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Analysis of treatment pathways for three chronic diseases using OMOP CDM

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 medica...

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Autores principales: Zhang, Xin, Wang, Li, Miao, Shumei, Xu, Hua, Yin, Yuechuchu, Zhu, Yueshi, Dai, Zuolei, Shan, Tao, Jing, Shenqi, Wang, Jian, Zhang, Xiaoliang, Huang, Zhongqiu, Wang, Zhongmin, Guo, Jianjun, Liu, Yun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer US 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6244882/
https://www.ncbi.nlm.nih.gov/pubmed/30421323
http://dx.doi.org/10.1007/s10916-018-1076-5
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author Zhang, Xin
Wang, Li
Miao, Shumei
Xu, Hua
Yin, Yuechuchu
Zhu, Yueshi
Dai, Zuolei
Shan, Tao
Jing, Shenqi
Wang, Jian
Zhang, Xiaoliang
Huang, Zhongqiu
Wang, Zhongmin
Guo, Jianjun
Liu, Yun
author_facet Zhang, Xin
Wang, Li
Miao, Shumei
Xu, Hua
Yin, Yuechuchu
Zhu, Yueshi
Dai, Zuolei
Shan, Tao
Jing, Shenqi
Wang, Jian
Zhang, Xiaoliang
Huang, Zhongqiu
Wang, Zhongmin
Guo, Jianjun
Liu, Yun
author_sort Zhang, Xin
collection PubMed
description 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.
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spelling pubmed-62448822018-12-11 Analysis of treatment pathways for three chronic diseases using OMOP CDM Zhang, Xin Wang, Li Miao, Shumei Xu, Hua Yin, Yuechuchu Zhu, Yueshi Dai, Zuolei Shan, Tao Jing, Shenqi Wang, Jian Zhang, Xiaoliang Huang, Zhongqiu Wang, Zhongmin Guo, Jianjun Liu, Yun J Med Syst Systems-Level Quality Improvement 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. Springer US 2018-11-13 2018 /pmc/articles/PMC6244882/ /pubmed/30421323 http://dx.doi.org/10.1007/s10916-018-1076-5 Text en © The Author(s) 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
spellingShingle Systems-Level Quality Improvement
Zhang, Xin
Wang, Li
Miao, Shumei
Xu, Hua
Yin, Yuechuchu
Zhu, Yueshi
Dai, Zuolei
Shan, Tao
Jing, Shenqi
Wang, Jian
Zhang, Xiaoliang
Huang, Zhongqiu
Wang, Zhongmin
Guo, Jianjun
Liu, Yun
Analysis of treatment pathways for three chronic diseases using OMOP CDM
title Analysis of treatment pathways for three chronic diseases using OMOP CDM
title_full Analysis of treatment pathways for three chronic diseases using OMOP CDM
title_fullStr Analysis of treatment pathways for three chronic diseases using OMOP CDM
title_full_unstemmed Analysis of treatment pathways for three chronic diseases using OMOP CDM
title_short Analysis of treatment pathways for three chronic diseases using OMOP CDM
title_sort analysis of treatment pathways for three chronic diseases using omop cdm
topic Systems-Level Quality Improvement
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6244882/
https://www.ncbi.nlm.nih.gov/pubmed/30421323
http://dx.doi.org/10.1007/s10916-018-1076-5
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