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Petit P, Chamot S, Al-Salameh A, Cancé C, Desailloud R, Bonneterre V. Farming activity and risk of treated thyroid disorders: Insights from the TRACTOR project, a nationwide cohort study. Environ Res 2024; 249:118458. [PMID: 38365059 DOI: 10.1016/j.envres.2024.118458] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/20/2023] [Revised: 02/08/2024] [Accepted: 02/09/2024] [Indexed: 02/18/2024]
Abstract
BACKGROUND Epidemiological data regarding thyroid diseases are lacking, in particular for occupationally exposed populations. OBJECTIVES To compare the risk of hypothyroidism and hyperthyroidism between farming activities within the complete population of French farm managers (FMs). METHODS Digital health data from retrospective administrative databases, including insurance claims and electronic health/medical records, was employed. This cohort data spanned the entirety of French farm managers (FMs) who had undertaken work at least once from 2002 to 2016. Survival analysis with the time to initial medication reimbursement as timescale was used to examine the association (hazard ratio, HR) between 26 specific farming activities and both treated hypothyroidism and hyperthyroidism. A distinct model was developed for each farming activity, comparing FMs who had never engaged in the specific farming activity between 2002 and 2016 with those who had. All analyses were adjusted for potential confounders (e.g., age), and sensitivity analyses were conducted. RESULTS Among 1088561 FMs (mean age 46.6 [SD 14.1]; 31% females), there were 31834 hypothyroidism cases (75% females) and 620 hyperthyroidism cases (67% females), respectively. The highest risks were observed for cattle activities for both hyperthyroidism (HR ranging from 1.75 to 2.42) and hypothyroidism (HR ranging from 1.41 to 1.44). For hypothyroidism, higher risks were also observed for several animal farming activities (pig, poultry, and rabbit), as well as fruit arboriculture (HR = 1.22 [1.14-1.31]). The lowest risks were observed for activities involving horses. Sex differences in the risk of hypothyroidism were observed for eight activities, with the risk being higher for males (HR = 1.09 [1.01-1.20]) than females in viticulture (HR = 0.97 [0.93-1.00]). The risk of hyperthyroidism was two times higher for male dairy farmers than females. DISCUSSION Our findings offer a comprehensive overview of thyroid disease risks within the FM community. Thyroid ailments might not stem from a single cause but likely arise from the combined effects of various causal agents and triggering factors (agricultural exposome). Further investigation into distinct farming activities-especially those involving cattle-is essential to pinpoint potential risk factors that could enhance thyroid disease monitoring in agriculture.
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Affiliation(s)
- Pascal Petit
- CHU Grenoble Alpes, Centre Régional de Pathologies Professionnelles et Environnementales, 38000, Grenoble, France; Univ. Grenoble Alpes, AGEIS, 38000, Grenoble, France.
