Search

Background: Residential mobility is believed to influence the occurrence and development of cancer; however, the results are inconclusive. Furthermore, limited studies have been conducted on Asian populations. This study aimed to evaluate the relationship between residential mobility and liver cancer risk among Chinese women. Methods: We enrolled 72,818 women from urban Shanghai between 1996 and 2000, and then followed them until the end of 2016. Cox regression models were used to calculate hazard ratios (HRs) and 95% confidence intervals (CIs) to assess the association between residential mobility and liver cancer risk. A linear trend test was conducted by ranking variables. A sensitivity analysis was also conducted, excluding participants with follow-up times of less than 2 years, to prevent potential bias. Results: During the 1,269,765 person-years of follow-up, liver cancer was newly diagnosed in 259 patients. Domestic migration (HR = 1.47, 95% CI, 1.44–1.50), especially immigration to Shanghai (HR = 1.47, 95% CI, 1.44–1.50) was associated with an increased risk of liver cancer. In addition, migration frequency, age at initial migration and first immigration to Shanghai had linear trends with an increased liver cancer risk (Ptrend <0.001). The results were similar when excluding participants with less than two years of follow-up. Conclusions: The possible association between residential mobility and a higher risk of liver cancer in women could suggest the need for effective interventions to reduce adverse environmental exposures and enhance people's health. [ABSTRACT FROM AUTHOR]

Background: The formal risk assessment is essential in the management of acute coronary syndrome (ACS). In this study, we develop a risk model for the prediction of 3-year mortality for Chinese ACS patients with machine learning algorithms.Methods: A total of 2174 consecutive patients who underwent angiography with ACS were enrolled. The missing data among baseline characteristics were imputed using the MissForest algorithm based on random forest method. In model development, a least absolute shrinkage and selection operator (LASSO) derived Cox regression with internal tenfold cross-validation was used to identify the predictors for 3-year mortality. The clinical performance was assessed with decision curve analysis.Results: The average follow-up period was 27.82 ± 13.73 months; during the 3 years of follow up, 193 patients died (mortality rate 8.88%). The Kaplan-Meier estimate of 3-year mortality was 0.91 (95% confidence interval (CI): 0.890.92). After feature selection, 6 predictors were identified: Age," "Creatinine," "Hemoglobin," "Platelets," "aspartate transaminase (AST)" and "left ventricular ejection fraction (LVEF)". At tenfold internal validation, our risk model performed well in both discrimination (area under curve (AUC) of receiver operating characteristic (ROC) analysis was 0.768) and calibration (calibration slope was approximately 0.711). As a comparison, the AUC and calibration slope were 0.701 and 0.203 in Global Registry of Acute Coronary Events (GRACE) risk score, respectively. Additionally, the highest net benefit of our model within the entire range of threshold probability for clinical intervention by decision curve analysis demonstrated the superiority of it in daily practice.Conclusion: Our study developed a prediction model for 3-year morality in Chinese ACS patients. The methods of missing data imputation and model derivation base on machine learning algorithms improved the ability of prediction. . Trial registration ChiCTR, ChiCTR-OOC-17010433. Registered 17 February 2017-Retrospectively registered. [ABSTRACT FROM AUTHOR]