Protocol: Triple Diamond method for problem solving and design thinking. Rubric validation

[EN] There is a set of tools that we can use to improve the results of each of the phases that continuous improvement projects must go through (8D, PDCA, DMAIC, Double diamond, etc.). These methods use divergent techniques, which help generate multiple alternatives, and convergent techniques that help analyze and filter the generated options. However, the tools used in all those frameworks are often very similar. Our goal, in this research, is to develop a comprehensive model that allows it to be used both for problem-solving and for taking advantage of opportunities. This protocol defines the main terms related to our research, makes a framework proposal, proposes a rubric that identifies observable milestones at each stage of the model and proposes the action plan to validate this rubric and the model in a given context. The action plan will be implemented in a future research.Marin-Garcia, JA.; Garcia-Sabater, JJ.; Garcia-Sabater, JP.; Maheut, J. (2020). Protocol: Triple Diamond method for problem solving and design thinking. Rubric validation. WPOM-Working Papers on Operations Management. 11(2):49-68. https://doi.org/10.4995/wpom.v11i2.14776OJS496811

A data generator for covid-19 patients’ care requirements inside hospitals

[EN] A Spanish version of the article is provided (see section before references). This paper presents the generation of a plausible data set related to the needs of COVID-19 patients with severe or critical symptoms. Possible illness’ stages were proposed within the context of medical knowledge as of January 2021. The parameters chosen in this data set were customized to fit the population data of the Valencia region (Spain) with approximately 2.5 million inhabitants. They were based on the evolution of the pandemic between September 2020 and March 2021, a period that included two complete waves of the pandemic. Contrary to expectation and despite the European and national transparency laws (BOE-A2013-12887, 2013; European Parliament and Council of the European Union, 2019), the actual COVID-19 pandemic-related data, at least in Spain, took considerable time to be updated and made available (usually a week or more). Moreover, some relevant data necessary to develop and validate hospital bed management models were not publicly accessible. This was either because these data were not collected, because public agencies failed to make them public (despite having them indexed in their databases), the data were processed within indicators and not shown as raw data, or they simply published the data in a format that was difficult to process (e.g., PDF image documents versus CSV tables). Despite the potential of hospital information systems, there were still data that were not adequately captured within these systems. Moreover, the data collected in a hospital depends on the strategies and practices specific to that hospital or health system. This limits the generalization of "real" data, and it encourages working with "realistic" or plausible data that are clean of interactions with local variables or decisions (Gunal, 2012; Marin-Garcia et al., 2020). Besides, one can parameterize the model and define the data structure that would be necessary to run the model without delaying till the real data become available. Conversely, plausible data sets can be generated from publicly available information and, later, when real data become available, the accuracy of the model can be evaluated (Garcia-Sabater and Maheut, 2021). This work opens lines of future research, both theoretical and practical. From a theoretical point of view, it would be interesting to develop machine learning tools that, by analyzing specific data samples in real hospitals, can identify the parameters necessary for the automatic prototyping of generators adapted to each hospital. Regarding the lines of research applied, it is evident that the formalism proposed for the generation of sound patients is not limited to patients affected by SARS-CoV-2 infection. The generation of heterogeneous patients can represent the needs of a specific population and serve as a basis for studying complex health service delivery systems.