A semantic autonomous video surveillance system for dense camera networks in smart cities

Producción CientíficaThis paper presents a proposal of an intelligent video surveillance system able to detect and identify abnormal and alarming situations by analyzing object movement. The system is designed to minimize video processing and transmission, thus allowing a large number of cameras to be deployed on the system, and therefore making it suitable for its usage as an integrated safety and security solution in Smart Cities. Alarm detection is performed on the basis of parameters of the moving objects and their trajectories, and is performed using semantic reasoning and ontologies. This means that the system employs a high-level conceptual language easy to understand for human operators, capable of raising enriched alarms with descriptions of what is happening on the image, and to automate reactions to them such as alerting the appropriate emergency services using the Smart City safety network

Application of mixed reality to ultrasound-guided femoral arterial cannulation during real-time practice in cardiac interventions

Producción CientíficaMixed reality opens interesting possibilities as it allows physicians to interact with both, the real physical and the virtual computer-generated environment and objects, in a powerful way. A mixed reality system, based in the HoloLens 2 glasses, has been developed to assist cardiologists in a quite complex interventional procedure: the ultrasound-guided femoral arterial cannulations, during real-time practice in interventional cardiology. The system is divided into two modules, the transmitter module, responsible for sending medical images to HoloLens 2 glasses, and the receiver module, hosted in the HoloLens 2, which renders those medical images, allowing the practitioner to watch and manage them in a 3D environment. The system has been successfully used, between November 2021 and August 2022, in up to 9 interventions by 2 different practitioners, in a large public hospital in central Spain. The practitioners using the system confirmed it as easy to use, reliable, real-time, reachable, and cost-effective, allowing a reduction of operating times, a better control of typical errors associated to the interventional procedure, and opening the possibility to use the medical imagery produced in ubiquitous e-learning. These strengths and opportunities were only nuanced by the risk of potential medical complications emerging from system malfunction or operator errors when using the system (e.g., unexpected momentary lag). In summary, the proposed system can be taken as a realistic proof of concept of how mixed reality technologies can support practitioners when performing interventional and surgical procedures during real-time daily practice.Junta de Castilla y León - Gerencia Regional de Salud (SACyL) (grant number GRS 2275/A/2020)Instituto de Salud Carlos III (grant number DTS21/00158)Publicación en abierto financiada por el Consorcio de Bibliotecas Universitarias de Castilla y León (BUCLE), con cargo al Programa Operativo 2014ES16RFOP009 FEDER 2014-2020 DE CASTILLA Y LEÓN, Actuación:20007-CL - Apoyo Consorcio BUCL

An intelligent surveillance platform for large metropolitan areas with dense sensor deployment

Producción CientíficaThis paper presents an intelligent surveillance platform based on the usage of large numbers of inexpensive sensors designed and developed inside the European Eureka Celtic project HuSIMS. With the aim of maximizing the number of deployable units while keeping monetary and resource/bandwidth costs at a minimum, the surveillance platform is based on the usage of inexpensive visual sensors which apply efficient motion detection and tracking algorithms to transform the video signal in a set of motion parameters. In order to automate the analysis of the myriad of data streams generated by the visual sensors, the platform’s control center includes an alarm detection engine which comprises three components applying three different Artificial Intelligence strategies in parallel. These strategies are generic, domain-independent approaches which are able to operate in several domains (traffic surveillance, vandalism prevention, perimeter security, etc.). The architecture is completed with a versatile communication network which facilitates data collection from the visual sensors and alarm and video stream distribution towards the emergency teams. The resulting surveillance system is extremely suitable for its deployment in metropolitan areas, smart cities, and large facilities, mainly because cheap visual sensors and autonomous alarm detection facilitate dense sensor network deployments for wide and detailed coveraMinisterio de Industria, Turismo y Comercio and the Fondo de Desarrollo Regional (FEDER) and the Israeli Chief Scientist Research Grant 43660 inside the European Eureka Celtic project HuSIMS (TSI-020400-2010-102)

