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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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Cano I, Tényi Á, Schueller C, Wolff M, Huertas Migueláñez MM, Gomez-Cabrero D, Antczak P, Roca J, Cascante M, Falciani F, Maier D. The COPD Knowledge Base: enabling data analysis and computational simulation in translational COPD research. J Transl Med 2014; 12 Suppl 2:S6. [PMID: 25471253 PMCID: PMC4255911 DOI: 10.1186/1479-5876-12-s2-s6] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022] Open
Abstract
Background Previously we generated a chronic obstructive pulmonary disease (COPD) specific knowledge base (http://www.copdknowledgebase.eu) from clinical and experimental data, text-mining results and public databases. This knowledge base allowed the retrieval of specific molecular networks together with integrated clinical and experimental data. Results The COPDKB has now been extended to integrate over 40 public data sources on functional interaction (e.g. signal transduction, transcriptional regulation, protein-protein interaction, gene-disease association). In addition we integrated COPD-specific expression and co-morbidity networks connecting over 6 000 genes/proteins with physiological parameters and disease states. Three mathematical models describing different aspects of systemic effects of COPD were connected to clinical and experimental data. We have completely redesigned the technical architecture of the user interface and now provide html and web browser-based access and form-based searches. A network search enables the use of interconnecting information and the generation of disease-specific sub-networks from general knowledge. Integration with the Synergy-COPD Simulation Environment enables multi-scale integrated simulation of individual computational models while integration with a Clinical Decision Support System allows delivery into clinical practice. Conclusions The COPD Knowledge Base is the only publicly available knowledge resource dedicated to COPD and combining genetic information with molecular, physiological and clinical data as well as mathematical modelling. Its integrated analysis functions provide overviews about clinical trends and connections while its semantically mapped content enables complex analysis approaches. We plan to further extend the COPDKB by offering it as a repository to publish and semantically integrate data from relevant clinical trials. The COPDKB is freely available after registration at http://www.copdknowledgebase.eu.
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Miralles F, Gomez-Cabrero D, Lluch-Ariet M, Tegnér J, Cascante M, Roca J. Predictive medicine: outcomes, challenges and opportunities in the Synergy-COPD project. J Transl Med 2014; 12 Suppl 2:S12. [PMID: 25472742 PMCID: PMC4255885 DOI: 10.1186/1479-5876-12-s2-s12] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND Chronic Obstructive Pulmonary Disease (COPD) is a major challenge for healthcare. Heterogeneities in clinical manifestations and in disease progression are relevant traits in COPD with impact on patient management and prognosis. It is hypothesized that COPD heterogeneity results from the interplay of mechanisms governing three conceptually different phenomena: 1) pulmonary disease, 2) systemic effects of COPD and 3) co-morbidity clustering. OBJECTIVES To assess the potential of systems medicine to better understand non-pulmonary determinants of COPD heterogeneity. To transfer acquired knowledge to healthcare enhancing subject-specific health risk assessment and stratification to improve management of chronic patients. METHOD Underlying mechanisms of skeletal muscle dysfunction and of co-morbidity clustering in COPD patients were explored with strategies combining deterministic modelling and network medicine analyses using the Biobridge dataset. An independent data driven analysis of co-morbidity clustering examining associated genes and pathways was done (ICD9-CM data from Medicare, 13 million people). A targeted network analysis using the two studies: skeletal muscle dysfunction and co-morbidity clustering explored shared pathways between them. RESULTS (1) Evidence of abnormal regulation of pivotal skeletal muscle biological pathways and increased risk for co-morbidity clustering was observed in COPD; (2) shared abnormal pathway regulation between skeletal muscle dysfunction and co-morbidity clustering; and, (3) technological achievements of the projects were: (i) COPD Knowledge Base; (ii) novel modelling approaches; (iii) Simulation Environment; and, (iv) three layers of Clinical Decision Support Systems. CONCLUSIONS The project demonstrated the high potential of a systems medicine approach to address COPD heterogeneity. Limiting factors for the project development were identified. They were relevant to shape strategies fostering 4P Medicine for chronic patients. The concept of Digital Health Framework and the proposed roadmap for its deployment constituted relevant project outcomes.
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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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Cano I, Lluch-Ariet M, Gomez-Cabrero D, Maier D, Kalko S, Cascante M, Tegnér J, Miralles F, Herrera D, Roca J. Biomedical research in a Digital Health Framework. J Transl Med 2014; 12 Suppl 2:S10. [PMID: 25472554 PMCID: PMC4255881 DOI: 10.1186/1479-5876-12-s2-s10] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022] Open
Abstract
This article describes a Digital Health Framework (DHF), benefitting from the lessons learnt during the three-year life span of the FP7 Synergy-COPD project. The DHF aims to embrace the emerging requirements--data and tools--of applying systems medicine into healthcare with a three-tier strategy articulating formal healthcare, informal care and biomedical research. Accordingly, it has been constructed based on three key building blocks, namely, novel integrated care services with the support of information and communication technologies, a personal health folder (PHF) and a biomedical research environment (DHF-research). Details on the functional requirements and necessary components of the DHF-research are extensively presented. Finally, the specifics of the building blocks strategy for deployment of the DHF, as well as the steps toward adoption are analyzed. The proposed architectural solutions and implementation steps constitute a pivotal strategy to foster and enable 4P medicine (Predictive, Preventive, Personalized and Participatory) in practice and should provide a head start to any community and institution currently considering to implement a biomedical research platform.