[ES] En este trabajo se presenta cómo se ha generado un conjunto de datos verosímiles relacionados con las necesidades de pacientes covid-19 con síntomas severe or critical. Se considerarán las etapas posibles con los conocimientos médicos a fecha de enero de 2021. Los parámetros elegidos en este data set están personalizados para adecuarse a los valores poblacionales de la región de Valencia (Spain), unos 2.5 Millones de habitantes y la evolución de la pandemia entre los meses de septiembre 2020 y marzo 2021, un periodo de tiempo que contemple dos olas completas de pandemia.En contra de lo que cabría esperar, a pesar de la ley de transparencia europea y nacional (BOE-A-2013-12887, 2013; Parlamento Europeo y del Consejo de la Unión Europea, 2019), los datos reales relacionados con la pandemia covid-19, al menos en España, tardan mucho en actualizarse y estar disponibles (normalmente una semana o más días). Además, algunos datos relevantes para trabajar los modelos de gestión de camas de hospital no están accesibles públicamente. Bien porque no se hayan recogido esos datos, o porque los organismos públicos no los ofrecen (a pesar de tenerlos indexados en sus bases de datos), o los ofrecen camuflados en indicadores procesados y no muestran los datos en bruto, o simplemente los publican en un formato de difícil reutilización (por ejemplo, en documentos PDF en lugar de en tablas CSV). A pesar de que los sistemas de información de los hospitales son bastante potentes, siguen existiendo datos que ni siquiera están recogidos adecuadamente en el sistema de información de salud.Por otra parte, los datos recogidos en un hospital dependen de las estrategias y practicas propias de ese hospital o sistema de salud. Este efecto limita la generalización de los datos “reales” y es necesario trabajar con datos “realistas” o verosímiles que están limpios de interacciones con variables o decisiones locales (Gunal, 2012; Marin-Garcia et al., 2020). Por un lado, se puede parametrizar el modelo y definir la estructura de datos que sería necesaria para ejecutar el modelo con datos reales. Por otro lado, se pueden generar conjuntos de datos verosímiles a partir de la información pública disponible y, posteriormente, cuando se disponga de los datos reales evaluar la bondad del modelo (Garcia-Sabater & Maheut, 2021).Marin-Garcia, JA.; Ruiz, A.; Julien, M.; Garcia-Sabater, JP. (2021). A data generator for covid-19 patients’ care requirements inside hospitals. WPOM-Working Papers on Operations Management. 12(1):76-115. https://doi.org/10.4995/wpom.1533276115121Alexander, G. L. (2007). The nurse-patient trajectory framework. Medinfo. MEDINFO, 12(Pt 2), 910- 914.Belciug, S., Bejinariu, S. I., & Costin, H. (2020). An artificial immune system approach for a multicompartment queuing model for improving medical resources and inpatient bed occupancy in pandemics. Advances in Electrical and Computer Engineering, 20(3). https://doi.org/10.4316/AECE.2020.03003BOE-A-2013-12887. (2013). Ley 19/2013, de 9 de diciembre, de transparencia acceso a la información pública y buen gobierno. 