New to MyHealth?
Manage Your Care From Anywhere.
Access your health information from any device with MyHealth. You can message your clinic, view lab results, schedule an appointment, and pay your bill.
ALREADY HAVE AN ACCESS CODE?
DON'T HAVE AN ACCESS CODE?
NEED MORE DETAILS?
MyHealth for Mobile
A Predictive Model for Postembolization Syndrome after Transarterial Hepatic Chemoembolization of Hepatocellular Carcinoma.
A Predictive Model for Postembolization Syndrome after Transarterial Hepatic Chemoembolization of Hepatocellular Carcinoma. Radiology Khalaf, M. H., Sundaram, V., AbdelRazek Mohammed, M. A., Shah, R., Khosla, A., Jackson, K., Desai, M., Kothary, N. 2018: 180257Abstract
Purpose To develop and validate a predictive model for postembolization syndrome (PES) following transarterial hepatic chemoembolization (TACE) for hepatocellular carcinoma. Materials and Methods In this single-center, retrospective study, 370 patients underwent 513 TACE procedures between October 2014 and September 2016. Seventy percent of the patients were randomly assigned to a training data set and the remaining 30% were assigned to a testing data set. Variables included demographic, laboratory, clinical, and procedural details. PES was defined as pain and/or nausea beyond 6 hours after TACE that required intravenous medication for symptom control. The predictive model was developed by using conditional inference trees and Lasso regression. Results Demographics, laboratory data, performance, tumor characteristics, and procedural details were statistically similar for the training and testing data sets. Overall, 83 of 370 patients (22.4%) after 107 of 513 TACE procedures (20.8%) met the predefined criteria. Factors identified at univariable analysis included large tumor burden (P = .004), drug-eluting embolic TACE (P = .03), doxorubicin dose (P = .003), history of PES (P < .001) and chronic pain (P < .001), of which history of PES, tumor burden, and drug-eluting embolic TACE were identified as the strongest predictors by the multivariable analysis and were used to develop the predictive model. When applied to the testing data set, the model demonstrated an area under the curve of 0.62, sensitivity of 79% (22 of 28), specificity of 44.2% (53 of 120), and a negative predictive value of 90% (53 of 59). Conclusion The model identified history of postembolization syndrome, tumor burden, and drug-eluting embolic chemoembolization as predictors of protracted recovery because of postembolization syndrome. © RSNA, 2018.
View details for PubMedID 30299233