The Association of the Neutrophil-to-Lymphocyte Ratio, Platelet-to-Lymphocyte Ratio, Lymphocyte-to-Monocyte Ratio and Systemic Inflammation Response Index with Short-Term Functional Outcome in Patients with Acute Ischemic Stroke

Values of lymphocyte-related ratios in predicting the clinical outcome of acute ischemic stroke patients receiving intravenous thrombolysis based on different etiologies

Background

While neutrophil-to-lymphocyte ratio (NLR), lymphocyte-to-monocyte ratio (LMR), and platelet-to-lymphocyte ratio (PLR) have been associated with acute ischemic stroke (AIS) outcomes, their differential predictive value across etiological subtypes (TOAST classification) in thrombolysis-treated patients remains underexplored.

Methods

In this retrospective cohort study, we analyzed 381 AIS patients receiving intravenous thrombolysis. Hematological indices were calculated from pre-thrombolysis. Using multivariable logistic regression adjusted for age, NIHSS, and comorbidities, we assessed associations between baseline ratios and 90-day unfavorable outcomes (mRS 3-6). Receiver operating characteristic (ROC) analysis was used to determine optimal cutoffs stratified by TOAST subtypes.

Results

A total of 381 patients were included in the study. NLR showed superior predictive performance: large-artery atherosclerosis: AUC = 0.702 (aOR = 1.35, 95%CI = 1.14-1.61, p = 0.001), small-artery occlusion: AUC = 0.750 (aOR = 1.51, 95%CI = 1.08-2.10, p = 0.015), cardioembolic stroke: AUC = 0.679 (aOR = 1.82, 95%CI = 1.07-3.10, p = 0.028). LMR showed predictive value only in large-artery atherosclerosis (AUC = 0.632, p = 0.004). Optimal NLR cutoffs: 3.19 (large-artery), 3.94 (small-artery), 3.17 (cardioembolic stroke).

Conclusion

NLR emerged as a robust, subtype-specific predictor of post-thrombolysis outcomes, particularly in atherosclerotic stroke variants. These findings supported NLR's clinical utility for risk stratification in thrombolysis-eligible AIS patients.

Machine learning consensus clustering for inflammatory subtype analysis in stroke and its impact on mortality risk: a study based on NHANES (1999-2018)

Background

Our study aims to utilize unsupervised machine learning methods to perform inflammation clustering on stroke patients via novel CBC-derived inflammatory indicators (NLR, PLR, NPAR, SII, SIRI, and AISI), evaluate the mortality risk among these different clusters and construct prognostic models to provide reference for clinical management.

Methods

A cross-sectional analysis was conducted using data from stroke participants in the U.S. NHANES 1999-2018. Weighted multivariate logistic regression was used to construct different models; consensus clustering methods were employed to subtype stroke patients based on inflammatory marker levels; LASSO regression analysis was used to construct an inflammatory risk score model to analyze the survival risks of different inflammatory subtypes; WQS regression, Cox regression, as well as XGBoost, random forest, and SVMRFE machine learning methods were used to screen hub markers which affected stroke prognosis; finally, a prognostic nomogram model based on hub inflammatory markers was constructed and evaluated using calibration and DCA curves.

Results

A total of 918 stroke patients with a median follow-up of 79 months and 369 deaths. Weighted multivariate logistic regression analysis revealed that high SIRI and NPAR levels were significantly positively correlated with increased all-cause mortality risk in stroke patients (p < 0.001), independent of potential confounders; Consensus clustering divided patients into two inflammatory subgroups via SIRI and NPAR, with subgroup 2 having significantly higher markers and mortality risks than subgroup 1 (p < 0.001); LASSO regression analysis showed subgroup 2 had higher risk scores and shorter overall survival than subgroup 1 [HR, 1.99 (1.61-2.45), p < 0.001]; WQS regression, Cox regression, and machine learning methods identified NPAR and SIRI as hub prognostic inflammatory markers; The nomogram prognostic model with NPAR and SIRI demonstrated the best net benefit for predicting 1, 3, 5 and 10-year overall survival in stroke patients.

Conclusion

This study shows NPAR and SIRI were key prognostic inflammatory markers and positively correlated with mortality risk (p < 0.001) for stroke patients. Patients would been divided into 2 inflammatory subtypes via them, with subtype 2 having higher values and mortality risks (p < 0.001). It suggests that enhanced monitoring and management for patients with high SIRI and NPAR levels to improve survival outcomes.

