Decreased Thrombin Generation is Associated with Increased Thrombin Generation Biomarkers and Blood Cellular Indices in Pulmonary Embolism.

Pulmonary embolism (PE) is a heterogenous condition with variable clinical presentations. Thrombin generation potential (TGP) and biomarkers, and blood cellular indices can reflect the underlying pathophysiology and risk stratification of PE. This case-control study analyzed TGP in 209 PE patients from Loyola University, Pulmonary Embolism Response Team program compared to normal human plasma (NHP) controls. The present study evaluates TGP and biomarkers, and cellular indices in relation to PE severity, according to the European Society of Cardiology (ESC) guidelines. Statistical analysis including median with interquartile range (IQR), 2-tailed Wilcoxon Mann-Whitney test, Chi-square test, and Spearman Correlational analysis were performed. There were 209 patients with PE, with an almost equal distribution between sex, and a median age of 63 years. Significant downregulation in peak thrombin and endogenous thrombin potential (ETP), as well as upregulation in lag time, were observed in PE patients versus controls. Biomarker analysis revealed pronounced elevations, with D-dimer demonstrating the most significant increase. Blood cellular indices also rose in PE patients, correlating with disease severity. PE severity was associated with higher TGP and biomarker levels. Mortality rates differed significantly across risk categories and were highest in patients with elevated cellular indices. TGP and biomarkers are intricately linked to PE severity and can aid in risk stratification. Elevated cellular indices are associated with increased mortality, highlighting their potential as prognostic markers. These findings could enhance the precision of PE management strategies.

Association of systemic immune-inflammation index with asthma and asthma-related events: a cross-sectional NHANES-based study.

Background: Asthma is associated with persistent airway inflammation, and numerous studies have investigated inflammatory markers causing asthma. However, the systemic immune-inflammation index (SII) is a novel inflammatory marker, with scarce research reporting on the correlation between SII and asthma and asthma-related events. Objective: The purpose of this study was to assess the relationship between SII and asthma and asthma-related events (including whether asthma is still present, asthma flare-ups in the past year, and asthma duration) using data from the National Health and Nutrition Examination Survey (NHANES). Methods: The study utilized data from NHANES 2009-2018 with asthma and asthma-related events as dependent variables and SII as an independent variable. Multifactor logistic regression was employed to assess the correlation between the independent and dependent variables. Smoothed curve-fitting and threshold effect analyses were also carried out to determine the presence of non-linear relationships. Subgroup analyses were then performed to identify sensitive populations. Results: In this study, we analyzed data from 40,664 participants to elucidate the association between SII and asthma and its related events. The study findings indicated a positive correlation between SII and asthma, with a relative risk increase of 0.03% for asthma incidence per one percentage point increase in SII (OR = 1.0003, 95% CI: 1.0002, 1.0004). For individuals still suffering from asthma, higher SII also indicated a positive correlation with ongoing asthma (OR = 1.0004, 95% CI: 1.0001, 1.0006). However, no statistically significant association was observed between SII and asthma exacerbations within the following year (OR = 1.0001, p > 0.05). When considering the duration of asthma, we observed a slight positive correlation with SII (ß = 0.0017, 95% CI: 0.0005, 0.0029). Additionally, a significant non-linear relationship between SII and asthma duration emerged at the threshold of 504.3 (ß = 0.0031, 95% CI: 0.0014-0.0048, p = 0.0003). Subgroup analysis revealed a stronger correlation between SII and asthma in male patients (OR = 1.0004, 95% CI: 1.0002-1.0006) and individuals aged 60 and above (OR = 1.0005, 95% CI: 1.0003-1.0007). No gender differences were observed for individuals still suffering from asthma. However, the positive correlation between SII and asthma was more pronounced in participants under 20 years old (OR = 1.0004 in Model 3, 95% CI: 1.0002-1.0006). Specific sensitive subgroups for asthma exacerbation recurrence within the past year were not identified. When considering asthma duration, we observed this association to be significant in male individuals (ß = 0.0031 in Model 3, 95% CI: 0.0014-0.0049) as well as individuals aged 20 to 39 (ß = 0.0023 in Model 3, 95% CI: 0.0005-0.0040). Conclusion: Our study concludes that SII is positively correlated with the persistence of asthma yet has limited predictive power for asthma recurrence. This highlights SII's potential as a tool for assessing asthma risk and formulating targeted management strategies.

A Clinical Nomogram for Predicting Overall Survival in Patients With T1/T2 Penile Squamous Cell Carcinoma.

