Articulos Científicos Mat. Inf. Rad.

A Real-World Study Comparing Advanced Hybrid Closed-Loop Systems During Pregnancy in Women with Type 1 Diabetes

2026-07-30T04:10:55Z

A Real-World Study Comparing Advanced Hybrid Closed-Loop Systems During Pregnancy in Women with Type 1 Diabetes Qurós, Carmen; Wägner, Ana M; Azriel, Sharona; Soldevila, Berta; Beato-Vibora, Pilar; Herrera Arranz, Mayte; Nattero, Lía; Picón-César, Maria José; Climent, Elisenda; Amigó, Judit; Colomo, Natalia; Durán-Martínez, María; Alpañes Buesa, Macarena; Megía, Ana; Vinagre, Irene; Vega Guedes, Begoña; Díaz-Soto, Gonzalo; Bandrés, Orosia; Barquiel, Beatriz; López Tinoco, Cristina; Márquez Pardo, Rosa; Martinez Brocca, Maria Asuncion; Corcoy, Rosa; Codina, Mercedes; Piedra, María; Rebollo Román, Ángel; Cuesta, Martín; López-Gallardo, Gema; Goya Canino, María M.; Bugatto González, Fernando; Mendoza, Lilian C.; Olvera Márquez, María Del Pilar; Perea, Verónica Este estudio multicéntrico nacional aporta evidencia en vida real crítica para el SNS, al realizar una comparación directa (head-to-head) de tres sistemas avanzados de asa cerrada híbrida (AHCL) en gestantes con diabetes tipo 1. La relevancia traslacional es inmediata y cubre un vacío de evidencia clínica sustancial, permitiendo optimizar el control glucémico y reducir el riesgo neonatal mediante medicina de precisión. Su impacto se refleja en la mejora de la práctica clínica asistencial y en su alta potencialidad para informar futuras guías y recomendaciones de manejo tecnológico en diabetes pregestacional.

Customized versus population-based curves for the prediction of fetal and neonatal malnutrition

2026-04-16T23:06:36Z

Customized versus population-based curves for the prediction of fetal and neonatal malnutrition Fernández Alba, Juan Jesús; González Macías, María del Carmen; León Del Pino, Raquel; Prado Fernandes, Fabiana; Lagares Franco, Carolina María; Moreno Corral, Luis Javier; Torrejón Cardoso, Rafael Objetivos Construir un modelo de curvas customizadas de peso al nacer basado en población española. Comparar la capacidad de este modelo customizado frente a nuestra gráfica poblacional para predecir un índice ponderal neonatal inferior al percentil 10. Métodos Desarrollamos un modelo capaz de predecir el percentil 10 para un feto en función de la edad gestacional, el sexo y el peso, la talla y la edad maternos. Comparamos la capacidad de este modelo customizado frente a nuestro propio modelo poblacional para predecir un índice ponderal neonatal inferior al percentil 10. Se utilizaron datos de una amplia BASE DE DATOS (32.854 recién nacidos vivos entre 1993 y 2012). Se incluyeron únicamente gestaciones únicas con edad gestacional al parto entre 32 y 42 semanas. Resultados En la población gestante global, el método customizado fue superior al método poblacional para la detección de recién nacidos con índice ponderal inferior al percentil 10 (sensibilidad 55% frente a 40,96%; especificidad 99,6% frente a 91,23%; VPP 11,49% frente a 9,55%; VPN 98,84% frente a 98,55%). En gestantes con IMC superior al percentil 90, la sensibilidad fue del 75% frente al 50% del método poblacional. En gestantes con talla superior al percentil 90, la sensibilidad fue casi el doble que la del método poblacional (61,53% frente a 33,33%). Conclusión La curva customizada de peso al nacer es superior al método poblacional para la detección de recién nacidos con índice ponderal inferior al percentil 10. Esto es especialmente así en mujeres situadas en los rangos más altos de talla y peso, y en recién nacidos pretérmino.; Objectives: The aim of our study was to construct a model of customized birth weight curves based on a Spanish population and to compare the ability of this customized model to our population-based chart to predict a neonatal ponderal index (PI) <10th percentile. Methods: We developed a model that can predict the 10th percentile for a fetus according to gestational age and gender as well as maternal weight, height, and age. We compared the ability of this customized model to that of our own population-based model to predict a neonatal PI <10th percentile. Data from a large database were used (32,854 live newborns, from 1993 through 2012). Only singleton pregnancies with a gestational age at delivery of 32-42 weeks were included. Results: In the entire pregnant population, the customized method was superior to the population-based method for detecting newborns with a PI <10th percentile (sensitivity: 55 vs. 40.96%; specificity: 99.6 vs. 91.23%; positive predictive value: 11.49 vs. 9.55%, and negative predictive value: 98.84 vs. 98.55%, respectively). In pregnant women with a BMI >90th percentile, the sensitivity was 75%, compared to 50% in the population-based method. In pregnant women with a height >90th percentile, the sensitivity was almost as high as in the population-based method (61.53 vs. 33.33%). Conclusion: The customized birth weight curve is superior to the population-based method for the detection of newborns with a PI <10th percentile. This is especially the case in women in the higher scales of height and weight as well as in preterm babies.