Interpretable prediction of stroke prognosis: SHAP for SVM and nomogram for logistic regression

Background

Ischemic Stroke (IS) stands as a leading cause of mortality and disability globally, with an anticipated increase in IS-related fatalities by 2030. Despite therapeutic advancements, many patients still lack effective interventions, underscoring the need for improved prognostic assessment tools. Machine Learning (ML) models have emerged as promising tools for predicting stroke prognosis, surpassing traditional methods in accuracy and speed.

Objective

The aim of this study was to develop and validate ML algorithms for predicting the 6-month prognosis of patients with Acute Cerebral Infarction, using clinical data from two medical centers in China, and to assess the feasibility of implementing Explainable ML in clinical settings.

Methods

A retrospective observational cohort study was conducted involving 398 patients diagnosed with Acute Cerebral Infarction from January 2023 to February 2024. The dataset included demographic information, medical histories, clinical evaluations, and laboratory results. Six ML models were constructed: Logistic Regression, Naive Bayes, Support Vector Machine (SVM), Random Forest, XGBoost, and AdaBoost. Model performance was evaluated using the Area Under the Receiver Operating Characteristic curve (AUC), sensitivity, specificity, predictive values, and F1 score, with five-fold cross-validation to ensure robustness.

Results

The training set, identified key variables associated with stroke prognosis, including hypertension, diabetes, and smoking history. The SVM model demonstrated exceptional performance, with an AUC of 0.9453 on the training set and 0.9213 on the validation set. A Nomogram based on Logistic Regression was developed for visualizing prognostic risk, incorporating factors such as the National Institutes of Health Stroke Scale (NIHSS) score, Barthel Index (BI), Watanabe Drinking Test (KWST) score, Platelet Distribution Width (PDW), and others. Our models showed high predictive accuracy and stability across both datasets.

Conclusion

This study presents a robust ML approach for predicting stroke prognosis, with the SVM model and Nomogram providing valuable tools for clinical decision-making. By incorporating advanced ML techniques, we enhance the precision of prognostic assessments and offer a theoretical and practical framework for clinical application.

The Complex Inflammatory and Nutritional Indices to Predict Prognostic Risk for Patients With Acute Coronary Syndrome Undergoing Percutaneous Coronary Intervention

PURPOSE

To investigate the role of the systemic inflammatory response index (SIRI) and high-density lipoprotein cholesterol (HDL-C) and low-density lipoprotein cholesterol (LDL-C) levels in predicting the risk of major adverse cardiovascular events (MACEs) in patients with acute coronary syndrome (ACS) undergoing percutaneous coronary intervention (PCI).

PATIENTS AND METHODS

Overall, 1377 patients with ACS who underwent PCI between January 2016 and December 2018 were consecutively enrolled. The patients were divided into MACEs (n = 60) and non-MACEs (n = 1317) groups. The study endpoints were MACEs, including cardiac-related mortality and rehospitalization for severe heart failure (HF), myocardial infarction (MI), and in-stent restenosis.

RESULTS

Both groups showed significant differences in the patients with age > 65 years, history of HF, acute MI, cardiogenic shock, left ventricular ejection fraction < 40%, SIRI ≥ 2.848, SIRI/HDL-C ≥ 1.977, and SIRI × LDL-C ≥ 4.609. The Kaplan-Meier curve showed that the low SIRI group had higher cumulative survival than the high SIRI group. Additionally, the univariate and multivariate Cox proportional hazards model demonstrated that SIRI ≥ 2.848, SIRI/HDL-C ≥ 1.977, and SIRI × LDL-C ≥ 4.609 were independent risk factors for patients with ACS undergoing PCI. Restricted cubic spline models were generated to visualize the relationship between SIRI, SIRI/HDL-C, and SIRI × LDL-C and the prognostic risk.

CONCLUSION

SIRI ≥ 2.848, SIRI/HDL-C ≥ 1.977, and SIRI × LDL-C ≥ 4.609 were all independent prognostic risk factors in patients with ACS undergoing PCI, which may be useful markers for assessment for long prognosis.