BACKGROUND: To evaluate the overall survival (OS) and construct a nomogram to predict the OS of patients with penile squamous cell carcinoma (PSCC). METHODS: This retrospective study analyzed data of patients with PSCC from the First Affiliated Hospital of Soochow University between 2012 and 2022. R software was used to explore factors influencing OS in PSCC. Kaplan-Meier method and log-rank test were employed for OS estimation. Univariate and multivariate Cox proportional hazards regression analyses were performed to identify these factors. A nomogram was created to identify the independent prognostic factors. The model was evaluated by concordance index, receiver operating characteristic (ROC) curves, and calibration plots. RESULTS: A total of 159 patients with T1/T2 PSCC were included in the analysis. Patients with T2/N2 stage, older age, larger tumor size, high preoperative systemic immune-inflammation index (SII), and poor preoperative nutrition had a higher incidence of poor OS. Age, T/N stage, tumor size, and SII were identified as independent prognostic indicators. A prognostic nomogram was formulated, and its predictive accuracy for estimating OS in PSCC patients was validated through ROC curves and calibration plots. CONCLUSION: The nomograms, based on age, T/N stage, tumor size, and high preoperative SII, provide a valuable tool for predicting 1-, 2-, and 3-year OS in patients with T1/T2 PSCC without distant metastases.

Systemic immune-inflammation index in predicting hospitalized bronchiectasis exacerbation risks and disease severity.

Background: Bronchiectasis is a common respiratory disease with neutrophilic inflammation being the predominant pathophysiology. Systemic immune-inflammation index (SII) is a simple and readily available biomarker being studied in various conditions including asthma, chronic obstructive pulmonary disease, and interstitial lung disease, but not in bronchiectasis. We aim to investigate the prognostic role of SII in bronchiectasis with this study. Methods: A retrospective cohort study in Chinese patients with non-cystic fibrosis (CF) bronchiectasis was conducted in Hong Kong, to investigate the association between baseline SII and of hospitalized bronchiectasis exacerbation risk over 4.5 years of follow-up, as well as correlating with disease severity in bronchiectasis. The baseline SII in 2018 was calculated based on stable-state complete blood count. Results: Among 473 Chinese patients with non-CF bronchiectasis were recruited, 94 of the patients had hospitalized bronchiectasis exacerbation during the follow-up period. Higher SII was associated with increased hospitalized bronchiectasis exacerbation risks with adjusted odds ratio (aOR) of 1.001 [95% confidence interval (CI): 1.000-1.001, P=0.003] for 1 unit (cells/µL) increase in SII count and aOR of 1.403 (95% CI: 1.126-1.748, P=0.003) for 1 standard deviation (SD) increase in SII. SII was found to have significant negative association with baseline forced expiratory volume in the first second (FEV1) (in litre and percentage predicted), forced vital capacity (FVC) in percentage; and significant positive correlation with the extent of bronchiectasis and baseline neutrophil to lymphocyte ratio (NLR). Conclusions: SII could serve as biomarker to predict the risks of hospitalized exacerbation in bronchiectasis patients, as well as correlating with the disease severity.

Value of computed tomography radiomics combined with inflammation indices in predicting the efficacy of immunotherapy in patients with locally advanced and metastatic non-small cell lung cancer.

Background: Although immunotherapy has revolutionized the treatment landscape of lung cancer and improved the prognosis of this malignancy, many patients with lung cancer still are not able to benefit from it because of many different reasons. The expression of programmed death ligand-1 (PD-L1) in tumor cells has been approved for the prediction of immunotherapy efficacy; however, its clinical application has been limited by the invasiveness of PD-L1 determination and the heterogeneity of tumor cells. As a promising technology, radiomics has made significant progress in the diagnosis and treatment of lung cancer. Thus, we constructed a noninvasive predictive model which based on radiomics to predict the immunotherapy efficacy of lung caner patients. Methods: Data of 82 patients with stage IIIa/IVb NSCLC who received immunotherapy at the First Affiliated Hospital of Soochow University from December 2019 to January 2023 were retrospectively collected. These patients were followed up for durable clinical benefit (DCB), as defined by whether progression-free survival (PFS) reached 12 months. The least absolute shrinkage and selection operator (LASSO) algorithm was used to screen for the radiomic features in the training set, and a radiomics score (Rad-score) was calculated. The clinical baseline data were analyzed, and the peripheral blood inflammation indices were calculated. Univariate and multivariate analyses were performed to identify the applicable indices, which were combined with the Rad-score to create a comprehensive forecasting model (CFM) and nomograms. Internal validation was performed in the validation set. Results: Up to the last follow-up time, 48 of 82 patients had a PFS of more than 12 months. The area under the receiver operating characteristic (ROC) curve (AUC) of the Rad-score was 0.858 and 0.812, respectively, in the training set and validation set. A systemic immune-inflammation index (SII) score of <500.88 after two cycles of immunotherapy was a protective factor for PFS >12 months [odds ratio (OR) 0.054; P=0.003]. The CFM had an AUC of 0.930 and 0.922, respectively, in the training and validation sets. The calibration curves and decision curve analysis (DCA) demonstrated the reliability and clinical applicability of the model, respectively. Conclusions: The radiomics model performed well in predicting whether patients with locally advanced or metastatic NSCLC can achieve DCB after receiving immunotherapy. The CFM had good predictive performance and reliability.

