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The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
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Cómo predecir el reflujo ácido anormal: desarrollos recientes

Yu Kyung Cho1, Donghoon Kang1, Jae Myung Park1

  • 1Division of Gastroenterology, Department of Internal Medicine, College of Medicine, The Catholic University of Korea, Seoul, Korea.

Expert review of gastroenterology & hepatology
|February 6, 2026
PubMed
Resumen
Este resumen es generado por máquina.

Nuevos marcadores fisiológicos y la integración de IA mejoran el diagnóstico de la enfermedad por reflujo gastroesofágico (ERGE), aumentando la precisión en casos no concluyentes. Estos avances ofrecen precisión en la identificación del reflujo ácido anormal más allá del monitoreo tradicional.

Palabras clave:
Reflujo gastroesofágicotiempo de exposición al ácidointeligencia artificialimpedancia basalonda peristáltica inducida por deglución posreflujo

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Área de la Ciencia:

  • Gastroenterología
  • Fisiología
  • Tecnología Médica

Sus antecedentes:

  • El diagnóstico de la enfermedad por reflujo gastroesofágico (ERGE) se basa tradicionalmente en la monitorización del pH y el tiempo de exposición al ácido (AET).
  • Los resultados no concluyentes de los métodos tradicionales requieren enfoques diagnósticos avanzados.
  • Los avances fisiológicos y tecnológicos recientes ofrecen nuevos parámetros para la evaluación de la ERGE.

Objetivo del estudio:

  • Revisar los avances recientes en la predicción del reflujo ácido anormal.
  • Destacar los nuevos marcadores fisiológicos y las aplicaciones de inteligencia artificial (IA) en el diagnóstico de la ERGE.
  • Discutir el potencial de estas nuevas herramientas para mejorar la precisión diagnóstica.

Principales métodos:

  • Revisión de nuevos índices fisiológicos que incluyen la impedancia basal y la onda peristáltica inducida por deglución posreflujo (PSPW).
  • Inclusión de parámetros de manometría esofágica de alta resolución (HRM) como el integral contráctil de la unión gastroesofágica (EGJ-CI).
  • Exploración de la inteligencia artificial (IA) para automatizar la detección y clasificación de eventos de reflujo.

Principales resultados:

  • Las nuevas métricas como la impedancia basal y la PSPW ofrecen información sobre la integridad de la mucosa y la depuración química.
  • La manometría de alta resolución (HRM) proporciona evidencia indirecta de la función esofágica.
  • La IA demuestra potencial en la automatización de análisis complejos, reduciendo la variabilidad y apoyando el diagnóstico de precisión.

Conclusiones:

  • Las nuevas métricas fisiológicas y la integración de IA mejoran significativamente la predicción del reflujo ácido anormal, especialmente en casos límite.
  • Estas herramientas avanzadas prometen una mayor precisión diagnóstica para la ERGE.
  • La estandarización y la validación multicéntrica son cruciales para la adopción clínica.