1
|
Mariani S, Metting E, Lahr MMH, Vargiu E, Zambonelli F. Developing an ML pipeline for asthma and COPD: The case of a Dutch primary care service. INT J INTELL SYST 2021. [DOI: 10.1002/int.22568] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Affiliation(s)
- Stefano Mariani
- Department of Sciences and Methods for Engineering University of Modena and Reggio Emilia Reggio Emilia Italy
| | - Esther Metting
- Health Technology Assessment, Department of Epidemiology, University of Groningen University Medical Center Groningen The Netherlands
| | - Maarten M. H. Lahr
- Health Technology Assessment, Department of Epidemiology, University of Groningen University Medical Center Groningen The Netherlands
| | - Eloisa Vargiu
- EURECAT Technology Centre Digital Health Unit Barcelona Spain
| | - Franco Zambonelli
- Department of Sciences and Methods for Engineering University of Modena and Reggio Emilia Reggio Emilia Italy
| |
Collapse
|
2
|
Digital Health for Enhanced Understanding and Management of Chronic Conditions: COPD as a Use Case. SYSTEMS MEDICINE 2021. [DOI: 10.1016/b978-0-12-801238-3.11690-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
|
3
|
Saqi M, Lysenko A, Guo YK, Tsunoda T, Auffray C. Navigating the disease landscape: knowledge representations for contextualizing molecular signatures. Brief Bioinform 2019; 20:609-623. [PMID: 29684165 PMCID: PMC6556902 DOI: 10.1093/bib/bby025] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2017] [Revised: 02/05/2018] [Indexed: 12/14/2022] Open
Abstract
Large amounts of data emerging from experiments in molecular medicine are leading to the identification of molecular signatures associated with disease subtypes. The contextualization of these patterns is important for obtaining mechanistic insight into the aberrant processes associated with a disease, and this typically involves the integration of multiple heterogeneous types of data. In this review, we discuss knowledge representations that can be useful to explore the biological context of molecular signatures, in particular three main approaches, namely, pathway mapping approaches, molecular network centric approaches and approaches that represent biological statements as knowledge graphs. We discuss the utility of each of these paradigms, illustrate how they can be leveraged with selected practical examples and identify ongoing challenges for this field of research.
Collapse
Affiliation(s)
- Mansoor Saqi
- Mansoor Saqi Data Science Institute, Imperial College London, UK
| | - Artem Lysenko
- Artem Lysenko Laboratory for Medical Science Mathematics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
| | - Yi-Ke Guo
- Yi-Ke Guo Data Science Institute, Imperial College London, UK
| | - Tatsuhiko Tsunoda
- Tatsuhiko Tsunoda Laboratory for Medical Science Mathematics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan CREST, JST, Tokyo, Japan Department of Medical Science Mathematics, Medical Research Institute, Tokyo Medical and Dental University, Tokyo, Japan
| | - Charles Auffray
- Charles Auffray European Institute for Systems Biology and Medicine, Lyon, France
| |
Collapse
|
4
|
Roca J, Tenyi A, Cano I. Paradigm changes for diagnosis: using big data for prediction. ACTA ACUST UNITED AC 2018; 57:317-327. [DOI: 10.1515/cclm-2018-0971] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2018] [Accepted: 11/21/2018] [Indexed: 11/15/2022]
Abstract
Abstract
Due to profound changes occurring in biomedical knowledge and in health systems worldwide, an entirely new health and social care scenario is emerging. Moreover, the enormous technological potential developed over the last years is increasingly influencing life sciences and driving changes toward personalized medicine and value-based healthcare. However, the current slow progression of adoption, limiting the generation of healthcare efficiencies through technological innovation, can be realistically overcome by fostering convergence between a systems medicine approach and the principles governing Integrated Care. Implicit with this strategy is the multidisciplinary active collaboration of all stakeholders involved in the change, namely: citizens, professionals with different profiles, academia, policy makers, industry and payers. The article describes the key building blocks of an open and collaborative hub currently being developed in Catalonia (Spain) aiming at generation, deployment and evaluation of a personalized medicine program addressing highly prevalent chronic conditions that often show co-occurrence, namely: cardiovascular disorders, chronic obstructive pulmonary disease, type 2 diabetes mellitus; metabolic syndrome and associated mental disturbances (anxiety-depression and altered behavioral patterns leading to unhealthy life styles).
