ObjectiveTo explore the correlation of pretreatment systemic immune inflammation index (SII) with prognosis in esophageal cancer patients.MethodsWe searched the PubMed, Web of Science, Embase, Cochrane Library, China National Knowledge Infrastructure, VIP, Chinese Biology Medicine, and Wanfang databases to identify eligible studies evaluating the relation between pretreatment SII and prognosis in patients with esophageal cancer from establishment of databases to December 2018. SII was defined as the absolute neutrophil count multiplied by the absolute platelet count divided by the absolute lymphocyte count. The primary endpoint was overall survival (OS), and the secondary endpoints were cancer-specific survival and disease-free survival. The Stata 12.0 software was applied for the meta-analysis, and the hazard ratio (HR) and 95% confidence interval (CI) were assessed.ResultsA total of six retrospective studies involving 2 376 esophageal cancer patients were included and all patients were from China or Japan. The results revealed that elevated pretreatment SII was significantly associated with poor OS in esophageal cancer [HR=1.50, 95%CI (1.15, 1.95), P=0.002]. Subgroup analyses of OS indicated that SII had a high prognostic value in patients who received surgery [HR=1.54, 95%CI (1.14, 2.08), P=0.005] and were diagnosed as esophageal squamous cell carcinoma [HR=1.50, 95%CI (1.11, 2.02), P=0.007]; however, no significant relation was observed between SII and prognosis in esophageal cancer patients who were treated with radiotherapy [HR=1.318, 95%CI (0.611, 2.841), P=0.482]. Furthermore, compared with neutrophil to lymphocyte ratio and platelet to lymphocyte ratio, SII showed a higher predictive value for the prognosis of esophageal cancer.ConclusionsPretreatment SII may serve as an independent risk factor for prognosis of Chinese and Japanese esophageal cancer patients, especially patients who were treated with surgery and with esophageal squamous cell carcinoma. However, more prospective studies with big samples from other countries or regions are still needed to verify our findings.
ObjectiveTo systematically review the relationship between systemic immune inflammatory index (SII) and the prognosis of coronary heart disease. MethodsThe CNKI, VIP, CBM, WanFang Data, PubMed, EMbase, Web of Science, Ovid, Cochrane Library and Scopus databases were electronically searched to collect cohort studies related to the relationship between SII and the prognosis of patients with coronary heart disease from inception to December 10, 2022. Two researchers independently screened the literature, extracted the data and assessed the risk of bias of the included studies. Meta-analysis was then performed by using RevMan 5.3 and Stata 15.0 software. ResultsA total of 7 cohort studies involving 18 413 patients were included. The results of meta-analysis showed that the group of high level SII was higher risk of major adverse cardiovascular events (MACE) (OR=2.2, 95%Cl 1.5 to 3.3, P<0.01), all-cause death (OR=2.0, 95%Cl 1.1 to 3.4, P=0.02), and cardiogenic death (OR=2.4, 95%Cl 1.5 to 3.9, P<0.01) than the group of low level SII. However, no significant difference was found in the risk of re-hospitalization for heart failure. ConclusionThe current evidence shows that high levels of SII can increase the risk of MACE, all-cause death and cardiogenic death in patients with coronary heart disease. Due to the limited quality and quantity of the included studies, more high quality studies are needed to verify the above conclusion.
