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Modelo funcional de ecuaciones de estimación generalizadas para detectar la progresión del campo visual glaucomatoso

Sanghun Jeong1, Hwayeong Kim2, Sangwoo Moon3

  • 1Department of Statistics, Pusan National University, Busan 46241, Republic of Korea.

International journal of ophthalmology
|January 23, 2026
PubMed
Resumen
Este resumen es generado por máquina.

Un nuevo modelo funcional de ecuación de estimación generalizada (GEE) detecta eficazmente la progresión del campo visual glaucomatoso en pacientes con glaucoma primario de ángulo abierto (GPAA). Este método identifica más casos más rápido que los algoritmos existentes.

Palabras clave:
modelo funcional de ecuación de estimación generalizadaprogresión perimétricaglaucoma primario de ángulo abierto

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Área de la Ciencia:

  • Oftalmología
  • Bioestadística
  • Tecnología Médica

Sus antecedentes:

  • La progresión del campo visual glaucomatoso es un indicador clave del avance de la enfermedad.
  • La detección temprana y precisa de la progresión es crucial para una intervención oportuna.
  • Los métodos actuales para detectar la progresión del campo visual tienen limitaciones en sensibilidad y velocidad.

Objetivo del estudio:

  • Desarrollar y validar un modelo funcional de ecuación de estimación generalizada (GEE) para detectar la progresión del campo visual glaucomatoso.
  • Comparar el rendimiento del modelo funcional GEE propuesto con algoritmos establecidos.

Principales métodos:

  • Se analizó una cohorte de 716 ojos de 716 pacientes con glaucoma primario de ángulo abierto (GPAA) con datos suficientes de pruebas de campo visual y seguimiento.
  • Se entrenó un modelo GEE funcional en 501 ojos y se probó en 215 ojos.
  • Se evaluó el rendimiento comparando el modelo GEE funcional con las tasas de desviación media (MD) e índice de campo visual (VFI), las puntuaciones de Advanced Glaucoma Intervention Study (AGIS) y Collaborative Initial Glaucoma Treatment Study (CIGTS), y la regresión lineal punto por punto (PLR).

Principales resultados:

  • El modelo GEE funcional identificó la mayor proporción de ojos con progresión perimétrica (54,4%), superando significativamente la tasa de VFI (34,4%), PLR (23,3%), tasa de MD (21,4%), CIGTS (7,9%) y AGIS (5,1%).
  • La progresión se detectó significativamente más rápido utilizando el modelo GEE funcional en comparación con otros métodos (P≤0,019 ajustada).
  • Se observó un acuerdo moderado entre el modelo GEE funcional y la tasa de VFI (κ=0,47).

Conclusiones:

  • El modelo GEE funcional demuestra un rendimiento superior en la detección de la progresión perimétrica en pacientes con GPAA.
  • Este enfoque novedoso ofrece un menor tiempo para la detección de la progresión, lo que podría mejorar el manejo del paciente.
  • El modelo GEE funcional representa un avance prometedor en el análisis de los cambios del campo visual glaucomatoso.