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Type 1 diabetes mellitus is a disease that affects millions of people around the world. Recent progress in embedded devices has allowed the development of artificial pancreas that can pump insulin subcutaneously to automatically regulate blood glucose levels in diabetic patients. In this work, a Lyapunov-based intelligent controller using artificial neural networks is proposed for application in automated insulin delivery systems. The adoption of an adaptive radial basis function network within the control scheme allows regulation of blood glucose levels without the need for a dynamic model of the system. The proposed model-free approach does not require the patient to inform when they are going to have a meal and is able to deal with inter- and intrapatient variability. To ensure safe operating conditions, the stability of the control law is rigorously addressed through a Lyapunov-like analysis. In silico analysis using virtual patients are provided to demonstrate the effectiveness of the proposed control scheme, showing its ability to maintain normoglycemia in patients with type 1 diabetes mellitus. Three different scenarios were considered: one long- and two short-term simulation studies. In the short-term analyses, 20 virtual patients were simulated for a period of 7 days, with and without prior basal therapy, while in the long-term simulation, 1 virtual patient was assessed over 63 days. The results show that the proposed approach was able to guarantee a time in the range above 95% for the target glycemia in all scenarios studied, which is in fact well above the desirable 70%. Even in the long-term analysis, the intelligent control scheme was able to keep blood glucose metrics within clinical care standards: mean blood glucose of 119.59 mg/dL with standard deviation of 32.02 mg/dL and coefficient of variation of 26.78%, all below the respective reference values.

Introdução: A escala Confusion Assessment Method for Intensive Care Unit (CAM-ICU) apresenta um grande potencial de aplicação no Serviço de Urgência (SU). No entanto, existem ainda incertezas relativas à sua acurácia e fiabilidade, requerendo estudos com maiores coortes. Numa das maiores e mais heterogéneas coortes conhecidas até o momento, aqui o objetivo foi validar e explorar a aplicabilidade da CAM-ICU (versão Português Europeu) como ferramenta de rastreio e diagnóstico de delirium no SU de um hospital terciário. Métodos: Os dados foram colhidos aleatoriamente e prospectivamente numa amostra de conveniência em doentes adultos admitidos no SU do Hospital de Braga. O delirium foi primeiramente avaliado usando a CAM-ICU. Para a validação, o padrão de referência foi a avaliação psiquiátrica, efetuada em intervalo de 3 horas da CAM-ICU, por psiquiatra oculto ao seu resultado, usando os critérios de diagnósticos da DSM-5, sem interferência nos procedimentos do SU relativa à operacionalização das avaliações psiquiátricas. Resultados: Após aplicar os critérios de exclusão/inclusão, dos 592 doentes avaliados, 19,8% foram diagnosticados com delirium usando a CAM-ICU. O padrão de referência foi aplicado a 81 doentes. A CAM-ICU apresentou uma sensibilidade de 95,2% (IC 95% = 74,1 a 99,8) e especificidade de 88,3% (IC 95% = 76,8 a 94,8). A CAM-ICU mostrou uma acurácia de 90,1% em relação ao padrão de referência. Conclusão: A CAM-ICU (versão Portuguesa) é um instrumento prático, válido e fiável para a avaliação de delirium em ambiente real de SU, mostrando uma sensibilidade, especificidade e acurácia para o diagnóstico de delirium., Medicina Interna, Vol. 29 N.º 3 (2022): Julho/Setembro

Introduction: The Confusion Assessment Method for Intensive Care Unit (CAM-ICU) is of great potential value in the emergency department (ED). However, there is still uncertainty about its diagnostic accuracy and reliability, with studies warranting larger cohorts. In one of the largest and most heterogeneous cohorts considered to date, the aim was to validate and explore the applicability of the CAM-ICU (European Portuguese) as a delirium screening and diagnostic tool in the ED setting of a main hospital referral centre. Methods: Data were randomly and prospectively recorded in a convenience sample of patients with 18 years or older admitted to the ED of Hospital de Braga (Braga, Portugal). Delirium status was primarily assessed using the CAM-ICU. For the validation aspect, the reference standard was a psychiatric evaluation, conducted within 3 hours of the CAM-ICU by a psychiatrist blind to the finding, using the DSM-5 diagnostic criteria, without external interference on the regular proceedings in the ED on the timing, number and operationalization of psychiatric evaluations in the setting. Results: After exclusion/inclusion criteria, of the 592 patients evaluated, 19.8% were diagnosed with delirium using the CAM-ICU. The reference standard was applied to 81 patients. The CAM-ICU presented a sensitivity of 95.2% (95% CI= 74.1 to 99.8) and specificity of 88.3% (95% CI= 76.8 to 94.8). CAM-ICU showed an accuracy of 90.1% with the reference standard. Conclusion: The CAM-ICU (Portuguese version) is practical, valid and reliable instrument for the assessment of delirium in the real setting of an ED showing sensibility, specificity and accuracy for delirium diagnosis.