Background: To explore the correlation between the preoperative systemic immune inflammation index (SII) and the prognosis of patients with gastric carcinoma (GC). Methods: The clinical data of 771 GC patients surgically treated in the Department of Gastrointestinal Surgery, Qinghai University Affiliated Hospital from June 2010 to June 2015 were retrospectively analyzed, and their preoperative SII was calculated. The optimal cut-off value of preoperative SII was determined using the receiver operating characteristic (ROC) curve, the confounding factors between the two groups were eliminated using the propensity score matching (PSM) method, and the correlation between preoperative SII and clinicopathological characteristics was assessed by chi-square test. Moreover, the overall survival was calculated using Kaplan-Meier method, the survival curve was plotted, and log-rank test was performed for the significance analysis between the curves. Univariate and multivariate analyses were also conducted using the Cox proportional hazards model. Results: It was determined by the ROC curve that the optimal cut-off value of preoperative SII was 489.52, based on which 771 GC patients were divided into high SII (H-SII) group and low SII (L-SII) group, followed by PSM in the two groups. The results of Kaplan-Meier analysis showed that before and after PSM, the postoperative 1-, 3-, and 5-year survival rates in L-SII group were superior to those in H-SII group, and the overall survival rate had a statistically significant difference between the two groups (P < 0.05). Before PSM, preoperative SII [hazard ratio (HR) = 2.707, 95% confidence interval (CI) 2.074-3.533, P < 0.001] was an independent risk factor for the prognosis of GC patients. After 1:1 PSM, preoperative SII (HR = 2.669, 95%CI 1.881–3.788, P < 0.001) was still an independent risk factor for the prognosis of GC patients. Conclusions: Preoperative SII is an independent risk factor for the prognosis of GC patients. The increase in preoperative SII in peripheral blood indicates a worse prognosis.

Background: To explore the relationship between the preoperative immune inflammation index (SII) and the prognostic nutritional index (PNI) and the overall survival rate (OS) of patients with alveolar hydatid disease.Methods: The clinical data of patients with hepatic alveolar echinococcosis treated by surgery in the Department of Hepatobiliary and Pancreatic Surgery, Affiliated Hospital of Qinghai University from January 2015 to January 2019 were analyzed retrospectively, and the SII, PNI, PLR and NLR were calculated. Spearman correlation analysis was utilized to analyze the correlation among SII, PNI, PLR and NLR. Receiver operating characteristic curve (ROC) was utilized to determine the best intercept values of SII, PNI, PLR and NLR, and Chi-square test was used to evaluate the relationship between SII, PNI and various clinicopathological features in patients with hepatic alveolar echinococcosis. The kaplan-Meier method was used to draw survival curves and analyze the relationship between them and the total survival time of patients. A cox regression model was used to analyze the relationship between SII, PNI and the prognosis of patients with hepatic alveolar echinococcosis. Finally, ROC curve was used to estimate the predictive efficacy of SII, PNI and COSII-PNI for the prognosis of patients with hepatic alveolar echinococcosis.Results: A total of 242 patients were included, including 96 males and 146 females, aged 11.0-67.0 (36.6 ± 11.7) years. The values of SII, PNI, PLR and NLR are calculated, and the best truncation values of SII, PNI, PLR and NLR are given in ROC curve. The kaplan-Meier survival curve was used to analyze the relationship between SII, PNI, PLR, NLR and the overall survival time of patients with hepatic alveolar echinococcosis. The results showed that the median follow-up time was 45 months (95%CI: 39.484-50.516), and the average survival time was 49 months (95%CI: 47.300-51.931), which was low p<0.001); The 5-year OS rate of low PNI was significantly lower than that of high PNI group (37.7% vs 71.6%; p<0.001); The 5-year OS rate in low PLR group was significantly higher than that in high PLR group (70.4% vs 24.3%; p<0.001); The 5-year OS rate in low NLR group was significantly higher than that in high NLR group (67.2% vs 28.8%; p<0.001). Cox unifoliate analysis showed that SII, PNI, PLR and NLR were important prognostic factors related to OS. Cox multivariate analysis showed that SII(HR=4.678, 95% CI: 2.581-8.480, P<0.001) and PNI(HR=0.530, 95%CI: 0.305-0.920, P<0.05) were identified as independent risk indicators of OS, while NL was identified as independent risk indicators of OS ROC curve analysis showed that AUC of SII, PNI, PLR, NLR and COSII-PNI were 0.670(95%CI: 0.601-0.738), 0.638(95%CI: 0.561-0.716) and 0.618(95% CI: 0.541-0.694), respectively COSII-PNI is superior to SII and PNI in evaluating prognosis (P < 0.05).Conclusions: SII and PNI can be regarded as independent risk factors reflecting the prognosis of patients with hepatic alveolar echinococcosis. The lower SII and the higher PNI before operation, the better the prognosis of patients, and the combined application of SII and PNI before operation can improve the accuracy of prediction.

