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Modelado predictivo del riesgo de trastorno del sueño preoperatorio en adultos mayores mediante el uso de datos de

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Un nuevo modelo de riesgo que utiliza datos de anillos inteligentes predice con precisión los trastornos del sueño preoperatorio en pacientes quirúrgicos mayores. Esto ayuda a personalizar la atención y mejorar los resultados quirúrgicos para este grupo vulnerable.

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regresión logística de regresión.adultos mayores mayores adultos mayores.trastorno del sueño preoperatorio preoperatorio.modelo de predicción de riesgos modelo de predicción de riesgosDispositivos portátiles inteligentes dispositivos portátiles inteligentes.

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Área de la Ciencia:

  • Medicina Geriátrica La medicina geriátrica es una especialidad de la medicina geriátrica.
  • Medicina del sueño Medicina del sueño.
  • Evaluación del riesgo quirúrgico.

Sus antecedentes:

  • Los trastornos del sueño son frecuentes en pacientes quirúrgicos mayores, lo que aumenta las complicaciones y los costos de atención médica.
  • Las herramientas de riesgo quirúrgico existentes pasan por alto factores específicos que afectan el sueño en adultos mayores.
  • Los dispositivos portátiles ofrecen datos objetivos del sueño, pero los modelos a menudo carecen de contexto quirúrgico.

Objetivo del estudio:

  • Desarrollar y validar un modelo de predicción de riesgos para los trastornos del sueño preoperatorio en pacientes quirúrgicos mayores.
  • Integrar los datos de los dispositivos inteligentes portátiles con las evaluaciones clínicas para una evaluación integral del riesgo.
  • Permitir la identificación temprana de los factores que influyen en la planificación personalizada de la atención perioperatoria.

Principales métodos:

  • Estudio prospectivo de 242 pacientes quirúrgicos mayores que usan anillos inteligentes antes de la cirugía.
  • Recopilación de datos sociodemográficos, cognitivos y psicológicos junto con métricas objetivas del sueño.
  • Desarrolló y validó un modelo de riesgo utilizando regresión logística y muestreo de arranque, evaluando el rendimiento a través de curvas ROC, calibración y análisis de decisiones.

Principales resultados:

  • Se identificaron predictores clave: puntaje de la Escala de Ansiedad y Depresión Hospitalaria, número de despertares y duración del sueño REM y ligero.
  • El modelo demostró una fuerte discriminación (AUC = 0,92) y una buena calibración.
  • El análisis de la curva de decisión confirmó una alta utilidad clínica a través de varios umbrales de riesgo.

Conclusiones:

  • Un modelo de riesgo basado en anillos inteligentes identifica eficazmente a pacientes quirúrgicos mayores con trastornos del sueño preoperatorio.
  • Esto facilita las intervenciones oportunas e individualizadas para mejorar la atención perioperatoria.
  • El modelo ofrece una base científica para mejorar los resultados quirúrgicos y reducir la carga de atención médica en adultos mayores.