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Un modelo de aprendizaje automático predice los pacientes que necesitan opioides después de la cirugía, lo que ayuda al manejo personalizado del dolor. Esta estrategia de ahorro de opioides reduce los riesgos asociados con el uso de opioides postoperatorios.

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Área de la Ciencia:

  • Anestesiología
  • Manejo del Dolor
  • Aprendizaje Automático en Salud

Sus antecedentes:

  • El uso de opioides postoperatorios presenta riesgos de dependencia y desvío.
  • El desarrollo de estrategias efectivas de ahorro de opioides es crucial para la seguridad del paciente.
  • La identificación de pacientes con alto riesgo de uso de opioides es esencial para un manejo personalizado del dolor.

Objetivo del estudio:

  • Desarrollar y evaluar un régimen de ahorro de opioides para cirugía ambulatoria.
  • Crear un modelo de aprendizaje automático (ML) para predecir el uso de opioides postoperatorios.
  • Identificar los factores clave asociados con el consumo de opioides postoperatorios.

Principales métodos:

  • Se implementó el programa Toward Opioid-Free Ambulatory Surgery (TOFAS), alternando ibuprofeno y paracetamol con dosis limitadas de rescate de oxicodona.
  • Se desarrolló un modelo de ML para predecir el uso de opioides postoperatorios en pacientes adultos sometidos a cirugía ambulatoria.
  • Se validó el modelo de ML utilizando el área bajo la curva característica operativa del receptor (AUC) con una división de entrenamiento-prueba de 80/20 y 10 semillas aleatorias.

Principales resultados:

  • 42% de los 223 pacientes inscritos recibieron su receta de opioides, con una mediana de 4 dosis utilizadas.
  • El modelo de ML logró una AUC de prueba media de 0.674, con una sensibilidad de 0.70 y una especificidad de 0.68.
  • Los predictores clave del uso de opioides incluyeron cáncer activo, edad, tipo de anestesia, raza/etnia, antecedentes de EPOC, complicaciones intraoperatorias, uso preoperatorio de paracetamol e intensidad del dolor.

Conclusiones:

  • El modelo de ML desarrollado identifica de manera confiable a los pacientes con alto riesgo de uso de opioides postoperatorios.
  • Esta capacidad predictiva apoya estrategias personalizadas de manejo del dolor que ahorran opioides en entornos ambulatorios.
  • El modelo facilita la planificación personalizada del manejo del dolor, reduciendo potencialmente la dependencia y el desvío de opioides.