Objective To investigate the efficacy of systemic immune inflammation index (SII) at admission and National Institutes of Health Stroke Scale (NIHSS) score immediately after thrombolysis on evaluating the short-term prognosis of neurological function in patients with acute ischemic stroke (AIS) receiving intravenous thrombolysis. Methods Patients with AIS treated with intravenous thrombolysis in the Second People’s Hospital of Chengdu between March 2022 and March 2023 were retrospectively analyzed. The basic data of the patients, NIHSS score at emergency admission, NIHSS score immediately after thrombolysis, modified Rankin Scale (mRS) score 3 months after discharge, and laboratory data at admission were collected, and SII at admission was calculated. According to the mRS score 3 months after discharge, the patients were divided into the good prognosis group (mRS≤2) and the poor prognosis group (mRS>2). Multivariate logistic regression analysis was used to screen out the factors affecting the prognosis of patients, and the receiver operating characteristic curve was drawn to analyze the evaluation effect of SII at admission and NIHSS score immediately after thrombolysis on the poor prognosis of neurological function of patients in the short term. Results A total of 213 patients were enrolled, and the prognosis was poor in 88 patients. Multivariate logistic regression analysis showed that age, onset-to-needle time, uric acid at admission, SII at admission, fasting blood glucose after admission, and NIHSS score immediately after thrombolysis were independent risk factors for poor prognosis in AIS patients (P<0.05). The area under the receiver operating characteristic curve (AUC) of SII at admission for predicting poor prognosis was 0.715, the sensitivity was 55.7%, and the specificity was 84.0%. The AUC of NIHSS score immediately after thrombolysis for predicting poor prognosis of patients was 0.866, the sensitivity was 87.5%, and the specificity was 72.8%. The AUC of SII at admission combined with NIHSS score immediately after thrombolysis for predicting poor prognosis of patients was 0.875, the sensitivity was 84.1%, the specificity was 77.6%, the positive predictive value was 72.5%, and the negative predictive value was 87.4%. SII at admission was positively correlated with NIHSS score at emergency admission, NIHSS score immediately after thrombolysis, and mRS score 3 months after discharge (P<0.05). Conclusion SII at admission can predict the short-term prognosis of neurological function of patients with AIS after thrombolysis therapy, and the combination of SII at admission and NIHSS score immediately after thrombolysis can improve the prediction efficiency.
Objective To investigate the correlation between systemic immune inflammatory index (SII) and other metabolic indicators and diabetic epiretinal membranes (dERM). MethodsA retrospective case-control study. From March 2022 to July 2023, 81 patients (81 eyes) with dERM in Department of Ophthalmology, Affiliated Jinhua Hospital of Zhejiang University of Medicine School diagnosed by fundus screening were included in the study. A total of 81 patients (81 eyes) with diabetes who were matched in age, gender, and duration of diabetes and had no dERM or diabetic macular edema in both eyes during fundus screening were selected as the control group. All patients underwent optical coherence tomography (OCT) examination and laboratory tests for peripheral blood neutrophil, lymphocyte, platelet counts, serum albumin, blood lipids, uric acid, and glycosylated hemoglobin (HbA1c). SII was calculated. Random urine samples were collected for urinary albumin/creatinine ratio (ACR) testing. The OCT device's own analysis software obtained the macular volume coefficient, including central foveal thickness (CMT), macular volume, and average macular thickness. The macular volume coefficient, SII, serum albumin, blood lipids, uric acid, HbA1c, and ACR between the two groups were compared using paired t tests or Mann-Whitney U tests. Conditional logistic regression analysis was performed to evaluate the risk factors for dERM; Spearman correlation test was used to analyze the correlation between CMT, SII, ACR, disorganization of retinal inner layers (DRIL), intraretinal cyst (IRC), and hyper-reflective foci (HRF) in patients with dERM. ResultsThere were significant differences in CMT, macular volume, average macular thickness, SII, serum albumin, and ACR between the dERM group and the control group (Z=−7.234, −6.306, −6.400, −3.063, −2.631, −3.868; P＜0.05). Conditional logistics regression analysis showed that high SII [odds ratio (OR)= 3.919, 95% confidence interval (CI) 1.591-9.654, P=0.003] and ACR (OR=4.432, 95%CI 1.885-10.420, P=0.001) were risk factors for dERM. Spearman correlation analysis showed that HRF, IRC, DRIL were positively correlated with CMT (Rs=0.234, 0.330, 0.248; P=0.036, 0.003, 0.026); HRF was positively correlated with SII and ACR (Rs=0.233, 0.278; P=0.036, 0.012). ConclusionElevated SII and ACR are independent risk factors for the occurrence of dERM.