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Alonso SG, de la Torre Díez I, Zapiraín BG. Predictive, Personalized, Preventive and Participatory (4P) Medicine Applied to Telemedicine and eHealth in the Literature. J Med Syst 2019; 43:140. [PMID: 30976942 DOI: 10.1007/s10916-019-1279-4] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2019] [Accepted: 04/05/2019] [Indexed: 10/27/2022]
Abstract
The main objective of this work is to provide a review of existing research work into predictive, personalized, preventive and participatory medicine in telemedicine and ehealth. The academic databases used for searches are IEEE Xplore, PubMed, Science Direct, Web of Science and ResearchGate, taking into account publication dates from 2010 up to the present day. These databases cover the greatest amount of information on scientific texts in multidisciplinary fields, from engineering to medicine. Various search criteria were established, such as ("Predictive" OR "Personalized" OR "Preventive" OR "Participatory") AND "Medicine" AND ("eHealth" OR "Telemedicine") selecting the articles of most interest. A total of 184 publications about predictive, personalized, preventive and participatory (4P) medicine in telemedicine and ehealth were found, of which 48 were identified as relevant. Many of the publications found show how the P4 medicine is being developed in the world and the benefits it provides for patients with different illnesses. After the revision that was undertaken, it can be said that P4 medicine is a vital factor for the improvement of medical services. It is hoped that one of the main contributions of this study is to provide an insight into how P4 medicine in telemedicine and ehealth is being applied, as well as proposing outlines for the future that contribute to the improvement of prevention and prediction of illnesses.
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Affiliation(s)
- Susel Góngora Alonso
- Department of Signal Theory and Communications, and Telematics Engineering, University of Valladolid, Paseo de Belén, 15, 47011, Valladolid, Spain
| | - Isabel de la Torre Díez
- Department of Signal Theory and Communications, and Telematics Engineering, University of Valladolid, Paseo de Belén, 15, 47011, Valladolid, Spain.
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Marín de Mas I, Fanchon E, Papp B, Kalko S, Roca J, Cascante M. Molecular mechanisms underlying COPD-muscle dysfunction unveiled through a systems medicine approach. Bioinformatics 2016; 33:95-103. [PMID: 27794560 DOI: 10.1093/bioinformatics/btw566] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2016] [Revised: 08/26/2016] [Accepted: 08/29/2016] [Indexed: 01/04/2023] Open
Abstract
MOTIVATION Skeletal muscle dysfunction is a systemic effect in one-third of patients with chronic obstructive pulmonary disease (COPD), characterized by high reactive-oxygen-species (ROS) production and abnormal endurance training-induced adaptive changes. However, the role of ROS in COPD remains unclear, not least because of the lack of appropriate tools to study multifactorial diseases. RESULTS We describe a discrete model-driven method combining mechanistic and probabilistic approaches to decipher the role of ROS on the activity state of skeletal muscle regulatory network, assessed before and after an 8-week endurance training program in COPD patients and healthy subjects. In COPD, our computational analysis indicates abnormal training-induced regulatory responses leading to defective tissue remodeling and abnormal energy metabolism. Moreover, we identified tnf, insr, inha and myc as key regulators of abnormal training-induced adaptations in COPD. The tnf-insr pair was identified as a promising target for therapeutic interventions. Our work sheds new light on skeletal muscle dysfunction in COPD, opening new avenues for cost-effective therapies. It overcomes limitations of previous computational approaches showing high potential for the study of other multi-factorial diseases such as diabetes or cancer. CONTACT jroca@clinic.ub.es or martacascante@ub.eduSupplementary information: Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Igor Marín de Mas
