Development and validation of risk prediction model for new-onset cardiovascular diseases among breast cancer patients: Based on regional medical data of Inner Mongolia / 北京大学学报(医学版)

OBJECTIVE@#To develop and validate a three-year risk prediction model for new-onset cardiovascular diseases (CVD) among female patients with breast cancer.@*METHODS@#Based on the data from Inner Mongolia Regional Healthcare Information Platform, female breast cancer patients over 18 years old who had received anti-tumor treatments were included. The candidate predictors were selected by Lasso regression after being included according to the results of the multivariate Fine & Gray model. Cox proportional hazard model, Logistic regression model, Fine & Gray model, random forest model, and XGBoost model were trained on the training set, and the model performance was evaluated on the testing set. The discrimination was evaluated by the area under the curve (AUC) of the receiver operator characteristic curve (ROC), and the calibration was evaluated by the calibration curve.@*RESULTS@#A total of 19 325 breast cancer patients were identified, with an average age of (52.76±10.44) years. The median follow-up was 1.18 [interquartile range (IQR): 2.71] years. In the study, 7 856 patients (40.65%) developed CVD within 3 years after the diagnosis of breast cancer. The final selected variables included age at diagnosis of breast cancer, gross domestic product (GDP) of residence, tumor stage, history of hypertension, ischemic heart disease, and cerebrovascular disease, type of surgery, type of chemotherapy and radiotherapy. In terms of model discrimination, when not considering survival time, the AUC of the XGBoost model was significantly higher than that of the random forest model [0.660 (95%CI: 0.644-0.675) vs. 0.608 (95%CI: 0.591-0.624), P < 0.001] and Logistic regression model [0.609 (95%CI: 0.593-0.625), P < 0.001]. The Logistic regression model and the XGBoost model showed better calibration. When considering survival time, Cox proportional hazard model and Fine & Gray model showed no significant difference for AUC [0.600 (95%CI: 0.584-0.616) vs. 0.615 (95%CI: 0.599-0.631), P=0.188], but Fine & Gray model showed better calibration.@*CONCLUSION@#It is feasible to develop a risk prediction model for new-onset CVD of breast cancer based on regional medical data in China. When not considering survival time, the XGBoost model and the Logistic regression model both showed better performance; Fine & Gray model showed better performance in consideration of survival time.

Arsenic speciation in rat plasma after oral administration of realgar and Niu Huang Jie Du Pian / 药学学报

The arsenic species in rat plasma were studied after oral administration of realgar and Niu Huang Jie Du Pian (NHJDP) and the possible compatible effects of realgar was evaluated by comparing the pharmacokinetics of arsenic species after administration of realgar and NHJDP. The separation of the arsenicals was performed by a high performance liquid chromatography-hydride generation-atomic fluorescence spectrometry (HPLC-HG-AFS) technique. Dimethylarsinic acid (DMA) was found to be the main species in rats' plasma after dosing. No traces of arsenite[As(Ⅲ)], monomethylarsonic acid (MMA) or arsenate[As(V)] were detected at any sampling time points. Compared with realgar administration alone, dose-normalized peak concentration (Cmax) and AUC0-t of DMA were significantly decreased by NHJDP administration, while the tmax was significantly delayed with the clearance and apparent volume of distribution significantly increased, indicating that the pharmacokinetics of As from realgar was affected by other ingredients in the compound prescription of NHJDP.