Purpose We aimed to explore the relationship between the systemic immune-inflammation index (SII) and rheumatoid arthritis (RA) using NHANES from 1999 to 2018. Methods We collected data from the NHANES database from 1999 to 2018. The SII is calculated from the counts of lymphocytes (LC), neutrophils (NC), and platelets (PC). The RA patients were derived from questionnaire data. We used weighted multivariate regression analysis and subgroup analysis to explore the relationship between SII and RA. Furthermore, the restricted cubic splines were used to explore the non-linear relationships. Result Our study included a total of 37,604 patients, of which 2642 (7.03%) had rheumatoid arthritis. After adjusting for all covariates, the multivariate logistic regression analysis showed that high SII (In-transform) levels were associated with an increased likelihood of rheumatoid arthritis (OR=1.167, 95% CI=1.025–1.328, P=0.020). The interaction test revealed no significant effect on this connection. In the restricted cubic spline regression model, the relationship between ln-SII and RA was non-linear. The cutoff value of SII for RA was 578.25. The risk of rheumatoid arthritis increases rapidly when SII exceeds the cutoff value. Conclusion In general, there is a positive correlation between SII and rheumatoid arthritis. Our study shows that SII is a novel, valuable, and convenient inflammatory marker that can be used to predict the risk of rheumatoid arthritis in US adults.

PurposeOur aim was to identify the clinical characteristics and develop and validate diagnostic and prognostic web-based dynamic prediction models for gastric cancer (GC) with bone metastasis (BM) using the SEER database.MethodOur study retrospectively analyzed and extracted the clinical data of patients aged 18-85 years who were diagnosed with gastric cancer between 2010 and 2015 in the SEER database. We randomly divided all patients into a training set and a validation set according to the ratio of 7 to 3. Independent factors were identified using logistic regression and Cox regression analyses. Furthermore, we developed and validated two web-based clinical prediction models. We evaluated the prediction models using the C-index, ROC, calibration curve, and DCA.ResultA total of 23,156 patients with gastric cancer were included in this study, of whom 975 developed bone metastases. Age, site, grade, T stage, N stage, brain metastasis, liver metastasis, and lung metastasis were identified as independent risk factors for the development of BM in GC patients. T stage, surgery, and chemotherapy were identified as independent prognostic factors for GC with BM. The AUCs of the diagnostic nomogram were 0.79 and 0.81 in the training and test sets, respectively. The AUCs of the prognostic nomogram at 6, 9, and 12 months were 0.93, 0.86, 0.78, and 0.65, 0.69, 0.70 in the training and test sets, respectively. The calibration curve and DCA showed good performance of the nomogram.ConclusionsWe established two web-based dynamic prediction models in our study. It could be used to predict the risk score and overall survival time of developing bone metastasis in patients with gastric cancer. In addition, we also hope that these two web-based applications will help physicians comprehensively manage gastric cancer patients with bone metastases.

PurposeIt is well known that the CD4/CD8 ratio is a special immune-inflammation marker. We aimed to explore the relationship between the CD4/CD8 ratio and the risk of surgical site infections (SSI) among human immunodeficiency virus (HIV)-positive adults undergoing orthopedic surgery.MethodsWe collected and analyzed data from 216 HIV-positive patients diagnosed with fractures at the department of orthopedics, Beijing Ditan Hospital between 2011 and 2019. The demographic, surgical, and hematological data for all patients were collected in this retrospective cohort study. We explored the risk factors for SSI using univariate and multivariate logistic regression analysis. Then, the clinical correlation between the CD4 count, CD4/CD8 ratio, and SSI was studied using multivariate logistic regression models after adjusting for potential confounders. Furthermore, the association between the CD4/CD8 ratio and SSI was evaluated on a continuous scale with restricted cubic spline (RCS) curves based on logistic regression models.ResultsA total of 23 (10.65%) patients developed SSI during the perioperative period. Patients with hepatopathy (OR=6.10, 95%CI=1.46-28.9), HIV viral load (OR=8.68, 95%CI=1.42-70.2; OR=19.4, 95%CI=3.09-179), operation time (OR=7.84, 95%CI=1.35-77.9), and CD4 count (OR=0.05, 95%CI=0.01-0.23) were risk factors for SSI (P-value < 0.05). Our study demonstrated that a linear relationship between CD4 count and surgical site infection risk. In other words, patients with lower CD4 counts had a higher risk of developing SSI. Furthermore, the relationship between CD4/CD8 ratio and SSI risk was non-linear, inverse ‘S’ shaped. The risk of SSI increased substantially when the ratio was below 0.913; above 0.913, the risk of SSI was almost unchanged. And there is a ‘threshold-saturation’ effect between them.ConclusionOur research shows the CD4/CD8 ratio could be a useful predictor and immune-inflammation marker of the risk of SSI in HIV-positive fracture patients. These results, from a Chinese hospital, support the beneficial role of immune reconstitution in HIV-positive patients prior to orthopedic surgery.