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Clustering probabilístico utilizando el modelo multivariante de mezcla de crecimiento en entornos clínicos: un

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  • 1Division of Rheumatology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.

Statistics in medicine
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Resumen
Este resumen es generado por máquina.

Este estudio identifica dos subgrupos de pacientes con esclerodermia (esclerosis sistémica; SSc): un grupo estable y un grupo progresor con disminución de la función pulmonar. El algoritmo desarrollado predice la progresión de la SSc para una mejor toma de decisiones clínicas.

Palabras clave:
modelos jerárquicos bayesianosmodelado multivariante de mezcla de crecimientoesclerodermiaalgoritmo de actualización secuencialmembresía de clúster basada en tendencias

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Área de la Ciencia:

  • Inmunología; Reumatología; Neumología

Sus antecedentes:

  • La esclerodermia (esclerosis sistémica; SSc) es una enfermedad autoinmune heterogénea con progresión variable en los sistemas orgánicos.
  • La estratificación precisa de los pacientes es crucial para guiar la atención clínica y el manejo de la SSc.
  • La comprensión de la trayectoria de la enfermedad ayuda a predecir los resultados y adaptar los tratamientos.

Objetivo del estudio:

  • Clasificar a pacientes con SSc en subpoblaciones clínicamente significativas.
  • Desarrollar un marco de clasificación en tiempo real basado en características de referencia y patrones de progresión de la enfermedad.
  • Guiar la atención clínica identificando a los pacientes en riesgo de progresión rápida de la enfermedad.

Principales métodos:

  • Se empleó un modelo bayesiano multivariante de mezcla de crecimiento para analizar las trayectorias de la función pulmonar.
  • Se modelaron conjuntamente la capacidad vital forzada (CVF) y la capacidad de difusión de monóxido de carbono (DLCO) en 289 pacientes con SSc.
  • Se desarrolló un marco para actualizar secuencialmente las probabilidades de los subgrupos de pacientes utilizando datos longitudinales.

Principales resultados:

  • Se identificaron dos subgrupos distintos de pacientes: un grupo "estable" (n=150) con cambios mínimos en la función pulmonar durante 10 años.
  • Un grupo "progresor" (n=139) exhibió una disminución significativa en la CVF y la DLCO poco después del inicio de la enfermedad.
  • El algoritmo calcula la probabilidad de pertenecer al grupo progresor utilizando datos de referencia y mediciones longitudinales de CVF/DLCO.

Conclusiones:

  • El método desarrollado permite el cálculo de la probabilidad de referencia de progresión rápida, actualizada secuencialmente con los datos acumulados del paciente.
  • Este enfoque facilita la identificación temprana de pacientes que probablemente experimentarán un rápido declive de la enfermedad.
  • La integración y clasificación secuencial de datos tienen el potencial de mejorar la toma de decisiones clínicas y los resultados de los pacientes en la SSc.