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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| Veröffentlicht in: | Journal of medical systems 2018-12, Vol.42 (12), p.260-12, Article 260 |
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| creator | 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 |
| 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 ana |
| doi_str_mv | 10.1007/s10916-018-1076-5 |
| format | Article |
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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.</description><identifier>ISSN: 0148-5598</identifier><identifier>EISSN: 1573-689X</identifier><identifier>DOI: 10.1007/s10916-018-1076-5</identifier><identifier>PMID: 30421323</identifier><language>eng</language><publisher>New York: Springer US</publisher><subject>Acarbose ; Big Data ; China ; Chronic Disease - drug therapy ; Chronic illnesses ; Critical Pathways ; Data analysis ; Data management ; Data processing ; Databases, Factual ; Diabetes ; Diabetes mellitus ; Diabetes mellitus (non-insulin dependent) ; Diseases ; Drug development ; Drugs ; Electronic Health Records ; Guidelines ; Health Informatics ; Health Sciences ; Health services ; Humans ; Hypertension ; Institutions ; Medical research ; Medical treatment ; Medicine ; Medicine & Public Health ; Mental depression ; Metformin ; Models, Theoretical ; Observation ; Patients ; Standardization ; Statistics for Life Sciences ; Systems-Level Quality Improvement</subject><ispartof>Journal of medical systems, 2018-12, Vol.42 (12), p.260-12, Article 260</ispartof><rights>The Author(s) 2018</rights><rights>Journal of Medical Systems is a copyright of Springer, (2018). All Rights Reserved. © 2018. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c470t-a8d3243429911b2649a2391b4673da14db9cb2d2d41fc3f2d9a43b323b0591df3</citedby><cites>FETCH-LOGICAL-c470t-a8d3243429911b2649a2391b4673da14db9cb2d2d41fc3f2d9a43b323b0591df3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s10916-018-1076-5$$EPDF$$P50$$Gspringer$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s10916-018-1076-5$$EHTML$$P50$$Gspringer$$Hfree_for_read</linktohtml><link.rule.ids>230,314,778,782,883,27911,27912,41475,42544,51306</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/30421323$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Zhang, Xin</creatorcontrib><creatorcontrib>Wang, Li</creatorcontrib><creatorcontrib>Miao, Shumei</creatorcontrib><creatorcontrib>Xu, Hua</creatorcontrib><creatorcontrib>Yin, Yuechuchu</creatorcontrib><creatorcontrib>Zhu, Yueshi</creatorcontrib><creatorcontrib>Dai, Zuolei</creatorcontrib><creatorcontrib>Shan, Tao</creatorcontrib><creatorcontrib>Jing, Shenqi</creatorcontrib><creatorcontrib>Wang, Jian</creatorcontrib><creatorcontrib>Zhang, Xiaoliang</creatorcontrib><creatorcontrib>Huang, Zhongqiu</creatorcontrib><creatorcontrib>Wang, Zhongmin</creatorcontrib><creatorcontrib>Guo, Jianjun</creatorcontrib><creatorcontrib>Liu, Yun</creatorcontrib><title>Analysis of treatment pathways for three chronic diseases using OMOP CDM</title><title>Journal of medical systems</title><addtitle>J Med Syst</addtitle><addtitle>J Med Syst</addtitle><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.</description><subject>Acarbose</subject><subject>Big Data</subject><subject>China</subject><subject>Chronic Disease - drug therapy</subject><subject>Chronic illnesses</subject><subject>Critical Pathways</subject><subject>Data analysis</subject><subject>Data management</subject><subject>Data processing</subject><subject>Databases, Factual</subject><subject>Diabetes</subject><subject>Diabetes mellitus</subject><subject>Diabetes mellitus (non-insulin dependent)</subject><subject>Diseases</subject><subject>Drug development</subject><subject>Drugs</subject><subject>Electronic Health Records</subject><subject>Guidelines</subject><subject>Health Informatics</subject><subject>Health Sciences</subject><subject>Health services</subject><subject>Humans</subject><subject>Hypertension</subject><subject>Institutions</subject><subject>Medical