Collapse
Affiliation(s)
- Josep Roca
- Hospital Clínic, IDIBAPS, Facultat de Medicina , Universitat de Barcelona , Barcelona, Catalunya , Spain
- Centro de Investigación Biomédica en Red de Enfermedades Respiratorias (CIBERES) , Av. Monforte de Lemos, 3-5. Pabellón 11. Planta 0 , 28029, Madrid, Catalunya , Spain , Phone: +34-932275747, Fax: +34-932275455
| | - Akos Tenyi
- Hospital Clínic, IDIBAPS, Facultat de Medicina , Universitat de Barcelona , Barcelona, Catalunya , Spain
- Centro de Investigación Biomédica en Red de Enfermedades Respiratorias (CIBERES) , Madrid, Catalunya , Spain
| | - Isaac Cano
- Hospital Clínic, IDIBAPS, Facultat de Medicina , Universitat de Barcelona , Barcelona, Catalunya , Spain
- Centro de Investigación Biomédica en Red de Enfermedades Respiratorias (CIBERES) , Madrid, Catalunya , Spain
| |
Collapse
|
5
|
Protocol for regional implementation of collaborative self-management services to promote physical activity. BMC Health Serv Res 2018; 18:560. [PMID: 30016944 PMCID: PMC6050723 DOI: 10.1186/s12913-018-3363-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2017] [Accepted: 07/05/2018] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND Chronic diseases are generating a major health and societal burden worldwide. Healthy lifestyles, including physical activity (PA), have proven efficacy in the prevention and treatment of many chronic conditions. But, so far, national PA surveillance systems, as well as strategies for promotion of PA, have shown low impact. We hypothesize that personalized modular PA services, aligned with healthcare, addressing the needs of a broad spectrum of individual profiles may show cost-effectiveness and sustainability. METHODS The current manuscript describes the protocol for regional implementation of collaborative self-management services to promote PA in Catalonia (7.5 M habitants) during the period 2017-2019. The protocols of three implementation studies encompassing a broad spectrum of individual needs are reported. They have a quasi-experimental design. That is, a non-randomized intervention group is compared to a control group (usual care) using propensity score methods wherein age, gender and population-based health risk assessment are main matching variables. The principal innovations of the PA program are: i) Implementation of well-structured modular interventions promoting PA; ii) Information and communication technologies (ICT) to facilitate patient accessibility, support collaborative management of individual care plans and reduce costs; and iii) Assessment strategies based on the Triple Aim approach during and beyond the program deployment. DISCUSSION The manuscript reports a precise roadmap for large scale deployment of community-based ICT-supported integrated care services to promote healthy lifestyles with high potential for comparability and transferability to other sites. TRIAL REGISTRATION This study protocol has been registered at ClinicalTrials.org ( NCT02976064 ). Registered November 24th, 2016.
Collapse
|
6
|
Vela E, Tényi Á, Cano I, Monterde D, Cleries M, Garcia-Altes A, Hernandez C, Escarrabill J, Roca J. Population-based analysis of patients with COPD in Catalonia: a cohort study with implications for clinical management. BMJ Open 2018; 8:e017283. [PMID: 29511004 PMCID: PMC5855237 DOI: 10.1136/bmjopen-2017-017283] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND Clinical management of patients with chronic obstructive pulmonary disease (COPD) shows potential for improvement provided that patients' heterogeneities are better understood. The study addresses the impact of comorbidities and its role in health risk assessment. OBJECTIVE To explore the potential of health registry information to enhance clinical risk assessment and stratification. DESIGN Fixed cohort study including all registered patients with COPD in Catalonia (Spain) (7.5 million citizens) at 31 December 2014 with 1-year (2015) follow-up. METHODS A total of 264 830 patients with COPD diagnosis, based on the International Classification of Diseases (Ninth Revision) coding, were assessed. Performance of multiple logistic regression models for the six main dependent variables of the study: mortality, hospitalisations (patients with one or more admissions; all cases and COPD-related), multiple hospitalisations (patients with at least two admissions; all causes and COPD-related) and users with high healthcare costs. Neither clinical nor forced spirometry data were available. RESULTS Multimorbidity, assessed with the adjusted morbidity grouper, was the covariate with the highest impact in the predictive models, which in turn showed high performance measured by the C-statistics: (1) mortality (0.83), (2 and 3) hospitalisations (all causes: 0.77; COPD-related: 0.81), (4 and 5) multiple hospitalisations (all causes: 0.80; COPD-related: 0.87) and (6) users with high healthcare costs (0.76). Fifteen per cent of individuals with highest healthcare costs to year ratio represented 59% of the overall costs of patients with COPD. CONCLUSIONS The results stress the impact of assessing multimorbidity with the adjusted morbidity grouper on considered health indicators, which has implications for enhanced COPD staging and clinical management. TRIAL REGISTRATION NUMBER NCT02956395.