1-32.Brochard, L. (2003). Mechanical ventilation: Invasive versus noninvasive. European Respiratory Journal, Supplement, 22(47), 31s-37s. https://doi.org/10.1183/09031936.03.00050403Buckley, D., & Gillham, M. (2007). Invasive Respiratory Support. In Cardiothoracic Critical Care (pp. 419-436). Elsevier Inc. https://doi.org/10.1016/B978-075067572-7.50032-1Casas-Rojo, J. M., Antón-Santos, J. M., Millán-Núñez-Cortés, J., Lumbreras-Bermejo, C., RamosRincón, J. M., Roy-Vallejo, E., Artero-Mora, A., Arnalich-Fernández, F., García-Bruñén, J. M., Vargas-Núñez, J. A., Freire-Castro, S. J., Manzano-Espinosa, L., Perales-Fraile, I., CresteloViéitez, A., Puchades-Gimeno, F., Rodilla-Sala, E., Solís-Marquínez, M. N., Bonet-Tur, D., Fidalgo-Moreno, M. P., … Gómez-Huelgas, R. (2020). Clinical characteristics of patients hospitalized with COVID-19 in Spain: Results from the SEMI-COVID-19 Registry. Revista Clinica Espanola, 220(8), 480-494. https://doi.org/10.1016/j.rce.2020.07.003Castelnuovo, F., Marchese, V., Cristini, G., Crosato, V., Pennati, F., Renisi, G., Izzo, I., Paraninfo, G., Van Hauwermeiren, E., & Castelli, F. (2020). Discharge ward during the sars-cov-2 pandemic: An effective way to increase patient turnover when human resources are scarce. Infezioni in Medicina, 28(4), 539-544. https://doi.org/10.1007/s15010-020-01522-4Celeux, G., Lavergne, C., Vernaz, Y., Celeux, G., Lavergne, C., Vernaz, Y., Material, A., & Censored, D. (2006). Assessing Material Aging from Doubly Censored Data : Weibull Distribution vs . Poisson Process To cite this version : HAL Id : inria-00072799 Assessing material aging from doubly censored data : Weibull distribution vs . Poisson process apport. [Research Report] RR-3857, INRIA. 2000. inria-00072799.Claudio, D., Cosgriff, V., Nino, V., & Valladares, L. (2021). An Agile Standardized Work Procedure for Cleaning the Operating Room. Journal of Industrial Engineering and Management, 14, in press. https://doi.org/https://doi.org/jiem.3440CNE -Centro Nacional de Epidemiología. (2020). Información científico-técnica, enfermedad por coronavirus, COVID-19 (actualizado 20201112).Comtois, D. (2021). summarytools: Tools to Quickly and Neatly Summarize Data.Corbin, J. M., & Strauss, A. L. (1988). Unending Work and Care: Managing Chronic Illness at Home. Jossey-Bass Inc.Daniel, P., Mecklenburg, M., Massiah, C., Joseph, M. A., Wilson, C., Parmar, P., Rosengarten, S., Maini, R., Kim, J., Oomen, A., & Zehtabchi, S. (2021). Non-invasive positive pressure ventilation versus endotracheal intubation in treatment of COVID-19 patients requiring ventilatory support. American Journal of Emergency Medicine, 43, 103-108. https://doi.org/10.1016/j.ajem.2021.01.068Dominguez-Lara, S. A. (2018). Odds-ratios and their interpretation as effect size in research. In Educacion Medica (Vol. 19, Issue 1, pp. 65-66). Fundacion Educacion Medica. https://doi.org/10.1016/j.edumed.2017.01.008ECDP. (2020). Guidance for discharge and ending isolation in the context of widespread community transmission of COVID-19-first update Scope of this document. In European Centre for Disease Prevention (Issue April, pp. 1-8).Epstein, R. H., & Dexter, F. (2020). A Predictive Model for Patient Census and Ventilator Requirements at Individual Hospitals During the Coronavirus Disease 2019 (COVID-19) Pandemic: A Preliminary Technical Report. Cureus. https://doi.org/10.7759/cureus.8501European center for disease prevention and control. (2020). Coronavirus disease 2019 (COVID-19) pandemic: increased transmission in the EU/EEA and the UK - seventh update. 2019 (March).Fowler, R., Hatchette, T., Salvadori, M., Baclic, O., Volling, C., Murthy, S., Emeriaud, G., Money, D., Brooks, J., Decou, M., & Ofner, M. (2020). Clinical management of patients with COVID-19: Second interim guidance. Canadian Critical Care Society and Association of Medical Microbiology and Infectious Disease (AMMI) Canada, 1-67.Garcia-Sabater, J. P., & Maheut, J. (2021). Introducción al Modelado Matematico, Nota Técnica. RiuNet. Repositorio Institucional UPV. https://doi.org/http://hdl.handle.net/10251/158555Garcia-Sabater, J. P., Maheut, J., Ruiz, A., & Garcia-Sabater, J. J. (2020). A framework for capacity and operations planning in services organizations employing workers with intellectual disabilities. Sustainability (Switzerland), 12(22), 1-17. https://doi.org/10.3390/su12229713Generalitat Valenciana. Conselleria de Sanitat Universal i Salut Pública. (2019). Memoria de gestión conselleria de sanitat universal i salut pública 2019. 