Increased expression of Ki-67 is a poor prognostic marker for colorectal cancer patients: a meta analysis.

BACKGROUND: The prognostic value of Ki-67 expression in colorectal cancer patients was controversial. Therefore, this meta analysis was conducted to ascertain the prognostic value of Ki-67 expression in colorectal cancer patients. METHODS: The electronic databases, including EMBASE, PubMed, Cochrane Library and Web of Knowledge database, were searched from January 1970 to July 2017. The pooled hazard ratios and 95% confidence intervals were calculated to evaluate the prognostic value of Ki-67 expression for colorectal cancer patients. RESULTS: Totally 34 eligible studies and 6180 colorectal cancer patients were included in the present meta analysis. The pooled hazard ratios were 1.54(95% CI 1.17-2.02, P = 0.005) for overall survival and 1.43(1.12-1.83, P = 0.008) for disease free survival in univariate analysis. After adjustment of other prognostic factors, the pooled HR was 1.50(95% CI 1.02-2.22, P = 0.03) for overall survival in multivariate analysis. CONCLUSION: The present meta analysis demonstrated that high Ki-67 expression is significantly correlated with poor overall survival and disease free survival, indicating that high Ki-67 expression may serve as a valuable predictive method for poor prognosis of colorectal cancer patients.

Individual mortality risk predictive system of patients with acute-on-chronic liver failure based on a random survival forest model.

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.