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Affiliation(s)
- Isaac Cano
- IDIBAPS-Hospital Clínic, CIBERES, Universitat de Barcelona, 08036, Barcelona, Catalunya, Spain
| | - Magí Lluch-Ariet
- Department of eHealth, Barcelona Digital, Roc Boronat 117, 08017 Barcelona, Catalunya, Spain
| | - David Gomez-Cabrero
- Unit of Computational Medicine, Department of Medicine, Center for Molecular Medicine, Karolinska Institutet, Karolinska University Hospital, Stockholm, Sweden
| | - Dieter Maier
- Biomax Informatics AG, Robert-Koch-Str. 2, Planegg, Germany
| | - Susana Kalko
- IDIBAPS-Hospital Clínic, CIBERES, Universitat de Barcelona, 08036, Barcelona, Catalunya, Spain
| | - Marta Cascante
- IDIBAPS-Hospital Clínic, CIBERES, Universitat de Barcelona, 08036, Barcelona, Catalunya, Spain
- Departament de Bioquimica i Biologia Molecular i IBUB, Facultat de Biologia, Universitat de Barcelona, 08028 Barcelona, Spain
| | - Jesper Tegnér
- Unit of Computational Medicine, Department of Medicine, Center for Molecular Medicine, Karolinska Institutet, Karolinska University Hospital, Stockholm, Sweden
| | - Felip Miralles
- Department of eHealth, Barcelona Digital, Roc Boronat 117, 08017 Barcelona, Catalunya, Spain
| | - Diego Herrera
- Departament de Bioquimica i Biologia Molecular i IBUB, Facultat de Biologia, Universitat de Barcelona, 08028 Barcelona, Spain
- Almirall R&D, 08980 Sant Feliu de Llobregat, Barcelona, Spain
| | - Josep Roca
- IDIBAPS-Hospital Clínic, CIBERES, Universitat de Barcelona, 08036, Barcelona, Catalunya, Spain
- Centro de Investigacíon Biomédica en Red de Enfermedades Respiratorias (CIBERES), Bunyola, Balearic Islands
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Velickovski F, Ceccaroni L, Roca J, Burgos F, Galdiz JB, Marina N, Lluch-Ariet M. Clinical Decision Support Systems (CDSS) for preventive management of COPD patients. J Transl Med 2014; 12 Suppl 2:S9. [PMID: 25471545 PMCID: PMC4255917 DOI: 10.1186/1479-5876-12-s2-s9] [Citation(s) in RCA: 44] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
BACKGROUND The use of information and communication technologies to manage chronic diseases allows the application of integrated care pathways, and the optimization and standardization of care processes. Decision support tools can assist in the adherence to best-practice medicine in critical decision points during the execution of a care pathway. OBJECTIVES The objectives are to design, develop, and assess a clinical decision support system (CDSS) offering a suite of services for the early detection and assessment of chronic obstructive pulmonary disease (COPD), which can be easily integrated into a healthcare providers' work-flow. METHODS The software architecture model for the CDSS, interoperable clinical-knowledge representation, and inference engine were designed and implemented to form a base CDSS framework. The CDSS functionalities were iteratively developed through requirement-adjustment/development/validation cycles using enterprise-grade software-engineering methodologies and technologies. Within each cycle, clinical-knowledge acquisition was performed by a health-informatics engineer and a clinical-expert team. RESULTS A suite of decision-support web services for (i) COPD early detection and diagnosis, (ii) spirometry quality-control support, (iii) patient stratification, was deployed in a secured environment on-line. The CDSS diagnostic performance was assessed using a validation set of 323 cases with 90% specificity, and 96% sensitivity. Web services were integrated in existing health information system platforms. CONCLUSIONS Specialized decision support can be offered as a complementary service to existing policies of integrated care for chronic-disease management. The CDSS was able to issue recommendations that have a high degree of accuracy to support COPD case-finding. Integration into healthcare providers' work-flow can be achieved seamlessly through the use of a modular design and service-oriented architecture that connect to existing health information systems.
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Affiliation(s)
- Filip Velickovski
- Barcelona Digital Technology Centre, 5th floor, 08018 Barcelona, Spain
- ViCOROB, Universitat de Girona, Campus Montilivi, 17071 Girona, Spain
| | | | - Josep Roca
- Hospital Clínic, IDIBAPS, Universitat de Barcelona, 08036 Barcelona, Spain
- Centro de Investigacíon Biomédica en Red Enfermedades Respiratorias (CIBERES), 07110 Bunyola, Mallorca, Illes Balears, Spain
| | - Felip Burgos
- Hospital Clínic, IDIBAPS, Universitat de Barcelona, 08036 Barcelona, Spain
- Centro de Investigacíon Biomédica en Red Enfermedades Respiratorias (CIBERES), 07110 Bunyola, Mallorca, Illes Balears, Spain
| | - Juan B Galdiz
- Servicio de Neumología, Hospital Universitario Cruces, 48903 Barakaldo, Bizkaia, Spain
- Centro de Investigacíon Biomédica en Red Enfermedades Respiratorias (CIBERES), 07110 Bunyola, Mallorca, Illes Balears, Spain
| | - Nuria Marina
- Servicio de Neumología, Hospital Universitario Cruces, 48903 Barakaldo, Bizkaia, Spain
| | - Magí Lluch-Ariet
- Barcelona Digital Technology Centre, 5th floor, 08018 Barcelona, Spain
- Departament d'Enginyeria Telemática (ENTEL), Universitat Politécnica de Catalunya (UPC), 08034 Barcelona, Spain
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