Predictive significance of systemic immune-inflammation index combined with prealbumin for postoperative pneumonia following lung resection surgery.

BACKGROUND: We aimed to determine whether systemic immune-inflammation index (SII) combined with prealbumin can provide better predictive power for postoperative pneumonia in patients undergoing lung resection surgery. METHODS: We identified eligible patients undergoing lung resection surgery at the Affiliated Hospital of Nantong University from March 2021 to March 2022. Demographic characteristics, clinical data, and laboratory information were collected and reviewed from the electronic medical records of the patients. To test the effect of the combined detection of SII and prealbumin, we made an equation using logistic regression analysis. The receiver operating characteristic curve (ROC) was plotted to evaluate the predictive powers, sensitivity, and specificity of prealbumin, SII, and SII combined with prealbumin. Decision curve analysis (DCA) was used to determine the clinical validity and net benefit of different methods of detection. RESULTS: Totally 386 eligible patients were included with a median age of 62.0 years (IQR: 55.0, 68.0), and 57 (14.8%) patients presented with postoperative pneumonia within 7 days after surgery. The multivariate regression analysis showed that preoperative SII as continuous variable was associated with an increased risk of postoperative pneumonia (OR: 1.38, 95% CI: 1.19-2.83, P = 0.011), whereas the prealbumin as continuous variable remained as an independent protective predictor of postoperative pneumonia in the adjusted analysis (OR: 0.80, 95% CI: 0.37-0.89, P = 0.023). Compared to SII or prealbumin, the combined detection of preoperative SII and prealbumin showed a higher predictive power with area under curve of 0.79 (95% CI: 0.71-0.86, P < 0.05 for all). Additionally, DCA indicated that the combined detection was superior over preoperative SII or prealbumin alone in clinical validity and net benefit. CONCLUSION: Both preoperative SII and prealbumin are independent influencing factors for postoperative pneumonia after lung resection surgery. The combined detection of preoperative SII and prealbumin can significantly improve prediction capability to identify potential postoperative pneumonia-susceptible patients, facilitating early interventions to improve postoperative quality of life for surgical lung resection patients.

Comparative Analysis of Inflammatory Indexes in Lung Cancer Patients With and Without Brain Metastases.

Introduction Lung cancer is the leading cause of oncological deaths worldwide. Various combined inflammatory indexes, such as the systemic immune-inflammation index (SII), neutrophil-to-lymphocyte ratio (NLR), lymphocyte-to-monocyte ratio (LMR), and platelet-to-lymphocyte ratio (PLR) have shown associations with pretreatment survival prognosis in patients suffering of lung cancer with or without brain metastases. This study aimed to compare the average values of NLR, PLR, LMR, and SII in healthy patients, patients with lung cancer without any other metastases, and patients with lung cancer and brain metastases. Materials and methods In this prospective study, we have divided the patients into three groups: Group 1 included patients diagnosed with lung cancer and one or more brain metastases of lung cancer origin, Group 2 included patients diagnosed with lung cancer without known metastases, and Group 3 was the control group which included healthy subjects. Preoperative complete blood counts were extracted for all included patients and we calculated the values of SII, NLR, PLR, and LMR for each individual patient in each group. The next step was to calculate the average values of SII, NLR, PLR, and LMR for each group of patients and to identify the differences between groups. Results A total number of 228 patients were enrolled in the study. Group 1 included 67 patients with average values of SII = 2020.98, NLR = 7.25, PLR = 199.46, and LMR = 2.97. Group 2 included 88 patients with average values of SII = 1638.01, NLR = 4.58, PLR = 188.42, and LMR = 3.43. Group 3 included 73 subjects with the following average values of the inflammatory indexes: SII = 577.41, NLR = 2.34, PLR = 117.84, and LMR = 3.56. Conclusion We observed statistically significant differences in SII, NLR, and PLR among the three groups of patients, suggesting their potential role as prognostic markers. Furthermore, our analysis revealed significant correlations between inflammatory markers within lung cancer patients, highlighting their involvement in tumor microenvironment modulation. Our findings demonstrate an escalation in SII, NLR, and PLR values as the disease progresses. These parameters of inflammation and immune status are readily and cost-effectively, and repeatedly assessable in routine clinical practice.

Prognostic significance of systemic immune inflammatory index in NSCLC: a meta-analysis.