14493-14496.Generalitat Valenciana. (2018). Memoria de Gestión de la Conselleria de Sanitat Universal i Salut Pública.Guan, W., Ni, Z., Hu, Y., Liang, W., Ou, C., He, J., Liu, L., Shan, H., Lei, C., Hui, D. S. C., Du, B., Li, L., Zeng, G., Yuen, K.-Y., Chen, R., Tang, C., Wang, T., Chen, P., Xiang, J., … Zhong, N. (2020). Clinical Characteristics of Coronavirus Disease 2019 in China. New England Journal of Medicine, 382(18), 1708-1720. https://doi.org/10.1056/NEJMoa2002032Gunal, M. M. (2012). A guide for building hospital simulation models. Health Systems, 1(1), 17-25. https://doi.org/10.1057/hs.2012.8Hair, J. F., Black, W. C., Babin, B., & Anderson, R. E. (2009). Multivariate data analysis (7th edition). Prentice Hall. Huang, C., Wang, Y., Li, X., Ren, L., Zhao, J., Hu, Y., Zhang, L., Fan, G., Xu, J., Gu, X., Cheng, Z., Yu, T., Xia, J., Wei, Y., Wu, W., Xie, X., Yin, W., Li, H., Liu, M., … Cao, B. (2020). Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. The Lancet, 395(10223), 497- 506. https://doi.org/10.1016/S0140-6736(20)30183-5Lagarda-Leyva, E. A., & Ruiz, A. (2019). A Systems Thinking Model to Support Long-Term Bearability of the Healthcare System: The Case of the Province of Quebec. Sustainability, 11(24), 7028. https://doi.org/10.3390/su11247028Manninen, K. (2020). Typical progress of covid-19. Marin-Garcia, J. A. (2015). Publishing in two phases for focused research by means of "research collaborations." WPOM-Working Papers on Operations Management, 6(2), 76. https://doi.org/10.4995/wpom.v6i2.4459Marin-Garcia, J. A., Bonavia, T., & Losilla, J.-M. (2020). Changes in the Association between European Workers' Employment Conditions and Employee Well-Being in 2005, 2010 and 2015. International Journal of Environmental Research and Public Health, 17(3), 1048. https://doi.org/10.3390/ijerph17031048Marin-Garcia, J. A., Garcia-Sabater, J. P., Ruiz, A., Maheut, J., & Garcia-Sabater, J. J. (2020). Operations Management at the service of health care management: Example of a proposal for action research to plan and schedule health resources in scenarios derived from the COVID-19 outbreak. Journal of Industrial Engineering and Management, 13(2), 213. https://doi.org/10.3926/jiem.3190Marin-Garcia, J. A., Vidal-Carreras, P. I., Garcia Sabater, J. J., & Escribano-Martinez, J. (2019). Protocol: Value Stream Maping in Healthcare. A systematic literature review. WPOM-Working Papers on Operations Management, 10(2), 36. https://doi.org/10.4995/wpom.v10i2.12297Ministerio De Sanidad, Servicios Sociales e Igualdad. (2017). Hábitos de Vida Informe Anual del Sistema Nacional de salud 2016 (INFORMES,). MINISTERIO DE SANIDAD, SERVICIOS SOCIALES E IGUALDAD.Mun, J. (2008). Appendix C. Understanding and Choosing the Right Probability Distributions. Advanced Analytical Models: Over 800 Models and 300 Applications from the Basel II Accord to Wall Street and Beyond, 899-917. https://doi.org/10.1002/9781119197096.app03Nino, V., Gomez, K., Martinez, K., & Claudio, D. (2021). Improving the registration process in a healthcare facility with lean principles. Journal of Industrial Engineering and Management, 14, in press. https://doi.org/https://doi.org/jiem.3432Olivieri, A., Palù, G., & Sebastiani, G. (2021). COVID-19 cumulative incidence, intensive care, and mortality in Italian regions compared to selected European countries. International Journal of Infectious Diseases, 102. https://doi.org/10.1016/j.ijid.2020.10.070Parlamento Europeo y del Consejo de la Unión Europea. (2019). Directiva(UE) 2019/1024 DEL PARLAMENTO EUROPEO Y DEL CONSEJO de la Unión Europea de 20 de junio de 2019 relativa a los datos abiertos y la reutilización de la información del sector público (versión refundida). 