2 research outputs found
Proyecto Apertura de una Unidad de Fractura 脫sea por Fragilidad (FLS) en el H. De la Plana.
Treball Final de Grau en Medicina. Codi: MD1158. Curs acad猫mic: 2018/2019.Introduction: ostheoporosis (OP) is a chronic disease characterized by alterations of the bone tissue.
These changes in the bone produce a decrease in the resistance and increase the risk of fragility fractures
that entail a serious social, clinical and economical problem.
Objective: project to open a fracture liaison service (FLS) at the Hospital de La Plana.
Methods: we have made an analysis and synthesis of the most up-to-date scientific evidence (using the
levels of evidence from the SIGN and critical reading CASPe) and the formulation of recommendations
based on these evidence and consensus techniques.
Elaboration process: development of the FLS protocol of the Hospital de La Plana with the
identification and evaluation of fractured patients as well as the application of a treatment, education
and favoring the link between the FLS and primary care to the follow-up of the patients.
Conclusion: improve the quality of life of the patients in the La Plana area decreasing the number of
fractures through the FLS.Introducci贸n: la osteoporosis (OP) es una enfermedad cr贸nica caracterizada por alteraciones del tejido
贸seo. Estos cambios en el hueso implican una disminuci贸n de la resistencia e incremento de fracturas
por fragilidad 贸sea que conllevan un grave problema de inter茅s cl铆nico, social y econ贸mico.
Objetivo: proyecto de apertura de una unidad de fractura 贸sea por fragilidad (FLS) en el Hospital de La
Plana.
M茅todos: se ha realizado un an谩lisis y s铆ntesis de la evidencia cient铆fica m谩s actualizada (utilizando los
niveles de evidencia del SIGN y lectura cr铆tica CASPe) y formulaci贸n de recomendaciones a partir de
estas evidencia y t茅cnicas de consenso.
Resultados: desarrollo del protocolo de la FLS del Hospital de La Plana con la identificaci贸n,
evaluaci贸n de los pacientes fracturados as铆 como la aplicaci贸n de un tratamiento, educaci贸n y favorecer
el enlace entre la FLS y atenci贸n primaria (AP) en el seguimiento de los pacientes.
Conclusi贸n: aumentar la calidad de vida de los pacientes del 脕rea de La Plana disminuyendo el n煤mero
de fracturas a trav茅s de la FLS.
Palabras clave: fracturas por fragilidad, prevenci贸n secundaria, osteoporosis, unidad de fractura por
fragilidad
Machine Learning Approaches to Predict Major Adverse Cardiovascular Events in Atrial Fibrillation
The increasing prevalence of atrial fibrillation (AF) and its association with Major Adverse Cardiovascular Events (MACE) presents challenges in early identification and treatment. Although existing risk factors, biomarkers, genetic variants, and imaging parameters predict MACE, emerging factors may be more decisive. Artificial intelligence and machine learning techniques (ML) offer a promising avenue for more effective AF evolution prediction. Five ML models were developed to obtain predictors of MACE in AF patients. Two-thirds of the data were used for training, employing diverse approaches and optimizing to minimize prediction errors, while the remaining third was reserved for testing and validation. AdaBoost emerged as the top-performing model (accuracy: 0.9999; recall: 1; F1 score: 0.9997). Noteworthy features influencing predictions included the Charlson Comorbidity Index (CCI), diabetes mellitus, cancer, the Wells scale, and CHA2DS2-VASc, with specific associations identified. Elevated MACE risk was observed, with a CCI score exceeding 2.67 卤 1.31 (p p < 0.001), and an intermediate-risk Wells scale classification. Overall, the AdaBoost ML offers an alternative predictive approach to facilitate the early identification of MACE risk in the assessment of patients with AF