Experimental Analysis of the Input Variables' Relevance to Forecast Next Day's Aggregated Electric Demand Using Neural Networks

Thanks to the built in intelligence (deployment of new intelligent devices and sensors in places where historically they were not present), the Smart Grid and Microgrid paradigms are able to take advantage from aggregated load forecasting, which opens the door for the implementation of new algorithms to seize this information for optimization and advanced planning. Therefore, accuracy in load forecasts will potentially have a big impact on key operation factors for the future Smart Grid/Microgrid-based energy network like user satisfaction and resource saving, and new methods to achieve an efficient prediction in future energy landscapes (very different from the centralized, big area networks studied so far). This paper proposes different improved models to forecast next day's aggregated load using artificial neural networks, taking into account the variables that are most relevant. In particular, seven models based on the multilayer perceptron will be proposed, progressively adding input variables after analyzing the influence of climate factors on aggregated load. The results section presents the forecast from the proposed models, obtained from real data.Hernández, L.; Baladrón Zorita, C.; Aguiar Pérez, JM.; Calavia Domínguez, L.; Carro Martínez, B.; Sanchez-Esguevillas, A.; Garcia Fernandez, P.... (2013). Experimental Analysis of the Input Variables' Relevance to Forecast Next Day's Aggregated Electric Demand Using Neural Networks. Energies. 6(6):2927-2948. doi:10.3390/en6062927S2927294866Zhang, Q., Lai, K. K., Niu, D., Wang, Q., & Zhang, X. (2012). A Fuzzy Group Forecasting Model Based on Least Squares Support Vector Machine (LS-SVM) for Short-Term Wind Power. Energies, 5(9), 3329-3346. doi:10.3390/en5093329Hsu, C.-C., & Chen, C.-Y. (2003). Regional load forecasting in Taiwan––applications of artificial neural networks. Energy Conversion and Management, 44(12), 1941-1949. doi:10.1016/s0196-8904(02)00225-xCarpaneto, E., & Chicco, G. (2008). Probabilistic characterisation of the aggregated residential load patterns. IET Generation, Transmission & Distribution, 2(3), 373. doi:10.1049/iet-gtd:20070280Shu Fan, Methaprayoon, K., & Wei-Jen Lee. (2009). Multiregion Load Forecasting for System With Large Geographical Area. IEEE Transactions on Industry Applications, 45(4), 1452-1459. doi:10.1109/tia.2009.2023569Pudjianto, D., Ramsay, C., & Strbac, G. (2007). Virtual power plant and system integration of distributed energy resources. IET Renewable Power Generation, 1(1), 10. doi:10.1049/iet-rpg:20060023Ruiz, N., Cobelo, I., & Oyarzabal, J. (2009). A Direct Load Control Model for Virtual Power Plant Management. IEEE Transactions on Power Systems, 24(2), 959-966. doi:10.1109/tpwrs.2009.2016607Hernandez, L., Baladron, C., Aguiar, J. M., Carro, B., Sanchez-Esguevillas, A., Lloret, J., … Cook, D. (2013). A multi-agent system architecture for smart grid management and forecasting of energy demand in virtual power plants. IEEE Communications Magazine, 51(1), 106-113. doi:10.1109/mcom.2013.6400446Mousavi, S. M., & Abyaneh, H. A. (2011). Effect of Load Models on Probabilistic Characterization of Aggregated Load Patterns. IEEE Transactions on Power Systems, 26(2), 811-819. doi:10.1109/tpwrs.2010.2062542Ipakchi, A., & Albuyeh, F. (2009). Grid of the future. IEEE Power and Energy Magazine, 7(2), 52-62. doi:10.1109/mpe.2008.931384Naphade, M., Banavar, G., Harrison, C., Paraszczak, J., & Morris, R. (2011). Smarter Cities and Their Innovation Challenges. Computer, 44(6), 