Collapse
Affiliation(s)
- Emili Vela
- Area d’Atenció Sanitària, Servei Català de la Salut, Barcelona, Catalonia, Spain
| | - Ákos Tényi
- Hospital Clinic de Barcelona, Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Universitat de Barcelona, Barcelona, Spain
- Center for Biomedical Network Research in Respiratory Diseases (CIBERES), Madrid, Spain
| | - Isaac Cano
- Hospital Clinic de Barcelona, Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Universitat de Barcelona, Barcelona, Spain
- Center for Biomedical Network Research in Respiratory Diseases (CIBERES), Madrid, Spain
| | - David Monterde
- Institut Català de la Salut, Serveis Centrals, Catalunya, Spain
| | - Montserrat Cleries
- Area d’Atenció Sanitària, Servei Català de la Salut, Barcelona, Catalonia, Spain
| | - Anna Garcia-Altes
- Agencia de Qualitat i Avaluació Sanitaries de Catalunya (AQuAS), Catalunya, Spain
| | - Carme Hernandez
- Hospital Clinic de Barcelona, Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Universitat de Barcelona, Barcelona, Spain
- Center for Biomedical Network Research in Respiratory Diseases (CIBERES), Madrid, Spain
| | - Joan Escarrabill
- Hospital Clinic de Barcelona, Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Universitat de Barcelona, Barcelona, Spain
- Master Plan for Respiratory Diseases (PDMAR), Ministry of Health (Catalonia) REDISSEC, Health Services Research on Chronic Patients Network, Instituto de Salud Carlos III, Barcelona, Spain
| | - Josep Roca
- Hospital Clinic de Barcelona, Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Universitat de Barcelona, Barcelona, Spain
- Center for Biomedical Network Research in Respiratory Diseases (CIBERES), Madrid, Spain
| |
Collapse
|
7
|
Digital Health Research Methods and Tools: Suggestions and Selected Resources for Researchers. ADVANCES IN BIOMEDICAL INFORMATICS 2018. [DOI: 10.1007/978-3-319-67513-8_2] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
|
8
|
Sapci AH, Sapci HA. The Effectiveness of Hands-on Health Informatics Skills Exercises in the Multidisciplinary Smart Home Healthcare and Health Informatics Training Laboratories. Appl Clin Inform 2017; 8:1184-1196. [PMID: 29272900 DOI: 10.4338/aci-2017-08-ra-0136] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022] Open
Abstract
OBJECTIVE This article aimed to evaluate the effectiveness of newly established innovative smart home healthcare and health informatics laboratories, and a novel laboratory course that focuses on experiential health informatics training, and determine students' self-confidence to operate wireless home health monitoring devices before and after the hands-on laboratory course. MATERIALS AND METHODS Two web-based pretraining and posttraining questionnaires were sent to 64 students who received hands-on training with wireless remote patient monitoring devices in smart home healthcare and health informatics laboratories. RESULTS All 64 students completed the pretraining survey (100% response rate), and 49 students completed the posttraining survey (76% response rate). The quantitative data analysis showed that 95% of students had an interest in taking more hands-on laboratory courses. Sixty-seven percent of students had no prior experience with medical image, physiological data acquisition, storage, and transmission protocols. After the hands-on training session, 75.51% of students expressed improved confidence about training patients to measure blood pressure monitor using wireless devices. Ninety percent of students preferred to use a similar experiential approach in their future learning experience. Additionally, the qualitative data analysis demonstrated that students were expecting to have more courses with hands-on exercises and integration of technology-enabled delivery and patient monitoring concepts into the curriculum. CONCLUSION This study demonstrated that the multidisciplinary smart home healthcare and health informatics training laboratories and the hands-on exercises improved students' technology adoption rates and their self-confidence in using wireless patient monitoring devices.