| | - Sylvain Chamot
- Regional Center for Occupational and Environmental Diseases of Hauts-de-France, Amiens University Hospital, 1 rond point du Pr Christian Cabrol, 80000, Amiens, France; Péritox (UMR_I 01), UPJV/INERIS, University of Picardy Jules Verne, Chemin du Thil, 80025, Amiens, France
| | - Abdallah Al-Salameh
- Péritox (UMR_I 01), UPJV/INERIS, University of Picardy Jules Verne, Chemin du Thil, 80025, Amiens, France; Department of Endocrinology, Diabetes Mellitus and Nutrition, Amiens University Hospital, 1 rond point du Pr Christian Cabrol, 80054, Amiens, France
| | - Christophe Cancé
- Univ. Grenoble Alpes, CNRS, UMR 5525, VetAgro Sup, Grenoble INP, CHU Grenoble Alpes, TIMC, 38000, Grenoble, France
| | - Rachel Desailloud
- Péritox (UMR_I 01), UPJV/INERIS, University of Picardy Jules Verne, Chemin du Thil, 80025, Amiens, France; Department of Endocrinology, Diabetes Mellitus and Nutrition, Amiens University Hospital, 1 rond point du Pr Christian Cabrol, 80054, Amiens, France
| | - Vincent Bonneterre
- CHU Grenoble Alpes, Centre Régional de Pathologies Professionnelles et Environnementales, 38000, Grenoble, France; Univ. Grenoble Alpes, CNRS, UMR 5525, VetAgro Sup, Grenoble INP, CHU Grenoble Alpes, TIMC, 38000, Grenoble, France
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Chamot S, Al-Salameh A, Petit P, Bonneterre V, Cancé C, Decocq G, Boullier A, Braun K, Desailloud R. Does prenatal exposure to multiple airborne and tap-water pollutants increase neonatal thyroid-stimulating hormone concentrations? Data from the Picardy region, France. Sci Total Environ 2023; 905:167089. [PMID: 37717745 DOI: 10.1016/j.scitotenv.2023.167089] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Revised: 09/12/2023] [Accepted: 09/13/2023] [Indexed: 09/19/2023]
Abstract
OBJECTIVE Systematic screening for congenital hypothyroidism by heel-stick sampling has revealed unexpected heterogeneity in the geographic distribution of newborn thyroid-stimulating hormone concentrations in Picardy, France. We explored a possible relationship with environmental pollutants. METHODS Zip code geolocation data from mothers of newborns without congenital hypothyroidism born in 2021 were linked to ecological data for a set of airborne (particulate matter with a diameter of 2.5 μm or less [PM2.5] or 10 μm or less [PM10]) and tap-water (nitrate and perchlorate ions and atrazine) pollutants. Statistical associations between mean exposure levels during the third trimester of pregnancy and Thyroid-stimulating hormone (TSH) concentrations in 6249 newborns (51 % male) were investigated using linear regression models. RESULTS Median neonatal TSH concentration (interquartile range, IQR) was 1.7 (1-2.8) mIU/L. An increase of one IQR in prenatal exposure to perchlorate ions (3.6 μg/L), nitrate ions (19.2 mg/L), PM2.5 (3.7 μg/m3) and PM10 (3.4 μg/m3), were associated with increases in TSH concentrations of 2.30 % (95 % CI: 0.95-3.66), 5.84 % (95 % CI: 2.81-8.87), 13.44 % (95 % CI: 9.65-17.28) and 6.26 % (95 % CI: 3.01-9.56), respectively. CONCLUSIONS Prenatal exposure to perchlorate and nitrate ions in tap water and to airborne PM over the third trimester of pregnancy was significantly associated with increased neonatal TSH concentrations.
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Affiliation(s)
- Sylvain Chamot
- Regional Center for Occupational and Environmental Diseases of Hauts-de-France, Amiens University Hospital, 1 rond point du Pr Christian Cabrol, 80000 Amiens, France; Péritox (UMR_I 01), UPJV/INERIS, University of Picardy Jules Verne, 1 rond point du Pr Christian Cabrol, 80000 Amiens, France.