32-39. doi:10.1109/mc.2011.187Hernández, L., Baladrón, C., Aguiar, J. M., Calavia, L., Carro, B., Sánchez-Esguevillas, A., … Gómez, J. (2012). A Study of the Relationship between Weather Variables and Electric Power Demand inside a Smart Grid/Smart World Framework. Sensors, 12(9), 11571-11591. doi:10.3390/s120911571Hernandez, L., Baladrón, C., Aguiar, J., Carro, B., Sanchez-Esguevillas, A., & Lloret, J. (2013). Short-Term Load Forecasting for Microgrids Based on Artificial Neural Networks. Energies, 6(3), 1385-1408. doi:10.3390/en6031385Perez, E., Beltran, H., Aparicio, N., & Rodriguez, P. (2013). Predictive Power Control for PV Plants With Energy Storage. IEEE Transactions on Sustainable Energy, 4(2), 482-490. doi:10.1109/tste.2012.2210255Ogliari, E., Grimaccia, F., Leva, S., & Mussetta, M. (2013). Hybrid Predictive Models for Accurate Forecasting in PV Systems. Energies, 6(4), 1918-1929. doi:10.3390/en6041918Douglas, A. P., Breipohl, A. M., Lee, F. N., & Adapa, R. (1998). The impacts of temperature forecast uncertainty on Bayesian load forecasting. IEEE Transactions on Power Systems, 13(4), 1507-1513. doi:10.1109/59.736298Sadownik, R., & Barbosa, E. P. (1999). Short-term forecasting of industrial electricity consumption in Brazil. Journal of Forecasting, 18(3), 215-224. doi:10.1002/(sici)1099-131x(199905)18:33.0.co;2-bHuang, S. R. (1997). Short-term load forecasting using threshold autoregressive models. IEE Proceedings - Generation, Transmission and Distribution, 144(5), 477. doi:10.1049/ip-gtd:19971144Infield, D. G., & Hill, D. C. (1998). Optimal smoothing for trend removal in short term electricity demand forecasting. IEEE Transactions on Power Systems, 13(3), 1115-1120. doi:10.1109/59.709108Sargunaraj, S., Sen Gupta, D. P., & Devi, S. (1997). Short-term load forecasting for demand side management. IEE Proceedings - Generation, Transmission and Distribution, 144(1), 68. doi:10.1049/ip-gtd:19970599Hong-Tzer Yang, & Chao-Ming Huang. (1998). A new short-term load forecasting approach using self-organizing fuzzy ARMAX models. IEEE Transactions on Power Systems, 13(1), 217-225. doi:10.1109/59.651639Hong-Tzer Yang, Chao-Ming Huang, & Ching-Lien Huang. (1996). Identification of ARMAX model for short term load forecasting: an evolutionary programming approach. IEEE Transactions on Power Systems, 11(1), 403-408. doi:10.1109/59.486125Yu, Z. (1996). A temperature match based optimization method for daily load prediction considering DLC effect. IEEE Transactions on Power Systems, 11(2), 728-733. doi:10.1109/59.496146Charytoniuk, W., Chen, M. S., & Van Olinda, P. (1998). Nonparametric regression based short-term load forecasting. IEEE Transactions on Power Systems, 13(3), 725-730. doi:10.1109/59.708572Taylor, J. W., & Majithia, S. (2000). Using combined forecasts with changing weights for electricity demand profiling. Journal of the Operational Research Society, 51(1), 72-82. doi:10.1057/palgrave.jors.2600856Ramanathan, R., Engle, R., Granger, C. W. J., Vahid-Araghi, F., & Brace, C. (1997). Short-run forecasts of electricity loads and peaks. International Journal of Forecasting, 13(2), 161-174. doi:10.1016/s0169-2070(97)00015-0Elman, J. L. (1990). Finding Structure in Time. Cognitive Science, 14(2), 179-211. doi:10.1207/s15516709cog1402_1Elman, J. L. (1991). Distributed representations, simple recurrent networks, and grammatical structure. Machine Learning, 7(2-3), 195-225. doi:10.1007/bf00114844Kohonen, T. (1990). The self-organizing map. Proceedings of the IEEE, 78(9), 1464-1480. doi:10.1109/5.58325Razavi, S., & Tolson, B. A. (2011). A New Formulation for Feedforward Neural Networks. IEEE Transactions on Neural Networks, 22(10), 1588-1598. doi:10.1109/tnn.2011.216316