Collapse
Affiliation(s)
- A H Sapci
- Department of Allied Health, Adelphi University, Garden City, New York, United States
| | | |
Collapse
|
9
|
Cano I, Dueñas-Espín I, Hernandez C, de Batlle J, Benavent J, Contel JC, Baltaxe E, Escarrabill J, Fernández JM, Garcia-Aymerich J, Mas MÀ, Miralles F, Moharra M, Piera J, Salas T, Santaeugènia S, Soler N, Torres G, Vargiu E, Vela E, Roca J. Protocol for regional implementation of community-based collaborative management of complex chronic patients. NPJ Prim Care Respir Med 2017; 27:44. [PMID: 28710482 PMCID: PMC5511202 DOI: 10.1038/s41533-017-0043-9] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2016] [Revised: 05/22/2017] [Accepted: 05/31/2017] [Indexed: 12/17/2022] Open
Affiliation(s)
- Isaac Cano
- Hospital Clinic de Barcelona, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Universitat de Barcelona, Barcelona, Spain.
- Center for Biomedical Network Research in Respiratory Diseases (CIBERES), Majadahonda (Madrid), Spain.
| | - Ivan Dueñas-Espín
- Hospital Clinic de Barcelona, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Universitat de Barcelona, Barcelona, Spain
- ISGlobal, Centre for Research in Environmental Epidemiology (CREAL), Universitat Pompeu Fabra (UPF), CIBER Epidemiología y Salud Pública (CIBERESP), Barcelona, Spain
| | - Carme Hernandez
- Hospital Clinic de Barcelona, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Universitat de Barcelona, Barcelona, Spain
- Center for Biomedical Network Research in Respiratory Diseases (CIBERES), Majadahonda (Madrid), Spain
| | - Jordi de Batlle
- Center for Biomedical Network Research in Respiratory Diseases (CIBERES), Majadahonda (Madrid), Spain
- Respiratory Department, Institut de Recerca Biomedica (IRBLeida), Lleida, Spain
| | - Jaume Benavent
- Consorci d'Atenció Primària de Salut Barcelona Esquerra (CAPSBE), Barcelona, Spain
| | - Juan Carlos Contel
- Departament de Salut, Generalitat de Catalunya, Barcelona, Catalonia, Spain
| | - Erik Baltaxe
- Hospital Clinic de Barcelona, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Universitat de Barcelona, Barcelona, Spain
- Center for Biomedical Network Research in Respiratory Diseases (CIBERES), Majadahonda (Madrid), Spain
| | - Joan Escarrabill
- Hospital Clinic de Barcelona, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Universitat de Barcelona, Barcelona, Spain
| | | | - Judith Garcia-Aymerich
- ISGlobal, Centre for Research in Environmental Epidemiology (CREAL), Universitat Pompeu Fabra (UPF), CIBER Epidemiología y Salud Pública (CIBERESP), Barcelona, Spain
| | - Miquel Àngel Mas
- Badalona Serveis Assistencials (BSA), Badalona, Catalonia, Spain
| | - Felip Miralles
- Eurecat. Technological Center of Catalonia, Barcelona, Catalunya, Spain
| | - Montserrat Moharra
- Agència de Qualitat i Avaluació Sanitàries de Catalunya (AQuAS), Barcelona, Catalonia, Spain
| | - Jordi Piera
- Badalona Serveis Assistencials (BSA), Badalona, Catalonia, Spain
| | - Tomas Salas
- Agència de Qualitat i Avaluació Sanitàries de Catalunya (AQuAS), Barcelona, Catalonia, Spain
| | | | - Nestor Soler
- Hospital Clinic de Barcelona, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Universitat de Barcelona, Barcelona, Spain
- Center for Biomedical Network Research in Respiratory Diseases (CIBERES), Majadahonda (Madrid), Spain
| | - Gerard Torres
- Center for Biomedical Network Research in Respiratory Diseases (CIBERES), Majadahonda (Madrid), Spain
- Respiratory Department, Institut de Recerca Biomedica (IRBLeida), Lleida, Spain
| | - Eloisa Vargiu
- Eurecat. Technological Center of Catalonia, Barcelona, Catalunya, Spain
| | - Emili Vela
- CatSalut, Servei Català de la Salut, Barcelona, Catalonia, Spain
| | - Josep Roca
- Hospital Clinic de Barcelona, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Universitat de Barcelona, Barcelona, Spain.
