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

Abstract

Meng-Tsang Hsieh,1– 3 Cheng-Yang Hsieh,4,5 Tzu-Tung Tsai,1 Sheng-Feng Sung6,7 1Stroke Center and Department of Neurology, E-Da Hospital, Kaohsiung, Taiwan; 2School of Medicine, College of Medicine, I-Shou University, Kaohsiung, Taiwan; 3Institute of Clinical Medicine, College of Medicine, National Cheng Kung University, Tainan, Taiwan; 4Department of Neurology, Tainan Sin Lau Hospital, Tainan, Taiwan; 5School of Pharmacy, Institute of Clinical Pharmacy and Pharmaceutical Sciences, College of Medicine, National Cheng Kung University, Tainan, Taiwan; 6Division of Neurology, Department of Internal Medicine, Ditmanson Medical Foundation Chia-Yi Christian Hospital, Chiayi City, Taiwan; 7Department of Nursing, Min-Hwei Junior College of Health Care Management, Tainan, TaiwanCorrespondence: Sheng-Feng Sung, Division of Neurology, Department of Internal Medicine, Ditmanson Medical Foundation Chia-Yi Christian Hospital, 539 Zhongxiao Road, East District, Chiayi City, 60002, Taiwan, Tel [phone] ext 7283, Fax [phone], Email [email protected]: 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.Keywords: administrative claims data, diagnosis, ICD-10-CM, stroke, risk factors

Keywords

• administrative claims data
• diagnosis
• icd-10-cm
• stroke
• risk factors