Predicción de mortalidad en UCI de pacientes con COVID-19: un modelo predictivo supervisado con redes neuronales artificiales (RNA)

Introducción. Se han descrito muchos factores de riesgo de la COVID-19, pero no disponemos de un indicador de mortalidad en UCI para individualizar el pronóstico del paciente y establecer medidas de tratamiento más precoces y eficaces. Las Redes Neuronales Artificiales (RNA) son una forma de análisis multivariante alternativo.Objetivo. Diseñar un indicador mediante RNA para predecir la mortalidad del paciente crítico ingresado en UCI por COVID-19, a partir de las variables presentes en las primeras 24 horas de ingreso en UCI, analizar las variables relacionadas y evaluar su capacidad predictiva.Material y Método. Se ha realizado un estudio observacional, retrospectivo en el que se recogieron las variables presentes en las primeras 24 horas tras el ingreso en el Servicio de Medicina Intensiva del HCU “Lozano Blesa”. Tras realizar un análisis estadístico se ha obtenido una Red MLP, siendo el indicador de mortalidad el valor de la neurona de la capa de salida que predice exitus. El software informático calcula la importancia de las variables (IV) incluidas y asigna cuales de ellas eran altamente predictivas.Resultados. El valor medio del Indicador en el grupo que sobrevivió fue de 0.322 + 0.215, frente a 0.592 + 0.213 en el grupo de falleció; p Conclusiones. Es posible crear un Indicador de Mortalidad del paciente ingresado en UCI por COVID-19 utilizando la metodología del aprendizaje automático de las redes neuronales artificiales. Las variables más importantes relacionadas con su mortalidad son la edad, la ferritina y la LDH. El Indicador propuesto muestra una capacidad predictiva aceptable y podría mejorarse aún más si fuese creado a partir de una base de datos multicéntrica.<br /

Clonal chromosomal mosaicism and loss of chromosome Y in elderly men increase vulnerability for SARS-CoV-2

The pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2, COVID-19) had an estimated overall case fatality ratio of 1.38% (pre-vaccination), being 53% higher in males and increasing exponentially with age. Among 9578 individuals diagnosed with COVID-19 in the SCOURGE study, we found 133 cases (1.42%) with detectable clonal mosaicism for chromosome alterations (mCA) and 226 males (5.08%) with acquired loss of chromosome Y (LOY). Individuals with clonal mosaic events (mCA and/or LOY) showed a 54% increase in the risk of COVID-19 lethality. LOY is associated with transcriptomic biomarkers of immune dysfunction, pro-coagulation activity and cardiovascular risk. Interferon-induced genes involved in the initial immune response to SARS-CoV-2 are also down-regulated in LOY. Thus, mCA and LOY underlie at least part of the sex-biased severity and mortality of COVID-19 in aging patients. Given its potential therapeutic and prognostic relevance, evaluation of clonal mosaicism should be implemented as biomarker of COVID-19 severity in elderly people. Among 9578 individuals diagnosed with COVID-19 in the SCOURGE study, individuals with clonal mosaic events (clonal mosaicism for chromosome alterations and/or loss of chromosome Y) showed an increased risk of COVID-19 lethality

Clinical and genetic characteristics of late-onset Huntington's disease

Background: The frequency of late-onset Huntington's disease (&gt;59 years) is assumed to be low and the clinical course milder. However, previous literature on late-onset disease is scarce and inconclusive. Objective: Our aim is to study clinical characteristics of late-onset compared to common-onset HD patients in a large cohort of HD patients from the Registry database. Methods: Participants with late- and common-onset (30–50 years)were compared for first clinical symptoms, disease progression, CAG repeat size and family history. Participants with a missing CAG repeat size, a repeat size of ≤35 or a UHDRS motor score of ≤5 were excluded. Results: Of 6007 eligible participants, 687 had late-onset (11.4%) and 3216 (53.5%) common-onset HD. Late-onset (n = 577) had significantly more gait and balance problems as first symptom compared to common-onset (n = 2408) (P &lt;.001). Overall motor and cognitive performance (P &lt;.001) were worse, however only disease motor progression was slower (coefficient, −0.58; SE 0.16; P &lt;.001) compared to the common-onset group. Repeat size was significantly lower in the late-onset (n = 40.8; SD 1.6) compared to common-onset (n = 44.4; SD 2.8) (P &lt;.001). Fewer late-onset patients (n = 451) had a positive family history compared to common-onset (n = 2940) (P &lt;.001). Conclusions: Late-onset patients present more frequently with gait and balance problems as first symptom, and disease progression is not milder compared to common-onset HD patients apart from motor progression. The family history is likely to be negative, which might make diagnosing HD more difficult in this population. However, the balance and gait problems might be helpful in diagnosing HD in elderly patients