- Center for Biomedical Network Research in Respiratory Diseases (CIBERES), Majadahonda (Madrid), Spain.
| |
Collapse
|
10
|
Cano I, Tenyi A, Vela E, Miralles F, Roca J. Perspectives on Big Data applications of health information. ACTA ACUST UNITED AC 2017. [DOI: 10.1016/j.coisb.2017.04.012] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
|
11
|
Auffray C, Balling R, Barroso I, Bencze L, Benson M, Bergeron J, Bernal-Delgado E, Blomberg N, Bock C, Conesa A, Del Signore S, Delogne C, Devilee P, Di Meglio A, Eijkemans M, Flicek P, Graf N, Grimm V, Guchelaar HJ, Guo YK, Gut IG, Hanbury A, Hanif S, Hilgers RD, Honrado Á, Hose DR, Houwing-Duistermaat J, Hubbard T, Janacek SH, Karanikas H, Kievits T, Kohler M, Kremer A, Lanfear J, Lengauer T, Maes E, Meert T, Müller W, Nickel D, Oledzki P, Pedersen B, Petkovic M, Pliakos K, Rattray M, I Màs JR, Schneider R, Sengstag T, Serra-Picamal X, Spek W, Vaas LAI, van Batenburg O, Vandelaer M, Varnai P, Villoslada P, Vizcaíno JA, Wubbe JPM, Zanetti G. Making sense of big data in health research: Towards an EU action plan. Genome Med 2016; 8:71. [PMID: 27338147 PMCID: PMC4919856 DOI: 10.1186/s13073-016-0323-y] [Citation(s) in RCA: 124] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023] Open
Abstract
Medicine and healthcare are undergoing profound changes. Whole-genome sequencing and high-resolution imaging technologies are key drivers of this rapid and crucial transformation. Technological innovation combined with automation and miniaturization has triggered an explosion in data production that will soon reach exabyte proportions. How are we going to deal with this exponential increase in data production? The potential of "big data" for improving health is enormous but, at the same time, we face a wide range of challenges to overcome urgently. Europe is very proud of its cultural diversity; however, exploitation of the data made available through advances in genomic medicine, imaging, and a wide range of mobile health applications or connected devices is hampered by numerous historical, technical, legal, and political barriers. European health systems and databases are diverse and fragmented. There is a lack of harmonization of data formats, processing, analysis, and data transfer, which leads to incompatibilities and lost opportunities. Legal frameworks for data sharing are evolving. Clinicians, researchers, and citizens need improved methods, tools, and training to generate, analyze, and query data effectively. Addressing these barriers will contribute to creating the European Single Market for health, which will improve health and healthcare for all Europeans.
Collapse
Affiliation(s)
- Charles Auffray
- European Institute for Systems Biology and Medicine, 1 avenue Claude Vellefaux, 75010, Paris, France.
- CIRI-UMR5308, CNRS-ENS-INSERM-UCBL, Université de Lyon, 50 avenue Tony Garnier, 69007, Lyon, France.
| | - Rudi Balling
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, 7 Avenue des Hauts Fourneaux, 4362, Esch-sur-Alzette, Luxembourg.