El Sistema de Predicción por conjuntos para el Corto Plazo de AEMET (AEMET-SREPS): migración a la mesoescala

Ponencia presentada en: XXXII Jornadas Científicas de la AME y el XIII Encuentro Hispano Luso de Meteorología celebrado en Alcobendas (Madrid), del 28 al 30 de mayo de 2012.El Sistema de Predicción por Conjuntos para el Corto Plazo desarrollado en la AEMET (AEMET-SREPS), pionero en Europa como multi-modelo en la predicción probabilística con modelos de área limitada (Limited Area Models, LAM), pasa a una fase de transición a la mesoescala gamma. Se revisa en este artículo tanto su historia, aplicaciones y logros como su configuración actual, perspectiva y líneas de investigación

Exercise Ventilatory Inefficiency in Post-COVID-19 Syndrome: Insights from a Prospective Evaluation

The present study was partially granted by Gerencia Regional de Salud de Castilla y León under grant number GRS COVID 111/A/20 and a grant from the Spanish Society of Cardiology SEC/FEC-INVCLI 2.Introduction: Coronavirus disease 2019 (COVID-19) is a systemic disease characterized by a disproportionate inflammatory response in the acute phase. This study sought to identify clinical sequelae and their potential mechanism. Methods: We conducted a prospective single-center study (NCT04689490) of previously hospitalized COVID-19 patients with and without dyspnea during mid-term follow-up. An outpatient group was also evaluated. They underwent serial testing with a cardiopulmonary exercise test (CPET), transthoracic echocardiogram, pulmonary lung test, six-minute walking test, serum biomarker analysis, and quality of life questionaries. Results: Patients with dyspnea (n = 41, 58.6%), compared with asymptomatic patients (n = 29, 41.4%), had a higher proportion of females (73.2 vs. 51.7%; p = 0.065) with comparable age and prevalence of cardiovascular risk factors. There were no significant differences in the transthoracic echocardiogram and pulmonary function test. Patients who complained of persistent dyspnea had a significant decline in predicted peak VO2 consumption (77.8 (64–92.5) vs. 99 (88–105); p 50% of COVID-19 survivors present a symptomatic functional impairment irrespective of age or prior hospitalization. Our findings suggest a potential ventilation/perfusion mismatch or hyperventilation syndrome.Fac. de MedicinaTRUEJunta de Castilla y León. Gerencia Regional de Salud de Castilla y LeónSociedad Española de Cardiologíapu

Artificial Neural Networks for Short-Term Load Forecasting in Microgrids Environment Energy

The adaptation of energy production to demand has been traditionally very important for utilities in order to optimize resource consumption. This is especially true also in microgrids where many intelligent elements have to adapt their behaviour depending on the future generation and consumption conditions. However, traditional forecasting has been performed only for extremely large areas, such as nations and regions. This work aims at presenting a solution for short-term load forecasting (STLF) in microgrids, based on a three-stage architecture which starts with pattern recognition by a self-organizing map (SOM), a clustering of the previous partition via k-means algorithm, and finally demand forecasting for each cluster with a multilayer perceptron. Model validation was performed with data from a microgrid-sized environment provided by the Spanish company Iberdrola. (C) 2014 Elsevier Ltd. All rights reserved.Hernandez, L.; Baladron, C.; Aguiar, JM.; Carro, B.; Sanchez-Esguevillas, A.; Lloret, J. (2014). Artificial Neural Networks for Short-Term Load Forecasting in Microgrids Environment Energy. Energy. 75:252-264. doi:10.1016/j.energy.2014.07.065S2522647

