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Article: Conversion of CPRD AURUM Data into the OMOP Common Data Model.

Mayer, Craig S

Informatics in medicine unlocked

2023 Volume 43

Abstract: Introduction: Efforts to standardize clinical data using Common Data Models (CDMS) has grown in recent years. Use of CDMs allows for quicker understanding of data structure and reuse of existing tools. One CDM is the Observational Medical Outcomes ... ...

Abstract	Introduction: Efforts to standardize clinical data using Common Data Models (CDMS) has grown in recent years. Use of CDMs allows for quicker understanding of data structure and reuse of existing tools. One CDM is the Observational Medical Outcomes Partnership (OMOP) CDM. Clinical Practice Research Datalink (CPRD) is a data collection program collecting general practitioner data in the UK. Objective: Our objective was to convert a static copy of CPRD AURUM data into the OMOP CDM and run existing tools on the converted data. Methods: Two methods were used to convert each CPRD file into the OMOP CDM. The first was direct mapping used when converting CPRD files that had comparable tables in the OMOP CDM. The original names were changed to the OMOP equivalent and source values converted to standardized OMOP concepts. CPRD files: Patient (to OMOP Person), Staff (to Provider), Drug Issue (to Drug Exposure) and Practice (to Care Site) were directly mapped. The second method was indirect where for the CPRD Observation file the domain of each data row was used to assign data to proper OMOP tables or columns done by converting all source values to standard concepts. Results: The OMOP CDM conversion populated 12 tables and 20,240,453,339 rows, with the largest table being the Measurement table (5,202,579,174 data row). Mapping source values to OMOP standard concepts, we found 60.2% (46,413 of 77,149) of source concepts were also standard concepts. The Drug Exposure table had the fewest source values already in the standard form as only 4.7% (1,433 of 30,194) of the source concepts were standard concepts. On a data retention level, only 2.00% of all data rows were excluded as they did not have a clear fit in the developed CDM and were not able to stand alone without additional information which was not present. Conclusion: CPRD AURUM was successfully converted into the OMOP CDM with minimal data loss. Existing OHDSI tools were used with the converted data to show efficacy of the converted data. The existence of a standardized version of CPRD AURUM data vastly increases its reusability in future research due to increased understanding and tools available.
Language	English
Publishing date	2023-11-10
Publishing country	England
Document type	Journal Article
ISSN	2352-9148
ISSN	2352-9148
DOI	10.1016/j.imu.2023.101407
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Conversion of CPRD AURUM data into the OMOP common data model

Craig S. Mayer

Informatics in Medicine Unlocked, Vol 43, Iss , Pp 101407- (2023)

2023

Abstract	Introduction: Efforts to standardize clinical data using Common Data Models (CDMS) has grown in recent years. Use of CDMs allows for quicker understanding of data structure and reuse of existing tools. One CDM is the Observational Medical Outcomes Partnership (OMOP) CDM. Clinical Practice Research Datalink (CPRD) is a data collection program collecting general practitioner data in the UK. Objective: Our objective was to convert a static copy of CPRD AURUM data into the OMOP CDM and run existing tools on the converted data. Methods: Two methods were used to convert each CPRD file into the OMOP CDM. The first was direct mapping used when converting CPRD files that had comparable tables in the OMOP CDM. The original names were changed to the OMOP equivalent and source values converted to standardized OMOP concepts. CPRD files: Patient (to OMOP Person), Staff (to Provider), Drug Issue (to Drug Exposure) and Practice (to Care Site) were directly mapped. The second method was indirect where for the CPRD Observation file the domain of each data row was used to assign data to proper OMOP tables or columns done by converting all source values to standard concepts. Results: The OMOP CDM conversion populated 12 tables and 20,240,453,339 rows, with the largest table being the Measurement table (5,202,579,174 data row). Mapping source values to OMOP standard concepts, we found 60.2% (46,413 of 77,149) of source concepts were also standard concepts. The Drug Exposure table had the fewest source values already in the standard form as only 4.7% (1433 of 30,194) of the source concepts were standard concepts. On a data retention level, only 2.00% of all data rows were excluded as they did not have a clear fit in the developed CDM and were not able to stand alone without additional information which was not present. Conclusion: CPRD AURUM was successfully converted into the OMOP CDM with minimal data loss. Existing OHDSI tools were used with the converted data to show efficacy of the converted data. The existence of a standardized ...
Keywords	Data science ; Clinical informatics ; Real world data ; Common data model ; Computer applications to medicine. Medical informatics ; R858-859.7
Subject code	310
Language	English
Publishing date	2023-01-01T00:00:00Z
Publisher	Elsevier
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book: New insights in intracerebral hemorrhage

Toyoda, Kazunori / Anderson, Craig S. / Mayer, Stephan A.