research</subject><subject>Medical treatment</subject><subject>Medicine</subject><subject>Medicine & Public Health</subject><subject>Mental depression</subject><subject>Metformin</subject><subject>Models, Theoretical</subject><subject>Observation</subject><subject>Patients</subject><subject>Standardization</subject><subject>Statistics for Life Sciences</subject><subject>Systems-Level Quality 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of treatment pathways for three chronic diseases using OMOP CDM</title><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></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c470t-a8d3243429911b2649a2391b4673da14db9cb2d2d41fc3f2d9a43b323b0591df3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2018</creationdate><topic>Acarbose</topic><topic>Big Data</topic><topic>China</topic><topic>Chronic Disease - drug therapy</topic><topic>Chronic illnesses</topic><topic>Critical Pathways</topic><topic>Data analysis</topic><topic>Data management</topic><topic>Data processing</topic><topic>Databases, Factual</topic><topic>Diabetes</topic><topic>Diabetes mellitus</topic><topic>Diabetes mellitus (non-insulin dependent)</topic><topic>Diseases</topic><topic>Drug development</topic><topic>Drugs</topic><topic>Electronic Health Records</topic><topic>Guidelines</topic><topic>Health Informatics</topic><topic>Health Sciences</topic><topic>Health services</topic><topic>Humans</topic><topic>Hypertension</topic><topic>Institutions</topic><topic>Medical research</topic><topic>Medical treatment</topic><topic>Medicine</topic><topic>Medicine & Public Health</topic><topic>Mental depression</topic><topic>Metformin</topic><topic>Models, Theoretical</topic><topic>Observation</topic><topic>Patients</topic><topic>Standardization</topic><topic>Statistics for Life Sciences</topic><topic>Systems-Level Quality Improvement</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Zhang, Xin</creatorcontrib><creatorcontrib>Wang, Li</creatorcontrib><creatorcontrib>Miao, Shumei</creatorcontrib><creatorcontrib>Xu, Hua</creatorcontrib><creatorcontrib>Yin, 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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.</abstract><cop>New York</cop><pub>Springer US</pub><pmid>30421323</pmid><doi>10.1007/s10916-018-1076-5</doi><tpages>12</tpages><oa>free_for_read</oa></addata></record> |
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| identifier | ISSN: 0148-5598 |
| ispartof | Journal of medical systems, 2018-12, Vol.42 (12), p.260-12, Article 260 |
| issn | 0148-5598 1573-689X |
| language | eng |
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| source | MEDLINE; Springer Nature - Complete Springer Journals |
| subjects | Acarbose Big Data China Chronic Disease - drug therapy Chronic illnesses Critical Pathways Data analysis Data management Data processing Databases, Factual Diabetes Diabetes mellitus Diabetes mellitus (non-insulin dependent) Diseases Drug development Drugs Electronic Health Records Guidelines Health Informatics Health Sciences Health services Humans Hypertension Institutions Medical research Medical treatment Medicine Medicine & Public Health Mental depression Metformin Models, Theoretical Observation Patients Standardization Statistics for Life Sciences Systems-Level Quality Improvement |
| title | Analysis of treatment pathways for three chronic diseases using OMOP CDM |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-16T05%3A23%3A04IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_pubme&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Analysis%20of%20treatment%20pathways%20for%20three%20chronic%20diseases%20using%20OMOP%20CDM&rft.jtitle=Journal%20of%20medical%20systems&rft.au=Zhang,%20Xin&rft.date=2018-12-01&rft.volume=42&rft.issue=12&rft.spage=260&rft.epage=12&rft.pages=260-12&rft.artnum=260&rft.issn=0148-5598&rft.eissn=1573-689X&rft_id=info:doi/10.1007/s10916-018-1076-5&rft_dat=%3Cproquest_pubme%3E2132737499%3C/proquest_pubme%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2132193874&rft_id=info:pmid/30421323&rfr_iscdi=true |