Impacto clínico de las aberrancias inmunofenotípicas y perfil mutacional en síndromes mielodisplásicos

Los síndromes mielodisplásicos (SMD) constituyen un grupo heterogéneo de entidades clínicas. La citomorfología y la citogenética constituyen el estándar para el estudio de pacientes con sospecha de SMD. Sin embargo, todavía existen dificultades a la hora de establecer el diagnóstico de SMD, sobre todo en muestras con una sola citopenia, y sin exceso de blastos. Las anomalías citogenéticas representan un factor pronóstico fundamental y apoyo a la hora del diagnóstico, aunque sólo existen alteraciones en un 30-50% de los pacientes. Las nuevas herramientas diagnósticas de las que disponemos en la actualidad (en citometría de flujo y en biología molecular) pueden contribuir no sólo al diagnóstico, sino también al pronóstico en pacientes con SMD. OBJETIVO Desarrollar una metodología por citometría de flujo que nos permita realizar el diagnóstico diferencial entre pacientes con SMD y con citopenias de otro origen. Evaluar la aplicabilidad de la técnica de secuenciación masiva de nuevo generación y de alta sensibilidad (NGS) para el diagnóstico molecular y pronóstico de pacientes con SMD..

Talleres de propuestas y sugerencias de alumnos de Grado para la mejora del Programa Docentia

El Proyecto tiene como objetivo la implementacion de una Cultura de Calidad en los alumnos de grados de diferentes Grados de Ciencias de la Salud. Y el estudio de posibles vias y mecanismos para aumentar la implicacion de los alumnos de la UCM en el Programa Docentia

Machine Learning Improves Risk Stratification in Myelodysplastic Neoplasms : An Analysis of the Spanish Group of Myelodysplastic Syndromes

Myelodysplastic neoplasms (MDS) are a heterogeneous group of hematological stem cell disorders characterized by dysplasia, cytopenias, and increased risk of acute leukemia. As prognosis differs widely between patients, and treatment options vary from observation to allogeneic stem cell transplantation, accurate and precise disease risk prognostication is critical for decision making. With this aim, we retrieved registry data from MDS patients from 90 Spanish institutions. A total of 7202 patients were included, which were divided into a training (80%) and a test (20%) set. A machine learning technique (random survival forests) was used to model overall survival (OS) and leukemia-free survival (LFS). The optimal model was based on 8 variables (age, gender, hemoglobin, leukocyte count, platelet count, neutrophil percentage, bone marrow blast, and cytogenetic risk group). This model achieved high accuracy in predicting OS (c-indexes; 0.759 and 0.776) and LFS (c-indexes; 0.812 and 0.845). Importantly, the model was superior to the revised International Prognostic Scoring System (IPSS-R) and the age-adjusted IPSS-R. This difference persisted in different age ranges and in all evaluated disease subgroups. Finally, we validated our results in an external cohort, confirming the superiority of the Artificial Intelligence Prognostic Scoring System for MDS (AIPSS-MDS) over the IPSS-R, and achieving a similar performance as the molecular IPSS. In conclusion, the AIPSS-MDS score is a new prognostic model based exclusively on traditional clinical, hematological, and cytogenetic variables. AIPSS-MDS has a high prognostic accuracy in predicting survival in MDS patients, outperforming other well-established risk-scoring systems

Machine Learning Improves Risk Stratification in Myelodysplastic Neoplasms: An Analysis of the Spanish Group of Myelodysplastic Syndromes

Myelodysplastic neoplasms (MDS) are a heterogeneous group of hematological stem cell disorders characterized by dysplasia, cytopenias, and increased risk of acute leukemia. As prognosis differs widely between patients, and treatment options vary from observation to allogeneic stem cell transplantation, accurate and precise disease risk prognostication is critical for decision making. With this aim, we retrieved registry data from MDS patients from 90 Spanish institutions. A total of 7202 patients were included, which were divided into a training (80%) and a test (20%) set. A machine learning technique (random survival forests) was used to model overall survival (OS) and leukemia-free survival (LFS). The optimal model was based on 8 variables (age, gender, hemoglobin, leukocyte count, platelet count, neutrophil percentage, bone marrow blast, and cytogenetic risk group). This model achieved high accuracy in predicting OS (c-indexes; 0.759 and 0.776) and LFS (c-indexes; 0.812 and 0.845). Importantly, the model was superior to the revised International Prognostic Scoring System (IPSS-R) and the age-adjusted IPSS-R. This difference persisted in different age ranges and in all evaluated disease subgroups. Finally, we validated our results in an external cohort, confirming the superiority of the Artificial Intelligence Prognostic Scoring System for MDS (AIPSS-MDS) over the IPSS-R, and achieving a similar performance as the molecular IPSS. In conclusion, the AIPSS-MDS score is a new prognostic model based exclusively on traditional clinical, hematological, and cytogenetic variables. AIPSS-MDS has a high prognostic accuracy in predicting survival in MDS patients, outperforming other well-established risk-scoring systems