31 figures and 20 tables

(Frontiers of neurology and neuroscience ; vol. 37)

2016

Author's details	volume editors Kazunori Toyoda, Suita, Osaka; Craig S. Anderson, Sydney, N.S.W.; Stephan A. Mayer, New York, N.Y
Series title	Frontiers of neurology and neuroscience ; vol. 37
Collection
Keywords	Cerebral Hemorrhage / diagnosis ; Cerebral Hemorrhage / therapy ; Neurology ; Neurosurgery ; Radiology ; Hirnblutung
Subject	Intrazerebrales Hämatom ; Intrazerebralhämatom ; Intrazerebrale Blutung ; Intrakranielle Blutung
Language	English
Size	VIII, 197 Seiten, Illustrationen, Diagramme, 26 cm
Publisher	Karger
Publishing place	Basel
Publishing country	Switzerland
Document type	Book
HBZ-ID	HT018847875
ISBN	978-3-318-05596-2 ; 978-3-318-05597-9 ; 3-318-05596-4 ; 3-318-05597-2
Database	Catalogue ZB MED Medicine, Health

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Article ; Online: Learning important common data elements from shared study data: The All of Us program analysis.

Mayer, Craig S / Huser, Vojtech

PloS one

2023 Volume 18, Issue 7, Page(s) e0283601

Abstract: There are many initiatives attempting to harmonize data collection across human clinical studies using common data elements (CDEs). The increased use of CDEs in large prior studies can guide researchers planning new studies. For that purpose, we analyzed ...

Abstract	There are many initiatives attempting to harmonize data collection across human clinical studies using common data elements (CDEs). The increased use of CDEs in large prior studies can guide researchers planning new studies. For that purpose, we analyzed the All of Us (AoU) program, an ongoing US study intending to enroll one million participants and serve as a platform for numerous observational analyses. AoU adopted the OMOP Common Data Model to standardize both research (Case Report Form [CRF]) and real-world (imported from Electronic Health Records [EHRs]) data. AoU standardized specific data elements and values by including CDEs from terminologies such as LOINC and SNOMED CT. For this study, we defined all elements from established terminologies as CDEs and all custom concepts created in the Participant Provided Information (PPI) terminology as unique data elements (UDEs). We found 1 033 research elements, 4 592 element-value combinations and 932 distinct values. Most elements were UDEs (869, 84.1%), while most CDEs were from LOINC (103 elements, 10.0%) or SNOMED CT (60, 5.8%). Of the LOINC CDEs, 87 (53.1% of 164 CDEs) originated from previous data collection initiatives, such as PhenX (17 CDEs) and PROMIS (15 CDEs). On a CRF level, The Basics (12 of 21 elements, 57.1%) and Lifestyle (10 of 14, 71.4%) were the only CRFs with multiple CDEs. On a value level, 61.7% of distinct values are from an established terminology. AoU demonstrates the use of the OMOP model for integrating research and routine healthcare data (64 elements in both contexts), which allows for monitoring lifestyle and health changes outside the research setting. The increased inclusion of CDEs in large studies (like AoU) is important in facilitating the use of existing tools and improving the ease of understanding and analyzing the data collected, which is more challenging when using study specific formats.
MeSH term(s)	Humans ; Common Data Elements ; Population Health ; Data Collection ; Systematized Nomenclature of Medicine ; Delivery of Health Care
Language	English
Publishing date	2023-07-07
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Intramural
ZDB-ID	2267670-3
ISSN	1932-6203 ; 1932-6203
ISSN (online)	1932-6203
ISSN	1932-6203
DOI	10.1371/journal.pone.0283601
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Learning important common data elements from shared study data

Craig S Mayer / Vojtech Huser

PLoS ONE, Vol 18, Iss 7, p e

The All of Us program analysis.

2023 Volume 0283601

Abstract	There are many initiatives attempting to harmonize data collection across human clinical studies using common data elements (CDEs). The increased use of CDEs in large prior studies can guide researchers planning new studies. For that purpose, we analyzed the All of Us (AoU) program, an ongoing US study intending to enroll one million participants and serve as a platform for numerous observational analyses. AoU adopted the OMOP Common Data Model to standardize both research (Case Report Form [CRF]) and real-world (imported from Electronic Health Records [EHRs]) data. AoU standardized specific data elements and values by including CDEs from terminologies such as LOINC and SNOMED CT. For this study, we defined all elements from established terminologies as CDEs and all custom concepts created in the Participant Provided Information (PPI) terminology as unique data elements (UDEs). We found 1 033 research elements, 4 592 element-value combinations and 932 distinct values. Most elements were UDEs (869, 84.1%), while most CDEs were from LOINC (103 elements, 10.0%) or SNOMED CT (60, 5.8%). Of the LOINC CDEs, 87 (53.1% of 164 CDEs) originated from previous data collection initiatives, such as PhenX (17 CDEs) and PROMIS (15 CDEs). On a CRF level, The Basics (12 of 21 elements, 57.1%) and Lifestyle (10 of 14, 71.4%) were the only CRFs with multiple CDEs. On a value level, 61.7% of distinct values are from an established terminology. AoU demonstrates the use of the OMOP model for integrating research and routine healthcare data (64 elements in both contexts), which allows for monitoring lifestyle and health changes outside the research setting. The increased inclusion of CDEs in large studies (like AoU) is important in facilitating the use of existing tools and improving the ease of understanding and analyzing the data collected, which is more challenging when using study specific formats.
Keywords	Medicine ; R ; Science ; Q
Subject code	005
Language	English
Publishing date	2023-01-01T00:00:00Z
Publisher	Public Library of Science (PLoS)
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: regCOVID: Tracking publications of registered COVID-19 studies.

Mayer, Craig S / Huser, Vojtech

BMC medical research methodology

2022 Volume 22, Issue 1, Page(s) 221

Abstract: Background: In response to the COVID-19 pandemic many clinical studies have been initiated leading to the need for efficient ways to track and analyze study results. We expanded our previous project that tracked registered COVID-19 clinical studies to ... ...

Abstract	Background: In response to the COVID-19 pandemic many clinical studies have been initiated leading to the need for efficient ways to track and analyze study results. We expanded our previous project that tracked registered COVID-19 clinical studies to also track result articles generated from these studies. Our objective was to develop a data science approach to identify and analyze all publications linked to COVID-19 clinical studies and generate a prioritized list of publications for efficient understanding of the state of COVID-19 clinical research. Methods: We conducted searches of ClinicalTrials.gov and PubMed to identify articles linked to COVID-19 studies, and developed criteria based on the trial phase, intervention, location, and record recency to develop a prioritized list of result publications. Results: The performed searchers resulted in 1 022 articles linked to 565 interventional trials (17.8% of all 3 167 COVID-19 interventional trials as of 31 January 2022). 609 publications were identified via abstract-link in PubMed and 413 via registry-link in ClinicalTrials.gov, with 27 articles linked from both sources. Of the 565 trials publishing at least one article, 197 (34.9%) had multiple linked publications. An attention score was assigned to each publication to develop a prioritized list of all publications linked to COVID-19 trials and 83 publications were identified that are result articles from late phase (Phase 3) trials with at least one US site and multiple study record updates. For COVID-19 vaccine trials, 108 linked result articles for 64 trials (14.7% of 436 total COVID-19 vaccine trials) were found. Conclusions: Our method allows for the efficient identification of important COVID-19 articles that report results of registered clinical trials and are connected via a structured article-trial link. Our data science methodology also allows for consistent and as needed data updates and is generalizable to other conditions of interest.
MeSH term(s)	COVID-19 ; COVID-19 Vaccines ; Humans ; Pandemics ; Periodicals as Topic ; PubMed ; Publications ; Registries
Chemical Substances	COVID-19 Vaccines
Language	English
Publishing date	2022-08-10
Publishing country	England
Document type	Journal Article
ZDB-ID	2041362-2
ISSN	1471-2288 ; 1471-2288
ISSN (online)	1471-2288
ISSN	1471-2288
DOI	10.1186/s12874-022-01703-9
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: regCOVID

Craig S. Mayer / Vojtech Huser

BMC Medical Research Methodology, Vol 22, Iss 1, Pp 1-

Tracking publications of registered COVID-19 studies

2022 Volume 10

Abstract: Abstract Background In response to the COVID-19 pandemic many clinical studies have been initiated leading to the need for efficient ways to track and analyze study results. We expanded our previous project that tracked registered COVID-19 clinical ... ...

Abstract	Abstract Background In response to the COVID-19 pandemic many clinical studies have been initiated leading to the need for efficient ways to track and analyze study results. We expanded our previous project that tracked registered COVID-19 clinical studies to also track result articles generated from these studies. Our objective was to develop a data science approach to identify and analyze all publications linked to COVID-19 clinical studies and generate a prioritized list of publications for efficient understanding of the state of COVID-19 clinical research. Methods We conducted searches of ClinicalTrials.gov and PubMed to identify articles linked to COVID-19 studies, and developed criteria based on the trial phase, intervention, location, and record recency to develop a prioritized list of result publications. Results The performed searchers resulted in 1 022 articles linked to 565 interventional trials (17.8% of all 3 167 COVID-19 interventional trials as of 31 January 2022). 609 publications were identified via abstract-link in PubMed and 413 via registry-link in ClinicalTrials.gov, with 27 articles linked from both sources. Of the 565 trials publishing at least one article, 197 (34.9%) had multiple linked publications. An attention score was assigned to each publication to develop a prioritized list of all publications linked to COVID-19 trials and 83 publications were identified that are result articles from late phase (Phase 3) trials with at least one US site and multiple study record updates. For COVID-19 vaccine trials, 108 linked result articles for 64 trials (14.7% of 436 total COVID-19 vaccine trials) were found. Conclusions Our method allows for the efficient identification of important COVID-19 articles that report results of registered clinical trials and are connected via a structured article-trial link. Our data science methodology also allows for consistent and as needed data updates and is generalizable to other conditions of interest.
Keywords	COVID-19 ; Data Science ; Clinical Trials ; Result Publications ; Informatics ; Medicine (General) ; R5-920
Subject code	001
Language	English
Publishing date	2022-08-01T00:00:00Z
Publisher	BMC
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article: Computerized monitoring of COVID-19 trials, studies and registries in ClinicalTrials.gov registry.

Mayer, Craig S / Huser, Vojtech

PeerJ

2020 Volume 8, Page(s) e10261

Abstract: Clinical trial registries can provide important information about relevant studies for a given condition to other researchers and the public. We developed a computerized informatics based approach to provide an overview and analysis of COVID-19 studies ... ...

Abstract	Clinical trial registries can provide important information about relevant studies for a given condition to other researchers and the public. We developed a computerized informatics based approach to provide an overview and analysis of COVID-19 studies registered on ClinicalTrials.gov registry. Using the perspective of analyzing active or completed COVID-19 studies, we identified 401 interventional clinical trials, 287 observational studies and 64 registries. We analyzed features of each study type separately such as location, design, interventions and update history. Our results show that the United States had the most COVID-19 interventional trials, France had the most COVID-19 observational studies and France and the United States tied for the most COVID-19 registries on ClinicalTrials.gov. The majority of studies in all three study types had a single study site. For update history "Study Status" is the most updated information and we found that studies located in Canada (2.70 updates per study) and the United States (1.76 updates per study) update their studies more often than studies in any other country. Using normalization and mapping techniques, we identified Hydroxychloroquine (92 studies) as the most common drug intervention, while convalescent plasma (20 studies) is the most common biological intervention. The primary purpose of most interventional trials is for treatment with 298 studies (74.3%). For COVID-19 registries we found the most common proposed follow-up time is 1 year (15 studies). Of specific importance and interest is COVID-19 vaccine trials, of which 12 were identified. Our informatics based approach allows for constant monitoring and updating as well as multiple applications to other conditions and interests.
Keywords	covid19
Language	English
Publishing date	2020-10-23
Publishing country	United States
Document type	Clinical Trial
ZDB-ID	2703241-3
ISSN	2167-8359
ISSN	2167-8359
DOI	10.7717/peerj.10261
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Article ; Online: Computerized monitoring of COVID-19 trials, studies and registries in ClinicalTrials.gov registry

Craig S. Mayer / Vojtech Huser

PeerJ, Vol 8, p e

2020 Volume 10261

Abstract	Clinical trial registries can provide important information about relevant studies for a given condition to other researchers and the public. We developed a computerized informatics based approach to provide an overview and analysis of COVID-19 studies registered on ClinicalTrials.gov registry. Using the perspective of analyzing active or completed COVID-19 studies, we identified 401 interventional clinical trials, 287 observational studies and 64 registries. We analyzed features of each study type separately such as location, design, interventions and update history. Our results show that the United States had the most COVID-19 interventional trials, France had the most COVID-19 observational studies and France and the United States tied for the most COVID-19 registries on ClinicalTrials.gov. The majority of studies in all three study types had a single study site. For update history “Study Status” is the most updated information and we found that studies located in Canada (2.70 updates per study) and the United States (1.76 updates per study) update their studies more often than studies in any other country. Using normalization and mapping techniques, we identified Hydroxychloroquine (92 studies) as the most common drug intervention, while convalescent plasma (20 studies) is the most common biological intervention. The primary purpose of most interventional trials is for treatment with 298 studies (74.3%). For COVID-19 registries we found the most common proposed follow-up time is 1 year (15 studies). Of specific importance and interest is COVID-19 vaccine trials, of which 12 were identified. Our informatics based approach allows for constant monitoring and updating as well as multiple applications to other conditions and interests.
Keywords	COVID-19 ; Clinical trials ; Data science ; Medicine ; R ; Biology (General) ; QH301-705.5
Subject code	610
Language	English
Publishing date	2020-10-01T00:00:00Z
Publisher	PeerJ Inc.
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Assessing the validity of self-report of psychopathy short-form (SRP-SF) in incarcerated offenders from Chile and Uruguay.

Trajtenberg, Nicolás / de Ribera, Olga Sánchez / Nivette, Amy / León-Mayer, Elizabeth / Neumann, Craig S

International journal of law and psychiatry

2023 Volume 87, Page(s) 101867

Abstract: Psychopathy remains a relatively unexplored concept in Latin America. The abbreviated Self-Report Psychopathy Scale (SRP-SF) seems promising in this under-resourced context. However, the SRP-SF should be tested for measurement invariance to achieve ... ...

Abstract	Psychopathy remains a relatively unexplored concept in Latin America. The abbreviated Self-Report Psychopathy Scale (SRP-SF) seems promising in this under-resourced context. However, the SRP-SF should be tested for measurement invariance to achieve meaningful comparison across countries in Latin America. Therefore the aims of this study were to examine the underlying factor structure of the SRP-SF in incarcerated adult male offenders from Uruguay (n = 331) and Chile (n = 208), to examine the measurement invariance of the SRP-SF across countries, and to assess the utility of SRP-SF to classify first time offenders from offenders with criminal history. Findings showed a good fit for the four-factor model in Uruguay, and both Chile and Uruguay showed invariance. Conversely, the Interpersonal and Affective factors were not associated with criminal history in the Uruguayan sample. Therefore, more studies are needed before using the SRP-SF as screening tool to classify first-time offenders and reoffenders in different countries in Latin America.
MeSH term(s)	Adult ; Humans ; Male ; Self Report ; Criminals ; Chile ; Uruguay ; Antisocial Personality Disorder/psychology ; Prisoners/psychology
Language	English
Publishing date	2023-02-15
Publishing country	Netherlands
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	304429-4
ISSN	1873-6386 ; 0160-2527
ISSN (online)	1873-6386
ISSN	0160-2527
DOI	10.1016/j.ijlp.2023.101867
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