Developing an interpretable machine learning model for predicting COVID-19 patients deteriorating prior to intensive care unit admission using laboratory markers.

Coronavirus disease (COVID-19) remains a significant global health challenge, prompting a transition from emergency response to comprehensive management strategies. Furthermore, the emergence of new variants of concern, such as BA.2.286, underscores the need for early detection and response to new variants, which continues to be a crucial strategy for mitigating the impact of COVID-19, especially among the vulnerable population. This study aims to anticipate patients requiring intensive care or facing elevated mortality risk throughout their COVID-19 infection while also identifying laboratory predictive markers for early diagnosis of patients. Therefore, haematological, biochemical, and demographic variables were retrospectively evaluated in 8,844 blood samples obtained from 2,935 patients before intensive care unit admission using an interpretable machine learning model. Feature selection techniques were applied using precision-recall measures to address data imbalance and evaluate the suitability of the different variables. The model was trained using stratified cross-validation with k=5 and internally validated, achieving an accuracy of 77.27%, sensitivity of 78.55%, and area under the receiver operating characteristic (AUC) of 0.85; successfully identifying patients at increased risk of severe progression. From a medical perspective, the most important features of the progression or severity of patients with COVID-19 were lactate dehydrogenase, age, red blood cell distribution standard deviation, neutrophils, and platelets, which align with findings from several prior investigations. In light of these insights, diagnostic processes can be significantly expedited through the use of laboratory tests, with a greater focus on key indicators. This strategic approach not only improves diagnostic efficiency but also extends its reach to a broader spectrum of patients. In addition, it allows healthcare professionals to take early preventive measures for those most at risk of adverse outcomes, thereby optimising patient care and prognosis.

Heart Cycle: facilitating the deployment of advanced care processes.

Current trends in health management improvement demand the standardization of care protocols to achieve better quality and efficiency. The use of Clinical Pathways is an emerging solution for that problem. However, current Clinical Pathways are big manuals written in natural language and highly affected by human subjectivity. These problems make their deployment and dissemination extremely difficult in real practice environments. Furthermore, the intrinsic difficulties for the design of formal Clinical Pathways requires new specific design tools to help making them relly useful and cost-effective. Process Mining techniques can help to automatically infer processes definition from execution samples and, thus, support the automatization of the standardization and continuous control of healthcare processes. This way, they can become a relevant helping tool for clinical experts and healthcare systems for reducing variability in clinical practice and better understand the performance of the system.

Tratamiento del síndrome de déficit atencional, SDA, en niños: evaluación de la moclobemida, una alternativa no psicoestimulante / Treatment of attention deficit hyperactivity disorder in children: assessment of moclobemide a non-psichostimulant alternative

La terapia actual del síndrome de déficit atencional (SDA) con psicoestimulantes conlleva riesgos de abuso/mal uso y otras limitaciones. Este estudio evaluó la eficacia y tolerabilidad del antidepresivo moclobemida como alternativa no psicoestimulante de este trastorno en niños. Se estudiaron 21 niños entre 6-13 años de edad sin tratamiento SDA previo, cuyo diagnóstico comprendió: historia clínica según DSM-IV; antecedentes de los progenitores; examen neurológico de inmadurez según edad; información del colegio y la aplicación semanal de: test de Conners, test Maze-Porteus (laberinto), familiar figure matching test (FFMT) e impresión clínica global. Tratamiento: 150 mg moclobemida 2 veces al día durante un mes, controlándoseles semanalmente. El 95 por ciento de los pacientes evidenció descensos de los puntajes de severidad en el test de Conners, convirtiéndose los casos severos en moderados, leves y normales. En el laberinto y FFMT hubo reducciones significativas del tiempo de ejecución y del número de errores cometidos, con mejorías en el 86 por ciento de los niños. Según impresión clínica global, un 63 por ciento normalizó o redujo su cuadro a un SDA leve. No hubo alteraciones cardiovasculares, centrales o del apetito, ni del hemograma, perfil bioquímico o EEG. Una niña refirió sensación de frío. Los resultados indican que la moclobemida puede ser una alternativa eficaz y bien tolerada en el tratamiento del SDA en niños. Estudios de largo plazo podrán confirmar estos hallazgos