| | - Abdallah Al-Salameh
- Péritox (UMR_I 01), UPJV/INERIS, University of Picardy Jules Verne, 1 rond point du Pr Christian Cabrol, 80000 Amiens, France; Department of Endocrinology, Diabetes Mellitus and Nutrition, Amiens University Hospital, 1 rond point du Pr Christian Cabrol, 80054 Amiens, France
| | - Pascal Petit
- CHU Grenoble Alpes, Centre Régional de Pathologies Professionnelles et Environnementales, 38000 Grenoble, France; Univ. Grenoble Alpes, AGEIS, 38000 Grenoble, France
| | - Vincent Bonneterre
- CHU Grenoble Alpes, Centre Régional de Pathologies Professionnelles et Environnementales, 38000 Grenoble, France; Univ. Grenoble Alpes, CNRS, Grenoble INP, TIMC, 38000 Grenoble, France
| | - Christophe Cancé
- Univ. Grenoble Alpes, CNRS, Grenoble INP, TIMC, 38000 Grenoble, France
| | - Guillaume Decocq
- UF PRiMAX (Prévention des Risques liés aux Médicaments et Autres Xénobiotiques), Service de Pharmacologie clinique, Centre hospitalier universitaire d'Amiens - Picardie, 1 rond point du Pr Christian Cabrol, F-80054 Amiens Cedex 1, France; Ecologie et Dynamique des Systèmes Anthropisés (EDYSAN, UMR CNRS 7058), Jules Verne University of Picardy, 1 rue des Louvels, 80037 Amiens Cedex 1, France
| | - Agnès Boullier
- Department of Biochemistry, Amiens University Hospital, 1 rond point du Pr Christian Cabrol, 80054 Amiens, France; Regional Center of Newborn Screening of Picardy, Amiens University Hospital, 1 rond point du Pr Christian Cabrol, 80054 Amiens, France
| | - Karine Braun
- Regional Center of Newborn Screening of Picardy, Amiens University Hospital, 1 rond point du Pr Christian Cabrol, 80054 Amiens, France; Department of Paediatrics, Amiens University Hospital, 80054 Amiens, France
| | - Rachel Desailloud
- Péritox (UMR_I 01), UPJV/INERIS, University of Picardy Jules Verne, 1 rond point du Pr Christian Cabrol, 80000 Amiens, France; Department of Endocrinology, Diabetes Mellitus and Nutrition, Amiens University Hospital, 1 rond point du Pr Christian Cabrol, 80054 Amiens, France
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Artemova S, von Schenck U, Fa R, Stoessel D, Nowparast Rostami H, Madiot PE, Januel JM, Pagonis D, Landelle C, Gallouche M, Cancé C, Olive F, Moreau-Gaudry A, Prieur S, Bosson JL. Cohort profile for development of machine learning models to predict healthcare-related adverse events (Demeter): clinical objectives, data requirements for modelling and overview of data set for 2016-2018. BMJ Open 2023; 13:e070929. [PMID: 37591641 PMCID: PMC10441093 DOI: 10.1136/bmjopen-2022-070929] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/09/2022] [Accepted: 07/27/2023] [Indexed: 08/19/2023] Open
Abstract
PURPOSE In-hospital health-related adverse events (HAEs) are a major concern for hospitals worldwide. In high-income countries, approximately 1 in 10 patients experience HAEs associated with their hospital stay. Estimating the risk of an HAE at the individual patient level as accurately as possible is one of the first steps towards improving patient outcomes. Risk assessment can enable healthcare providers to target resources to patients in greatest need through adaptations in processes and procedures. Electronic health data facilitates the application of machine-learning methods for risk analysis. We aim, first to reveal correlations between HAE occurrence and patients' characteristics and/or the procedures they undergo during their hospitalisation, and second, to build models that allow the early identification of patients at an elevated risk of HAE. PARTICIPANTS 143 865 adult patients hospitalised at Grenoble Alpes University Hospital (France) between 1 January 2016 and 31 December 2018. FINDINGS TO DATE In this set-up phase of the project, we describe the preconditions for big data analysis using machine-learning methods. We present an overview of the retrospective de-identified multisource data for a 2-year period extracted from the hospital's Clinical Data Warehouse, along with social determinants of health data from the National Institute of Statistics and Economic Studies, to be used in machine learning (artificial intelligence) training and validation. No supplementary information or evaluation on the part of medical staff will be required by the information system for risk assessment. FUTURE PLANS We are using this data set to develop predictive models for several general HAEs including secondary intensive care admission, prolonged hospital stay, 7-day and 30-day re-hospitalisation, nosocomial bacterial infection, hospital-acquired venous thromboembolism, and in-hospital mortality.