| | - Inês Barroso
- Wellcome Trust Sanger Institute, Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SA, UK
| | - László Bencze
- Health Services Management Training Centre, Faculty of Health and Public Services, Semmelweis University, Kútvölgyi út 2, 1125, Budapest, Hungary
| | - Mikael Benson
- Centre for Personalised Medicine, Linköping University, 581 85, Linköping, Sweden
| | - Jay Bergeron
- Translational & Bioinformatics, Pfizer Inc., 300 Technology Square, Cambridge, MA, 02139, USA
| | - Enrique Bernal-Delgado
- Institute for Health Sciences, IACS - IIS Aragon, San Juan Bosco 13, 50009, Zaragoza, Spain
| | - Niklas Blomberg
- ELIXIR, Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
| | - Christoph Bock
- CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, Lazarettgasse 14, AKH BT25.2, 1090, Vienna, Austria
- Department of Laboratory Medicine, Medical University of Vienna, Lazarettgasse 14, AKH BT25.2, 1090, Vienna, Austria
- Max Planck Institute for Informatics, Campus E1 4, 66123, Saarbrücken, Germany
| | - Ana Conesa
- Príncipe Felipe Research Center, C/ Eduardo Primo Yúfera 3, 46012, Valencia, Spain
- University of Florida, Institute of Food and Agricultural Sciences (IFAS), 2033 Mowry Road, Gainesville, FL, 32610, USA
| | | | - Christophe Delogne
- Technology, Data & Analytics, KPMG Luxembourg, Société Coopérative, 39 Avenue John F. Kennedy, 1855, Luxembourg, Luxembourg
| | - Peter Devilee
- Department of Human Genetics, Department of Pathology, Leiden University Medical Centre, Einthovenweg 20, 2333 ZC, Leiden, The Netherlands
| | - Alberto Di Meglio
- Information Technology Department, European Organization for Nuclear Research (CERN), 385 Route de Meyrin, 1211, Geneva 23, Switzerland
| | - Marinus Eijkemans
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Heidelberglaan 100, 3508 GA, Utrecht, The Netherlands
| | - Paul Flicek
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
| | - Norbert Graf
- Department of Pediatric Oncology/Hematology, Saarland University, Campus Homburg, Building 9, 66421, Homburg, Germany
| | - Vera Grimm
- Project Management Jülich, Forschungszentrum Jülich GmbH, Wilhelm-Johnen-Straße, 52428, Jülich, Germany
| | - Henk-Jan Guchelaar
- Department of Clinical Pharmacy & Toxicology, Leiden University Medical Center, Albinusdreef 2, 2333 ZA, Leiden, The Netherlands
| | - Yi-Ke Guo
- Data Science Institute, Imperial College London, South Kensington, London, SW7 2AZ, UK
| | - Ivo Glynne Gut
- CNAG-CRG, Center for Genomic Regulation, Barcelona Institute for Science and Technology (BIST), C/Baldiri Reixac 4, 08029, Barcelona, Spain
| | - Allan Hanbury
- Institute of Software Technology and Interactive Systems, TU Wien, Favoritenstrasse 9-11/188, 1040, Vienna, Austria
| | - Shahid Hanif
- The Association of the British Pharmaceutical Industry, 7th Floor, Southside, 105 Victoria Street, London, SW1E 6QT, UK
| | - Ralf-Dieter Hilgers
- Department of Medical Statistics, RWTH-Aachen University, Universitätsklinikum Aachen, Pauwelsstraße 30, 52074, Aachen, Germany
| | - Ángel Honrado
- SYNAPSE Research Management Partners, Diputació 237, Àtic 3ª, 08007, Barcelona, Spain
| | - D Rod Hose
- Department of Infection, Immunity and Cardiovascular Disease and Insigneo Institute for In-Silico Medicine, Medical School, University of Sheffield, Beech Hill Road, Sheffield, S10 2RX, UK
| | | | - Tim Hubbard
- Department of Medical & Molecular Genetics, King's College London, London, SE1 9RT, UK
- Genomics England, London, EC1M 6BQ, UK
| | - Sophie Helen Janacek
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
| | - Haralampos Karanikas
- National and Kapodistrian University of Athens, Medical School, Xristou Lada 6, 10561, Athens, Greece
| | - Tim Kievits
- Vitromics Healthcare Holding B.V., Onderwijsboulevard 225, 5223 DE, 's-Hertogenbosch, The Netherlands
| | - Manfred Kohler
- Fraunhofer Institute for Molecular Biology and Applied Ecology ScreeningPort, Schnackenburgallee 114, 22525, Hamburg, Germany
| | - Andreas Kremer
- ITTM S.A., 9 avenue des Hauts Fourneaux, 4362, Esch-sur-Alzette, Luxembourg
| | - Jerry Lanfear
- Research Business Technology, Pfizer Ltd, GP4 Building, Granta Park, Cambridge, CB21 6GP, UK
| | - Thomas Lengauer
- Max Planck Institute for Informatics, Campus E1 4, 66123, Saarbrücken, Germany
| | - Edith Maes
- Health Economics & Outcomes Research, Deloitte Belgium, Berkenlaan 8A, 1831, Diegem, Belgium
| | - Theo Meert
- Janssen Pharmaceutica N.V., R&D G3O, Turnhoutseweg 30, 2340, Beerse, Belgium
| | - Werner Müller
- Faculty of Life Sciences, University of Manchester, AV Hill Building, Oxford Road, Manchester, M13 9PT, UK