Artificial Neural Network for Short-Term Load Forecasting in Distribution Systems

The new paradigms and latest developments in the Electrical Grid are based on the introduction of distributed intelligence at several stages of its physical layer, giving birth to concepts such as Smart Grids, Virtual Power Plants, microgrids, Smart Buildings and Smart Environments. Distributed Generation (DG) is a philosophy in which energy is no longer produced exclusively in huge centralized plants, but also in smaller premises which take advantage of local conditions in order to minimize transmission losses and optimize production and consumption. This represents a new opportunity for renewable energy, because small elements such as solar panels and wind turbines are expected to be scattered along the grid, feeding local installations or selling energy to the grid depending on their local generation/consumption conditions. The introduction of these highly dynamic elements will lead to a substantial change in the curves of demanded energy. The aim of this paper is to apply Short-Term Load Forecasting (STLF) in microgrid environments with curves and similar behaviours, using two different data sets: the first one packing electricity consumption information during four years and six months in a microgrid along with calendar data, while the second one will be just four months of the previous parameters along with the solar radiation from the site. For the first set of data different STLF models will be discussed, studying the effect of each variable, in order to identify the best one. That model will be employed with the second set of data, in order to make a comparison with a new model that takes into account the solar radiation, since the photovoltaic installations of the microgrid will cause the power demand to fluctuate depending on the solar radiation.Our gratitude to CEDER-CIEMAT for providing the data to the presented work. In the same way, we want to convey our gratitude to the project partners MIRED-CON (IPT-2012-0611-120000), funded by the INNPACTO agreement of the Ministry of Economy and Competitiveness of the Government of Spain. Finally, a special mention to the help of the students Fatih Selim Bayraktar and Guniz Betul Yasar of Gazi University (Turkey), and Cristina Gil Valverde of UNED (Spain).Hernandez, L.; Baladron, C.; Aguiar, JM.; Calavia, L.; Carro, B.; Sanchez-Esguevillas, A.; Perez, F.... (2014). Artificial Neural Network for Short-Term Load Forecasting in Distribution Systems. Energies. 7(3):1576-1598. https://doi.org/10.3390/en7031576S1576159873Spencer, H. H., & Hazen, H. L. (1925). Artificial Representation of Power Systems. Transactions of the American Institute of Electrical Engineers, XLIV, 72-79. doi:10.1109/t-aiee.1925.5061095Hamilton, R. F. (1944). The Summation or Load Curves. Transactions of the American Institute of Electrical Engineers, 63(10), 729-735. doi:10.1109/t-aiee.1944.5058782Davies, M. (1959). The relationship between weather and electricity demand. Proceedings of the IEE Part C: Monographs, 106(9), 27. doi:10.1049/pi-c.1959.0007Matthewman, P. D., & Nicholson, H. (1968). Techniques