Clinical effect of allogeneic hematopoietic stem cell transplantation in children with hyper-IgM syndrome / 中国当代儿科杂志

OBJECTIVES@#To evaluate the clinical effect of allogeneic hematopoietic stem cell transplantation (allo-HSCT) in children with hyper-IgM syndrome (HIGM).@*METHODS@#A retrospective analysis was performed on the medical data of 17 children with HIGM who received allo-HSCT. The Kaplan Meier method was used for the survival analysis of the children with HIGM after allo-HSCT.@*RESULTS@#After allo-HSCT, 16 children were diagnosed with sepsis; 14 tested positive for virus within 100 days after allo-HSCT, among whom 11 were positive for Epstein-Barr virus, 7 were positive for cytomegalovirus, and 2 were positive for JC virus; 9 children were found to have invasive fungal disease. There were 6 children with acute graft-versus-host disease and 3 children with chronic graft-versus-host disease. The median follow-up time was about 2 years, and 3 children died in the early stage after allo-HSCT. The children had an overall survival (OS) rate of 82.35%, an event-free survival (EFS) rate of 70.59%, and a disease-free survival (DFS) rate of 76.47%. The univariate analysis showed that the children receiving HLA-matched allo-HSCT had a significantly higher EFS rate than those receiving HLA-mismatched allo-HSCT (P=0.019) and that the children receiving HLA-matched unrelated allo-HSCT had significantly higher OS, EFS, and DFS rates than those receiving HLA-mismatched unrelated allo-HSCT (P<0.05). Compared with the children with fungal infection after allo-HSCT, the children without fungal infection had significantly higher EFS rate (P=0.02) and DFS rate (P=0.04).@*CONCLUSIONS@#Allo-HSCT is an effective treatment method for children with HIGM. HLA-matched allo-HSCT and active prevention and treatment of fungal infection and opportunistic infection may help to improve the prognosis of such children.

Risk assessment of malignancy in solitary pulmonary nodules in lung computed tomography: a multivariable predictive model study / 中华医学杂志(英文版)

BACKGROUND@#Computed tomography images are easy to misjudge because of their complexity, especially images of solitary pulmonary nodules, of which diagnosis as benign or malignant is extremely important in lung cancer treatment. Therefore, there is an urgent need for a more effective strategy in lung cancer diagnosis. In our study, we aimed to externally validate and revise the Mayo model, and a new model was established.@*METHODS@#A total of 1450 patients from three centers with solitary pulmonary nodules who underwent surgery were included in the study and were divided into training, internal validation, and external validation sets (n = 849, 365, and 236, respectively). External verification and recalibration of the Mayo model and establishment of new logistic regression model were performed on the training set. Overall performance of each model was evaluated using area under receiver operating characteristic curve (AUC). Finally, the model validation was completed on the validation data set.@*RESULTS@#The AUC of the Mayo model on the training set was 0.653 (95% confidence interval [CI]: 0.613-0.694). After re-estimation of the coefficients of all covariates included in the original Mayo model, the revised Mayo model achieved an AUC of 0.671 (95% CI: 0.635-0.706). We then developed a new model that achieved a higher AUC of 0.891 (95% CI: 0.865-0.917). It had an AUC of 0.888 (95% CI: 0.842-0.934) on the internal validation set, which was significantly higher than that of the revised Mayo model (AUC: 0.577, 95% CI: 0.509-0.646) and the Mayo model (AUC: 0.609, 95% CI, 0.544-0.675) (P < 0.001). The AUC of the new model was 0.876 (95% CI: 0.831-0.920) on the external verification set, which was higher than the corresponding value of the Mayo model (AUC: 0.705, 95% CI: 0.639-0.772) and revised Mayo model (AUC: 0.706, 95% CI: 0.640-0.772) (P < 0.001). Then the prediction model was presented as a nomogram, which is easier to generalize.@*CONCLUSIONS@#After external verification and recalibration of the Mayo model, the results show that they are not suitable for the prediction of malignant pulmonary nodules in the Chinese population. Therefore, a new model was established by a backward stepwise process. The new model was constructed to rapidly discriminate benign from malignant pulmonary nodules, which could achieve accurate diagnosis of potential patients with lung cancer.