General Hospital, Boston, MA, USA
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
- Cardiovascular Disease Initiative, Broad Institute of Harvard and the Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Puneet Batra
- Data Sciences Platform, Broad Institute of Harvard and the Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Steven A Lubitz
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA.
- Cardiovascular Disease Initiative, Broad Institute of Harvard and the Massachusetts Institute of Technology, Cambridge, MA, USA.
- Demoulas Center for Cardiac Arrhythmias, Massachusetts General Hospital, Boston, MA, USA.
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