Aim: The aim of this meta-analysis was to investigate the relationship between the baseline systemic immune inflammatory index (SII) and prognosis in patients with NSCLC. Materials & methods: The relation between pretreatment SII and overall survival, disease-free survival, cancer-specific survival, progression-free survival and recurrence-free survival in NSCLC patients was analyzed combined with hazard ratio and 95% CI. Results: The results showed that high SII was significantly correlated with overall survival and progression-free survival of NSCLC patients, but not with disease-free survival, cancer-specific survival and recurrence-free survival. Conclusion: The study suggests that a higher SII has association with worse prognosis in NSCLC patients. PROSPERO registration number: CRD42022336270.

Evaluation of the inflammatory parameters as potential biomarkers of systemic inflammation extent and the disease severity in psoriasis patients.

The disease severity of psoriasis is mainly assessed subjectively via  psoriasis area and severity index (PASI) and body surface area (BSA), while an optimal measure of cutaneous response, may overlook systemic inflammation in psoriasis patients. The neutrophil to lymphocyte ratio (NLR), platelet to lymphocyte ratio (PLR), monocyte to lymphocyte ratio (MLR), monocyte to high density lipoprotein ratio (MHR), and systemic immune-inflammation index (SII) exhibit notable associations with the inflammation severity in multiple diseases. The aim of this retrospective study was to explore the associations between inflammatory parameters and the skin lesions' severity of psoriasis. After analysis, we found that patients with psoriasis had higher NLR, MLR, PLR, MHR, and SII levels compared to the control group. At baseline, the parameters of NLR (r = 0.124, P = 0.003), MLR (r = 0.153, P < 0.001), MHR (r = 0.217, P < 0.001) and SII (r = 0.141, P = 0.001) had a positive correlation with PASI in psoriasis patients. At the same time, we analyzed the patients who received different systemic therapy. We observed a significant decrease in NLR, PLR, MLR, and SII in psoriasis patients after treatment. Notably, TNF-α inhibitors and IL-17A inhibitors subgroups showed a more significant reduction than IL-23/IL-12/23 inhibitors and MTX medication. Additionally, we found the change of NLR (r = 0.194, P < 0.001), PLR (r = 0.104, P = 0.014), MLR (r = 0.191, P < 0.001), MHR (r = 0.106, P = 0.012), and SII (r = 0.228, P < 0.001) had a positive correlation with the change of PASI in psoriasis patients. In conclusion, this study suggests that NLR, MLR, and SII may serve as useful biomarkers for assessing systemic inflammation extent and disease severity in psoriasis patients.

Is there a relationship between the severity of obstructive sleep apnea syndrome and the systemic immune inflammation index?

AIM: Vascular dysfunction, oxidative stress and systemic inflammation are considered responsible for the pathophysiology of Obstructive sleep apnea syndrome (OSAS). It is thought that desaturation due to apnea-hypopnea attacks in OSAS patients activates inflammatory pathways. In this study, we aimed to reveal the relationship between inflammation parameters Systemic immune inflammation index (SII), neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratios (PLR) severity of OSAS in patients who underwent polysomnography in our hospital's sleep laboratory. METHODS: We grouped our 171 patients who were followed up in our sleep laboratory with the diagnosis of OSAS according to their AHI values. We evaluated the correlation of SII, NLR, and PLR values obtained from the complete blood tests of our patients with OSAS diagnosis and OSAS severity. RESULTS: The mean NLR, PLR and SII values of patients with OSAS were statistically significantly higher than those without OSAS (p < 0.05). A positive correlation of 18% was found between the presence of OSAS and the SII value (p = 0.016). No statistically significant difference was found when comparing OSAS severity and NLR, PLR and SII values (p > 0.05). CONCLUSION: We observed that SII, NLR and PLR parameters, which are rapidly assessable systemic inflammation markers of this process, were independently associated in patients diagnosed with OSAS and that there was no change in SII, NLR, and PLR parameters with OSAS severity.

Assessment of the systemic immune-inflammation index in type 2 diabetic patients with and without dry eye disease: A case-control study.

