Determinación del estado metabólico de pacientes con diabetes gestacional mediante autómatas finitos

Los nuevos criterios de diagnóstico de la diabetes gestacional recomendados por la IADPSGC disminuyen los efectos adversos de la hiperglucemia tanto en la madre como en el recién nacido, pero su aplicación supondría un aumento de la prevalencia llegando a triplicar el número de casos actual. Para que los Servicios de Endocrinología y Nutrición puedan hacer frente a la carga que supondría este aumento de prevalencia es necesario emplear nuevos procesos asistenciales que incluyan el uso de las TICs. Este trabajo presenta una herramienta de análisis automático de datos de monitorización que determina el estado metabólico de las pacientes con diabetes gestacional a partir de sus datos de glucemia, dieta y cetonuria. Su diseño se basa en dos autómatas finitos, uno para el análisis de la glucemia y de la dieta y el otro para el análisis de la cetonuria. La salida de ambos autómatas se combina para determinar el estado metabólico de la paciente a lo largo del tiempo. La herramienta se ha evaluado con datos retrospectivos de 25 pacientes pertenecientes al Hospital Parc Taulí de Sabadell comparando los 1288 estados metabólicos resultantes con los 47 ajustes de terapia realizados por el equipo médico. Se observó que el 91,49% de los cambios de tratamiento coincidieron con estados metabólicos deficientes determinados por la herramienta de análisis. La herramienta ayuda a diferenciar pacientes complejas que requieren una evaluación exhaustiva y un ajuste de terapia de las que tienen buen control metabólico y no necesitan ser evaluadas por el personal médico

Clasificación de medidas de glucemia en función de ingestas en diabetes gestacional

Este trabajo presenta un clasificador de medidas de glucemia en función de las ingestas asociadas para pacientes con diabetes gestacional. Se presentan los resultados obtenidos al comparar la relevancia de diferentes atributos así como del uso de dos de los algoritmos más populares en el mundo del aprendizaje automático: las redes neuronales y los árboles de decisión. El estudio se ha realizado con los datos de 53 pacientes pertenecientes al Hospital de Sabadell y al Hospital Mutua de Terrassa obteniendo un 91,72% de precisión en el caso de la red neuronal, y un 95.92% con el árbol de decisión. La clasificación automática de medidas de glucemia permitirá a los especialistas pautar un tratamiento más acertado en base a la información obtenida directamente del glucómetro de las pacientes, contribuyendo así al desarrollo de los sistemas automáticos de ayuda a la decisión para diabetes gestacional

Serological and molecular survey of hepatitis E virus in cats and dogs in Spain

Hepatitis E virus (HEV) is an emerging zoonotic pathogen that is currently recognized as one of themajor causes of acute human hepatitis worldwide. In Europe, the increasing number of hepatitis E cases is mainly associated with the consumption of animal food products or contact with infected animals. Dogs and cats have been suggested as a zoonotic source of HEV infection. The aim of this study was to assess Orthohepevirus circulation, including HEV-A, HEV-B and HEV-C species, in sympatric urban cats and dogs in southern Spain. Between 2017 and 2020, blood samples were collected from 144 stray cats and 152 dogs, both strays and pets. The presence of antibodies againstHEV were tested using a double-antigen sandwich ELISA and seropositive simples were further analysed bywestern blot.ART-PCR was performed to detect RNAof Orthohepevirus species (HEV-A,HEV-B andHEV-C).Atotal of 19 (6.4%; 95%CI: 3.6-9.2) of the 296 animals tested showed anti-HEV antibodies by ELISA. Seropositivity was significantly higher in dogs (9.9%; 15/152; 95%CI: 5.1-14.6) than in cats (2.8%; 4/144; 95%CI: 0.1-5.5). Ten of the 18 ELISA-positive animals that could be further analysed by western blot, reacted against HEV-3 and/or HEV-C1 antigens, which suggest circulation of both genotypes in urban cats and dogs in the study area. However, HEV-A, HEV-B and HEV-C RNA were not detected in any of the tested sera. This is the first study to assess HEV circulation in both stray cats and dogs in Europe. Our results provide evidence of HEV exposure in sympatric urban cat and dog populations in southern Spain. Further studies are needed to determine the role of these species in the epidemiology of HEV

