Individual mortality risk predictive system of patients with acute-on-chronic liver failure based on a random survival forest model / 中华医学杂志(英文版)

BACKGROUND@#The basis of individualized treatment should be individualized mortality risk predictive information. The present study aimed to develop an online individual mortality risk predictive tool for acute-on-chronic liver failure (ACLF) patients based on a random survival forest (RSF) algorithm.@*METHODS@#The current study retrospectively enrolled ACLF patients from the Department of Infectious Diseases of The First People's Hospital of Foshan, Shunde Hospital of Southern Medical University, and Jiangmen Central Hospital. Two hundred seventy-six consecutive ACLF patients were included in the present study as a model cohort (n = 276). Then the current study constructed a validation cohort by drawing patients from the model dataset based on the resampling method (n = 276). The RSF algorithm was used to develop an individual prognostic model for ACLF patients. The Brier score was used to evaluate the diagnostic accuracy of prognostic models. The weighted mean rank estimation method was used to compare the differences between the areas under the time-dependent ROC curves (AUROCs) of prognostic models.@*RESULTS@#Multivariate Cox regression identified hepatic encephalopathy (HE), age, serum sodium level, acute kidney injury (AKI), red cell distribution width (RDW), and international normalization index (INR) as independent risk factors for ACLF patients. A simplified RSF model was developed based on these previous risk factors. The AUROCs for predicting 3-, 6-, and 12-month mortality were 0.916, 0.916, and 0.905 for the RSF model and 0.872, 0.866, and 0.848 for the Cox model in the model cohort, respectively. The Brier scores were 0.119, 0.119, and 0.128 for the RSF model and 0.138, 0.146, and 0.156 for the Cox model, respectively. The nonparametric comparison suggested that the RSF model was superior to the Cox model for predicting the prognosis of ACLF patients.@*CONCLUSIONS@#The current study developed a novel online individual mortality risk predictive tool that could predict individual mortality risk predictive curves for individual patients. Additionally, the current online individual mortality risk predictive tool could further provide predicted mortality percentages and 95% confidence intervals at user-defined time points.

Mutation of Notch1 gene and its protein expression in T-cell lymphoma / 白血病·淋巴瘤

Objective To investigate the effect of the expression of Notch1 protein and the mutation of Notch1 gene in.T-cell lymphoma (TCL).Methods Immunohistochemistry was used to detect the expression of Notch1 protein,and PCR amplification and DNA sequencing were used to detect the mutation of Notch1 gene in the 26th and 27th HD domain and the 34th PEST domain in 30 cases.10 cases of reactive hyperplasia tissues of lymph node were as the control.Results The positive rates of Notch1 protein expression and Notch1 gene mutation were 70.0 ％ (21/30) and 56.7 ％ (17/30).8 cases of Notch1 mutations were detected in the HD domain,6 cases in the PEST domain,and 3 case in both HD and PEST domains.Inscrtion,deletion,nonsense mutation and missense mutation were included in Notch 1 mutations.Conclusion Notch1 gene mutation may play an important role in the expression of Notch1 protein.The occur of TCL is related to the expression of Notch1 protein and the mutation of Notch1 gene.