| Autor: |
Jun Yup Kim, Keon-Joo Lee, Jihoon Kang, Beom Joon Kim, Moon-Ku Han, Seong-Eun Kim, Heeyoung Lee, Jong-Moo Park, Kyusik Kang, Soo Joo Lee, Jae Guk Kim, Jae-Kwan Cha, Dae-Hyun Kim, Tai Hwan Park, Moo-Seok Park, Sang-Soon Park, Kyung Bok Lee, Hong-Kyun Park, Yong-Jin Cho, Keun-Sik Hong, Kang-Ho Choi, Joon-Tae Kim, Dong-Eog Kim, Wi-Sun Ryu, Jay Chol Choi, Mi-Sun Oh, Kyung-Ho Yu, Byung-Chul Lee, Kwang-Yeol Park, Ji Sung Lee, Sujung Jang, Jae Eun Chae, Juneyoung Lee, Hee-Joon Bae, CRCS-K investigators |
| Jazyk: |
angličtina |
| Rok vydání: |
2020 |
| Předmět: |
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| Zdroj: |
PLoS ONE, Vol 15, Iss 2, p e0228997 (2020) |
| Druh dokumentu: |
article |
| ISSN: |
1932-6203 |
| DOI: |
10.1371/journal.pone.0228997 |
| Popis: |
BACKGROUND:Identifying acute ischemic stroke (AIS) among potential stroke cases is crucial for stroke research based on claims data. However, the accuracy of using the diagnostic codes of the International Classification of Diseases 10th revision was less than expected. METHODS:From the National Health Insurance Service (NHIS) claims data, stroke cases admitted to the hospitals participating in the multicenter stroke registry (Clinical Research Collaboration for Stroke in Korea, CRCS-K) during the study period with principal or additional diagnosis codes of I60-I64 on the 10th revision of International Classification of Diseases were extracted. The datasets were randomly divided into development and validation sets with a ratio of 7:3. A stroke identification algorithm using the claims data was developed and validated through the linkage between the extracted datasets and the registry database. RESULTS:Altogether, 40,443 potential cases were extracted from the NHIS claims data, of which 31.7% were certified as AIS through linkage with the CRCS-K database. We selected 17 key identifiers from the claims data and developed 37 conditions through combinations of those key identifiers. The key identifiers comprised brain CT, MRI, use of tissue plasminogen activator, endovascular treatment, carotid endarterectomy or stenting, antithrombotics, anticoagulants, etc. The sensitivity, specificity, and diagnostic accuracy of the algorithm were 81.2%, 82.9%, and 82.4% in the development set, and 80.2%, 82.0%, and 81.4% in the validation set, respectively. CONCLUSIONS:Our stroke identification algorithm may be useful to grasp stroke burden in Korea. However, further efforts to refine the algorithm are necessary. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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