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Validation of Stroke Risk Factors in Patients with Acute Ischemic Stroke, Transient Ischemic Attack, or Intracerebral Hemorrhage on Taiwan’s National Health Insurance Claims Data

PURPOSE: Taiwan has changed the coding system to the International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) coding since 2016. This study aimed to determine the optimal algorithms for identifying stroke risk factors in Taiwan’s National Health Insurance (NHI) cla...

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Autores principales: Hsieh, Meng-Tsang, Hsieh, Cheng-Yang, Tsai, Tzu-Tung, Sung, Sheng-Feng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Dove 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8938165/
https://www.ncbi.nlm.nih.gov/pubmed/35330593
http://dx.doi.org/10.2147/CLEP.S353435
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author Hsieh, Meng-Tsang
Hsieh, Cheng-Yang
Tsai, Tzu-Tung
Sung, Sheng-Feng
author_facet Hsieh, Meng-Tsang
Hsieh, Cheng-Yang
Tsai, Tzu-Tung
Sung, Sheng-Feng
author_sort Hsieh, Meng-Tsang
collection PubMed
description PURPOSE: Taiwan has changed the coding system to the International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) coding since 2016. This study aimed to determine the optimal algorithms for identifying stroke risk factors in Taiwan’s National Health Insurance (NHI) claims data. PATIENTS AND METHODS: We retrospectively enrolled 4538 patients hospitalized for acute ischemic stroke (AIS), transient ischemic attack (TIA), or intracerebral hemorrhage (ICH) from two hospitals’ stroke registries, which were linked to NHI claims data. We developed several algorithms based on ICD-10-CM diagnosis codes and prescription claims data to identify hypertension, diabetes, hyperlipidemia, atrial fibrillation (AF), and ischemic heart disease (IHD) using registry data as the reference standard. The agreement of risk factor status between claims and registry data was quantified by calculating the kappa statistic. RESULTS: According to the registry data, the prevalence of hypertension, diabetes, hyperlipidemia, AF, and IHD among all patients was 77.5%, 41.5%, 47.9%, 12.1%, and 7.1%, respectively. In general, including diagnosis codes from prior inpatient or outpatient claims to those from the stroke hospitalization claims improved the agreement. Incorporating prescription data could improve the agreement for hypertension, diabetes, hyperlipidemia, and AF, but not for IHD. The kappa values of the optimal algorithms were 0.552 (95% confidence interval 0.524–0.580) for hypertension, 0.802 (0.784–0.820) for diabetes, 0.514 (0.490–0.539) for hyperlipidemia, 0.765 (0.734–0.795) for AF, and 0.518 (0.473–0.564) for IHD. CONCLUSION: Algorithms using diagnosis codes alone are sufficient to identify hypertension, AF, and IHD whereas algorithms combining both diagnosis codes and prescription data are more suitable for identifying diabetes and hyperlipidemia. The study results may provide a reference for future studies using Taiwan’s NHI claims data.
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spelling pubmed-89381652022-03-23 Validation of Stroke Risk Factors in Patients with Acute Ischemic Stroke, Transient Ischemic Attack, or Intracerebral Hemorrhage on Taiwan’s National Health Insurance Claims Data Hsieh, Meng-Tsang Hsieh, Cheng-Yang Tsai, Tzu-Tung Sung, Sheng-Feng Clin Epidemiol Original Research PURPOSE: Taiwan has changed the coding system to the International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) coding since 2016. This study aimed to determine the optimal algorithms for identifying stroke risk factors in Taiwan’s National Health Insurance (NHI) claims data. PATIENTS AND METHODS: We retrospectively enrolled 4538 patients hospitalized for acute ischemic stroke (AIS), transient ischemic attack (TIA), or intracerebral hemorrhage (ICH) from two hospitals’ stroke registries, which were linked to NHI claims data. We developed several algorithms based on ICD-10-CM diagnosis codes and prescription claims data to identify hypertension, diabetes, hyperlipidemia, atrial fibrillation (AF), and ischemic heart disease (IHD) using registry data as the reference standard. The agreement of risk factor status between claims and registry data was quantified by calculating the kappa statistic. RESULTS: According to the registry data, the prevalence of hypertension, diabetes, hyperlipidemia, AF, and IHD among all patients was 77.5%, 41.5%, 47.9%, 12.1%, and 7.1%, respectively. In general, including diagnosis codes from prior inpatient or outpatient claims to those from the stroke hospitalization claims improved the agreement. Incorporating prescription data could improve the agreement for hypertension, diabetes, hyperlipidemia, and AF, but not for IHD. The kappa values of the optimal algorithms were 0.552 (95% confidence interval 0.524–0.580) for hypertension, 0.802 (0.784–0.820) for diabetes, 0.514 (0.490–0.539) for hyperlipidemia, 0.765 (0.734–0.795) for AF, and 0.518 (0.473–0.564) for IHD. CONCLUSION: Algorithms using diagnosis codes alone are sufficient to identify hypertension, AF, and IHD whereas algorithms combining both diagnosis codes and prescription data are more suitable for identifying diabetes and hyperlipidemia. The study results may provide a reference for future studies using Taiwan’s NHI claims data. Dove 2022-03-17 /pmc/articles/PMC8938165/ /pubmed/35330593 http://dx.doi.org/10.2147/CLEP.S353435 Text en © 2022 Hsieh et al. https://creativecommons.org/licenses/by-nc/3.0/This work is published and licensed by Dove Medical Press Limited. The full terms of this license are available at https://www.dovepress.com/terms.php and incorporate the Creative Commons Attribution – Non Commercial (unported, v3.0) License (http://creativecommons.org/licenses/by-nc/3.0/ (https://creativecommons.org/licenses/by-nc/3.0/) ). By accessing the work you hereby accept the Terms. Non-commercial uses of the work are permitted without any further permission from Dove Medical Press Limited, provided the work is properly attributed. For permission for commercial use of this work, please see paragraphs 4.2 and 5 of our Terms (https://www.dovepress.com/terms.php).
spellingShingle Original Research
Hsieh, Meng-Tsang
Hsieh, Cheng-Yang
Tsai, Tzu-Tung
Sung, Sheng-Feng
Validation of Stroke Risk Factors in Patients with Acute Ischemic Stroke, Transient Ischemic Attack, or Intracerebral Hemorrhage on Taiwan’s National Health Insurance Claims Data
title Validation of Stroke Risk Factors in Patients with Acute Ischemic Stroke, Transient Ischemic Attack, or Intracerebral Hemorrhage on Taiwan’s National Health Insurance Claims Data
title_full Validation of Stroke Risk Factors in Patients with Acute Ischemic Stroke, Transient Ischemic Attack, or Intracerebral Hemorrhage on Taiwan’s National Health Insurance Claims Data
title_fullStr Validation of Stroke Risk Factors in Patients with Acute Ischemic Stroke, Transient Ischemic Attack, or Intracerebral Hemorrhage on Taiwan’s National Health Insurance Claims Data
title_full_unstemmed Validation of Stroke Risk Factors in Patients with Acute Ischemic Stroke, Transient Ischemic Attack, or Intracerebral Hemorrhage on Taiwan’s National Health Insurance Claims Data
title_short Validation of Stroke Risk Factors in Patients with Acute Ischemic Stroke, Transient Ischemic Attack, or Intracerebral Hemorrhage on Taiwan’s National Health Insurance Claims Data
title_sort validation of stroke risk factors in patients with acute ischemic stroke, transient ischemic attack, or intracerebral hemorrhage on taiwan’s national health insurance claims data
topic Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8938165/
https://www.ncbi.nlm.nih.gov/pubmed/35330593
http://dx.doi.org/10.2147/CLEP.S353435
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