A phenotyping algorithm to identify acute ischemic stroke accurately from a national biobank: the Million Veteran Program.
Clin Epidemiol
; 10: 1509-1521, 2018.
Article
in En
| MEDLINE
| ID: mdl-30425582
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 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.
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Type of study:
Guideline
/
Prognostic_studies
Language:
En
Journal:
Clin Epidemiol
Year:
2018
Type:
Article