Background: The inflammation plays a role in the pathophysiology of type-2 diabetes progression, and the mechanism remains unclear. The systemic immune-inflammation index (SII) is a novel inflammatory marker for type 2 diabetes patients and integrates multiple indicators in complete blood counts and routine blood tests. Aim: Since there is no international diagnostic standard for dry eye disease (DED), this study uses low-cost inflammatory blood biomarkers to investigate the correlation between SII and DM2-DED and determine the diagnosis indices of other biomarkers in DM2-DED. Methodology: A case-control retrospective analysis of totel patients n = 293 randomly selected and categorized into four groups: DED, DM2, DM2-DED, and healthy subjects. Demographic and blood biomarker variables were classified as categorical and continuous variables. The platelet-to-lymphocyte ratio (PLR), lymphocytes-to-lymphocyte ratio, neutrophil-to-lymphocyte ratio (NLR), and SII were calculated platelet count multiply by NLR and analyzed for their correlation for all groups. Results: Focusing on DM2-DED patients was more common in females, 59.6%, than in males, 40.2%. The mean ages were 60.7 ± 11.85 years, a statistically significant difference with all groups. In the study group DM2-DED, there was an increase in all blood markers compared to all remaining groups except PLR. Only neutrophil, hemoglobin A1c (HbA1c), and fasting blood sugar levels were statistically significant differences in DM2-DED patients (p > 0.001, p < 0.001, and p < 0.001, respectively) compared to all groups. There was a positive correlation between HbA1c and PLR, HbA1c and NLR, and HbA1c and SII (r = 0.037, p = 0.705; r = 0.031, p = 0.754; and r = 0.066, p < 0.501, respectively) in the DM2-DED group. Conclusion: This study demonstrated that elevated SII values were linked to elevated HbA1c in DM2-DED patients. The potential of SII and HbA1c as early diagnostic indicators for ocular problems associated with diabetes mellitus is highlighted by their favorable connection in diagnosing DM2-DED.

The Predictors of Early Treatment Effectiveness of Intravitreal Bevacizumab Application in Patients with Diabetic Macular Edema.

The aim of this study was to establish whether multiple blood parameters might predict an early treatment response to intravitreal bevacizumab injections in patients with diabetic macular edema (DME). Seventy-eight patients with non-proliferative diabetic retinopathy (NPDR) and DME were included. The treatment response was evaluated with central macular thickness decrease and best corrected visual acuity increase one month after the last bevacizumab injection. Parameters of interest were the neutrophil-to-lymphocyte ratio (NLR), monocyte-to-lymphocyte ratio (MLR), platelet-to-lymphocyte ratio (PLR), systemic immune-inflammation index (SII), vitamin D, and apolipoprotein B to A-I ratio (ApoB/ApoA-I). The NLR (2.03 ± 0.70 vs. 2.80 ± 1.08; p < 0.001), MLR (0.23 ± 0.06 vs. 0.28 ± 0.10; p = 0.011), PLR (107.4 ± 37.3 vs. 135.8 ± 58.0; p = 0.013), and SII (445.3 ± 166.3 vs. 675.3 ± 334.0; p < 0.001) were significantly different between responder and non-responder groups. Receiver operator characteristics analysis showed the NLR (AUC 0.778; 95% CI 0.669-0.864), PLR (AUC 0.628; 95% CI 0.511-0.735), MLR (AUC 0.653; 95% CI 0.536-0.757), and SII (AUC 0.709; 95% CI 0.595-0.806) could be predictors of response to bevacizumab in patients with DME and NPDR. Patients with severe NPDR had a significantly higher ApoB/ApoA-I ratio (0.70 (0.57-0.87) vs. 0.61 (0.49-0.72), p = 0.049) and lower vitamin D (52.45 (43.10-70.60) ng/mL vs. 40.05 (25.95-55.30) ng/mL, p = 0.025). Alterations in the NLR, PLR, MLR, and SII seem to provide prognostic information regarding the response to bevacizumab in patients with DME, whilst vitamin D deficiency and the ApoB/ApoA-I ratio could contribute to better staging.

Integrating systemic immune-inflammation index, fibrinogen, and T-SPOT.TB for precision distinction of active pulmonary tuberculosis in the era of mycobacterial disease research.