172/56-172/78.Petermann-Rocha, F., Hanlon, P., Gray, S. R., Welsh, P., Gill, J. M. R., Foster, H., Katikireddi, S. V., Lyall, D., Mackay, D. F., O'Donnell, C. A., Sattar, N., Nicholl, B. I., Pell, J. P., Jani, B. D., Ho, F. K., Mair, F. S., & Celis-Morales, C. (2020). Comparison of two different frailty measurements and risk of hospitalisation or death from COVID-19: findings from UK Biobank. BMC Medicine, 18(1). https://doi.org/10.1186/s12916-020-01822-4Pinaire, J., Azé, J., Bringay, S., & Landais, P. (2017). Patient healthcare trajectory. An essential monitoring tool: a systematic review. Health Information Science and Systems, 5(1), 1-18. https://doi.org/10.1007/s13755-017-0020-2Plaza, J. (2021). Informe Científico-Divulgativo: Un Año De Coronavirus Sars-Cov-2. Ministerio de Ciencia e Innovación.Popat, B., & Jones, A. T. (2012). Invasive and non-invasive mechanical ventilation. In Medicine (United Kingdom) (Vol. 40, Issue 6, pp. 298-304). Elsevier Ltd. https://doi.org/10.1016/j.mpmed.2012.03.010Posso, M., Comas, M., Román, M., Domingo, L., Louro, J., González, C., Sala, M., Anglès, A., Cirera, I., Cots, F., Frías, V.-M., Gea, J., Güerri-Fernández, R., Masclans, J. R., Noguès, X., Vázquez, O., Villar-García, J., Horcajada, J. P., Pascual, J., & Castells, X. (2020). Comorbidities and Mortality in Patients With COVID-19 Aged 60 Years and Older in a University Hospital in Spain. Archivos de Bronconeumología, 56(11), 756-758. https://doi.org/10.1016/j.arbres.2020.06.012R Core Team. (2020). R: A Language and Environment for Statistical Computing. Revelle, W. (2021). psych: Procedures for Psychological, Psychometric, and Personality Research.Roa-Martínez, S. M., Vidotti, S. A. B., & Santana, R. C. (2017). Estructura propuesta del artículo de datos como publicación científica. Revista Espanola de Documentacion Cientifica, 40(1), 1-12. https://doi.org/10.3989/redc.2017.1.1375Romeo Casabona, C. M., & Urruela Mora, A. (2020). Informe Del Ministerio De Sanidad Sobre Los Aspectos Éticos En Situaciones De Pandemia: El Sars-Cov-2. 12.RStudio Team. (2020). RStudio: Integrated Development for R. RStudio, PBC. Rubio-Rivas, M., Corbella, X., Mora-Luján, J. M., Loureiro-Amigo, J., López Sampalo, A., Yera Bergua, C., Esteve Atiénzar, P. J., Díez García, L. F., Gonzalez Ferrer, R., Plaza Canteli, S., Pérez Piñeiro, A., Cortés Rodríguez, B., Jorquer Vidal, L., Pérez Catalán, I., León Téllez, M., Martín Oterino, J. Á., Martín González, M. C., Serrano Carrillo de Albornoz, J. L., García Sardon, E., … GómezHuelgas, R. (2020). Predicting Clinical Outcome with Phenotypic Clusters in COVID-19 Pneumonia: An Analysis of 12,066 Hospitalized Patients from the Spanish Registry SEMI-COVID19. Journal of Clinical Medicine, 9(11), 3488. https://doi.org/10.3390/jcm9113488Ruckdeschel, P., Kohl, M., Stabla, T., & Camphausen, F. (2006). S4 Classes for Distributions. R News, 6(2), 2-6. Ruza, F. (2008). Cuidados Intensivos Pediatricos. 6(6), 336. Schauberger, P., & Walker, A. (2020). openxlsx: Read, Write and Edit xlsx Files.Stang, A., Stang, M., & Jöckel, K. H. (2020). Estimated use of intensive care beds due to COVID-19 in Germany over time. Deutsches Arzteblatt International, 117(19). https://doi.org/10.3238/arztebl.2020.0329Unroe, M., Kahn, J. M., Carson, S. S., Govert, J. A., Martinu, T., Sathy, S. J., Clay, A. S., Chia, J., Gray, A., Tulsky, J. A., & Cox, C. E. (2010). One-year trajectories of care and resource utilization for recipients of prolonged mechanical ventilation: A cohort study. Annals of Internal Medicine, 153(3), 167-175. https://doi.org/10.7326/0003-4819-153-3-201008030-00007Venables, W. N., & Ripley, B. D. (2002). Modern Applied Statistics with S (Fourth). Springer. https://doi.org/10.1007/978-0-387-21706-2Wang, Y., Wang, Y., Chen, Y., & Qin, Q. (2020). Unique epidemiological and clinical features of the emerging 2019 novel coronavirus pneumonia (COVID-19) implicate special control measures. In