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Insulin-replacement therapy usually includes both long-acting insulin (basal) and short-acting insulin (to cater to postprandial needs). In a diverse group of type 1 diabetes patients, the average daily insulin dose is typically 0.5-0.7 units/kg body weight. However, obese patients and pubertal adolescents may need more due to insulin resistance.
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Aprendizaje automático interpretable para predecir la efectividad de la metilprednisolona a dosis bajas en COVID

Jisheng Zhang1, Yang Chen2, Aijun Zhang2

  • 1The Third Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, Zhejiang, China.

iScience
|January 26, 2026
PubMed
Resumen

Este estudio desarrolló una herramienta predictiva para el tratamiento de COVID prolongada. Un modelo de regresión logística y un nomograma pueden ayudar a personalizar la terapia con metilprednisolona a dosis bajas para obtener mejores resultados en los pacientes.

Palabras clave:
Aplicaciones de inteligencia artificialTerapias

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Área de la Ciencia:

  • Investigación médica
  • Farmacología
  • Ciencia de datos en medicina

Sus antecedentes:

  • La COVID prolongada es una afección multitemática compleja con opciones de tratamiento limitadas.
  • Las respuestas individuales a la metilprednisolona a dosis bajas varían, lo que requiere herramientas predictivas.

Objetivo del estudio:

  • Desarrollar y validar un modelo predictivo para pacientes con COVID prolongada que reciben metilprednisolona a dosis bajas.
  • Identificar los factores clave que influyen en la eficacia del tratamiento.

Principales métodos:

  • Análisis retrospectivo de 330 pacientes con COVID prolongada tratados con metilprednisolona a dosis bajas.
  • Desarrollo de modelos de aprendizaje automático utilizando regresión LASSO.
  • Validación utilizando conjuntos de datos de entrenamiento, prueba y externos.

Principales resultados:

  • Un modelo de regresión logística (LR) demostró un rendimiento predictivo estable en todos los conjuntos de datos (AUCs que van desde 0.7198 hasta 0.8715).
  • Las explicaciones aditivas de SHapley (SHAP) identificaron siete variables predictivas clave.
  • Se construyó un nomograma basado en estas variables para la aplicación clínica.

Conclusiones:

  • El modelo LR y el nomograma desarrollados son herramientas eficaces para predecir la respuesta al tratamiento de COVID prolongada a la metilprednisolona a dosis bajas.
  • Estas herramientas apoyan las decisiones de tratamiento individualizadas y la gestión clínica.
  • La investigación adicional puede refinar la precisión predictiva para la gestión de enfermedades crónicas.