1.Value of preoperative peripheral blood albumin-to-globulin ratio in predicting the prognosis of non-metastatic clear cell renal cell carcinoma
Kai LI ; Qian CHEN ; Huaiding TANG ; Dashuai PENG ; Qizhong FU ; Ying LIU
Cancer Research and Clinic 2020;32(6):410-414
Objective:To investigate the prognostic value of preoperative peripheral blood albumin-to-globulin ratio (AGR) for patients with non-metastatic clear cell renal cell carcinoma (NMCCRCC).Methods:The clinicopathological and postoperative follow-up data of 61 patients with NMCCRCC confirmed by postoperative pathology who were admitted to Zhongshan Hospital of Dalian University from January 2012 to December 2014 was retrospectively analyzed. According to the receiver operating characteristic curve (ROC), the optimal cut-off value of AGR was determined. According to the cut-off value, the patients were divided into high AGR group (AGR≥1.59) and low AGR group (AGR<1.59). The factors affecting the prognosis of NMCCRCC patients were analyzed by using univariate and multivariate analyses.Results:Among 61 patients with NMCCRCC, 38 cases (62.3%) were in high AGR group and 23 cases (37.7%) were in low AGR group. The differences in age, globulin level, clinical staging, and recurrence status between the two groups were statistically significant (all P < 0.05). The overall survival time and disease-free survival time in low AGR group were shorter than those in high AGR group, and the differences were statistically significant (both P < 0.05). Multivariate Cox regression analysis showed that AGR < 1.59 was the independent influencing factor for overall survival and recurrence ( HR = 0.233, 95% CI 0.073-0.742, P = 0.014; HR = 0.343, 95% CI 0.134-0.873, P = 0.025). Conclusion:Preoperative AGR is valuable in predicting the prognosis of NMCCRCC patients.
2.Establishment and validation of a nomogram risk prediction model for infection complications in patients after hepatectomy for liver cancer
Mingqiang ZHU ; Dashuai YANG ; Xiangyun XIONG ; Junpeng PEI ; Yang PENG ; Youming DING
Journal of Clinical Hepatology 2023;39(1):110-117
Objective To investigate the risk factors of infection after hepatectomy for liver cancer, and to establish and validate a risk prediction model. Methods The clinical data of 167 patients with primary liver cancer who underwent hepatectomy in People's Hospital of Wuhan University from January 2020 to March 2022 were retrospectively collected. All patients were divided into postoperative infection group ( n =28) and non-infection group ( n =139) according to whether postoperative infection complications occurred. The t -test or Mann-Whitney U test was used for comparison of continuous data between two groups and the chi-square test was used for comparison of categorical data between two groups. Univariate analysis and logistic regression analysis were used to screen the risk factors of infection after hepatectomy for hepatocellular carcinoma, and a nomogram risk prediction model for postoperative infection was established. All patients were randomly divided into training cohort ( n =119) and the validation cohort ( n =48) according to the ratio of 7∶ 3, the Bootstrap method was used for internal validation of the model, and the model calibration curve and ROC curve were used to evaluate the calibration and discrimination of the nomogram model. Results Postoperative infection occurred in 28 of 167 patients (16.8%). Logistic regression analysis showed that diabetes, CONUT score ≥4 points, preoperative NLR, operation time, intraoperative blood loss, and drainage tube placement time > 7 d were independent risk factors for infection after hepatectomy for liver cancer (all P < 0.05). Based on the nomogram constructed from the above six risk factors, the area under the ROC curve of the training cohort and the validation cohort was 0.848, and 0.853, respectively. The calibration curve of the nomogram model shows that the predicted value is basically consistent with the actual observed value, indicating that the accuracy of the nomogram model prediction is better. Conclusion The individualized nomogram risk prediction model based on diabetes, CONUT score ≥4 points, preoperative NLR, operation time, intraoperative blood loss, and drainage tube placement time > 7 d has good predictive performance and has high predictive value for high-risk patients.