| | - Dörthe Nickel
- UMR3664 IC/CNRS, Institut Curie, Section Recherche, Pavillon Pasteur, 26 rue d'Ulm, 75248, Paris cedex 05, France
| | - Peter Oledzki
- Linguamatics Ltd, 324 Cambridge Science Park Milton Rd, Cambridge, CB4 0WG, UK
| | - Bertrand Pedersen
- PwC Luxembourg, 2 rue Gerhard Mercator, 2182, Luxembourg, Luxembourg
| | - Milan Petkovic
- Philips, HighTechCampus 36, 5656AE, Eindhoven, The Netherlands
| | - Konstantinos Pliakos
- Department of Public Health and Primary Care, KU Leuven Kulak, Etienne Sabbelaan 53, 8500, Kortrijk, Belgium
| | - Magnus Rattray
- Faculty of Life Sciences, University of Manchester, AV Hill Building, Oxford Road, Manchester, M13 9PT, UK
| | - Josep Redón I Màs
- INCLIVA Health Research Institute, University of Valencia, CIBERobn ISCIII, Avenida Menéndez Pelayo 4 accesorio, 46010, Valencia, Spain
| | - Reinhard Schneider
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, 7 Avenue des Hauts Fourneaux, 4362, Esch-sur-Alzette, Luxembourg
| | - Thierry Sengstag
- Swiss Institute of Bioinformatics (SIB) and University of Basel, Klingelbergstrasse 50/70, 4056, Basel, Switzerland
| | - Xavier Serra-Picamal
- Agency for Health Quality and Assessment of Catalonia (AQuAS), Carrer de Roc Boronat 81-95, 08005, Barcelona, Spain
| | - Wouter Spek
- EuroBioForum Foundation, Chrysantstraat 10, 3135 HG, Vlaardingen, The Netherlands
| | - Lea A I Vaas
- Fraunhofer Institute for Molecular Biology and Applied Ecology ScreeningPort, Schnackenburgallee 114, 22525, Hamburg, Germany
| | - Okker van Batenburg
- EuroBioForum Foundation, Chrysantstraat 10, 3135 HG, Vlaardingen, The Netherlands
| | - Marc Vandelaer
- Integrated BioBank of Luxembourg, 6 rue Nicolas-Ernest Barblé, 1210, Luxembourg, Luxembourg
| | - Peter Varnai
- Technopolis Group, 3 Pavilion Buildings, Brighton, BN1 1EE, UK
| | - Pablo Villoslada
- Hospital Clinic of Barcelona, Institute d'Investigacions Biomediques August Pi Sunyer (IDIBAPS), Rosello 149, 08036, Barcelona, Spain
| | - Juan Antonio Vizcaíno
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD, UK
| | - John Peter Mary Wubbe
- European Platform for Patients' Organisations, Science and Industry (Epposi), De Meeûs Square 38-40, 1000, Brussels, Belgium
| | - Gianluigi Zanetti
- CRS4, Ed.1 POLARIS, 09129, Pula, Italy
- BBMRI-ERIC, Neue Stiftingtalstrasse 2/B/6, 8010, Graz, Austria
| |
Collapse
|
12
|
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.
Collapse
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
| |
Collapse
|
13
|
Roca J, Cano I, Gomez-Cabrero D, Tegnér J. From Systems Understanding to Personalized Medicine: Lessons and Recommendations Based on a Multidisciplinary and Translational Analysis of COPD. Methods Mol Biol 2016; 1386:283-303. [PMID: 26677188 DOI: 10.1007/978-1-4939-3283-2_13] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Systems medicine, using and adapting methods and approaches as developed within systems biology, promises to be essential in ongoing efforts of realizing and implementing personalized medicine in clinical practice and research. Here we review and critically assess these opportunities and challenges using our work on COPD as a case study. We find that there are significant unresolved biomedical challenges in how to unravel complex multifactorial components in disease initiation and progression producing different clinical phenotypes. Yet, while such a systems understanding of COPD is necessary, there are other auxiliary challenges that need to be addressed in concert with a systems analysis of COPD. These include information and communication technology (ICT)-related issues such as data harmonization, systematic handling of knowledge, computational modeling, and importantly their translation and support of clinical practice. For example, clinical decision-support systems need a seamless integration with new models and knowledge as systems analysis of COPD continues to develop. Our experience with clinical implementation of systems medicine targeting COPD highlights the need for a change of management including design of appropriate business models and adoption of ICT providing and supporting organizational interoperability among professional teams across healthcare tiers, working around the patient. In conclusion, in our hands the scope and efforts of systems medicine need to concurrently consider these aspects of clinical implementation, which inherently drives the selection of the most relevant and urgent issues and methods that need further development in a systems analysis of disease.