Background: The clinical challenge of differentiating suspected tuberculosis with positive T-SPOT.TB results persist. This study aims to investigate the utility of the Systemic Immune-Inflammation Index (SII), Fibrinogen, and T-SPOT.TB in distinguishing between active pulmonary tuberculosis (PTB) and non-tuberculous lung diseases. Methods: A retrospective analysis included 1,327 cases of active PTB with positive T-SPOT.TB results and 703 cases of non-tuberculous lung diseases from May 2016 to December 2020 at Meizhou People's Hospital. These were designated as the case group and the control group, respectively. The detection indicators of T-SPOT.TB: Early Secreted Antigenic Target 6 (ESAT-6), Culture Filtrate Protein 10 (CFP-10), as well as SII and Fibrinogen levels-were compared and analyzed for association and joint diagnostic value between the two groups. Results: The case group showed higher values of ESAT-6, CFP-10, SII, and Fibrinogen compared to the control group (all p < 0.001). In the case group, SII and Fibrinogen did not correlate with ESAT-6 and CFP-10 (∣rs∣ all < 0.3) but were positively correlated with C-reactive protein (CRP; rs all > 0.3). SII and Fibrinogen values in smear-positive pulmonary tuberculosis were higher than in smear-negative cases (all p < 0.05). The optimal diagnostic thresholds for ESAT-6, CFP-10, SII, and Fibrinogen in differentiating between active PTB and non-tuberculous lung diseases were 21.50 SFCs/106 PBMC, 22.50 SFCs/106 PBMC, 2128.32, and 5.02 g/L, respectively. Regression logistic analysis showed that ESAT-6 < 21.5 (OR: 1.637, 95% CI: 1.311-2.043, p < 0.001), CFP-10 < 22.5 (OR: 3.918, 95% CI: 3.138-4.892, p = 0.025), SII < 2128.32 (OR: 0.763, 95% CI: 0.603-0.967, p < 0.001), and FIB < 5.02 (OR: 2.287, 95% CI: 1.865-2.806, p < 0.001) were independent risk factors for active PTB. The specificity for ESAT-6 + CFP-10, ESAT-6 + CFP-10 + SII, ESAT-6 + CFP-10 + FIB, and ESAT-6 + CFP-10 + SII + FIB was 82.5%, 83.2%, 95.8%, and 80.1%, respectively, while sensitivity was 52.6%, 53.0%, 55.8%, and 44.7%, and positive predictive values were 85.0%, 85.6%, 84.1%, and 89.6%, respectively. Conclusion: SII and Fibrinogen are positively correlated with the degree of tuberculosis inflammation and the bacterial load of Mycobacterium tuberculosis. The combined detection of SII, Fibrinogen, and T-SPOT.TB is significant in distinguishing between active PTB with positive T-SPOT.TB results and non-tuberculous lung diseases.

Association between systemic immune-inflammation index and post-stroke depression: a cross-sectional study of the national health and nutrition examination survey 2005-2020.

Background: Less research has linked the Systemic Immune Inflammatory Index (SII) with post-stroke depression (PSD). This study aims to look at any potential connections between SII and PSD. Methods: The National Health and Nutrition Examination Survey (NHANES), conducted in a population that embodied complete SII and stroke data from 2005 to 2020, was used to perform the current cross-sectional survey. A fitted smoothed curve was used to depict the nonlinear link between SII and PSD, and multiple linear regression analysis demonstrated a positive correlation between SII and PSD. Results: Multiple linear regression analysis showed that SII and PSD were markedly related [1.11(1.05, 1.17)]. Interaction tests showed that the association between SII and PSD was not statistically different between strata, and age, sex, BMI, income poverty ratio, education level, smoking status, diabetes mellitus, coronary heart disease, and heart failure did not have a significant effect on this positive association (p > 0.05 for interaction). In addition, a nonlinear association between SII and PSD was found using a two-stage linear regression model. Conclusion: The results of our research support the existence of a significant positive correlation between SII levels and PSD. Further prospective trials are required to comprehend SII, which is for the PSD thoroughly.

Association of systemic inflammatory markers with postoperative arrhythmias in esophageal cancer: a propensity score matching.

BACKGROUND: The severity and prognosis of an array of inflammatory diseases have been predicted using systemic inflammatory indices, such as the neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio, lymphocyte-to-monocyte ratio (LMR), derived neutrophil-to-lymphocyte ratio (dNLR), and systemic immune inflammation index (SII). The purpose of this study was to examine the association between systemic inflammatory markers and postoperative arrhythmias (PA) in esophageal cancer patients. METHODS: In the study, laboratory-related parameters were gathered and examined in 278 patients (non-PA = 221, PA = 57). Fit separate propensity score matching (PSM) within subgroup strata (surgery approaches); match within strata, and aggregate for main analysis. Finally, we established a 1:1(57:57) model. The ability of inflammatory makers on the first post-esophagectomy day to distinguish PA from postoperative non-arrhythmia (non-PA) by receiver operating characteristic (ROC) analysis. RESULTS: On the first post-esophagectomy day, there was a greater difference between PA and non-PA in terms of white blood cell (WBC) and neutrophil (NE), Neutrophil percentage (NE%), NLR, dNLR, LMR, and SII. After PSM, the following variables were substantially different between non-PA and PA: NE%, NLR, dNLR, and SII. It was found that WBC, NE, NE%, NLR, dNLR, LMR, and SII had the area under the curve (AUC) that was higher than 0.500 in ROC analysis, with NLR and SII having the highest AUC (AUC = 0.661). The indicators were subjected to binary logistic regression analysis, which increased the indicators' predictive ability (AUC = 0.707, sensitivity = 0.877). CONCLUSION: On the first post-esophagectomy day, systemic inflammatory indicators were significantly correlated with both PA and non-PA, and high SII and NLR are reliable markers of PA.

Depression heightened the association of the systemic immune-inflammation index with all-cause mortality among osteoarthritis patient.

