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基于 OMOP CDM 分析三种慢性病的治疗路径。

Analysis of treatment pathways for three chronic diseases using OMOP CDM.

机构信息

Department of Information, the First Affiliated Hospital, Nanjing Medical University, No.300 Guang Zhou Road, Nanjing, 210029, Jiangsu, China.

Institute of Medical Informatics and Management, Nanjing Medical University, No.300 Guang Zhou Road, Nanjing, 210029, Jiangsu, China.

出版信息

J Med Syst. 2018 Nov 13;42(12):260. doi: 10.1007/s10916-018-1076-5.


DOI:10.1007/s10916-018-1076-5
PMID:30421323
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6244882/
Abstract

The present study examined treatment pathways (the ordered sequence of medications that a patient is prescribed) for three chronic diseases (hypertension, type 2 diabetes, and depression), compared the pathways with recommendations from guidelines, discussed differences and standardization of medications in different medical institutions, explored population diversification and changes of clinical treatment, and provided clinical big data analysis-based data support for the development and study of drugs in China. In order to run the "Treatment Pathways in Chronic Disease" protocol in Chinese data sources,we have built a large data research and analysis platform for Chinese clinical medical data. Data sourced from the Clinical Data Repository (CDR) of the First Affiliated Hospital of Nanjing Medical University was extracted, transformed, and loaded into an observational medical outcomes partnership common data model (OMOP CDM) Ver. 5.0. Diagnosis and medication information for patients with hypertension, type 2 diabetes, and depression from 2005 to 2015 were extracted for observational research to obtain treatment pathways for the three diseases. The most common medications used to treat diabetes and hypertension were metformin and acarbose, respectively, at 28.5 and 20.9% as first-line medication. New drugs were emerging for depression; therefore, the favorite medication changed accordingly. Most patients with these three diseases had different treatment pathways from other patients with the same diseases. The proportions of monotherapy increased for the three diseases, especially in recent years. The recommendations presented in guidelines show some predominance. High-quality, effective guidelines incorporating domestic facts should be established to further guide medication and improve therapy at local hospitals. Medical institutions at all levels could improve the quality of medical services, and further standardize medications in the future. This research is the first application of the CDM model and OHDSI software in China, which were used to study, treatment pathways for three chronic diseases (hypertension, type 2 diabetes and depression), compare the pathways with recommendations from guidelines, discuss differences and standardization of medications in different medical institutions, demonstrate the urgent need for quality national guidelines, explores population diversification and changes of clinical treatment, and provide clinical big data analysis-based data support for the development and study of drugs in China.

摘要

本研究考察了三种慢性疾病(高血压、2 型糖尿病和抑郁症)的治疗路径(患者被开处方的药物的有序序列),将这些路径与指南建议进行了比较,讨论了不同医疗机构药物使用的差异和标准化,探索了人口多样化和临床治疗变化,并为中国药物的开发和研究提供了基于临床大数据分析的数据支持。为了在中国数据源中运行“慢性病治疗路径”方案,我们构建了一个大型的中国临床医疗数据研究和分析平台。从南京医科大学第一附属医院的临床数据仓库(CDR)中提取数据,并进行转换和加载到观察性医疗结果伙伴关系通用数据模型(OMOP CDM)V5.0 中。从 2005 年到 2015 年,提取高血压、2 型糖尿病和抑郁症患者的诊断和用药信息,进行观察性研究,获得三种疾病的治疗路径。治疗糖尿病和高血压最常用的一线药物分别是二甲双胍和阿卡波糖,分别占 28.5%和 20.9%。治疗抑郁症的新药不断涌现,因此用药偏好也随之改变。这三种疾病的大多数患者与同病患者的治疗路径不同。三种疾病的单药治疗比例增加,尤其是近年来。指南中提出的建议显示出一定的优势。应制定高质量、有效的指南,结合国内实际情况,进一步指导用药,提高当地医院的治疗效果。各级医疗机构今后可以提高医疗服务质量,进一步规范用药。本研究是 CDM 模型和 OHDSI 软件在中国的首次应用,用于研究三种慢性疾病(高血压、2 型糖尿病和抑郁症)的治疗路径,将这些路径与指南建议进行比较,讨论不同医疗机构药物使用的差异和标准化,展示制定高质量国家指南的迫切需求,探索人口多样化和临床治疗变化,并为中国药物的开发和研究提供基于临床大数据分析的数据支持。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cbe8/6244882/a51cea02369a/10916_2018_1076_Fig4a_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cbe8/6244882/406a1f014cd9/10916_2018_1076_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cbe8/6244882/9b90afe4a423/10916_2018_1076_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cbe8/6244882/eed5ba0e3e49/10916_2018_1076_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cbe8/6244882/a51cea02369a/10916_2018_1076_Fig4a_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cbe8/6244882/406a1f014cd9/10916_2018_1076_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cbe8/6244882/9b90afe4a423/10916_2018_1076_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cbe8/6244882/eed5ba0e3e49/10916_2018_1076_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cbe8/6244882/a51cea02369a/10916_2018_1076_Fig4a_HTML.jpg

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[6]
Association between depressive symptoms and diagnosis of diabetes and its complications: A network analysis in electronic health records.

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