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Affiliation(s)
- Svetlana Artemova
- Public Health Department, INSERM CIC1406, CHU Grenoble Alpes, Grenoble, France
- TIMC, CNRS UMR5525, Université Grenoble Alpes, Grenoble, France
| | | | - Rui Fa
- Elsevier Health Analytics, London, UK
| | | | | | | | | | - Daniel Pagonis
- Public Health Department, CHU Grenoble Alpes, Grenoble, France
| | - Caroline Landelle
- TIMC, CNRS UMR5525, Université Grenoble Alpes, Grenoble, France
- Public Health Department, CHU Grenoble Alpes, Grenoble, France
| | - Meghann Gallouche
- TIMC, CNRS UMR5525, Université Grenoble Alpes, Grenoble, France
- Public Health Department, CHU Grenoble Alpes, Grenoble, France
| | - Christophe Cancé
- Public Health Department, INSERM CIC1406, CHU Grenoble Alpes, Grenoble, France
- TIMC, CNRS UMR5525, Université Grenoble Alpes, Grenoble, France
| | - Frederic Olive
- Public Health Department, CHU Grenoble Alpes, Grenoble, France
| | - Alexandre Moreau-Gaudry
- Public Health Department, INSERM CIC1406, CHU Grenoble Alpes, Grenoble, France
- TIMC, CNRS UMR5525, Université Grenoble Alpes, Grenoble, France
| | - Sigurd Prieur
- Life Science Analytics, Elsevier BV, Berlin, Germany
| | - Jean-Luc Bosson
- Public Health Department, INSERM CIC1406, CHU Grenoble Alpes, Grenoble, France
- TIMC, CNRS UMR5525, Université Grenoble Alpes, Grenoble, France
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Cancé C, Lenne C, Artemova S, Mossuz P, Moreau-Gaudry A. Hypergraph Based Data Model for Complex Health Data Exploration and Its Implementation in PREDIMED Clinical Data Warehouse. Stud Health Technol Inform 2022; 290:335-339. [PMID: 35673030 DOI: 10.3233/shti220091] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Within the PREDIMED Clinical Data Warehouse (CDW) of Grenoble Alpes University Hospital (CHUGA), we have developed a hypergraph based operational data model, aiming at empowering physicians to explore, visualize and qualitatively analyze interactively the complex and massive information of the patients treated in CHUGA. This model constitutes a central target structure, expressed in a dual form, both graphical and formal, which gathers the concepts and their semantic relations into a hypergraph whose implementation can easily be manipulated by medical experts. The implementation is based on a property graph database linked to an interactive graphical interface allowing to navigate through the data and to interact in real time with a search engine, visualization and analysis tools. This model and its agile implementation allow for easy structural changes inherent to the evolution of techniques and practices in the health field. This flexibility provides adaptability to the evolution of interoperability standards.
