A phenotyping algorithm to identify acute ischemic stroke accurately from a national biobank: the Million Veteran Program.

ABSTRACT:

Background

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 a

SUBMITTER: Imran TF 

PROVIDER: S-EPMC6201999 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature