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Original Research

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

ORCID Icon, ORCID Icon, ORCID Icon & ORCID Icon
Pages 327-335 | Published online: 17 Mar 2022
 

Abstract

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.

Acknowledgments

This research was funded in part by the Ditmanson Medical Foundation Chia-Yi Christian Hospital-National Chung Cheng University Joint Research Program (CYCH-CCU-2022-03). The funders of the research had no role in the design and conduct of the study, interpretation of the data, or decision to submit for publication. The authors would like to thank Ms. Li-Ying Sung for English language review.

Disclosure

The authors report no conflicts of interest in this work.