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Affiliation(s)
- Christophe Cancé
- TIMC, Univ. Grenoble Alpes, CNRS, VetAgro'Sup, Grenoble INP, CHU Grenoble Alpes, F-38000, Grenoble, France
| | - Christian Lenne
- Clinical Investigation Center-Technological Innovation, Univ. Grenoble Alpes, INSERM CIC1406, CHUGA, Grenoble, France
| | - Svetlana Artemova
- CHU Grenoble Alpes (CHUGA), F-38000, Grenoble, France
- Clinical Investigation Center-Technological Innovation, Univ. Grenoble Alpes, INSERM CIC1406, CHUGA, Grenoble, France
- Public Health Department, CHU Grenoble Alpes, F-38000, Grenoble, France
| | - Pascal Mossuz
- CHU Grenoble Alpes (CHUGA), F-38000, Grenoble, France
- Department of Biological Hematology, Grenoble Alpes University Hospital, 38400 Grenoble, France
- IAB, Univ. Grenoble Alpes, CNRS, INSERM, Grenoble, France
| | - Alexandre Moreau-Gaudry
- CHU Grenoble Alpes (CHUGA), F-38000, Grenoble, France
- Clinical Investigation Center-Technological Innovation, Univ. Grenoble Alpes, INSERM CIC1406, CHUGA, Grenoble, France
- Public Health Department, CHU Grenoble Alpes, F-38000, Grenoble, France
- TIMC, Univ. Grenoble Alpes, CNRS, VetAgro'Sup, Grenoble INP, CHU Grenoble Alpes, F-38000, Grenoble, France
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Artemova S, Caporossi A, Cancé C, Madiot PE, Nemoz B, Larrat S, Moreau-Gaudry A, Bosson JL. COVID-19 Geographical Maps and Clinical Data Warehouse PREDIMED. Stud Health Technol Inform 2022; 290:1046-1047. [PMID: 35673198 DOI: 10.3233/shti220260] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
PREDIMED, Clinical Data Warehouse of Grenoble Alps University Hospital, is currently participating in daily COVID-19 epidemic follow-up via spatial and chronological analysis of geographical maps. This monitoring is aimed for cluster detection and vulnerable population discovery. Our real-time geographical representations allow us to track the epidemic both inside and outside the hospital.
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Affiliation(s)
- Svetlana Artemova
- CHU Grenoble Alpes, F-38000, Grenoble, France.,Clinical Investigation Center-Technological Innovation, Univ. Grenoble Alpes, INSERM CIC1406, CHU Grenoble Alpes, F-38000, Grenoble, France
| | - Alban Caporossi
- CHU Grenoble Alpes, F-38000, Grenoble, France.,Clinical Investigation Center-Technological Innovation, Univ. Grenoble Alpes, INSERM CIC1406, CHU Grenoble Alpes, F-38000, Grenoble, France.,TIMC, Univ. Grenoble Alpes, CNRS, VetAgro'Sup, CHU Grenoble Alpes, Grenoble INP, F-38000, Grenoble, France
| | - Christophe Cancé
- Clinical Investigation Center-Technological Innovation, Univ. Grenoble Alpes, INSERM CIC1406, CHU Grenoble Alpes, F-38000, Grenoble, France.,TIMC, Univ. Grenoble Alpes, CNRS, VetAgro'Sup, CHU Grenoble Alpes, Grenoble INP, F-38000, Grenoble, France
| | | | | | | | - Alexandre Moreau-Gaudry
- CHU Grenoble Alpes, F-38000, Grenoble, France.,Clinical Investigation Center-Technological Innovation, Univ. Grenoble Alpes, INSERM CIC1406, CHU Grenoble Alpes, F-38000, Grenoble, France.,TIMC, Univ. Grenoble Alpes, CNRS, VetAgro'Sup, CHU Grenoble Alpes, Grenoble INP, F-38000, Grenoble, France
| | - Jean-Luc Bosson
- CHU Grenoble Alpes, F-38000, Grenoble, France.,Clinical Investigation Center-Technological Innovation, Univ. Grenoble Alpes, INSERM CIC1406, CHU Grenoble Alpes, F-38000, Grenoble, France.,TIMC, Univ. Grenoble Alpes, CNRS, VetAgro'Sup, CHU Grenoble Alpes, Grenoble INP, F-38000, Grenoble, France
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Achard P, Maugard C, Cancé C, Spinosi J, Ozenfant D, Maître A, Bosson-Rieutort D, Bonneterre V. Medico-administrative data combined with agricultural practices data to retrospectively estimate pesticide use by agricultural workers. J Expo Sci Environ Epidemiol 2020; 30:743-755. [PMID: 31484997 DOI: 10.1038/s41370-019-0166-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/09/2019] [Revised: 06/10/2019] [Accepted: 06/17/2019] [Indexed: 06/10/2023]
Abstract