- Department of Biochemistry and Molecular Biology, Faculty of Biology, Institute of Biomedicine of University of Barcelona (IBUB) and IDIBAPS, Diagonal 645, Barcelona 08028, Spain.,Institut d' Investigacions Biomediques August Pi i Sunyer (IDIBAPS), Barcelona 08028, Spain.,Synthetic and Systems Biology Unit, Institute of Biochemistry, Biological Research Center of the Hungarian Academy of Sciences, Temesvári krt. 62, Szeged H-6726, Hungary
| | - Eric Fanchon
- Université Grenoble Alpes-CNRS, TIMC-IMAG UMR 5525, Faculté de Médecine, Grenoble 38041, France
| | - Balázs Papp
- Synthetic and Systems Biology Unit, Institute of Biochemistry, Biological Research Center of the Hungarian Academy of Sciences, Temesvári krt. 62, Szeged H-6726, Hungary
| | - Susana Kalko
- Bioinformatics Core Facility, IDIBAPS-CEK, Hospital Clínic, University de Barcelona, Barcelona 08036, Spain
| | - Josep Roca
- Institut d' Investigacions Biomediques August Pi i Sunyer (IDIBAPS), Barcelona 08028, Spain.,Department of Pulmonary Medicine, Hospital Clínic, IDIBAPS, CIBERES, Universitat de Barcelona, Barcelona 08036, Spain
| | - Marta Cascante
- Department of Biochemistry and Molecular Biology, Faculty of Biology, Institute of Biomedicine of University of Barcelona (IBUB) and IDIBAPS, Diagonal 645, Barcelona 08028, Spain.,Institut d' Investigacions Biomediques August Pi i Sunyer (IDIBAPS), Barcelona 08028, Spain
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Dueñas-Espín I, Vela E, Pauws S, Bescos C, Cano I, Cleries M, Contel JC, de Manuel Keenoy E, Garcia-Aymerich J, Gomez-Cabrero D, Kaye R, Lahr MMH, Lluch-Ariet M, Moharra M, Monterde D, Mora J, Nalin M, Pavlickova A, Piera J, Ponce S, Santaeugenia S, Schonenberg H, Störk S, Tegner J, Velickovski F, Westerteicher C, Roca J. Proposals for enhanced health risk assessment and stratification in an integrated care scenario. BMJ Open 2016; 6:e010301. [PMID: 27084274 PMCID: PMC4838738 DOI: 10.1136/bmjopen-2015-010301] [Citation(s) in RCA: 44] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/23/2022] Open
Abstract
OBJECTIVES Population-based health risk assessment and stratification are considered highly relevant for large-scale implementation of integrated care by facilitating services design and case identification. The principal objective of the study was to analyse five health-risk assessment strategies and health indicators used in the five regions participating in the Advancing Care Coordination and Telehealth Deployment (ACT) programme (http://www.act-programme.eu). The second purpose was to elaborate on strategies toward enhanced health risk predictive modelling in the clinical scenario. SETTINGS The five ACT regions: Scotland (UK), Basque Country (ES), Catalonia (ES), Lombardy (I) and Groningen (NL). PARTICIPANTS Responsible teams for regional data management in the five ACT regions. PRIMARY AND SECONDARY OUTCOME MEASURES We characterised and compared risk assessment strategies among ACT regions by analysing operational health risk predictive modelling tools for population-based stratification, as well as available health indicators at regional level. The analysis of the risk assessment tool deployed in Catalonia in 2015 (GMAs, Adjusted Morbidity Groups) was used as a basis to propose how population-based analytics could contribute to clinical risk prediction. RESULTS There was consensus on the need for a population health approach to generate health risk predictive modelling. However, this strategy was fully in place only in two ACT regions: Basque Country and Catalonia. We found marked differences among regions in health risk predictive modelling tools and health indicators, and identified key factors constraining their comparability. The research proposes means to overcome current limitations and the use of population-based health risk prediction for enhanced clinical risk assessment. CONCLUSIONS The results indicate the need for further efforts to improve both comparability and flexibility of current population-based health risk predictive modelling approaches. Applicability and impact of the proposals for enhanced clinical risk assessment require prospective evaluation.
