Department of Neurosurgery, Taipei Hospital, Ministry of Health and Welfare, New Taipei City, Taiwan.
Graduate Program in Biomedical Informatics, Yuan Ze University, Taoyuan City, Taiwan.
J Healthc Eng. 2022 Mar 25;2022:9733712. doi: 10.1155/2022/9733712. eCollection 2022.
Spontaneous intracerebral hemorrhage (sICH) has many predisposing/risk factors. Lag sequential analysis (LSA) is a method of analyzing sequential patterns and their associations within categorical data in different system states. The results of this study will assist in preventing sICH and improving the patient outcome after sICH. The correlations between a first sICH and previous clinic visits were examined using LSA with data obtained from the Taiwan National Health Insurance Research Database (NHIRD). In this study, LSA was employed to examine the data in the Taiwan NHIRD in order to identify predisposing and risk factors related to sICH, and the results increased our knowledge of the temporal relationships between diseases. This study employed LSA to identify predisposing/risk factors prior to the first occurrence of sICH using a healthcare administrative database in Taiwan. The data were managed using the clinical classification software (CCS). All cases of traumatic ICH were excluded. Ten disease groups were identified using CCS. Hypertension and dizziness/vertigo were identified as two important predisposing/risk factors for sICH, and early treatment of hypertension resulted in a greater survival rate. Five disease groups were found to have occurred prior to other diseases and affected mostly the elderly, resulting in subsequent sICH. The results of this study also showed that nutritional status and tooth health were highly associated with the occurrence of sICH owing to a poor state of the digestive system. In conclusion, there are many diseases that influence the risk of a subsequent sICH. This study demonstrated that LSA is a very useful tool for future study of healthcare administrative databases.
自发性脑出血 (sICH) 有许多诱发/风险因素。滞后序列分析 (LSA) 是一种分析分类数据中不同系统状态下顺序模式及其关联的方法。本研究的结果将有助于预防 sICH 和改善 sICH 后的患者预后。使用来自台湾全民健康保险研究数据库 (NHIRD) 的数据，通过 LSA 检查首次 sICH 与之前就诊之间的相关性。在这项研究中，LSA 被用于检查台湾 NHIRD 中的数据，以确定与 sICH 相关的诱发和风险因素，结果增加了我们对疾病之间时间关系的了解。本研究使用 LSA 通过使用台湾的医疗保健管理数据库来识别首次发生 sICH 之前的诱发/风险因素。数据由临床分类软件 (CCS) 管理。所有创伤性 ICH 病例均被排除在外。使用 CCS 确定了十个疾病组。高血压和头晕/眩晕被确定为 sICH 的两个重要诱发/风险因素，早期治疗高血压可提高生存率。发现五个疾病组先于其他疾病发生，主要影响老年人，导致随后发生 sICH。本研究的结果还表明，由于消化系统状况不佳，营养状况和牙齿健康与 sICH 的发生高度相关。总之，有许多疾病会影响随后发生 sICH 的风险。本研究表明，LSA 是未来医疗保健管理数据库研究的非常有用的工具。
