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Considering the difficulty in selecting correct insulin doses and the problem of hyper- and hypoglycemia episodes in type 1 diabetes, dosage-aid systems are very useful for these patients. A model-based approach to this problem must unavoidably consider uncertainty sources such as large intra-patient variability and food intake. In the present study, postprandial glucose is predicted considering this uncertain information using modal interval analysis. This approach calculates a safer prediction of possible hyper- and hypoglycemia episodes induced by insulin therapy for an individual patient's parameters and integrates this information into a dosage-aid system. Predictions of a patient's postprandial glucose at 5-h intervals are used to predict the risk for a given therapy. Then the insulin dose and injection-to-meal time with the lowest risk are calculated. The method has been validated for three different scenarios corresponding to preprandial glucose values of 100, 180 and 250 mg/dl.

The behavior of three insulin action and glucose kinetics models was assessed for an insulin therapy regime in the presence of patient variability. For this purpose, postprandial glucose in patients with type 1 diabetes was predicted by considering intra- and inter-patient variability using modal interval analysis. Equations to achieve optimal prediction are presented for models 1, 2 and 3, which are of increasing complexity. The model parameters were adjusted to reflect the “same” patient in the presence of variability. The glucose response envelope for model 1, the simplest insulin–glucose model assessed, included the responses of the other two models when a good fit of the model parameters was achieved. Thus, under variability, simple glucose–insulin models may be sufficient to describe patient dynamics in most situations.

Considering the difficulty in selecting correct insulin doses and the problem of hyper- and hypoglycemia episodes in type 1 diabetes, dosage-aid systems are very useful for these patients. A model-based approach to this problem must unavoidably consider uncertainty sources such as large intra-patient variability and food intake. In the present study, postprandial glucose is predicted considering this uncertain information using modal interval analysis. This approach calculates a safer prediction of possible hyper- and hypoglycemia episodes induced by insulin therapy for an individual patient's parameters and integrates this information into a dosage-aid system. Predictions of a patient's postprandial glucose at 5-h intervals are used to predict the risk for a given therapy. Then the insulin dose and injection-to-meal time with the lowest risk are calculated. The method has been validated for three different scenarios corresponding to preprandial glucose values of 100, 180 and 250 mg/dl.

The behavior of three insulin action and glucose kinetics models was assessed for an insulin therapy regime in the presence of patient variability. For this purpose, postprandial glucose in patients with type 1 diabetes was predicted by considering intra- and inter-patient variability using modal interval analysis. Equations to achieve optimal prediction are presented for models 1, 2 and 3, which are of increasing complexity. The model parameters were adjusted to reflect the “same” patient in the presence of variability. The glucose response envelope for model 1, the simplest insulin–glucose model assessed, included the responses of the other two models when a good fit of the model parameters was achieved. Thus, under variability, simple glucose–insulin models may be sufficient to describe patient dynamics in most situations.

Introducción: Existen dispositivos de captura de datos que se acoplan al sensor del FreeStyle Libre y convierten su comunicación de Near-field communication (NFC) a tecnología bluetooth para lograr una monitorización continua de glucosa en tiempo real. No se ha establecido la precisión de las mediciones en hipoglucemia mostradas por aplicaciones para smartphones que utiliza este dispositivo. Métodos: Estudio de pruebas diagnósticas. Se evaluó la exactitud numérica mediante la diferencia absoluta en los valores con respecto a la glucometría capilar (norma ISO 15197:2015) y la exactitud clínica, mediante las gradillas de error de Clarke y Parkes (Consensus), para mediciones de glucosa inferiores a 70 mg/dl realizadas con el sistema FreeStyle Libre y con la aplicación xDrip+ de estimación digital, en personas con diabetes con insulinoterapia. Resultados: Se incluyó a 27 pacientes (TIR 73,4%, TBR70 5,6%), quienes aportaron 83 eventos de hipoglucemia. La exactitud numérica fue adecuada en proporciones similares con el sistema FreeStyle Libre en comparación con la aplicación xDrip+ (81,92% vs. 68,67%, p = 0,0630). La evaluación de la precisión clínica mostró que el 92,8% de las mediciones para xDrip+ y el 98,8% para FreeStyle libre cumplieron el criterio según la gradilla de Parkes (Consensus) (p = 0,0535), y el 79,5 y el 91,6% de las mediciones cumplieron el criterio según la gradilla de Clarke (p = 0,0273) siendo superior con Libre. Conclusiones: El uso del transmisor NFC-Bluetooth (Miao-Miao) asociado a la aplicación xDrip+ no mejora la precisión numérica ni clínica para la detección de los eventos de hipoglucemia en los personas con diabetes.