ABSTRACT
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.
Subject(s)
Humans , Female , Adult , Middle Aged , Adolescent , Breast Neoplasms/epidemiology , Cardiovascular Diseases/etiology , Proportional Hazards Models , Logistic Models , China/epidemiologyABSTRACT
Objective: To understand the epidemiological characteristics of COVID-19 epidemic in Ejina banner, Inner Mongolia, in October 2021 and provide evidence for the improvement of COVID-19 prevention and control. Methods: The information about the time, area and population distributions of COVID-19 cases in Ejina before November 13, 2021 and the gene sequencing result of the isolates were collected for a statistical descriptive analysis. Results: The first COVID-19 case in Ejina occurred on 7 October, 2021. A total of 164 COVID-19 cases were reported from October 19 to November 12. Most cases were distributed in 6 communities in Darahub (156 cases, 95.12%). The result of full gene sequencing of the isolates indicted that the pathogen was Delta variant (B.1.617.2). The male to female ratio of the cases was 1.3∶1. The age of cases ranged from 1 to 85 years, and the cases aged 20-59 years accounted for 78.66%. The main clinical symptoms were sore throat (91 cases, 91.92%), cough (49 cases, 49.49%) and fever (23 cases, 23.23%). Most cases were ordinary ones (81 cases, 49.39%) and mild ones (68 cases, 41.46%). The cases were mainly detected at the isolation points (84 cases, 51.22%) and through population based nucleic acid testing (62 cases, 37.80%). The basic reproduction number (R0) of COVID-19 was 5.3, the average incubation period was 3.9 days. The local government rapidly started Ⅳ level emergency response and conducted 10 rounds of nucleic acid tests. The transferring of travelers reduced the risk for the further spread of COVID-19 in Ejina. Conclusions: The epidemic of COVID-19 in Ejina characterized by strong transmission, short incubation period, herd susceptibility and case clustering. Delta variant (B.1.617.2) was the pathogen, which might be imported from Zeke port. Comprehensive prevention and control measures, such as closed-loop management and vaccination, should be continued. The successful transferring of the patients and travelers provided evidence for the effective and precise prevention and control of COVID-19 in a routine manner.