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Affiliation(s)
- Ivan Dueñas-Espín
- Hospital Clinic de Barcelona, Institut d'Investigacions Biomèdiques August Pii Sunyer (IDIBAPS), CIBERES, Universitat de Barcelona, Barcelona, Spain
- Centre for Research in Environmental Epidemiology (CREAL), Universitat Pompeu Fabra, CIBER en Epidemiologia y Salud Pública (CIBERESP), Barcelona, Spain
| | - Emili Vela
- CatSalut, Servei Català de la Salut, Barcelona, Catalonia, Spain
| | - Steffen Pauws
- Royal Philips Netherlands BV acting through Philips Research, Eindhoven, The Netherlands
| | - Cristina Bescos
- Royal Philips Netherlands BV acting through Philips Homecare, Boeblingen, Germany
| | - Isaac Cano
- Hospital Clinic de Barcelona, Institut d'Investigacions Biomèdiques August Pii Sunyer (IDIBAPS), CIBERES, Universitat de Barcelona, Barcelona, Spain
| | | | - Joan Carles Contel
- Departament de Salut, Generalitat de Catalunya, Barcelona, Catalonia, Spain
| | | | - Judith Garcia-Aymerich
- Centre for Research in Environmental Epidemiology (CREAL), Universitat Pompeu Fabra, CIBER en Epidemiologia y Salud Pública (CIBERESP), Barcelona, Spain
| | | | | | - Maarten M H Lahr
- Department of Epidemiology, Health Technology Assessment, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Magí Lluch-Ariet
- Eurecat, Barcelona, Catalonia, Spain
- Networking Department, Technical University of Catalonia (UPC), Barcelona,Spain
| | - Montserrat Moharra
- Agència de Qualitat i Avaluació Sanitàries de Catalunya (AQuAS), Barcelona, Catalonia, Spain
| | - David Monterde
- Institut Català de la Salut, Barcelona, Catalonia, Spain
| | - Joana Mora
- Kronikgune—Centro de Investigación en Cronicidad, Basque Country, Bilbao, Spain
| | | | - Andrea Pavlickova
- NHS 24, Scottish Centre for Telehealth and Telecare (SCTT), Edinburgh, UK
| | - Jordi Piera
- Badalona Serveis Assistencials (BSA), Badalona, Catalonia, Spain
| | - Sara Ponce
- Kronikgune—Centro de Investigación en Cronicidad, Basque Country, Bilbao, Spain
| | | | - Helen Schonenberg
- Royal Philips Netherlands BV acting through Philips Homecare, Boeblingen, Germany
| | - Stefan Störk
- Comprehensive Heart Failure Center (CHFC), Universität Würsburg, Würsburg, Germany
| | - Jesper Tegner
- Department of Medicine, Karolinska Institute, Stockholm, Sweden
| | - Filip Velickovski
- Eurecat, Barcelona, Catalonia, Spain
- ViCOROB, Universitat de Girona, Girona, Spain
| | | | - Josep Roca
- Hospital Clinic de Barcelona, Institut d'Investigacions Biomèdiques August Pii Sunyer (IDIBAPS), CIBERES, Universitat de Barcelona, Barcelona, Spain
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Saqi M, Pellet J, Roznovat I, Mazein A, Ballereau S, De Meulder B, Auffray C. Systems Medicine: The Future of Medical Genomics, Healthcare, and Wellness. Methods Mol Biol 2016; 1386:43-60. [PMID: 26677178 DOI: 10.1007/978-1-4939-3283-2_3] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Abstract
Recent advances in genomics have led to the rapid and relatively inexpensive collection of patient molecular data including multiple types of omics data. The integration of these data with clinical measurements has the potential to impact on our understanding of the molecular basis of disease and on disease management. Systems medicine is an approach to understanding disease through an integration of large patient datasets. It offers the possibility for personalized strategies for healthcare through the development of a new taxonomy of disease. Advanced computing will be an important component in effectively implementing systems medicine. In this chapter we describe three computational challenges associated with systems medicine: disease subtype discovery using integrated datasets, obtaining a mechanistic understanding of disease, and the development of an informatics platform for the mining, analysis, and visualization of data emerging from translational medicine studies.
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Affiliation(s)
- Mansoor Saqi
- European Institute for Systems Biology and Medicine, CNRS-ENS-UCBL, Université de Lyon, 50 Avenue Tony Garnier, Lyon, 69007, France
| | - Johann Pellet
- European Institute for Systems Biology and Medicine, CNRS-ENS-UCBL, Université de Lyon, 50 Avenue Tony Garnier, Lyon, 69007, France
| | - Irina Roznovat
- European Institute for Systems Biology and Medicine, CNRS-ENS-UCBL, Université de Lyon, 50 Avenue Tony Garnier, Lyon, 69007, France
| | - Alexander Mazein
- European Institute for Systems Biology and Medicine, CNRS-ENS-UCBL, Université de Lyon, 50 Avenue Tony Garnier, Lyon, 69007, France
| | - Stéphane Ballereau
- European Institute for Systems Biology and Medicine, CNRS-ENS-UCBL, Université de Lyon, 50 Avenue Tony Garnier, Lyon, 69007, France
| | - Bertrand De Meulder
- European Institute for Systems Biology and Medicine, CNRS-ENS-UCBL, Université de Lyon, 50 Avenue Tony Garnier, Lyon, 69007, France
| | - Charles Auffray
- European Institute for Systems Biology and Medicine, CNRS-ENS-UCBL, Université de Lyon, 50 Avenue Tony Garnier, Lyon, 69007, France. .,Université Claude Bernard, 3e étage plot 2, 50 Avenue Tony Garnier, Lyon, Cedex 07, 69366, France.
