To investigate COVID‐19 vaccine coverage in immunosuppressed children, assess guardians' intention to vaccinate children, and determine reasons and associated factors. In addition, we attempted to capture the characteristics of them with Omicron. We obtained the vaccination coverage and guardian vaccine acceptance among pediatric transplant recipients through a web‐based questionnaire conducted from April 12 to 28, 2022, and performed the statistical analysis. Seven organ transplant recipient children with Omicron were also clinically analyzed. The three‐dose vaccine coverage for liver transplant (n = 563) and hematopoietic stem cell transplantation (n = 122) recipient children was 0.9% and 4.9%, and guardian vaccine acceptance was 63.8%. Independent risk factors for vaccine acceptance were the child's age, geographic location, type of transplant, guardian's vaccination status, guardian's level of distress about epidemic events, guardian's risk perception ability, anxiety, and knowledge of epidemic control. The main reasons for vaccine hesitancy were fear of vaccine‐induced adverse events and doubts about efficacy. Ultimately, most children infected with Omicron have mild or no symptoms and are infected by intra‐family. Since vaccine coverage and guardian acceptance are lowest among liver transplant children, and the infected are mainly intra‐family, we should devise more targeted education and vaccination instructions for their guardians.
Purpose:The indocyanine green retention rate at 15 min (ICG-R15) is of great importance in the accurate assessment of hepatic functional reserve for safe hepatic resection. To assist clinicians to evaluate hepatic functional reserve in medical institutions that lack expensive equipment, we aimed to explore a novel approach to predict ICG-R15 based on CT images and clinical data in patients with hepatocellular carcinoma (HCC).MethodsIn this retrospective study, 350 eligible patients were enrolled and randomly assigned to the training cohort (245 patients) and test cohort (105 patients). Radiomics features and clinical factors were analyzed to pick out the key variables, and based on which, we developed the random forest regression, extreme gradient boosting regression (XGBR), and artificial neural network models for predicting ICG-R15, respectively. Pearson's correlation coefficient (R) was adopted to evaluate the performance of the models.ResultsWe extracted 660 CT image features in total from each patient. Fourteen variables significantly associated with ICG-R15 were picked out for model development. Compared to the other two models, the XGBR achieved the best performance in predicting ICG-R15, with a mean difference of 1.59% (median, 1.53%) and an R-value of 0.90. Delong test result showed no significant difference in the area under the receiver operating characteristic (AUROCs) for predicting post hepatectomy liver failure between actual and estimated ICG-R15.ConclusionThe proposed approach that incorporates the optimal radiomics features and clinical factors can allow for individualized prediction of ICG-R15 value of patients with HCC, regardless of the specific equipment and detection reagent (NO. ChiCTR2100053042; URL, http://www.chictr.org.cn).
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