Artificial-intelligence-augmented telemedicine applied to the management of diet-treated gestational diabetes

Gestational diabetes (GD) confers an increased risk of complications as well as future type 2 diabetes. We assess the safety and efficacy of an artificial intelligence (AI)-augmented telemedicine system (ruled-based reasoning) that includes a blood glucose (BG) classifier (C4.5 Quinlan decision tree) in comparison with the standard care in the management of GD while insulin is not required

Patient-oriented computerized clinical guidelines for mobile decision support in gestational diabetes

The risks associated with gestational diabetes (GD) can be reduced with an active treatment able to improve glycemic control. Advances in mobile health can provide new patient-centric models for GD to create personalized health care services, increase patient independence and improve patients’ self-management capabilities, and potentially improve their treatment compliance. In these models, decision-support functions play an essential role. The telemedicine system MobiGuide provides personalized medical decision support for GD patients that is based on computerized clinical guidelines and adapted to a mobile environment. The patient’s access to the system is supported by a smartphone-based application that enhances the efficiency and ease of use of the system. We formalized the GD guideline into a computer-interpretable guideline (CIG). We identified several workflows that provide decision-support functionalities to patients and 4 types of personalized advice to be delivered through a mobile application at home, which is a preliminary step to providing decision-support tools in a telemedicine system: (1) therapy, to help patients to comply with medical prescriptions; (2) monitoring, to help patients to comply with monitoring instructions; (3) clinical assessment, to inform patients about their health conditions; and (4) upcoming events, to deal with patients’ personal context or special events. The whole process to specify patient-oriented decision support functionalities ensures that it is based on the knowledge contained in the GD clinical guideline and thus follows evidence-based recommendations but at the same time is patient-oriented, which could enhance clinical outcomes and patients’ acceptance of the whole system

Successful replacement of weekly face-to-face visits by unsupervised smart home telecare in diet-treated gestational diabetes (GD)

We have developed a computer-based smart telemedicine system with the aim of giving automated support to GD patients while insulin is not required. The smart telemedicine system combines a platform for remote monitoring of diabetes-related parameters with a decision-support system (DSS) based on expert knowledge that generates automatic feedback to patients and/or clinicians. The DSS generates initial and follow-up diet treatments and detects the need to start insulin. Blood glucose (BG) data downloaded to the system from the patient?s glucose meter is automatically classified into mealtime intervals and moments of measurement (preprandial,postprandial) by a classifier based on a decision tree. After downloading BG data and informing on ketonuria fasting status, the patient receives an evaluation of the data and, if needed, a proposal of diet adjustment. In case insulin therapy is advised, the system also informs the responsible doctor who schedules a face-to-face appointment. Sixty-nine patients diagnosed of GD following the NDDG criteria were randomized (2:1) to use the system (active group) or to attend the usual face-to-face visits (control group). At baseline, groups were comparable regarding all the clinical variables tested. During the follow-up period (36 days (1-141)), no correction of the automated-proposed treatment was done by doctors. Mean number of BG downloads by patients was 10.2±8 (1-29) and the mean number of changes in diet automatically proposed was 0.46. Mean number of BG values/day, mean BG and the % of BG values above 140 mg/dl, pre-partum HbA1c, and all the perinatal outcomes tested were similar between the groups. Mean number of face-to-face visits performed including first visit and training was 4.8±2.8 for the control group and 1.4±0.6 for the active group (p<0.001). In conclusion, this computer-based smart telemedicine system successfully replaced face-to-face follow-up visits in women diagnosed of GD while insulin therapy was not required

