Development and Validation of a Personalized, Sex-Specific Prediction Algorithm of Severe Atheromatosis in Middle-Aged Asymptomatic Individuals: The ILERVAS Study

Background: Although European guidelines recommend vascular ultrasound for the assessment of cardiovascular risk in low-to-moderate risk individuals, no algorithm properly identifies patients who could benefit from it. The aim of this study is to develop a sex-specific algorithm to identify those patients, especially women who are usually underdiagnosed. Methods: Clinical, anthropometrical, and biochemical data were combined with a 12-territory vascular ultrasound to predict severe atheromatosis (SA: ≥ 3 territories with plaque). A Personalized Algorithm for Severe Atheromatosis Prediction (PASAP-ILERVAS) was obtained by machine learning. Models were trained in the ILERVAS cohort (n = 8,330; 51% women) and validated in the control subpopulation of the NEFRONA cohort (n = 559; 47% women). Performance was compared to the Systematic COronary Risk Evaluation (SCORE) model. Results: The PASAP-ILERVAS is a sex-specific, easy-to-interpret predictive model that stratifies individuals according to their risk of SA in low, intermediate, or high risk. New clinical predictors beyond traditional factors were uncovered. In low- and high-risk (L&H-risk) men, the net reclassification index (NRI) was 0.044 (95% CI: 0.020-0.068), and the integrated discrimination index (IDI) was 0.038 (95% CI: 0.029-0.048) compared to the SCORE. In L&H-risk women, PASAP-ILERVAS showed a significant increase in the area under the curve (AUC, 0.074 (95% CI: 0.062-0.087), p-value: < 0.001), an NRI of 0.193 (95% CI: 0.162-0.224), and an IDI of 0.119 (95% CI: 0.109-0.129). Conclusion: The PASAP-ILERVAS improves SA prediction, especially in women. Thus, it could reduce the number of unnecessary complementary explorations selecting patients for a further imaging study within the intermediate risk group, increasing cost-effectiveness and optimizing health resources.This work was supported by grants from the Diputació de Lleida, Instituto de Salud Carlos III (RETIC RD16/0009/0011) and Ministerio de Ciencia, Innovación y Universidades (IJC2018-037792-I

Development and Validation of a Personalized, Sex-Specific Prediction Algorithm of Severe Atheromatosis in Middle-Aged Asymptomatic Individuals: The ILERVAS Study

BackgroundAlthough European guidelines recommend vascular ultrasound for the assessment of cardiovascular risk in low-to-moderate risk individuals, no algorithm properly identifies patients who could benefit from it. The aim of this study is to develop a sex-specific algorithm to identify those patients, especially women who are usually underdiagnosed.MethodsClinical, anthropometrical, and biochemical data were combined with a 12-territory vascular ultrasound to predict severe atheromatosis (SA: ≥ 3 territories with plaque). A Personalized Algorithm for Severe Atheromatosis Prediction (PASAP-ILERVAS) was obtained by machine learning. Models were trained in the ILERVAS cohort (n = 8,330; 51% women) and validated in the control subpopulation of the NEFRONA cohort (n = 559; 47% women). Performance was compared to the Systematic COronary Risk Evaluation (SCORE) model.ResultsThe PASAP-ILERVAS is a sex-specific, easy-to-interpret predictive model that stratifies individuals according to their risk of SA in low, intermediate, or high risk. New clinical predictors beyond traditional factors were uncovered. In low- and high-risk (L&amp;H-risk) men, the net reclassification index (NRI) was 0.044 (95% CI: 0.020–0.068), and the integrated discrimination index (IDI) was 0.038 (95% CI: 0.029–0.048) compared to the SCORE. In L&amp;H-risk women, PASAP-ILERVAS showed a significant increase in the area under the curve (AUC, 0.074 (95% CI: 0.062–0.087), p-value: &lt; 0.001), an NRI of 0.193 (95% CI: 0.162–0.224), and an IDI of 0.119 (95% CI: 0.109–0.129).ConclusionThe PASAP-ILERVAS improves SA prediction, especially in women. Thus, it could reduce the number of unnecessary complementary explorations selecting patients for a further imaging study within the intermediate risk group, increasing cost-effectiveness and optimizing health resources.Clinical Trial Registration[www.ClinicalTrials.gov], identifier [NCT03228459]

PROYECTO DE INNOVACIÓN DOCENTE AVANZADO (22-140) Dimensión 2: Tutoría y Orientación Académica, personal y profesional

Este proyecto de innovación docente tiene como objetivo principal conocer y analizar las trayectorias académicas y profesionales de los egresados del Grado en Relaciones Laborales y Recursos Humanos (RR.LL. y RR.HH.) de la Universidad de Granada, en los años 2018, 2019 y 2020. La investigación parte de la realización de un estudio propio con el objetivo de servir de instrumento de orientación profesional y académica de la Titulación; además de estar contemplado en el propio Sistema de Garantía de la Calidad de las Titulaciones. La realidad profesional constituye un factor determinante de las competencias que han de adquirir el estudiantado que, además, debe de estar en constante actualización en función de los cambios y avances que suceden en el ámbito laboral. Para ello resulta imprescindible conocer sus ámbitos académicos y profesionales, sus puntos fuertes y débiles, con el objetivo primordial de dotar al profesorado y al estudiantado de la información pertinente para la mejora de la orientación académica y profesional y poder así plantear acciones de mejora de la titulación que se ajusten al contexto real