An innovative method for assessing the relationship between longitudinal brain volume measurements and neurodevelopmental outcomes in preterm infants

2026-03-12T01:01:05Z

An innovative method for assessing the relationship between longitudinal brain volume measurements and neurodevelopmental outcomes in preterm infants Ortega León, Arantxa Mireya; Urda Muñoz, Daniel; Turias Domínguez, Ignacio José; Lubián López, Simón Pedro; Benavente Fernández, Isabel; Zhao, Qingyu; Jafrasteh, Bahram Predicting neurodevelopmental outcomes in very preterm infants is critical, but clinically-acquired data like longitudinal total brain volume (TBV), as macroscopic index of brain growth, are often sparse and irregular, hindering accurate prognosis. We studied 294 very preterm infants with TBV measured longitudinally and neurodevelopmental outcomes assessed at 2 years (Bayley-III) and 8 years (WISC-V). To handle missingness and irregular sampling, we compared six imputation strategies (mean, MissForest, MICE, GP, MGP, and MGP (initGP)) and trained a semi-supervised classifier on the imputed TBV trajectories. Performance was evaluated using multiple classification metrics, and results were summarized by averaging across outcomes and ages of study. Across analyses, a novel variant of Missing Gaussian Process (MGP) initialized with a GP fit (MGP (initGP)), which leverages individual patient trajectories to stabilize estimates, achieved the best average performance. Its advantages were most consistent and significant for 8-year outcomes, highlighting its strength in modeling longer developmental trajectories. While performance at 2 years was more modest, this likely reflects the intrinsic challenges of early-term prediction from TBV alone. We therefore recommend MGP (initGP) as a strong default for imputing longitudinal TBV for prognostic studies.

Glycemic Analysis and Stratification of Pediatric Patients with Type 1 Diabetes Using isCGM in Southern Spain: Insights from the Andiacare Digital Platform

2026-03-10T01:05:08Z

Glycemic Analysis and Stratification of Pediatric Patients with Type 1 Diabetes Using isCGM in Southern Spain: Insights from the Andiacare Digital Platform Leiva Gea, Isabel; Moreno Jabato, Fernando; Ariza Jiménez, Ana Belén; Lendíne Jurado, Alfonso; Gómez Perea, Ana; Romero Pérez, María del Mar; García García, Emilio; Santos Mata, María Ángeles; Martínez Moya, Gabriela; Momblan, Jerónimo; Lechuga Sancho, Alfonso María; Gómez Vida, José María; Mier Palacios, Merecedes; Ranchal Pérez, María del Pilar; Vivas González, Gustavo; Calleja Cabeza, Patricia; Fernández Hernández, Eugenio; Jiménez Martín, Ana Pilar; Guarino Narváez, Jessica; Rodríguez de Vera-Gómez, Pablo; Martinez Brocca, Maria Asuncion Background/Objectives: Type 1 diabetes mellitus (T1D) is the most common metabolic disorder in children, with significant physical and emotional impacts. Achieving optimal glucometric control is challenging due to the complex management and limitations of insulin therapy. Advances in pharmacology and technology, including continuous glucose monitoring (CGM) systems, offer new options for diabetes management. We developed Andiacare, an open-source platform for macro/micro-management of diabetes and analyzed its application in a pediatric T1D cohort to evaluate glucometric control patterns. Methods: A retrospective cohort study was conducted in a pediatric population (<18 years old) in Andalusia, Spain. Patients treated with Multiple Daily Injections of Insulin (MDI) and FreeStyle Libre 2 System (Abbott, Spain) were included. The patient data were analyzed using the Andiacare platform, which categorizes patients based on the Advanced Technologies and Treatments for Diabetes (ATTD) panel’s targets for glucometric control. Results: The study included 2215 patients from 18 pediatric hospitals. The Andiacare platform categorized patients into four groups based on glucometric control parameters, enabling patient stratification based on their glucometric control. Only 25.8% of the cohort achieved the recommended Time in Range (TIR), and 9.5% of the patients achieved all target parameters of glucometric control. Age is a determinant factor in adherence and achievement of set goals. Conclusions: This study offers insights into glucometric control in a large pediatric population with T1D in Andalusia. Few patients achieved the recommended glucometric control targets, highlighting the need for improved management strategies. The use of digital platforms such as Andiacare might contribute to facilitating the management of large pediatric cohorts. New algorithms integrating glucometric and non-glucometric parameters are required for improved individual and cohort categorization to optimize therapeutic interventions.