Propuesta y evaluación de un sistema de telemedicina y de ayuda a la decisión para el cuidado de pacientes con diabetes gestacional

La diabetes gestacional se define como cualquier grado de intolerancia a la glucosa que empieza por primera vez durante el embarazo y sus consecuencias asociadas incluyen un aumento de la mortalidad y morbilidad perinatal. A diferencia de las pacientes con diabetes tipo 1 o 2, las pacientes con diabetes gestacional necesitan un control más frecuente por parte del especialista, ya que sus niveles de glucosa en sangre aumentan cada semana requiriendo visitas semanales o quincenales durante el periodo de gestación. Los nuevos criterios de diagnóstico de la diabetes gestacional recomendados por la IADPSGC disminuyen los efectos adversos de la hiperglucemia tanto en la madre como en el recién nacido, pero su aplicación supondría un aumento de la prevalencia que llegaría a triplicar el número de casos actual en España. Para que los Servicios de Endocrinología y Nutrición del Sistema Nacional de Salud puedan hacer frente a la carga que supondría este aumento de prevalencia es necesario emplear nuevos procesos asistenciales que incluyan el uso de las tecnologías de la información. La telemedicina en diabetes gestacional ha demostrado ser eficaz en la reducción de visitas presenciales sin detrimento de la calidad asistencial recibida y consiguiendo una alta satisfacción de las pacientes. Sin embargo, la crítica que recibe este tipo de sistemas es la de no ahorrar tiempo a los profesionales clínicos al no descargarles de su trabajo cotidiano ni optimizar su quehacer diario. En esta tesis doctoral se propone y evalúa un sistema de telemedicina y de ayuda a la decisión para el seguimiento y control de la diabetes gestacional, cuyo objetivo es evitar a las pacientes desplazamientos innecesarios al hospital sin detrimento de la calidad asistencial recibida y anticipar ajustes de terapia mediante la evaluación frecuente y automática de sus datos de monitorización, todo ello sin aumentar la carga de trabajo de los profesionales sanitarios. El sistema se caracteriza por permitir la prescripción automática de terapias de dieta, la generación de alertas dirigidas al personal clínico sobre la necesidad de evaluar a ciertas pacientes con situaciones específicas y el cálculo automático de propuestas de modificaciones en la inicio y modificación de la terapia de insulina. El plataforma de telemedicina permite a las pacientes enviar sus valores de glucemia desde casa descargándolos directamente desde su medidor de glucosa, además de registrar otros parámetros de monitorización como la cetonuria, el cumplimiento de la terapia de dieta, la insulina administrada, la actividad física realizada o el estado de ánimo. Las medidas que no son etiquetadas por las pacientes con su ingesta relacionada, son etiquetadas de manera automática por un clasificador basado en una combinación del algoritmo de clustering Expectation Maximization y de un árbol de decisión C4.5. La plataforma desarrollada incluye dos módulos de ayuda a la decisión: 1) Módulo de análisis para detectar el estado metabólico de las pacientes; y 2) Módulo de recomendaciones que para determinar la recomendación terapéutica más apropiada según el análisis de la situación de la paciente. La base de conocimiento de ambos módulos se basa en reglas lógicas y fue creada mediante la formalización de guías clínicas y las especificaciones de un equipo de profesionales expertas en endocrinología. El diseño del módulo de análisis que detecta el estado metabólico de las pacientes a partir de sus datos de monitorización se basa en dos autómatas finitos, uno para el análisis de la glucemia y de la dieta y el otro para el análisis de la cetonuria. La salida de ambos autómatas se combina para determinar el estado metabólico de las pacientes a lo largo del tiempo. El módulo de recomendaciones es el encargado de determinar si es necesario realizar un cambio de terapia y el tipo de ajuste recomendado. Si el módulo recomienda un ajuste de dieta, éste se realiza de manera automática notificando a las pacientes del mismo, mientras que si la recomendación es de iniciar la terapia de insulina, se alerta al médico responsable y se sugiere un posible tratamiento inicial de insulina. El sistema propuesto en esta tesis doctoral ha sido evaluado mediante un estudio clínico controlado y aleatorizado en el Hospital Universitario Parc Taulí de Sabadell y en el Hospital Mutua de Terrassa durante 17 meses con la participación de 119 pacientes. Los resultados de evaluación han permitido comprobar que el sistema presentado es capaz de identificar a las pacientes que tienen un buen control metabólico y gestionar su tratamiento de manera automática hasta que requieran la administración de insulina, así como identificar a las pacientes complejas que requieren una evaluación más exhaustiva por parte del personal médico. Durante el estudio clínico, el sistema detectó todas las situaciones que requirieron un cambio de terapia y todas las recomendaciones generadas fueron seguras y efectivas. El tiempo dedicado por las endocrinólogas a la evaluación de pacientes en el grupo activo fue menor comparado con la práctica tradicional y las visitas presenciales se redujeron, sin ocasionar un impacto negativo en los parámetros clínicos de las pacientes. Las principales aportaciones de este trabajo de investigación son: a) Evaluación de la seguridad y eficacia del seguimiento remoto de pacientes con diabetes gestacional mediante un sistema de telemedicina con herramientas de ayuda a la decisión integradas con prescripción automática de terapias de dieta, en términos de reducción de visitas, impacto en la carga de trabajo de los especialistas e impacto clínico. b) Diseño y desarrollo de una herramienta de análisis automático de datos para la determinación