Journal of Medical Virology (Vol. 92, Issue 6, pp. 568-576). John Wiley and Sons Inc. https://doi.org/10.1002/jmv.25748Wickham, H. (2007). Reshaping Data with the {reshape} Package. Journal of Statistical Software, 21(12), 1-20. https://doi.org/10.18637/jss.v021.i12Wickham, H. (2011). The Split-Apply-Combine Strategy for Data Analysis. Journal of Statistical Software, 40(1), 1-29. https://doi.org/10.18637/jss.v040.i01Wiersema, U. F. (2007). Noninvasive Respiratory Support. In Cardiothoracic Critical Care (pp. 410- 418). Elsevier Inc. https://doi.org/10.1016/B978-075067572-7.50031-XWinck, J. C., & Scala, R. (2021). Non-invasive respiratory support paths in hospitalized patients with COVID-19: proposal of an algorithm. Pulmonology. https://doi.org/10.1016/j.pulmoe.2020.12.005Wong, G. N., Weiner, Z. J., Tkachenko, A. V., Elbanna, A., Maslov, S., & Goldenfeld, N. (2020). Modeling COVID-19 dynamics in Illinois under non-pharmaceutical interventions. In medRxiv. https://doi.org/10.1101/2020.06.03.20120691Wu, H., Godfrey, A. J. R., Govindaraju, K., & Pirikahu, S. (2020). ExtDist: Extending the Range of Functions for Probability Distributions.Xia, W., & Sun, J. (2013). Simulation guided value stream mapping and lean improvement: A case study of a tubular machining facility. Journal of Industrial Engineering and Management, 6(2), 456-476. https://doi.org/10.3926/jiem.532Xu, X. W., Wu, X. X., Jiang, X. G., Xu, K. J., Ying, L. J., Ma, C. L., Li, S. B., Wang, H. Y., Zhang, S., Gao, H. N., Sheng, J. F., Cai, H. L., Qiu, Y. Q., & Li, L. J. (2020). Clinical findings in a group of patients infected with the 2019 novel coronavirus (SARS-Cov-2) outside of Wuhan, China: Retrospective case series. The BMJ, 368. https://doi.org/10.1136/bmj.m606Zheng, Z., Peng, F., Xu, B., Zhao, J., Liu, H., Peng, J., Li, Q., Jiang, C., Zhou, Y., Liu, S., Ye, C., Zhang, P., Xing, Y., Guo, H., & Tang, W. (2020). Risk factors of critical & mortal COVID-19 cases: A systematic literature review and meta-analysis. In Journal of Infection (Vol. 81, Issue 2, pp. e16- e25). W.B. Saunders Ltd. https://doi.org/10.1016/j.jinf.2020.04.02

A decision support system for modelling and implementing the supply network configuration and operations scheduling problem in the machine tool industry

[EN] This paper presents a decision support system to simultaneously solve the supply network configuration problem and the operations scheduling problem for the machine tool industry. A novel database structure, which is able to consider alternative operations and alternative bills of material, has been used. An algorithm for complete enumeration to determine all the feasible solutions using stroke graphs is introduced. A multiagent-based simulator evaluates the different key performance indicators that the supply network deals with for each alternative solution (e.g. workload, profits, delivery times, etc.) to determine that ‘satisficed’ by the collaborative decision-making among its members. A case study based on a Spanish company that assembles highly customised machines and tools in several European plants is considered. From the experiments results based on data linked to this industry, it will be demonstrated that the tool is potentially useful for stakeholders and for the central decision-maker to make decisions collaboratively in a multisite context caseWe thank the EWG-DSS and their four expert anonymous referees as well as the guest editorial board for their useful suggestions and criticism on earlier versions of this paper. The research leading to these results has received funding from the European Community's Seventh Framework Programme (FP7/2007-2013) under grant agreement no. NMP2-SL-2009-229333 and has been partially supported by the Spanish Ministry of Science and Innovation within the 'Proyectos de Investigacion Fundamental No Orientada Programme' through Project 'CORSARI MAGIC DPI2010-18243'. Julien Maheut holds a VALi+d grant funded by the Regional Valencian Government (Ref. ACIF/2010/222).Maheut, JPD.; Besga, JM.; Uribetxebarria, J.; García Sabater, JP. (2014). A decision support system for modelling and implementing the supply network configuration and operations scheduling problem in the machine tool industry. Production Planning and Control. 25(8):679-697. https://doi.org/10.1080/09537287.2013.798087S67969725

