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• W2896420497 endingPage "1521" @default.
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• W2896420497 abstract "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.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.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).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." @default.
• W2896420497 created "2018-10-26" @default.
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• W2896420497 date "2018-10-01" @default.
• W2896420497 modified "2023-10-16" @default.
• W2896420497 title "A phenotyping algorithm to identify acute ischemic stroke accurately from a national biobank: the Million Veteran Program" @default.
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• W2896420497 doi "https://doi.org/10.2147/clep.s160764" @default.
• W2896420497 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/6201999" @default.
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• W2896420497 hasPublicationYear "2018" @default.
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