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Predicción de la mortalidad y los resultados a corto plazo de las terapias de reemplazo renal continuas en recién
Anna Deja1,2, Kamil Deja3,4, Andrea Cappoli5
1Department of Pediatrics and Nephrology, Medical University of Warsaw, Warsaw, Poland.
Resumen
Los modelos de inteligencia artificial identificaron factores clave para la mortalidad y los problemas renales en bebés y recién nacidos sometidos a terapia de reemplazo renal continua (CKRT). Una menor producción de orina y un mayor tiempo hasta el inicio de la CKRT aumentaron el riesgo de mortalidad.
Área de la Ciencia:
- Nefrología pediátrica
- Medicina de cuidados intensivos
- Inteligencia artificial en el cuidado de la salud
Sus antecedentes:
- La terapia de reemplazo renal continua (TRC) es crucial para los recién nacidos en estado crítico y los lactantes con lesión renal aguda (IRA).
- Es esencial identificar los factores que influyen en la mortalidad y los resultados renales adversos en esta población vulnerable.
Objetivo del estudio:
- Aplicar la Inteligencia Artificial (IA), específicamente los modelos XGBoost, para identificar los factores clínicos asociados con los resultados a corto plazo en los recién nacidos y los lactantes sometidos a CKRT.
- Para predecir la mortalidad y la proteinuria en el alta hospitalaria en este grupo de pacientes.
Principales métodos:
- Se utilizaron datos del registro EurAKId (NCT02960867), incluidos los recién nacidos y los lactantes tratados con CKRT.
- Se emplearon modelos AI XGBoost para analizar las características clínicas, las características antropométricas y los ajustes técnicos de la CKRT.
- Se han identificado factores predictivos clave para la mortalidad y la proteinuria en el momento del alta.
Principales resultados:
- El estudio incluyó a 95 pacientes; la mortalidad global fue del 47,3%.
- Los modelos de IA indicaron una menor producción de orina, un mayor aumento de la creatinina sérica, un inicio retrasado de la CKRT y una presión arterial más baja como factores de riesgo de mortalidad.
- El aumento de la creatinina sérica al inicio de la admisión/CKRT y la enfermedad renal primaria predijeron proteinuria en el momento de la descarga.
Conclusiones:
- Los modelos XGBoost identifican eficazmente los predictores de resultados a corto plazo en pacientes pediátricos con TRC.
- Los indicadores tempranos de la gravedad de la IAC y el retraso en el tratamiento son críticos para el riesgo de mortalidad en recién nacidos y lactantes.
- La enfermedad renal primaria y los marcadores bioquímicos son predictores significativos de la proteinuria.
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