ABSTRACT
The following paper reports the case of a woman on in-center hemodialysis through an arteriovenous graft, who attended with an acute vascular access thrombosis. Post percutaneous thrombectomy, the patient presented a rare case of self-limited acute hepatitis secondary to the revascularization procedure. We explain the probable trigger for this complication, its pathophysiology, management, and evolution.
Subject(s)
Arteriovenous Shunt, Surgical , Hepatitis , Thrombosis , Female , Humans , Graft Occlusion, Vascular/diagnostic imaging , Graft Occlusion, Vascular/etiology , Graft Occlusion, Vascular/surgery , Vascular Patency , Arteriovenous Shunt, Surgical/adverse effects , Thrombectomy/adverse effects , Thrombosis/diagnostic imaging , Thrombosis/etiology , Thrombosis/surgery , Renal Dialysis/adverse effects , Hepatitis/complications , Treatment Outcome , Retrospective StudiesABSTRACT
In an overwhelming demand scenario, such as the SARS-CoV-2 pandemic, pressure over health systems may outburst their predicted capacity to deal with such extreme situations. Therefore, in order to successfully face a health emergency, scientific evidence and validated models are needed to provide real-time information that could be applied by any health center, especially for high-risk populations, such as transplant recipients. We have developed a hybrid prediction model whose accuracy relative to several alternative configurations has been validated through a battery of clustering techniques. Using hospital admission data from a cohort of hospitalized transplant patients, our hybrid Data Envelopment Analysis (DEA)-Artificial Neural Network (ANN) model extrapolates the progression towards severe COVID-19 disease with an accuracy of 96.3%, outperforming any competing model, such as logistic regression (65.5%) and random forest (44.8%). In this regard, DEA-ANN allows us to categorize the evolution of patients through the values of the analyses performed at hospital admission. Our prediction model may help guiding COVID-19 management through the identification of key predictors that permit a sustainable management of resources in a patient-centered model. Supplementary Information: The online version contains supplementary material available at 10.1007/s10462-021-10008-0.
ABSTRACT
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