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dc.contributor.authorImran, T.F.
dc.contributor.authorPosner, D.
dc.contributor.authorHonerlaw, J.
dc.date.accessioned2019-06-21T18:46:26Z
dc.date.available2019-06-21T18:46:26Z
dc.date.issued2018
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85057751900&doi=10.2147%2fCLEP.S160764&partnerID=40&md5=954162a8311bb1451c994085e74786af
dc.identifier.urihttp://hdl.handle.net/10713/9693
dc.description.abstractBackground: Large databases provide an efficient way to analyze patient data. A challenge with these databases is the inconsistency of ICD codes and a potential for inaccurate ascertainment of cases. The purpose of this study was to develop and validate a reliable protocol to identify cases of acute ischemic stroke (AIS) from a large national database. Methods: Using the national Veterans Affairs electronic health-record system, Center for Medicare and Medicaid Services, and National Death Index data, we developed an algorithm to identify cases of AIS. Using a combination of inpatient and outpatient ICD9 codes, we selected cases of AIS and controls from 1992 to 2014. Diagnoses determined after medical-chart review were considered the gold standard. We used a machine-learning algorithm and a neural network approach to identify AIS from ICD9 codes and electronic health-record information and compared it with a previous rule-based stroke-classification algorithm. Results: We reviewed administrative hospital data, ICD9 codes, and medical records of 268 patients in detail. Compared with the gold standard, this AIS algorithm had a sensitivity of 91%, specificity of 95%, and positive predictive value of 88%. A total of 80,508 highly likely cases of AIS were identified using the algorithm in the Veterans Affairs national cardiovascular disease-risk cohort (n=2,114,458). Conclusion: Our algorithm had high specificity for identifying AIS in a nationwide electronic health-record system. This approach may be utilized in other electronic health databases to accurately identify patients with AIS. Copyright 2018 Imran et al.en_US
dc.description.sponsorshipThe Cardiovascular Health Study is funded under VA Merit Award I01-CX001025. The Million Veteran Program is funded by the Office of Research and Development, Department of Veterans Affairs, supported by grant CSPG002.en_US
dc.description.urihttps://dx.doi.org/10.2147/CLEP.S160764en_US
dc.language.isoen-USen_US
dc.publisherDove Medical Press Ltden_US
dc.relation.ispartofClinical Epidemiology
dc.subjectAcute ischemic strokeen_US
dc.subjectAdministrative health dataen_US
dc.subjectAlgorithmen_US
dc.subjectBig dataen_US
dc.subjectCerebrovascular accidenten_US
dc.subjectLarge databasesen_US
dc.titleA phenotyping algorithm to identify acute ischemic stroke accurately from a national biobank: The million veteran programen_US
dc.typeArticleen_US
dc.identifier.doi10.2147/CLEP.S160764


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