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Published in: BMC Medical Informatics and Decision Making 1/2017

Open Access 01-12-2017 | Research article

Validation of an algorithm that determines stroke diagnostic code accuracy in a Japanese hospital-based cancer registry using electronic medical records

Authors: Yasufumi Gon, Daijiro Kabata, Keichi Yamamoto, Ayumi Shintani, Kenichi Todo, Hideki Mochizuki, Manabu Sakaguchi

Published in: BMC Medical Informatics and Decision Making | Issue 1/2017

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Abstract

Background

This study aimed to validate an algorithm that determines stroke diagnostic code accuracy, in a hospital-based cancer registry, using electronic medical records (EMRs) in Japan.

Methods

The subjects were 27,932 patients enrolled in the hospital-based cancer registry of Osaka University Hospital, between January 1, 2007 and December 31, 2015. The ICD-10 (international classification of diseases, 10th revision) diagnostic codes for stroke were extracted from the EMR database. Specifically, subarachnoid hemorrhage (I60); intracerebral hemorrhage (I61); cerebral infarction (I63); and other transient cerebral ischemic attacks and related syndromes and transient cerebral ischemic attack (unspecified) (G458 and G459), respectively. Diagnostic codes, both “definite” and “suspected,” and brain imaging information were extracted from the database. We set the algorithm with the combination of the diagnostic code and/or the brain imaging information, and manually reviewed the presence or absence of the acute cerebrovascular disease with medical charts.

Results

A total of 2654 diagnostic codes, 1991 “definite” and 663 “suspected,” were identified. After excluding duplicates, the numbers of “definite” and “suspected” diagnostic codes were 912 and 228, respectively. The proportion of the presence of the disease in the “definite” diagnostic code was 22%; this raised 51% with the combination of the diagnostic code and the use of brain imaging information. When adding the interval of when brain imaging was performed (within 30 days and within 1 day) to the diagnostic code, the proportion increased to 84% and 90%, respectively. In the algorithm of “definite” diagnostic code, history of stroke was the most common in the diagnostic code, but in the algorithm of “definite” diagnostic code and the use of brain imaging within 1 day, stroke mimics was the most frequent.

Conclusions

Combining the diagnostic code and clinical examination improved the proportion of the presence of disease in the diagnostic code and achieved appropriate accuracy for research. Clinical research using EMRs require outcome validation prior to conducting a study.
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Metadata
Title
Validation of an algorithm that determines stroke diagnostic code accuracy in a Japanese hospital-based cancer registry using electronic medical records
Authors
Yasufumi Gon
Daijiro Kabata
Keichi Yamamoto
Ayumi Shintani
Kenichi Todo
Hideki Mochizuki
Manabu Sakaguchi
Publication date
01-12-2017
Publisher
BioMed Central
Published in
BMC Medical Informatics and Decision Making / Issue 1/2017
Electronic ISSN: 1472-6947
DOI
https://doi.org/10.1186/s12911-017-0554-x

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