CIBERER : Spanish national network for research on rare diseases: A highly productive collaborative initiative

Altres ajuts: Instituto de Salud Carlos III (ISCIII); Ministerio de Ciencia e Innovación.CIBER (Center for Biomedical Network Research; Centro de Investigación Biomédica En Red) is a public national consortium created in 2006 under the umbrella of the Spanish National Institute of Health Carlos III (ISCIII). This innovative research structure comprises 11 different specific areas dedicated to the main public health priorities in the National Health System. CIBERER, the thematic area of CIBER focused on rare diseases (RDs) currently consists of 75 research groups belonging to universities, research centers, and hospitals of the entire country. CIBERER's mission is to be a center prioritizing and favoring collaboration and cooperation between biomedical and clinical research groups, with special emphasis on the aspects of genetic, molecular, biochemical, and cellular research of RDs. This research is the basis for providing new tools for the diagnosis and therapy of low-prevalence diseases, in line with the International Rare Diseases Research Consortium (IRDiRC) objectives, thus favoring translational research between the scientific environment of the laboratory and the clinical setting of health centers. In this article, we intend to review CIBERER's 15-year journey and summarize the main results obtained in terms of internationalization, scientific production, contributions toward the discovery of new therapies and novel genes associated to diseases, cooperation with patients' associations and many other topics related to RD research

Report from Working Group 3: Beyond the standard model physics at the HL-LHC and HE-LHC

This is the third out of five chapters of the final report [1] of the Workshop on Physics at HL-LHC, and perspectives on HE-LHC [2]. It is devoted to the study of the potential, in the search for Beyond the Standard Model (BSM) physics, of the High Luminosity (HL) phase of the LHC, defined as $3$ ab$^{-1}$ of data taken at a centre-of-mass energy of 14 TeV, and of a possible future upgrade, the High Energy (HE) LHC, defined as $15$ ab$^{-1}$ of data at a centre-of-mass energy of 27 TeV. We consider a large variety of new physics models, both in a simplified model fashion and in a more model-dependent one. A long list of contributions from the theory and experimental (ATLAS, CMS, LHCb) communities have been collected and merged together to give a complete, wide, and consistent view of future prospects for BSM physics at the considered colliders. On top of the usual standard candles, such as supersymmetric simplified models and resonances, considered for the evaluation of future collider potentials, this report contains results on dark matter and dark sectors, long lived particles, leptoquarks, sterile neutrinos, axion-like particles, heavy scalars, vector-like quarks, and more. Particular attention is placed, especially in the study of the HL-LHC prospects, to the detector upgrades, the assessment of the future systematic uncertainties, and new experimental techniques. The general conclusion is that the HL-LHC, on top of allowing to extend the present LHC mass and coupling reach by $20-50\%$ on most new physics scenarios, will also be able to constrain, and potentially discover, new physics that is presently unconstrained. Moreover, compared to the HL-LHC, the reach in most observables will, generally more than double at the HE-LHC, which may represent a good candidate future facility for a final test of TeV-scale new physics

