ABSTRACT
We performed a review of the literature (until August 01, 2019) on the occasion of the first transcaval approach for transcatheter aortic valve implantation in our hospital. This review focuses mainly on the indications of this alternative access route to the aorta. It may be useful for vascular surgeons in selected cases, such as the treatment of endoleaks after endovascular aneurysm repair and thoracic endovascular aneurysm repair. We describe historical aspects of transcaval access to the aorta, experimental studies, available case series and outcomes. Finally, we summarize the most significant technical aspects of this little-known access.
Subject(s)
Aortic Aneurysm, Abdominal , Blood Vessel Prosthesis Implantation , Endovascular Procedures , Surgeons , Aorta, Abdominal/surgery , Aortic Aneurysm, Abdominal/surgery , Blood Vessel Prosthesis , Humans , Male , Stents , Treatment OutcomeABSTRACT
Abstract We performed a review of the literature (until August 01, 2019) on the occasion of the first transcaval approach for transcatheter aortic valve implantation in our hospital. This review focuses mainly on the indications of this alternative access route to the aorta. It may be useful for vascular surgeons in selected cases, such as the treatment of endoleaks after endovascular aneurysm repair and thoracic endovascular aneurysm repair. We describe historical aspects of transcaval access to the aorta, experimental studies, available case series and outcomes. Finally, we summarize the most significant technical aspects of this little-known access.
Subject(s)
Humans , Male , Aortic Aneurysm, Abdominal/surgery , Blood Vessel Prosthesis Implantation , Endovascular Procedures , Surgeons , Aorta, Abdominal/surgery , Blood Vessel Prosthesis , Stents , Treatment OutcomeABSTRACT
BACKGROUNDEfficient and early triage of hospitalized Covid-19 patients to detect those with higher risk of severe disease is essential for appropriate case management. METHODSWe trained, validated, and externally tested a machine-learning model to early identify patients who will die or require mechanical ventilation during hospitalization from clinical and laboratory features obtained at admission. A development cohort with 918 Covid-19 patients was used for training and internal validation, and 352 patients from another hospital were used for external testing. Performance of the model was evaluated by calculating the area under the receiver-operating-characteristic curve (AUC), sensitivity and specificity. RESULTSA total of 363 of 918 (39.5%) and 128 of 352 (36.4%) Covid-19 patients from the development and external testing cohort, respectively, required mechanical ventilation or died during hospitalization. In the development cohort, the model obtained an AUC of 0.85 (95% confidence interval [CI], 0.82 to 0.87) for predicting severity of disease progression. Variables ranked according to their contribution to the model were the peripheral blood oxygen saturation (SpO2)/fraction of inspired oxygen (FiO2) ratio, age, estimated glomerular filtration rate, procalcitonin, C-reactive protein, updated Charlson comorbidity index and lymphocytes. In the external testing cohort, the model performed an AUC of 0.83 (95% CI, 0.81 to 0.85). This model is deployed in an open source calculator, in which Covid-19 patients at admission are individually stratified as being at high or non-high risk for severe disease progression. CONCLUSIONSThis machine-learning model, applied at hospital admission, predicts risk of severe disease progression in Covid-19 patients.