Collapse
Affiliation(s)
- Josep Roca
- IDIBAPS, Hospital Clínic, CIBERES, Universitat de Barcelona, Villarroel, 170, Barcelona, Catalunya, 08036, Spain. .,Centro de Investigación Biomédica en Red de Enfermedades Respiratorias (CIBERES), Bunyola, Balearic Islands.
| | - Isaac Cano
- IDIBAPS, Hospital Clínic, CIBERES, Universitat de Barcelona, Villarroel, 170, Barcelona, Catalunya, 08036, Spain.,Centro de Investigación Biomédica en Red de Enfermedades Respiratorias (CIBERES), Bunyola, Balearic Islands
| | - David Gomez-Cabrero
- Unit of Computational Medicine, Department of Medicine Solna, Center for Molecular Medicine, Karolinska Institutet, Karolinska University Hospital, Stockholm, Sweden
| | - Jesper Tegnér
- Unit of Computational Medicine, Department of Medicine Solna, Center for Molecular Medicine, Karolinska Institutet, Karolinska University Hospital, Stockholm, Sweden. .,L8:05 Karolinska University Hospital, Stockholm, 17176, Sweden.
| |
Collapse
|
14
|
An adaptive case management system to support integrated care services: Lessons learned from the NEXES project. J Biomed Inform 2015; 55:11-22. [DOI: 10.1016/j.jbi.2015.02.011] [Citation(s) in RCA: 33] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2014] [Revised: 02/23/2015] [Accepted: 02/28/2015] [Indexed: 01/11/2023]
|
15
|
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.
Collapse
|
16
|
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.
Collapse
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
| | | |
Collapse
|
17
|
Roca J, Vargas C, Cano I, Selivanov V, Barreiro E, Maier D, Falciani F, Wagner P, Cascante M, Garcia-Aymerich J, Kalko S, De Mas I, Tegnér J, Escarrabill J, Agustí A, Gomez-Cabrero D. Chronic Obstructive Pulmonary Disease heterogeneity: challenges for health risk assessment, stratification and management. J Transl Med 2014; 12 Suppl 2:S3. [PMID: 25472887 PMCID: PMC4255905 DOI: 10.1186/1479-5876-12-s2-s3] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023] Open
Abstract
Background and hypothesis Heterogeneity in clinical manifestations and disease progression in Chronic Obstructive Pulmonary Disease (COPD) lead to consequences for patient health risk assessment, stratification and management. Implicit with the classical "spill over" hypothesis is that COPD heterogeneity is driven by the pulmonary events of the disease. Alternatively, we hypothesized that COPD heterogeneities result from the interplay of mechanisms governing three conceptually different phenomena: 1) pulmonary disease, 2) systemic effects of COPD and 3) co-morbidity clustering, each of them with their own dynamics. Objective and method To explore the potential of a systems analysis of COPD heterogeneity focused on skeletal muscle dysfunction and on co-morbidity clustering aiming at generating predictive modeling with impact on patient management. To this end, strategies combining deterministic modeling and network medicine analyses of the Biobridge dataset were used to investigate the mechanisms of skeletal muscle dysfunction. An independent data driven analysis of co-morbidity clustering examining associated genes and pathways was performed using a large dataset (ICD9-CM data from Medicare, 13 million people). Finally, a targeted network analysis using the outcomes of the two approaches (skeletal muscle dysfunction and co-morbidity clustering) explored shared pathways between these phenomena. Results (1) Evidence of abnormal regulation of skeletal muscle bioenergetics and skeletal muscle remodeling showing a significant association with nitroso-redox disequilibrium was observed in COPD; (2) COPD patients presented higher risk for co-morbidity clustering than non-COPD patients increasing with ageing; and, (3) the on-going targeted network analyses suggests shared pathways between skeletal muscle dysfunction and co-morbidity clustering. Conclusions The results indicate the high potential of a systems approach to address COPD heterogeneity. Significant knowledge gaps were identified that are relevant to shape strategies aiming at fostering 4P Medicine for patients with COPD.
Collapse
|