BACKGROUND: Systemic immune-inflammatory index (SII) has been recognized as a novel inflammatory indicator in numerous diseases. It remains unknown how SII affects all-cause mortality among patients with osteoarthritis (OA). In this prospective cohort study, we intended to examine the relationship of SII with all-cause mortality among OA populations and assess the interaction between depression and SII. METHODS: Data was collected from National Health and Nutrition Examination Survey (NHANES) in 2005-2018. The National Death Index (NDI) provided vital status records. Multivariable Cox regression analyses with cubic spines were applied to estimate the association between SII and all-cause and CVD mortality. Stratified analysis and interaction tests assessed the interaction of SII and depression on all-cause mortality. RESULTS: In total 3174 OA adults were included. The lowest quartile Q1 (HR:1.44, 95%CI:1.02-2.04) and highest quartile Q4 (HR:1.44, 95%CI:1.02-2.04) of SII presented a higher risk of death compared with those in second quartile Q2 (Ref.) and third quartile Q3 (HR:1.23, 95%CI:0.89-1.68. Restricted cubic splines analysis revealed a U-shaped association of SII with all-cause mortality, the inflection points were 412.93 × 109/L. The interaction test observed a more significant relationship of SII with all-cause mortality in depression patients than in non-depression patients, indicating that depression can modify this association. LIMITATIONS: First, the observational study design failed to make causal inferences. Second, the baseline SII cannot reflect the long-term level of inflammation. Finally, there may be potential bias. CONCLUSION: SII was U-shaped associated with all-cause mortality in OA patients, and this association was significantly heightened by depression.

Prognostic value of preoperative combined with postoperative systemic immune-inflammation index for disease-free survival after radical rectal cancer surgery: a retrospective cohort study.

Background: Colorectal cancer (CRC) ranks highly in malignant tumor incidence and mortality rates, severely affecting human health. The predictive value of the systemic immune-inflammation index (SII) in CRC prognosis is gaining attention, but there is limited research on the combined preoperative and postoperative SII. This study aims to explore the prognostic value of combined SII on disease-free survival (DFS) in patients undergoing radical surgery for rectal cancer. Methods: We enrolled 292 patients with rectal cancer who underwent radical resection at the Affiliated Hospital of Xuzhou Medical University from May 2018 to September 2020, along with regular follow-ups to document the DFS. Patients' complete blood cell counts were assessed before surgery and between 21-56 days postoperatively. Calculating preoperative and postoperative SII, patients were categorized into four groups based on the optimal cutoff values: (I) low-low group (preoperative SII <449.325 and postoperative SII <568.13); (II) high-low group (preoperative SII ≥449.325 and postoperative SII <568.13); (III) low-high group (preoperative SII <449.325 and postoperative SII ≥568.13); and (IV) high-high group (preoperative SII ≥449.325 and postoperative SII ≥568.13). The receiver operating characteristic (ROC) curve analysis evaluated the prediction efficacy of preoperative, postoperative, and combined SII. Kaplan-Meier analysis generated DFS curves, and Cox regression analysis determined prognostic factors. Results: With a median follow-up of 41 months, 65.4% (191/292) patients reached DFS. The clinical pathological features between the four groups are balanced and comparable (P>0.05). The area under the ROC curve for preoperative, postoperative, and combined SII was 0.668 [95% confidence interval (CI): 0.6-0.737], 0.696 (95%CI: 0.63-0.763), and 0.741 (95% CI: 0.681-0.802), respectively. After adjusting for confounding factors such as adjuvant therapy, differentiation, vascular invasion, neural invasion, tumor-node-metastasis (TNM) stage, carcinoembryonic antigen (CEA), and carbohydrate antigen 19-9 (CA19-9), significant differences were observed between the high-low group [hazard ratio (HR) =2.403; 95% CI: 1.255-4.602; P=0.008], low-high group (HR =5.058; 95% CI: 2.389-10.71; P<0.001), and high-high group (HR =6.214; 95% CI: 3.474-11.115; P<0.001) compared to the low-low group, with higher risks of adverse outcomes. Conclusions: Combined SII has better predictive efficacy than monitoring preoperative or postoperative SII alone in rectal cancer patients undergoing radical surgery.

Inflammatory markers are associated with infertility prevalence: a cross-sectional analysis of the NHANES 2013-2020.