for load prediction in the electricity-supply industry. Proceedings of the Institution of Electrical Engineers, 115(10), 1451. doi:10.1049/piee.1968.0258Hippert, H. S., Pedreira, C. E., & Souza, R. C. (2001). Neural networks for short-term load forecasting: a review and evaluation. IEEE Transactions on Power Systems, 16(1), 44-55. doi:10.1109/59.910780García-Ascanio, C., & Maté, C. (2010). Electric power demand forecasting using interval time series: A comparison between VAR and iMLP. Energy Policy, 38(2), 715-725. doi:10.1016/j.enpol.2009.10.007Marin, F. J., Garcia-Lagos, F., Joya, G., & Sandoval, F. (2002). Global model for short-term load forecasting using artificial neural networks. IEE Proceedings - Generation, Transmission and Distribution, 149(2), 121. doi:10.1049/ip-gtd:20020224Hernández, L., Baladrón, C., Aguiar, J., Carro, B., & Sánchez-Esguevillas, A. (2012). Classification and Clustering of Electricity Demand Patterns in Industrial Parks. Energies, 5(12), 5215-5228. doi:10.3390/en5125215Hernández, L., Baladrón, C., Aguiar, J. M., Calavia, L., Carro, B., Sánchez-Esguevillas, A., … Gómez, J. (2012). A Study of the Relationship between Weather Variables and Electric Power Demand inside a Smart Grid/Smart World Framework. Sensors, 12(9), 11571-11591. doi:10.3390/s120911571Hagan, M. T., & Behr, S. M. (1987). The Time Series Approach to Short Term Load Forecasting. IEEE Transactions on Power Systems, 2(3), 785-791. doi:10.1109/tpwrs.1987.4335210Hernandez, L., Baladrón, C., Aguiar, J., Carro, B., Sanchez-Esguevillas, A., & Lloret, J. (2013). Short-Term Load Forecasting for Microgrids Based on Artificial Neural Networks. Energies, 6(3), 1385-1408. doi:10.3390/en6031385Kim, H., & Thottan, M. (2011). A two-stage market model for microgrid power transactions via aggregators. Bell Labs Technical Journal, 16(3), 101-107. doi:10.1002/bltj.20524Zhou, L., Rodrigues, J., & Oliveira, L. (2012). QoE-driven power scheduling in smart grid: architecture, strategy, and methodology. IEEE Communications Magazine, 50(5), 136-141. doi:10.1109/mcom.2012.6194394Liang Zhou, & Rodrigues, J. J. P. C. (2013). Service-oriented middleware for smart grid: Principle, infrastructure, and application. IEEE Communications Magazine, 51(1), 84-89. doi:10.1109/mcom.2013.6400443Wille-Haussmann, B., Erge, T., & Wittwer, C. (2010). Decentralised optimisation of cogeneration in virtual power plants. Solar Energy, 84(4), 604-611. doi:10.1016/j.solener.2009.10.009Hernandez, L., Baladron, C., Aguiar, J. M., Carro, B., Sanchez-Esguevillas, A., Lloret, J., … Cook, D. (2013). A multi-agent system architecture for smart grid management and forecasting of energy demand in virtual power plants. IEEE Communications Magazine, 51(1), 106-113. doi:10.1109/mcom.2013.6400446Razavi, S., & Tolson, B. A. (2011). A New Formulation for Feedforward Neural Networks. IEEE Transactions on Neural Networks, 22(10), 1588-1598. doi:10.1109/tnn.2011.2163169Bishop, C. M. (1994). Neural networks and their applications. Review of Scientific Instruments, 65(6), 1803-1832. doi:10.1063/1.114483

