Hsieh Meng-Tsang, Hsieh Cheng-Yang, Tsai Tzu-Tung, Sung Sheng-Feng
Stroke Center and Department of Neurology, E-Da Hospital, Kaohsiung, Taiwan.
School of Medicine, College of Medicine, I-Shou University, Kaohsiung, Taiwan.
Clin Epidemiol. 2022 Mar 17;14:327-335. doi: 10.2147/CLEP.S353435. eCollection 2022.
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.
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.
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.
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.
自2016年起,台湾已将编码系统变更为国际疾病分类第十次修订版临床修订本(ICD-10-CM)编码。本研究旨在确定台湾全民健康保险(NHI)理赔数据中识别中风危险因素的最佳算法。
我们从两家医院的中风登记处回顾性纳入了4538例因急性缺血性中风(AIS)、短暂性脑缺血发作(TIA)或脑出血(ICH)住院的患者,这些登记处与NHI理赔数据相关联。我们基于ICD-10-CM诊断编码和处方理赔数据开发了几种算法,以登记处数据作为参考标准来识别高血压、糖尿病、高脂血症、心房颤动(AF)和缺血性心脏病(IHD)。通过计算kappa统计量来量化理赔数据和登记处数据之间危险因素状态的一致性。
根据登记处数据,所有患者中高血压、糖尿病、高脂血症、AF和IHD的患病率分别为77.5%、41.5%、47.9%、12.1%和7.1%。总体而言,纳入先前住院或门诊理赔的诊断编码至中风住院理赔的诊断编码可提高一致性。纳入处方数据可提高高血压、糖尿病、高脂血症和AF的一致性,但对IHD无效。最佳算法的kappa值分别为:高血压0.552(95%置信区间0.524 - 0.580)、糖尿病0.802(0.784 - 0.820)、高脂血症0.514(0.490 - 0.539)、AF 0.765(0.734 - 0.795)、IHD 0.518(0.473 - 0.564)。
仅使用诊断编码的算法足以识别高血压、AF和IHD,而结合诊断编码和处方数据的算法更适合识别糖尿病和高脂血症。研究结果可为未来使用台湾NHI理赔数据的研究提供参考。