This work is part of a global project aiming to use medico-administrative big data from the whole French agricultural population (~3 millions), collected through their mandatory health insurance system (Mutualité Sociale Agricole), to highlight associations between chronic diseases and agricultural activities. At the request of the French Agency for Food, Environmental and Occupational Health & Safety (ANSES), our objective was to estimate which pesticides were probably used by each agricultural worker, in order to include this information in our analyses and search for association with diseases. We selected five databases to achieve this objective: the Graphical Land Parcel Registration (RPG), the French Agricultural Census, "Cultivation Practice" surveys from the Agriculture ministry, the MATPHYTO crop-exposure matrix and the Compilation of Phytosanitary Indexes from the French Public Health Agency. A geographical grid was designed to use geographical location while maintaining worker anonymity, dividing France into square tracts of variable surface each containing a minimum of 1500 agricultural workers. We developed an automated algorithm to predict each individual potential exposure by crossing her/his occupational activity, the geographical grid and the RPG to deduce cultivation practices and use it as a gateway to estimate pesticides use. This approach allowed drawing, from administrative data, a list of substances potentially used by each agricultural worker throughout France. Results of the algorithm are illustrated at collective level (descriptive statistics for the whole population), as well as at individual level (some workers taken as examples). The generalization of this method in other national contexts is discussed. By linking this information with the health insurance databases, this approach could contribute to the agricultural workers health surveillance.
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Affiliation(s)
- Pauline Achard
- Univ. Grenoble Alpes, CNRS, CHU Grenoble Alpes, Grenoble INP, TIMC-IMAG (EPSP Team), F-38000, Grenoble, France
| | - Charlotte Maugard
- Univ. Grenoble Alpes, CNRS, CHU Grenoble Alpes, Grenoble INP, TIMC-IMAG (EPSP Team), F-38000, Grenoble, France
| | - Christophe Cancé
- Univ. Grenoble Alpes, (UGA)/UMS GRICAD, F-38000, Grenoble, France
| | - Johan Spinosi
- Santé publique France, 12 Rue du Val d'Osne, F-94410, Saint-Maurice, France
- University of Lyon, Université Claude Bernard Lyon 1, Ifsttar, UMR T_9405, F-69373, Lyon, France
| | - Damien Ozenfant
- Caisse centrale Mutualité Sociale Agricole (CCMSA), 19 rue de Paris, F-93000, Bobigny, France
| | - Anne Maître
- Univ. Grenoble Alpes, CNRS, CHU Grenoble Alpes, Grenoble INP, TIMC-IMAG (EPSP Team), F-38000, Grenoble, France
| | - Delphine Bosson-Rieutort
- Univ. Grenoble Alpes, CNRS, CHU Grenoble Alpes, Grenoble INP, TIMC-IMAG (EPSP Team), F-38000, Grenoble, France
| | - Vincent Bonneterre
- Univ. Grenoble Alpes, CNRS, CHU Grenoble Alpes, Grenoble INP, TIMC-IMAG (EPSP Team), F-38000, Grenoble, France.
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7
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Cancé C, Madiot PE, Lenne C, Artemova S, Cohard B, Bodin M, Caporossi A, Blatier JF, Fauconnier J, Olive F, Pagonis D, Le Magny D, Bosson JL, Charriere K, Paturel I, Lavaire B, Schummer G, Eterno J, Ravey JN, Bricault I, Ferretti G, Chanoine S, Bedouch P, Barbier E, Thevenon J, Mossuz P, Moreau-Gaudry A. Cohort Creation and Visualization Using Graph Model in the PREDIMED Health Data Warehouse. Stud Health Technol Inform 2020; 270:108-112. [PMID: 32570356 DOI: 10.3233/shti200132] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Grenoble Alpes University Hospital (CHUGA) is currently deploying a health data warehouse called PREDIMED [1], a platform designed to integrate and analyze for research, education and institutional management the data of patients treated at CHUGA. PREDIMED contains healthcare data, administrative data and, potentially, data from external databases. PREDIMED is hosted by the CHUGA Information Systems Department and benefits from its strict security rules. CHUGA's institutional project PREDIMED aims to collaborate with similar projects in France and worldwide. In this paper, we present how the data model defined to implement PREDIMED at CHUGA is useful for medical experts to interactively build a cohort of patients and to visualize this cohort.
