El papel del fenotipado como herramienta de alerta precoz en el manejo prehospitalario del paciente COVID-19 con alto riesgo de deterioro

The pandemic caused by COVID-19 has posed great challenges to face a health, labor, economic and social crisis. The assistance to patients with acute pathology in the prehospital context has evolved exponentially in recent years, making diagnosis and treatment possible at the point of care on many occasions, since one of the challenges professionals face is the ability to to detect risk of mortality in fundamentally time-dependent pathologies, where a diagnostic or therapeutic delay can negatively influence the outcome. The use of early warning scales represents a tool that allows the systematized evaluation of patients and can predict possible serious adverse events, providing considerable help, especially when the outcomes are not initially suspected or detected. However, on certain occasions a scale that covers the entire population spectrum is unfeasible. Knowledge about the full spectrum of phenotypic abnormalities associated with a given disease can help prevent complications or at least recognize them early enough that effective treatments are available and personalize care for each patient. The main objective of this thesis was to develop a system of tools for risk stratification in the prehospital care process of patients with COVID-19 disease. To this end, three multicenter, retrospective, observational cohort studies have been carried out. We have obtained a bedside scale derived through variables obtained by telephone, through which professionals can very quickly and effectively discern the real short-term risk of patients. Second, after the results we conclude that the SpO2/FiO2 ratio is a simple, non-invasive, fast and promising tool to predict that it can help to make an early estimate of the degree of hypoxemia in infected patients even in patients with a high risk of deterioration. clinical but with low initial suspicion. Lastly, patients assessed at the bedside can be classified into four different phenotypes with differentiating characteristics and prognostic implications common to the group to which they belong. Through this tool, health professionals can discriminate risk and future implications, helping in the decision-making process with the proper use of resources.La pandemia provocada por la COVID-19 ha supuesto grandes retos para hacer frente a una crisis sanitaria, laboral, económica y social. La asistencia a pacientes con patología aguda en el contexto prehospitalario ha evolucionado exponencialmente en los últimos años, haciendo posible un diagnóstico y tratamiento en el punto de atención en muchas ocasiones, ya que uno de los desafíos a los que se enfrentan los profesionales es la capacidad de detectar riesgo de mortalidad en patologías fundamentalmente tiempo dependientes, donde un retraso diagnóstico o terapéutico puede influir negativamente en el desenlace. El uso de escalas de alerta precoz representa una herramienta que permite la evaluación sistematizada de los pacientes y puede predecir posibles eventos adversos graves, proporcionando una ayuda considerable sobre todo cuando los desenlaces no son sospechados o detectados inicialmente. Sin embargo, en determinadas ocasiones una escala que cubra todo el espectro poblacional es irrealizable. El conocimiento sobre el espectro completo de anomalías fenotípicas asociadas con una determinada enfermedad puede ayudar a prevenir complicaciones o al menos reconocerlas en una etapa lo suficientemente temprana como para que haya tratamientos efectivos disponibles y personalizar la atención de cada paciente. El objetivo principal de la presente tesis fue desarrollar un sistema de herramientas para la estratificación del riesgo en el proceso de atención prehospitalaria de los pacientes con enfermedad por COVID-19. Para ello se han realizado tres estudios observacionales de cohorte retrospectiva, multicéntricos. Hemos obtenido una escala a pie de cama derivada a través de variables obtenidas por teléfono, a través de la cual los profesionales pueden discernir de forma muy rápida y efectiva el riesgo real a corto plazo de los pacientes. En segundo lugar, tras los resultados concluimos que el cociente SpO2/FiO2 es una herramienta simple, no invasiva, rápida y prometedora para predecir que puede ayudar a realizar una estimación temprana del grado de hipoxemia en pacientes infectados, incluso en pacientes con alto riesgo de deterioro clínico pero con baja sospecha inicial. Por último, los pacientes valorados a pie de cama se pueden clasificar en cuatro fenotipos diferentes con características diferenciadoras e implicaciones pronósticas comunes al grupo al que pertenecen. Mediante esta herramienta los profesionales sanitarios pueden discriminar el riesgo y las implicaciones futuras, ayudando en el proceso de toma de decisiones con el adecuado uso de recursos.Escuela de DoctoradoDoctorado en Investigación en Ciencias de la Salu

Enabling cardiovascular multimodal, high dimensional, integrative analytics

While traditionally the understanding of cardiovascular morbidity relied on the acquisition and interpretation of health data, the advances in health technologies has enabled us to collect far larger amount of health data. This thesis explores the application of advanced analytics that utilise powerful mechanisms for integrating health data across different modalities and dimensions into a single and holistic environment to better understand different diseases, with a focus on cardiovascular conditions. Different statistical methodologies are applied across a number of case studies supported by a novel methodology to integrate and simplify data collection. The work culminates in the different dataset modalities explaining different effects on morbidity: blood biomarkers, electrocardiogram recordings, RNA-Seq measurements, and different population effects piece together the understanding of a person morbidity. More specifically, explainable artificial intelligence methods were employed on structured datasets from patients with atrial fibrillation to improve the screening for the disease. Omics datasets, including RNA-sequencing and genotype datasets, were examined and new biomarkers were discovered allowing a better understanding of atrial fibrillation. Electrocardiogram signal data were used to assess the early risk prediction of heart failure, enabling clinicians to use this novel approach to estimate future incidences. Population-level data were applied to the identification of associations and temporal trajectory of diseases to better understand disease dependencies in different clinical cohorts

Life Sciences Program Tasks and Bibliography for FY 1997

This document includes information on all peer reviewed projects funded by the Office of Life and Microgravity Sciences and Applications, Life Sciences Division during fiscal year 1997. This document will be published annually and made available to scientists in the space life sciences field both as a hard copy and as an interactive internet web page