Prepregnancy cardiovascular risk factors as predictors for hypertensive pregnancy disorders

Hypertensive disorders of pregnancy (HDP) are a group of conditions characterized by abnormally elevated blood pressure during pregnancy. HDP are one of the leading causes of maternal and fetal morbidity and mortality worldwide. There is growing evidence that prepregnancy cardiovascular risk factors significantly increase the risk of HDP. Along these lines, hypertension, diabetes mellitus, obesity, heart failure and hypercholesterolemia appear to be related to HDP. All of these can increase, to a lesser or greater degree, the risk of HDP. Although some of these variables are intertwined, they can also act as independent predictors. A preconceptional predictive tool could improve therapeutic decisions and pregnancy control in high-risk patients. This review aims to analyze the degree of correlation between HDP’s incidence and prepregnancy cardiovascular risk factors

Predictive factors for mortality in patients admitted to the intensive care unit

<p>Mortality prediction is a resource that allows optimized allocation of resources in the intensive care unit (ICU). Available scores provide an easy approach to mortality prediction in ICU patients. However, evidence has shown that these models are inadequate and imprecise for the intended purpose. Therefore, novel investigations are needed to optimize these models by including new variables. On the other hand, it has also been proposed to create new predictive models based on novel technology. Machine learning, artificial intelligence, models have been proposed as an alternative to solve this issue. However, more validation studies are needed to recommend further this novel approach toward mortality prediction. This review aims to analyze predictive mortality factors in the ICU context that can further optimize available scores or serve as an independent predictive factor.</p><p>La predicción de la mortalidad es un recurso que permite optimizar la asignación de recursos en la unidad de cuidados intensivos (UCI). Las puntuaciones disponibles proporcionan un enfoque sencillo para la predicción de la mortalidad en pacientes de la UCI. Sin embargo, la evidencia ha demostrado que estos modelos son inadecuados e imprecisos para el propósito previsto. Por tanto, se necesitan investigaciones novedosas para optimizar estos modelos incluyendo nuevas variables. Por otro lado, también se ha propuesto crear nuevos modelos predictivos basados en tecnología novedosa. Se han propuesto diversos modelos de aprendizaje automático, inteligencia artificial y como alternativa para solucionar este problema. Sin embargo, se necesitan más estudios de validación para recomendar más este novedoso enfoque hacia la predicción de la mortalidad. Esta revisión tiene como objetivo analizar los factores predictivos de mortalidad en el contexto de la UCI que pueden optimizar aún más las puntuaciones disponibles o servir como un factor predictivo independiente.</p&gt