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Gomez-Cabrero D, Menche J, Cano I, Abugessaisa I, Huertas-Migueláñez M, Tenyi A, Marin de Mas I, Kiani NA, Marabita F, Falciani F, Burrowes K, Maier D, Wagner P, Selivanov V, Cascante M, Roca J, Barabási AL, Tegnér J. Systems Medicine: from molecular features and models to the clinic in COPD. J Transl Med 2014; 12 Suppl 2:S4. [PMID: 25471042 PMCID: PMC4255907 DOI: 10.1186/1479-5876-12-s2-s4] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
Background and hypothesis Chronic Obstructive Pulmonary Disease (COPD) patients are characterized by heterogeneous clinical manifestations and patterns of disease progression. Two major factors that can be used to identify COPD subtypes are muscle dysfunction/wasting and co-morbidity patterns. We hypothesized that COPD heterogeneity is in part the result of complex interactions between several genes and pathways. We explored the possibility of using a Systems Medicine approach to identify such pathways, as well as to generate predictive computational models that may be used in clinic practice. Objective and method Our overarching goal is to generate clinically applicable predictive models that characterize COPD heterogeneity through a Systems Medicine approach. To this end we have developed a general framework, consisting of three steps/objectives: (1) feature identification, (2) model generation and statistical validation, and (3) application and validation of the predictive models in the clinical scenario. We used muscle dysfunction and co-morbidity as test cases for this framework. Results In the study of muscle wasting we identified relevant features (genes) by a network analysis and generated predictive models that integrate mechanistic and probabilistic models. This allowed us to characterize muscle wasting as a general de-regulation of pathway interactions. In the co-morbidity analysis we identified relevant features (genes/pathways) by the integration of gene-disease and disease-disease associations. We further present a detailed characterization of co-morbidities in COPD patients that was implemented into a predictive model. In both use cases we were able to achieve predictive modeling but we also identified several key challenges, the most pressing being the validation and implementation into actual clinical practice. Conclusions The results confirm the potential of the Systems Medicine approach to study complex diseases and generate clinically relevant predictive models. Our study also highlights important obstacles and bottlenecks for such approaches (e.g. data availability and normalization of frameworks among others) and suggests specific proposals to overcome them.
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Gomez-Cabrero D, Lluch-Ariet M, Tegnér J, Cascante M, Miralles F, Roca J. Synergy-COPD: a systems approach for understanding and managing chronic diseases. J Transl Med 2014; 12 Suppl 2:S2. [PMID: 25472826 PMCID: PMC4255903 DOI: 10.1186/1479-5876-12-s2-s2] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
Chronic diseases (CD) are generating a dramatic societal burden worldwide that is expected to persist over the next decades. The challenges posed by the epidemics of CD have triggered a novel health paradigm with major consequences on the traditional concept of disease and with a profound impact on key aspects of healthcare systems. We hypothesized that the development of a systems approach to understand CD together with the generation of an ecosystem to transfer the acquired knowledge into the novel healthcare scenario may contribute to a cost-effective enhancement of health outcomes. To this end, we designed the Synergy-COPD project wherein the heterogeneity of chronic obstructive pulmonary disease (COPD) was addressed as a use case representative of CD. The current manuscript describes main features of the project design and the strategies put in place for its development, as well the expected outcomes during the project life-span. Moreover, the manuscript serves as introductory and unifying chapter of the different papers associated to the Supplement describing the characteristics, tools and the objectives of Synergy-COPD.
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Affiliation(s)
- David Gomez-Cabrero
- Unit of Computational Medicine, Department of Medicine, Center for Molecular Medicine, Karolinska Institutet, Karolinska University Hospital, Stockholm, Sweden
| | - Magi Lluch-Ariet
- Department of eHealth, Barcelona Digital, 08017 Barcelona, Catalunya, Spain
| | - Jesper Tegnér
- Unit of Computational Medicine, Department of Medicine, Center for Molecular Medicine, Karolinska Institutet, Karolinska University Hospital, Stockholm, Sweden
| | - Marta Cascante
- Hospital Clinic - Institut d'Investigacions Biomediques August Pi i Sunyer (IDIBAPS). Universitat de Barcelona, 08036 Barcelona, Spain
- Departament de Bioquimica i Biologia Molecular i IBUB, Facultat de Biologia, Universitat de Barcelona, 08028 Barcelona, Spain
| | - Felip Miralles
- Department of eHealth, Barcelona Digital, 08017 Barcelona, Catalunya, Spain
| | - Josep Roca
- Hospital Clinic - Institut d'Investigacions Biomediques August Pi i Sunyer (IDIBAPS). Universitat de Barcelona, 08036 Barcelona, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Respiratorias (CIBERES), Bunyola, Balearic Islands
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