This pictorial review provides the principles of extracorporeal membrane oxygenation (ECMO) support and associated CT imaging features with emphasis on the hemodynamic changes and possible imaging pitfalls encountered. It is important that radiologists in ECMO centers apply well-designed imaging protocols and familiarize themselves with post-contrast CT imaging findings in patients on ECMO.
Adult ; Aorta, Thoracic/physiopathology/radiography ; Contrast Media/administration & dosage/pharmacokinetics ; Extracorporeal Membrane Oxygenation/classification/*methods ; Female ; Heart-Assist Devices ; Hemodynamics/*physiology ; Humans ; Intra-Aortic Balloon Pumping/instrumentation ; Male ; Middle Aged ; *Multidetector Computed Tomography ; Regional Blood Flow/physiology ; Retrospective Studies ; Ventricular Dysfunction, Left/physiopathology/radiography

Background/Aims: Despite the high efficacy of direct-acting antivirals (DAAs), approximately 1–3% of hepatitis C virus (HCV) patients fail to achieve a sustained virological response. We conducted a nationwide study to investigate risk factors associated with DAA treatment failure. Machine-learning algorithms have been applied to discriminate subjects who may fail to respond to DAA therapy. Methods: We analyzed the Taiwan HCV Registry Program database to explore predictors of DAA failure in HCV patients. Fifty-five host and virological features were assessed using multivariate logistic regression, decision tree, random forest, eXtreme Gradient Boosting (XGBoost), and artificial neural network. The primary outcome was undetectable HCV RNA at 12 weeks after the end of treatment. Results: The training (n=23,955) and validation (n=10,346) datasets had similar baseline demographics, with an overall DAA failure rate of 1.6% (n=538). Multivariate logistic regression analysis revealed that liver cirrhosis, hepatocellular carcinoma, poor DAA adherence, and higher hemoglobin A1c were significantly associated with virological failure. XGBoost outperformed the other algorithms and logistic regression models, with an area under the receiver operating characteristic curve of 1.000 in the training dataset and 0.803 in the validation dataset. The top five predictors of treatment failure were HCV RNA, body mass index, α-fetoprotein, platelets, and FIB-4 index. The accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of the XGBoost model (cutoff value=0.5) were 99.5%, 69.7%, 99.9%, 97.4%, and 99.5%, respectively, for the entire dataset. Conclusions Machine learning algorithms effectively provide risk stratification for DAA failure and additional information on the factors associated with DAA failure.