del estado metabólico de pacientes con diabetes gestacional en función de sus niveles de glucosa en sangre y de cetonuria. c) Metodología de diseño de un clasificador de alta precisión para el etiquetado automático de glucemias en relación a las ingestas principales de día para pacientes con diabetes gestacional. d) Diseño y desarrollo de una herramienta para generar recomendaciones sobre acciones terapéuticas relativas a la terapia de dieta e insulina de pacientes con diabetes gestacional. ----------ABSTRACT---------- Gestational diabetes mellitus is defined as glucose intolerance with onset during pregnancy and its associated consequences include increased perinatal mortality and morbidity. Unlike patients with diabetes type 1 or type 2, patients with gestational diabetes need a more frequent control by the specialist, as their blood glucose levels increase each week requiring weekly or biweekly clinical encounter during the gestational period. The new diagnostic criteria for gestational diabetes recommended by the IADPSGC reduce the adverse effects caused by hyperglycaemia in both mother and child, but their adoption would suppose an increase of the prevalence that would triplicate the number of cases in Spain. In order for the Endocrinology and Nutrition services of the National Health System to be able to cope with the burden of this increased prevalence, it is necessary to utilize new health care processes that include the use of information technologies. Telemedicine in gestational diabetes has proven to be effective in reducing face-to-face visits without deteriorate the quality of care and achieving high patient satisfaction. However, the criticism that such system receive is that they do not save clinician’s time or mitigate their workload or facilitate their daily work. This thesis proposes and evaluates a telemedicine and decision support system to manage the treatment of patients with gestational diabetes, whose aim is to improve access to specialized healthcare assistance, to prevent patients from unnecessary displacements maintaining the quality of care and to anticipate therapy adjustments by means of frequent and automatic evaluation of monitoring data, without increasing clinicians’ workload. The system is characterized by allowing to perform automatic diet adjustments, warn physicians about the need to evaluate certain patients with specific conditions and generate insulin therapy proposals. The telemedicine platform allows patients to upload their glycaemia data at home directly from their glucose meter, as well as report other monitoring variables like ketonuria, insulin dose administered, compliance to dietary treatment or physical activity. Glycaemia values, not tagged by patients, are automatically labelled with their associated meal by a classifier based on the Expectation Maximization cluster algorithm and a C4.5 decision tree learning algorithm. The platform developed include two decision support modules: 1) Analysis module to detect patients’ metabolic condition; and 2) Recommendation module to suggest the most appropriate therapeutic action according to patients’ condition. The knowledge base of both modules is based on production rules, and was created by formalizing clinical guidelines and the specifications of a team of experts in endocrinology. The design of the analysis module is based in two finite automata, one that analysis glycaemia and diet and the other one that analyzes the ketonuria. The output of both automata is combined to calculate patients’ daily metabolic condition. The second decision support module determines if a therapy adjustment is required and the type of adjustment recommended. If the module suggests a diet therapy adjustment, this is prescribed automatically and the patient is notified about it, whereas recommendations about insulin requirements are notified also to the physicians, who will decide if insulin needs to be prescribed. The system presented in this thesis has been evaluated with a randomized controlled trial clinical study at two hospitals, the Hospital Universitario Parc Taulí de Sabadell and the Hospital Mutua de Terrassa, during 17 months with the participation of 119 patients. The evaluation results verify that the system proposed is able to identify which patients have good metabolic control and to manage their treatment automatically until the administration of insulin is required, and which patients are complex and require a more exhaustive evaluation by the medical staff. During the clinical study, the system detected all situations that required a therapy adjustment and all recommendations generated by it were safe and effective. The time devoted by clinicians to patients’ evaluation in the intervention group was lower compare to conventional care and face-to-face visits were reduced, without causing a negative impact in patients’ clinical parameters and increasing their satisfaction. The main contributions of this research work are: a) Evaluation of the safety and effectiveness of remote follow-up of patients with gestational diabetes using a telemedicine system with decision support tools integrated with automatic diet prescriptions, in terms of face-to-face visits reduction, impact in clinicians’ workload and clinical impact in patients’’ parameters. b) Design and development of an automatic data analysis tool for the determination of the metabolic condition of patients with gestational diabetes, based on their blood glucose and ketonuria levels. c) A methodology to design a high precision classifier for the automatic labeling of glycaemia measurements in relation to daily intakes for patients with gestational diabetes. d) Design and development of a tool to generate recommendation on therapeutic actions related to diet and insulin therapies for patients with gestational diabetes