Background: Colorectal cancer (CRC) with liver metastasis is associated with poor prognosis and substantial heterogeneity in survival outcomes. Accurate risk stratification is essential for individualized treatment planning, yet reliable and interpretable prognostic tools remain limited. Therefore, this study aimed to develop and validate a prognostic model for overall survival (OS) in a predominantly surgically treated cohort of patients with CRC and synchronous liver metastasis at diagnosis based on Surveillance, Epidemiology, and End Results (SEER) data, and to enhance model interpretability using SHapley Additive exPlanations (SHAP) analysis. Methods: Data of patients with CRC with liver metastasis diagnosed between 2011 and 2018 were obtained from the SEER database. OS was used as the endpoint. Kaplan-Meier analysis and univariate and multivariable Cox regression were performed, and multivariable Cox regression was used to identify independent prognostic factors. A nomogram was constructed, and its performance was evaluated using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). SHAP analysis was further applied to improve model interpretability. Results: Kaplan-Meier analysis showed a poor overall prognosis, with a median OS of 25 months. Multivariable Cox regression identified age, tumor location, differentiation, metastatic stage, preoperative carcinoembryonic antigen (CEA), perineural invasion, bone metastases, metastasectomy, postoperative chemotherapy, and lymph node ratio (LNR) as independent prognostic factors for OS. Based on these variables, a nomogram was developed to predict 12-, 36-, and 60-month OS. The model showed good discrimination, calibration, and clinical utility in both the training and validation sets. SHAP analysis further improved model interpretability by showing the relative contribution of each predictor to survival estimation. Conclusions: We developed and internally validated a prognostic nomogram for OS in patients with CRC with liver metastasis based on SEER data. The model showed acceptable predictive performance and may provide supportive information for individualized prognosis evaluation in this population.

This study aimed to explore the changes of serum brain natriuretic peptide (BNP), procalcitonin (PCT), and recombinant single Ig IL-1-related receptor (SIGIRR) in patients with sepsis associated with abdominal infection in emergency and their relationship with prognosis and construct a nomogram prediction model. A total of 160 emergency patients with abdominal infection were divided into simple infection group (n = 80) and sepsis group (n = 80) according to whether sepsis occurred or not, and the baseline data, serum BNP, PCT, and SIGIRR levels of the 2 groups were compared. Patients in the sepsis group were divided into survival group (n = 52) and death group (n = 28) according to their survival conditions within 28 days. The baseline data, serum BNP, PCT, and SIGIRR levels of the 2 groups were compared, and the diagnostic value of serum BNP, PCT, and SIGIRR levels in the sepsis group was analyzed by receiver operating characteristic (ROC) curve. The related factors affecting the prognosis of sepsis patients were analyzed using a binary logistic equation. The nomogram risk prediction model of poor prognosis of sepsis patients is drawn and verified by calibration curve and ROC curve. The serum levels of BNP, PCT, and SIGIRR in the simple infection group were significantly lower than those in the sepsis group ( P < .05). In patients with sepsis group, the serum levels of BNP, PCT, and SIGIRR in the death group were significantly higher than those in the survival group ( P < .05). ROC curve analysis showed that the areas under the curve of serum BNP, PCT, and SIGIRR in predicting the poor prognosis of sepsis patients were 0.874, 0.824, and 0.784, respectively. Univariate and multivariate logistic regression analyses showed that serum BNP, PCT, and SIGIRR levels were independent risk factors for poor prognosis of sepsis patients ( P < .05). Based on the independent risk factors (BNP, PCT, and SIGIRR) screened by logistic regression model, the nomogram was constructed, with Concordance Index of 0.947, which indicated that the nomogram had good discrimination and consistency. The levels of BNP, PCT, and SIGIRR of patients with sepsis were significantly increased, which was significantly related to the poor prognosis of patients. The nomogram prediction model constructed on this basis is helpful for clinicians to provide better individualized treatment for the poor prognosis of patients with sepsis associated with abdominal infection.