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基于群组轨迹建模的脑卒中患者医疗轨迹探索。

Exploration of Medical Trajectories of Stroke Patients Based on Group-Based Trajectory Modeling.

机构信息

Department of Computer Science and Engineering, Yuan Ze University, Taoyuan City 320, Taiwan.

Graduate Program in Biomedical Informatics, Yuan Ze University, Taoyuan City 320, Taiwan.

出版信息

Int J Environ Res Public Health. 2019 Sep 18;16(18):3472. doi: 10.3390/ijerph16183472.

DOI:10.3390/ijerph16183472
PMID:31540463
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6765978/
Abstract

A high mortality rate is an issue with acute cerebrovascular disease (ACVD), as it often leads to a high medical expenditure, and in particular to high costs of treatment for emergency medical conditions and critical care. In this study, we used group-based trajectory modeling (GBTM) to study the characteristics of various groups of patients hospitalized with ACVD. In this research, the patient data were derived from the 1 million sampled cases in the National Health Insurance Research Database (NHIRD) in Taiwan. Cases who had been admitted to hospitals fewer than four times or more than eight times were excluded. Characteristics of the ACVD patients were collected, including age, mortality rate, medical expenditure, and length of hospital stay for each admission. We then performed GBTM to examine hospitalization patterns in patients who had been hospitalized more than four times and fewer than or equal to eight times. The patients were divided into three groups according to medical expenditure: high, medium, and low groups, split at the 33rd and 66th percentiles. After exclusion of unqualified patients, a total of 27,264 cases (male/female = 15,972/11,392) were included. Analysis of the characteristics of the ACVD patients showed that there were significant differences between the two gender groups in terms of age, mortality rate, medical expenditure, and total length of hospital stay. In addition, the data were compared between two admissions, which included interval, outpatient department (OPD) visit after discharge, OPD visit after hospital discharge, and OPD cost. Finally, the differences in medical expenditure between genders and between patients with different types of stroke-ischemic stroke, spontaneous intracerebral hemorrhage (sICH), and subarachnoid hemorrhage (SAH)-were examined using GBTM. Overall, this study employed GBTM to examine the trends in medical expenditure for different groups of stroke patients at different admissions, and some important results were obtained. Our results demonstrated that the time interval between subsequent hospitalizations decreased in the ACVD patients, and there were significant differences between genders and between patients with different types of stroke. It is often difficult to decide when the time has been reached at which further treatment will not improve the condition of ACVD patients, and the findings of our study may be used as a reference for assessing outcomes and quality of care for stroke patients. Because of the characteristics of NHIRD, this study had some limitations; for example, the number of cases for some diseases was not sufficient for effective statistical analysis.

摘要

高死亡率是急性脑血管病(ACVD)的一个问题,因为它通常会导致高额的医疗支出,特别是对急症和重症监护的治疗费用较高。在本研究中,我们使用基于群组的轨迹建模(GBTM)来研究因 ACVD 住院的患者的不同群组的特征。在这项研究中,患者数据来自台湾国民健康保险研究数据库(NHIRD)中抽样的 100 万例。排除住院次数少于 4 次或多于 8 次的病例。收集了 ACVD 患者的特征,包括年龄、死亡率、医疗支出和每次住院的住院时间。然后,我们使用 GBTM 来检查住院次数超过 4 次和小于或等于 8 次的患者的住院模式。根据医疗支出将患者分为高、中、低三组,分为第 33 和 66 百分位数。排除不合格患者后,共有 27264 例(男/女=15972/11392)被纳入。对 ACVD 患者特征的分析表明,两性在年龄、死亡率、医疗支出和总住院时间方面存在显著差异。此外,还比较了两次住院之间的间隔、出院后的门诊就诊、出院后的门诊就诊和门诊费用。最后,使用 GBTM 检查了不同性别和不同类型中风(缺血性中风、自发性脑出血(sICH)和蛛网膜下腔出血(SAH))患者之间的医疗支出差异。总的来说,本研究使用 GBTM 检查了不同入院次数不同类型中风患者的医疗支出趋势,并得出了一些重要结果。我们的研究结果表明,ACVD 患者的后续住院时间间隔缩短,且在性别和不同类型中风患者之间存在显著差异。对于何时达到进一步治疗不会改善 ACVD 患者病情的时间点,往往难以做出决策,我们的研究结果可能作为评估中风患者预后和护理质量的参考。由于 NHIRD 的特点,本研究存在一些局限性;例如,某些疾病的病例数不足,无法进行有效的统计分析。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a61f/6765978/ca4d7c5f53de/ijerph-16-03472-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a61f/6765978/dddd020e4772/ijerph-16-03472-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a61f/6765978/3ab28946328e/ijerph-16-03472-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a61f/6765978/ca4d7c5f53de/ijerph-16-03472-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a61f/6765978/dddd020e4772/ijerph-16-03472-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a61f/6765978/3ab28946328e/ijerph-16-03472-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a61f/6765978/ca4d7c5f53de/ijerph-16-03472-g003.jpg

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