A web-based clinical decision support system for gestational diabetes: Automatic diet prescription and detection of insulin needs

Background The growth of diabetes prevalence is causing an increasing demand in health care services which affects the clinicians’ workload as medical resources do not grow at the same rate as the diabetic population. Decision support tools can help clinicians with the inspection of monitoring data, providing a preliminary analysis to ease their interpretation and reduce the evaluation time per patient. This paper presents Sinedie, a clinical decision support system designed to manage the treatment of patients with gestational diabetes. Sinedie aims to improve access to specialized healthcare assistance, to prevent patients from unnecessary displacements, to reduce the evaluation time per patient and to avoid gestational diabetes adverse outcomes. Methods A web-based telemedicine platform was designed to remotely evaluate patients allowing them to upload their glycaemia data at home directly from their glucose meter, as well as report other monitoring variables like ketonuria and compliance to dietary treatment. Glycaemia values, not tagged by patients, are automatically labelled with their associated meal by a classifier based on the Expectation Maximization clustering algorithm and a C4.5 decision tree learning algorithm. Two finite automata are combined to determine the patient’s metabolic condition, which is analysed by a rule-based knowledge base to generate therapy adjustment recommendations. Diet recommendations are automatically prescribed and notified to the patients, whereas recommendations about insulin requirements are notified also to the physicians, who will decide if insulin needs to be prescribed. The system provides clinicians with a view where patients are prioritized according to their metabolic condition. A randomized controlled clinical trial was designed to evaluate the effectiveness and safety of Sinedie interventions versus standard care and its impact in the professionals’ workload in terms of the clinician’s time required per patient; number of face-to-face visits; frequency and duration of telematics reviews; patients’ compliance to self-monitoring; and patients’ satisfaction. Results Sinedie was clinically evaluated at “Parc Tauli University Hospital” in Spain during 17 months with the participation of 90 patients with gestational diabetes. Sinedie detected all situations that required a therapy adjustment and all the generated recommendations were safe. The time devoted by clinicians to patients’ evaluation was reduced by 27.389% and face-to-face visits per patient were reduced by 88.556%. Patients reported to be highly satisfied with the system, considering it useful and trusting in being well controlled. There was no monitoring loss and, in average, patients measured their glycaemia 3.890 times per day and sent their monitoring data every 3.477 days. Conclusions Sinedie generates safe advice about therapy adjustments, reduces the clinicians’ workload and helps physicians to identify which patients need a more urgent or more exhaustive examination and those who present good metabolic control. Additionally, Sinedie saves patients unnecessary displacements which contributes to medical centres’ waiting list reductio