Whole-genome sequencing reveals host factors underlying critical COVID-19

Critical COVID-19 is caused by immune-mediated inflammatory lung injury. Host genetic variation influences the development of illness requiring critical care1 or hospitalization2–4 after infection with SARS-CoV-2. The GenOMICC (Genetics of Mortality in Critical Care) study enables the comparison of genomes from individuals who are critically ill with those of population controls to find underlying disease mechanisms. Here we use whole-genome sequencing in 7,491 critically ill individuals compared with 48,400 controls to discover and replicate 23 independent variants that significantly predispose to critical COVID-19. We identify 16 new independent associations, including variants within genes that are involved in interferon signalling (IL10RB and PLSCR1), leucocyte differentiation (BCL11A) and blood-type antigen secretor status (FUT2). Using transcriptome-wide association and colocalization to infer the effect of gene expression on disease severity, we find evidence that implicates multiple genes—including reduced expression of a membrane flippase (ATP11A), and increased expression of a mucin (MUC1)—in critical disease. Mendelian randomization provides evidence in support of causal roles for myeloid cell adhesion molecules (SELE, ICAM5 and CD209) and the coagulation factor F8, all of which are potentially druggable targets. Our results are broadly consistent with a multi-component model of COVID-19 pathophysiology, in which at least two distinct mechanisms can predispose to life-threatening disease: failure to control viral replication; or an enhanced tendency towards pulmonary inflammation and intravascular coagulation. We show that comparison between cases of critical illness and population controls is highly efficient for the detection of therapeutically relevant mechanisms of disease

Whole-genome sequencing reveals host factors underlying critical COVID-19

Critical COVID-19 is caused by immune-mediated inflammatory lung injury. Host genetic variation influences the development of illness requiring critical care1 or hospitalization2,3,4 after infection with SARS-CoV-2. The GenOMICC (Genetics of Mortality in Critical Care) study enables the comparison of genomes from individuals who are critically ill with those of population controls to find underlying disease mechanisms. Here we use whole-genome sequencing in 7,491 critically ill individuals compared with 48,400 controls to discover and replicate 23 independent variants that significantly predispose to critical COVID-19. We identify 16 new independent associations, including variants within genes that are involved in interferon signalling (IL10RB and PLSCR1), leucocyte differentiation (BCL11A) and blood-type antigen secretor status (FUT2). Using transcriptome-wide association and colocalization to infer the effect of gene expression on disease severity, we find evidence that implicates multiple genes—including reduced expression of a membrane flippase (ATP11A), and increased expression of a mucin (MUC1)—in critical disease. Mendelian randomization provides evidence in support of causal roles for myeloid cell adhesion molecules (SELE, ICAM5 and CD209) and the coagulation factor F8, all of which are potentially druggable targets. Our results are broadly consistent with a multi-component model of COVID-19 pathophysiology, in which at least two distinct mechanisms can predispose to life-threatening disease: failure to control viral replication; or an enhanced tendency towards pulmonary inflammation and intravascular coagulation. We show that comparison between cases of critical illness and population controls is highly efficient for the detection of therapeutically relevant mechanisms of disease