Resultados del TAVI emergente comparado con el procedimiento electivo:: metanálisis

Introduction and objectives: Transcatheter aortic valve implantation (TAVI) has proven safe and effective in low-to-high risk patients, but emergency procedures have been excluded from the landmark trials. We aimed to assess the current outcomes and main factors conditioning the prognosis during emergency TAVI. Methods: A systematic search in PubMed and Google Scholar was conducted for all studies comparing elective vs emergency TAVI. Searched terms were “emergency” and/or “urgent”, “elective”, and “transcatheter valve replacement” and/or “heart failure” and/or “cardiogenic shock”. Emergency TAVI was considered as any unscheduled TAVI performed to treat refractory heart failure or cardiogenic shock. A random-effects model was used. Results: A total of 7 studies with 84 427 TAVI patients were included (14 241 emergency procedures; 70 186 elective TAVIs). Emergency cases presented higher risk scores (logistic EuroSCORE 65.9% ± 21% vs 29.4% ± 18%, P < .001; Society of Thoracic Surgeons Risk Score 29.4% ± 27.4% vs 13.7% ± 11.6%, P < .001). More advanced heart disease was observed with deterioration of left ventricular (LV) function (39.5% ± 17.8% vs 52.5% ± 12.8%; P < .001) and larger LV end-diastolic diameters (55 ± 9 mm vs 48 ± 7 mm; P < .001) despite similar aortic valve areas and gradients. Elective TAVIs presented a greater success rate (93.6% vs 92.5%; odds ratio [OR] = 0.84; 95%CI, 0.74-0.95; P = .005), less acute kidney injury, and a lower need for dialysis and mechanical circulatory support. Overall, non-emergency cases had lower in-hospital (3.3% vs 5.7%; P < .001), 30-day (4.4% vs 8.8%; P < .001) and 1-year mortality rates (19.7% vs 34.75%; P = .0001). The main determinants of mortality were need for new dialysis (OR = 2.26; 95%CI, 1.84-2.76; P < .001) or mechanical circulatory support (OR = 2.55; 95%CI, 1.14-5.67; P < .001). Conclusions: Emergency TAVI recipients presented worse baseline risk and more advanced cardiac disease that determined greater in-hospital, 30-day, and 1-year mortality rates. The early identification of patients at risk for requiring mechanical circulatory support or dialysis may contribute to a better indication of TAVI in emergency scenarios.Introducción y objetivos: El implante percutáneo de válvula aórtica (TAVI) ha demostrado ser seguro y eficaz en pacientes tanto de bajo como de alto riesgo, pero los procedimientos emergentes se han excluido en los principales estudios. El objetivo fue determinar los resultados actuales y los condicionantes del pronóstico durante el TAVI emergente. Métodos: Se realizó una búsqueda sistemática en PubMed y Google Scholar de cualquier estudio que comparara el TAVI electivo frente al emergente. Los términos empleados fueron «emergent» y/o «urgent», «elective», y «transcatheter valve replacement» y/o «heart failure» y/o «cardiogenic shock». Se consideró TAVI emergente todo procedimiento no programado realizado para tratar la insuficiencia cardiaca refractaria o el shock cardiogénico. Se utilizó un modelo de efectos aleatorios. Resultados: Se incluyeron 7 estudios (84.427 pacientes) tratados con TAVI (14.241 emergentes y 70.186 electivos). Los casos electivos presentaron una mayor puntuación de riesgo (EuroSCORE logístico 65,9 ± 21 frente a 29,4 ± 18%, p < 0,001; Society of Thoracic Surgeons Risk Score 29,4 ± 27,4 frente a 13,7 ± 11,6%, p < 0,001). Presentaron una enfermedad cardiaca más avanzada, con peor función ventricular izquierda (39,5 ± 17,8 frente a 52,5 ± 12,8%; p < 0,001) y mayor diámetro telediastólico del ventrículo izquierdo (55 ± 9 frente a 48 ± 7 mm; p < 0,001), pese a tener similar área valvular aórtica y gradientes. El TAVI electivo tuvo mayor tasa de éxito (93,6 frente a 92,5%; odds ratio [OR] = 0,84; IC95%, 0,74-0,95; p = 0,005), con menor tasa de fallo renal agudo y menos necesidad de diálisis y de soporte circulatorio mecánico. En conjunto, los casos no emergentes tuvieron menor mortalidad intrahospitalaria (3,3 frente a 5,7%; p < 0,001), a 30 días (4,4 frente a 8,8%; p < 0,001) y a 1 año (19,7 frente a 34,75%; p = 0,0001). Los principales determinantes de mortalidad fueron la nueva necesidad de diálisis (OR = 2.26; IC95%, 1,84-2,76; p < 0,001) o requerir soporte circulatorio mecánico (OR = 2,55; IC95%, 1,14-5,67; p < 0,001). Conclusiones: Los receptores de TAVI emergente presentaron peor riesgo basal y enfermedad cardiaca más avanzada, que determinaron una mayor mortalidad intrahospitalaria, a 30 días y a 1 año. La identificación precoz del riesgo de precisar soporte circulatorio mecánico o diálisis podría ayudar a una optimización de la indicación de TAVI emergente