Automatic classification of glycaemia measurements to enhance data interpretation in an expert system for gestational diabetes

Expert systems for diabetes care need to automatically evaluate glycaemia measurements in relationship to meals to correctly determine patients' metabolic condition and generate recommendations about therapy adjustments. Most glucose meters allow patients to manually label each measurement with a meal tag, but as this utility is not always used, a completion procedure is needed. Classification methods are usually based on predefined mealtimes and present insufficient accuracy that might affect the automatic data analysis. Expert systems in diabetes require a reliable method to manage incomplete glycaemia data so that they can determine if patients' metabolic condition is altered due to a specific meal or due to an extended fasting period. This paper presents the design and application of a classification module to automatically assign the appropriate meal and 'moment of measurement' to incomplete glycaemia data. Different machine learning techniques were studied in order to design the best classification algorithm in terms of accuracy. The selected classifier was implemented with a C4.5 decision tree with 7 input features selected with a wrapper evaluator and the genetic search algorithm, which achieved 95.45% of accuracy with the training set on cross-validation. The classification module was integrated in the Sinedie expert system for gestational diabetes care and was evaluated in a clinical environment for 8 months with 42 patients. A total of 7,113 glycaemia measurements were uploaded by patients into the Sinedie system and were completed by the 'classification module'. The 98.79% of the measurements were correctly classified, while patients modified the automatic classification of 1.21% of them. Classification results were improved by 21.04% compared to a classification based on predefined mealtimes. The automatic classification of glycaemia measurements minimizes the patient's intervention, allows structuring measurements in relationship to meals and makes automatic data interpretation by expert systems more reliable

Monitoring of kinetics and exhaustion markers of circulating CAR-T cells as early predictive factors in patients with B-cell malignancies

Purpose: CAR-T cell therapy has proven to be a disruptive treatment in the hematology field, however, less than 50% of patients maintain long-term response and early predictors of outcome are still inconsistently defined. Here, we aimed to optimize the detection of CD19 CAR-T cells in blood and to identify phenotypic features as early biomarkers associated with toxicity and outcomes. Experimental design: In this study, monitoring by flow cytometry and digital PCR (dPCR), and immunophenotypic characterization of circulating CAR-T cells from 48 patients treated with Tisa-cel or Axi-cel was performed. Results: Validation of the flow cytometry reagent for the detection of CAR-T cells in blood revealed CD19 protein conjugated with streptavidin as the optimal detection method. Kinetics of CAR-T cell expansion in blood confirmed median day of peak expansion at seven days post-infusion by both flow cytometry and digital PCR. Circulating CAR-T cells showed an activated, proliferative, and exhausted phenotype at the time of peak expansion. Patients with increased expansion showed more severe CRS and ICANs. Immunophenotypic characterization of CAR-T cells at the peak expansion identified the increased expression of co-inhibitory molecules PD1 and LAG3 and reduced levels of the cytotoxicity marker CD107a as predictors of a better long-term disease control. Conclusions: These data show the importance of CAR-T cells in vivo monitoring and identify the expression of PD1LAG3 and CD107a as early biomarkers of long-term disease control after CAR-T cell therapy