RT info:eu-repo/semantics/doctoralThesis
T1 Identificación de pacientes con un mayor riesgo de peor evolución clínica tras la infección por SARS-COV-2 mediante el diseño de algoritmos basados en la variabilidad genética
A1 Jaurrieta Largo, Sofía
A2 Universidad de Valladolid. Escuela de Doctorado
K1 Medicina Interna
K1 COVID-19
K1 Inflammation
K1 Sistema renina-angiotensina
K1 aprendizaje automático
K1 32 Ciencias Médicas
AB The genetic background influences the outcomes of COVID-19. The present study aimed to evaluate the incidence of polymorphisms in genes associated with the renin-angiotensin-aldosterone system (RAAS), cytokine production within the inflammatory system, and the vitamin D metabolic pathway, in relation to the severity of SARS-CoV-2 infection. To this end, a cohort of 338 patients with confirmed COVID-19 diagnosis was analyzed using machine learning algorithms to identify the genetic variants exerting the greatest impact on the clinical prognosis of the disease.The findings revealed that polymorphisms located in the IL6, IL6R, IL1α, IL1R, IFN-γ, TNF-α, CRP, VDR, VDBP, and ACE2 genes represent the most relevant genetic factors modulating the clinical severity of COVID-19. These genetic variants were significantly associated with an increased risk of developing COVID-19 pneumonia, higher mortality, hospital readmission within one year, and mortality related to such readmission. The applied machine learning models achieved an area under the curve (AUC) of 0.86 for predicting pneumonia, mortality, and readmission-associated mortality, as well as an AUC of 0.85 for predicting hospital readmission during the first year post-infection. These results reinforce the hypothesis that an individual’s genetic profile plays a determining role in the prognosis of COVID-19 and enable the stratification of patients with greater clinical susceptibility.In conclusion, this study demonstrates that predictive models based on machine learning and informed by genetic data constitute an effective tool for identifying individuals at increased risk of adverse outcomes, with particular relevance attributed to genetic variants related to the ACE2 gene, the inflammatory system, and vitamin D metabolism.
YR 2026
FD 2026
LK https://uvadoc.uva.es/handle/10324/83850
UL https://uvadoc.uva.es/handle/10324/83850
LA spa
NO Escuela de Doctorado
DS UVaDOC
RD 27-mar-2026