Associations of Inflammatory Markers With Neurological Dysfunction and Prognosis in Patients With Progressive Stroke

OBJECTIVE

This study aimed to explore the associations between inflammatory markers and the severity of early neurological dysfunction and prognosis in patients with progressive stroke (PS) and evaluated the predictive value of inflammatory markers for PS.

METHODS

Among 711 acute ischemic stroke (AIS) patients, 210 patients with PS and 501 patients without PS were included. Six inflammatory markers, including neutrophil to lymphocyte ratio (NLR), platelet to lymphocyte ratio (PLR), lymphocyte to monocyte ratio (LMR), systemic immune-inflammation index (SII), systemic inflammatory response index (SIRI), and pan-immune-inflammation value (PIV), were measured and compared between two groups. Correlation analysis was used to analyze the correlation between inflammatory markers and early neurological dysfunction in patients with PS. Univariate and multivariate regression analyses were applied to screen the factors for the prognosis of PS patients. The receiver operating characteristic (ROC) curve was utilized to evaluate the predictive value for the prognosis of PS patients.

RESULTS

Elevated levels of NLR, LMR, SII, and PIV were observed in PS patients. Correlation analysis revealed positive correlations between NLR, PLR, SII, SIRI, PIV, and early neurological deficits, while LMR showed a negative correlation in PS patients. Multivariate analysis identified LMR and the National Institutes of Health Stroke Score (NIHSS) as independent risk factors for poor outcome of PS patients. The predictive value of LMR alone was limited (AUC = 0.59), but combining it with NIHSS improved predictive accuracy (AUC = 0.73) (p < 0.05).

CONCLUSION

These findings suggest that inflammatory markers, particularly LMR, should be considered in PS management, and their combination with NIHSS enhances outcome prediction.

Elevated Platelet-to-Lymphocyte Ratio as a Predictor of All-Cause and Cardiovascular Mortality in Hypertensive Individuals

The platelet-to-lymphocyte ratio (PLR) has been proposed as a promising inflammatory biomarker, with potential implications for cardiovascular prognosis. However, its association with mortality outcomes in hypertensive individuals is not fully elucidated. This investigation sought to clarify the linkage between PLR and both overall and cardiovascular mortality in hypertensive individuals. Data from 15 483 hypertensive adults in the NHANES (2005-2018) were analyzed. Mortality data, including all-cause and cardiovascular deaths, were sourced from the National Death Index (NDI) up to December 31, 2019. The linkage between PLR and mortality risk was depicted using restricted cubic spline (RCS) models. Cox proportional hazards regression models assessed the independent association of PLR with mortality risk, with adjustments incrementally applied: Model 1 without adjustments; Model 2 adjusted for age and sex; Model 3 adjusted further for age, gender, race, marital status, diabetes, alcohol intake, smoking status, body mass index (BMI), history of cardiovascular disease (CVD), high-density lipoprotein cholesterol (HDL), low-density lipoprotein cholesterol (LDL), total cholesterol (TC), triglyceride (TG), and creatinine (CR). Over a median follow-up of 79 months, there were 2820 all-cause deaths and 758 cardiovascular deaths. The multivariate Cox analysis showed that those in the highest PLR quartile had significantly elevated risks of all-cause mortality (Model 1: HR = 1.28, 95% CI 1.16-1.42, p < 0.001; Model 2: HR = 1.14, 95% CI 1.03-1.26, p = 0.014; Model 3: HR = 1.16, 95% CI 1.05-1.29, p = 0.004)and cardiovascular mortality (Model 1: HR = 1.59, 95% CI 1.30-1.94, p < 0.001; Model 2: HR = 1.38, 95% CI 1.13-1.68, p = 0.001; Model 3: HR = 1.47, 95% CI 1.20-1.80, p < 0.001). The study reveals a U-shaped relationship between PLR and all-cause mortality, alongside a linear association with cardiovascular mortality. A PLR threshold of 118.83 has been identified as indicative of an adverse prognosis for all-cause mortality. Elevated PLR independently predicts heightened risks of both all-cause and cardiovascular mortality among hypertensive patients.

Risk Factors and Diagnostic Model Construction of Chronic Pain with Cognitive Impairment

Background

Cognitive impairment (CI) is frequently observed in patients with chronic pain (CP). CP progression increases the risk of dementia and accelerates Alzheimer's disease pathogenesis. However, risk diagnostic models and biomarkers for CP-related CI remain insufficient. Previous research has highlighted the relationships between several complete blood count parameters for CP or CI-related diseases, such as Alzheimer's disease, while the specific values of complete blood count parameters in CP-related CI patients remain unclear. This study aimed to explore the correlation between complete blood count parameters and CP-related CI to establish a risk diagnostic model for the early detection of CP-related CI.