BACKGROUND: Inflammation exerts a critical role in the pathogenesis of infertility. The relationship between inflammatory parameters from peripheral blood and infertility remains unclear. Aim of this study was to investigate the association between inflammatory markers and infertility among women of reproductive age in the United States. METHODS: Women aged 20-45 were included from the National Health and Nutrition Examination Survey (NHANES) 2013-2020 for the present cross-sectional study. Data of reproductive status was collected from the Reproductive Health Questionnaire. Six inflammatory markers, systemic immune inflammation index (SII), lymphocyte count (LC), product of platelet and neutrophil count (PPN), platelet-lymphocyte ratio (PLR), neutrophil-lymphocyte ratio (NLR) and lymphocyte-monocyte ratio (LMR) were calculated from complete blood counts in mobile examination center. Survey-weighted multivariable logistic regression was employed to assess the association between inflammatory markers and infertility in four different models, then restricted cubic spline (RCS) plot was used to explore non-linearity association between inflammatory markers and infertility. Subgroup analyses were performed to further clarify effects of other covariates on association between inflammatory markers and infertility. RESULTS: A total of 3,105 women aged 20-45 was included in the final analysis, with 431 (13.88%) self-reported infertility. A negative association was found between log2-SII, log2-PLR and infertility, with an OR of 0.95 (95% CI: 0.78,1.15; p = 0.60), 0.80 (95% CI:0.60,1.05; p = 0.10), respectively. The results were similar in model 1, model 2, and model 3. Compared with the lowest quartile (Q1), the third quartile (Q3) of log2-SII was negatively correlation with infertility, with an OR (95% CI) of 0.56 (95% CI: 0.37,0.85; p = 0.01) in model 3. Similarly, the third quartile (Q3) of log2-PLR was negatively correlation with infertility, with an OR (95% CI) of 0.61 (95% CI: 0.43,0.88; p = 0.01) in model 3. No significant association was observed between log2-LC, log2-PPN, log2-NLR, log2-LMR and infertility in model 3. A similar U-shaped relationship between log2-SII and infertility was found (p for non-linear < 0.05). The results of subgroup analyses revealed that associations between the third quartile (Q3) of log2-SII, log2-PLR and infertility were nearly consistent. CONCLUSION: The findings showed that SII and PLR were negatively associated with infertility. Further studies are needed to explore their association better and the underlying mechanisms.

The Systemic Immune-Inflammation Index (SII) and coronary artery lesions in Kawasaki disease.

Coronary artery lesions (CALs) are the most common complications of Kawasaki disease (KD) and play a crucial role in determining the prognosis of the disease. Consequently, the early identification of children with KD who are at risk of developing coronary artery damage is vitally important. We sought to investigate the relationship between the Systemic Immune-Inflammation Index (SII) and CALs in patients with KD and to assess its predictive value. We carried out a retrospective review and analysis of medical records for KD patients treated at the First Affiliated Hospital of Anhui Medical University between January 2017 and January 2023. We utilized single-variable tests, binary logistic regression analysis, ROC curve analysis, restricted cubic spline tests, and curve fitting to evaluate the association between SII and CALs. In our study, 364 patients were included, with 63 (17.3%) presenting with CALs at the time of admission. The binary logistic regression analysis indicated that SII was a significant risk factor for CALs at admission, evident in both unadjusted and models adjusted for confounders. The ROC curve analysis revealed an AUC (Area Under the Curve) value of 0.789 (95%CI 0.723-0.855, P < 0.001) for SII's predictive ability regarding CALs at admission. A consistent positive linear relationship between SII and the risk of CALs at admission was observed in both the raw and adjusted models. Our research findings suggest that SII serves as a risk factor for CALs and can be used as an auxiliary laboratory biomarker for predicting CALs.

The relationship between the systemic immune-inflammation index and obstructive sleep apnea.

AIM: To investigate the predictive value of the systemic immune-inflammation index (SII) in patients with obstructive sleep apnea (OSA). MATERIAL AND METHODS: Patients diagnosed with OSA formed the patient group, and those with a normal polysomnography (PSG) result formed the control group. The neutrophil, thrombocyte, monocyte, and lymphocyte counts obtained from the hemogram were used to calculate the neutrophil-to-lymphocyte ratio (NLR), thrombocyte-to-lymphocyte ratio (PLR), monocyte-to-lymphocyte ratio (MLR), and the SII values. The two groups were compared with respect to the NLR, PLR, MLR, and SII values. Correlations were examined between the PSG parameters and the NLR, PLR, MLR, and SII values in the patient group. RESULTS: Evaluation included 146 subjects with 85 in the patient group and 61 in the control group. Statistically significantly higher SII and NLR values were found in the patient group (p = 0.037, p < 0.05; p = 0.015, p < 0.05). A statistically significant negative correlation was observed between the SII and the lowest O2 saturation measurements (r = - 0.246; p = 0.003; p < 0.01). A statistically significant negative correlation was found between the NLR and the lowest O2 saturation measurement (r = - 0.255; p = 0.002; p < 0.01). The cutoff value for SII was found to be 290, with 84.7% sensitivity and 29.5% specificity. A cutoff value of 1.71 for NLR was determined to have 61.2% sensitivity and 60.7% specificity. CONCLUSION: SII may be a new, rapid, low-cost, and easy-to-measure biomarker for the prediction of obstructive sleep apnea.