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Affiliation(s)
| | | | | | - Svetlana Artemova
- CHU Grenoble Alpes (CHUGA), Grenoble, France
- Clinical Investigation Centre - Technological Innovation, Inserm, CHUGA, UGA, Grenoble, France
| | | | - Marjolaine Bodin
- CHU Grenoble Alpes (CHUGA), Grenoble, France
- Grenoble Institut des Neurosciences, Grenoble, France
| | - Alban Caporossi
- Univ. Grenoble Alpes (UGA), Grenoble, France
- CHU Grenoble Alpes (CHUGA), Grenoble, France
- Clinical Investigation Centre - Technological Innovation, Inserm, CHUGA, UGA, Grenoble, France
- TIMC-IMAG Laboratory, UGA, CNRS, Vet Agro, Grenoble, France
| | | | | | | | | | | | - Jean-Luc Bosson
- Univ. Grenoble Alpes (UGA), Grenoble, France
- CHU Grenoble Alpes (CHUGA), Grenoble, France
- Clinical Investigation Centre - Technological Innovation, Inserm, CHUGA, UGA, Grenoble, France
- TIMC-IMAG Laboratory, UGA, CNRS, Vet Agro, Grenoble, France
| | - Katia Charriere
- CHU Grenoble Alpes (CHUGA), Grenoble, France
- Clinical Investigation Centre - Technological Innovation, Inserm, CHUGA, UGA, Grenoble, France
| | | | | | | | | | | | - Ivan Bricault
- Univ. Grenoble Alpes (UGA), Grenoble, France
- CHU Grenoble Alpes (CHUGA), Grenoble, France
- Clinical Investigation Centre - Technological Innovation, Inserm, CHUGA, UGA, Grenoble, France
- TIMC-IMAG Laboratory, UGA, CNRS, Vet Agro, Grenoble, France
| | - Gilbert Ferretti
- Univ. Grenoble Alpes (UGA), Grenoble, France
- CHU Grenoble Alpes (CHUGA), Grenoble, France
- IAB, UGA, Inserm, CNRS, France
| | - Sébastien Chanoine
- Univ. Grenoble Alpes (UGA), Grenoble, France
- CHU Grenoble Alpes (CHUGA), Grenoble, France
- TIMC-IMAG Laboratory, UGA, CNRS, Vet Agro, Grenoble, France
- IAB, UGA, Inserm, CNRS, France
| | - Pierrick Bedouch
- Univ. Grenoble Alpes (UGA), Grenoble, France
- CHU Grenoble Alpes (CHUGA), Grenoble, France
- TIMC-IMAG Laboratory, UGA, CNRS, Vet Agro, Grenoble, France
| | | | - Julien Thevenon
- Univ. Grenoble Alpes (UGA), Grenoble, France
- CHU Grenoble Alpes (CHUGA), Grenoble, France
- IAB, UGA, Inserm, CNRS, France
| | - Pascal Mossuz
- Univ. Grenoble Alpes (UGA), Grenoble, France
- CHU Grenoble Alpes (CHUGA), Grenoble, France
- IAB, UGA, Inserm, CNRS, France
| | - Alexandre Moreau-Gaudry
- Univ. Grenoble Alpes (UGA), Grenoble, France
- CHU Grenoble Alpes (CHUGA), Grenoble, France
- Clinical Investigation Centre - Technological Innovation, Inserm, CHUGA, UGA, Grenoble, France
- TIMC-IMAG Laboratory, UGA, CNRS, Vet Agro, Grenoble, France
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