Objective:The prognosis of cancer patients depends not only on tumor related factors, but also on host related factors, especially systemic inflammatory response. Based on the ratio of neutrophils to lymphocytes (NLR), the ratio of platelets to lymphocytes (PLR) and the ratio of lymphocytes to monocytes (LMR), we constructed a systemic inflammation model to predict the survival time of patients with gastric cancer (GC) after radical gastrectomy.Methods:Two hundred and five patients with GC who underwent radical resection from January 2011 to January 2017 were selected in Qinghai Provincial Communications Hospital and Red Cross Hospital of Qinghai Province. NLR, PLR and LMR were collected before operation. The best truncation values of NLR, PLR and LMR were obtained by ROC curve and systemic inflammatory marker score (SIMS) was constructed. The clinical value of SIMS was analyzed by single factor and multi factor Cox risk proportion model.Results:All patients were followed up for an average of (63.47 ± 10.36) months (range 20 to 65 months). The median survival time was 56 months. The one-year mortality rate was 6.3%, the three-year mortality rate was 26.2%, and the five-year mortality rate was 34.6%. The AUC of NLR, PLR and LMR were 0.745, 0.805 and 0.866 respectively, and the best truncation values were 3.11, 144 and 3.34 respectively. The mortality of patients with NLR > 3.11, PLR > 144, LMR ≤ 3.34 was higher than that of patients with NLR ≤ 3.11, PLR ≤ 114, LMR > 3.34 ( χ2 = 10.491, 14.658 and 38.765; P<0.01); there were differences in survival curves among different groups of NLR, PLR, LMR ( P < 0.05). The survival curves of different scores of SIMS were different ( P < 0.05). Age ( HR = 1.358, 95% CI 1.153 to 1.599), T stage-T 3 ( HR = 2.739, 95% CI 1.200 to 6.248), T stage-T 4 ( HR = 3.013, 95% CI 1.312 to 6.920), N stage-N 2 ( HR = 5.832, 95% CI 2.974 to 11.455), pathological stage Ⅲ ( HR = 2.962, 95% CI 1.835 to 4.646), lymphovascular invasion ( HR = 1.813, 95% CI 1.274 to 3.642), SIMS-1 ( HR = 7.065, 95% CI 4.673 to 10.692), SIMS-2 ( HR = 7.885, 95% CI 4.991 to 12.435), SIMS-3 ( HR = 8.365, 95% CI 5.635 to 3.485) were the independent risk factors of GC patients′ death ( P < 0.05). Conclusions:This study successfully constructs Sims and confirms that preoperative Sims is a relatively easy, easy to obtain and low-cost prognosis index for GC patients, which can be used to evaluate the survival time of GC patients before operation.

ObjectiveTo investigate the clinical features of elderly patients with hepatitis B virus-related acute-on-chronic liver failure （HBV-ACLF） and the risk factors affecting the short-term prognosis of patients. MethodsA retrospective analysis was performed for 417 patients with HBV-ACLF who were admitted to The General Hospital of Western Theater Command from January 2015 to January 2023， and related clinical data were collected， including general status， routine blood test results， biochemical parameters， and conditions of liver cirrhosis and decompensated events （ascites， hepatic encephalopathy， and their severities）. The patients were followed up to observe 90-day survival. According to the age， the patients were divided into elderly group （with 106 patients aged ≥60 years） and non-elderly group （with 311 patients aged<60 years）， and according to the 90-day survival， the elderly group were further divided into survival group with 41 patients and death/transplantation group with 65 patients. The independent-samples t test or the Mann-Whitney U test was used for comparison of quantitative data between two groups， and the chi-square test was used for comparison of qualitative data between two groups. The binary logistic regression analysis was used to determine the independent influencing factors for the risk of death within 90 days in elderly patients with HBV-ACLF， and a nomogram model was constructed for predicting the risk of death. The receiver operating characteristic （ROC） curve was used to investigate the value of the model in predicting the prognosis of HBV-ACLF patients in both the training set and the validation set. Calibration curve and decision curve were plotted for the models constructed in the training set and the validation set， and the model was assessed in terms of the degree of fitness and predicting benefits. ResultsThe elderly patients had a significantly higher 90-day mortality rate than the non-elderly patients （P<0.05）， and compared with the non-elderly group， the elderly group had significantly higher incidence rate in female individuals， basic incidence rate of liver cirrhosis， incidence rate and grade of hepatic encephalopathy， incidence rate of ascites， and liver fibrosis markers （aspartate aminotransferase-to-platelet ratio index and fibrosis-4） （all P<0.05）， as well as significantly lower total cholesterol， high-density lipoprotein， albumin， alpha-fetoprotein， and lymphocytes （all P<0.05）. As for the elderly patients with HBV-ACLF， there were significant differences between the survival group and the death/transplantation group in total cholesterol， total bilirubin， international normalized ratio （INR）， alpha-fetoprotein， platelet， creatinine， serum sodium， monocytes， and the incidence rate and grade of hepatic encephalopathy （all P<0.05）. In addition， the multivariate logistic regression analysis showed that INR （odds ratio ［OR］=11.351， 95% confidence interval ［CI］： 1.942‍ ‍—‍ ‍66.362， P<0.05）， monocyte count （OR=23.636， 95%CI： 1.388‍ ‍—‍ ‍402.529， P<0.05）， total bilirubin （OR=1.007， 95%CI： 1.001‍ ‍—‍ ‍1.013， P<0.05）， and platelet count （OR=0.968， 95%CI： 0.945‍ ‍—‍ ‍0.993， P<0.05） were independent influencing factors for the 90-day prognosis of elderly patients with HBV-ACLF， and the nomogram model constructed based on these factors had a relatively high predictive value， with an area under the ROC curve of 0.915， a sensitivity of 88.0%， and a specificity of 86.7%. The nomogram model showed relatively high efficiency and degree of fitness in the verification set， and the decision curve suggested that the model had good benefits， with a higher prediction efficiency compared with the commonly used prediction models such as MELD score and COSSH-ACLF Ⅱ score. ConclusionElderly HBV-ACLF patients may have a high short-term mortality rate due to the reductions in liver synthesis， reserve function， and regenerative ability and immune dysfunction. INR， monocyte count， total bilirubin， and platelet count have a relatively high value in predicting the risk of death in elderly HBV-ACLF patients， and the nomogram model constructed based on these factors has a relatively high prediction efficiency.