Methods

This cross-sectional study was conducted at West China Hospital, Sichuan University. The Montreal Cognitive Assessment (MoCA) was used to classify patients into either the CP with CI group or the CP without CI group. Univariate analysis and multivariate logistic regression analysis were used to screen the related factors of CP-related CI for constructing a risk diagnostic model, and the model was evaluated using receiver operating characteristic (ROC) curve analysis.

Results

The study ultimately included 163 eligible patients. Based on analysis, age (OR, 1.037 [95% CI, 1.007-1.070]; P=0.018), duration of pain (OR, 2.546 [95% CI, 1.099-6.129]; P=0.032), VAS score (OR, 1.724 [95% CI, 0.819-3.672]; P=0.153), LMR (OR, 0.091 [95% CI, 0.024-0.275]; P<0.001), absolute neutrophil value (OR, 0.306 [95% CI, 0.115-0.767]; P=0.014), and lymphocyte percentage (OR, 6.551 [95% CI, 2.143-25.039]; P=0.002) were identified as critical factors of CP-related CI. The diagnostic model was evaluated by the ROC curve, demonstrating good diagnostic value with an area under the curve (AUC) of 0.803, a sensitivity of 0.603 and a specificity of 0.871.

Conclusion

The risk diagnostic model developed in this study for CP-related CI has significant value and enables clinicians to customize interventions based on each patient's needs.

Association of different inflammatory indices with risk of early natural menopause: a cross-sectional analysis of the NHANES 2013-2018

Background

Early natural menopause, characterized by the cessation of ovarian function before the age of 45, has been a subject of prior research indicating that inflammation may predict the onset of menopause. However, the specific relationship between peripheral blood inflammatory parameters and early natural menopause remains ambiguous.

Methods

This observational study utilized data from the National Health and Nutrition Examination Survey (NHANES) spanning the years 2013-2018. The age at menopause was ascertained through the Reproductive Health Questionnaire (RHQ), with early natural menopause defined as menopause occurring before the age of 45 years. Complete blood counts were derived from laboratory test data, and seven indices of inflammation were calculated, including lymphocyte count (LC), neutrophil count (NC), systemic immune inflammation index (SII), product of platelet and neutrophil count (PPN), platelet-lymphocyte ratio (PLR), neutrophil-lymphocyte ratio (NLR), and lymphocyte-monocyte ratio (LMR). A multivariate weighted logistic regression analysis was employed to estimate the association between these inflammatory indices and early natural menopause.

Results

A total of 2,034 participants were included in the analysis, of whom 460 reported experiencing menopause before the age of 45. Both Log2-NC and Log2-PPN were found to be positively correlated with early menopause, with odds ratios (OR) of 1.56 (95% CI: 1.16, 2.09; p = 0.005) and 1.36 (95% CI: 1.07, 1.72; p = 0.015), respectively. The results from models 1 and 2 were consistent with those from model 3. In the trend test, participants in the fourth quartile (Q4) of log2-LC exhibited a positive correlation with early menopause compared to those in the lowest quartile (Q1), with an OR of 1.41 (95% CI: 1.03, 1.93; p = 0.033). Similarly, the fourth quartile (Q4) of log2-NC and log2-PPN demonstrated a positive correlation with early menopause, with odds ratios (OR) of 1.76 (95% CI: 1.27-2.45; p < 0.001) and 1.66 (95% CI: 1.21-2.29; p = 0.002), respectively. In Model 3, log2-SII, log2-PLR, log2-NLR, and log2-LMR were not significantly associated with early menopause.

Conclusion

Our findings indicate that elevated levels of Log2-LC, Log2-NC, and Log2-PPN are positively correlated with an increased risk of early menopause among women in the United States.

The value of systemic immune inflammation index, white blood cell to platelet ratio, and homocysteine in predicting the instability of small saccular intracranial aneurysms

Inflammation has a destructive effect on the homeostasis of the vascular wall, which is involved in the formation, growth, and rupture of human intracranial aneurysms (IAs) disease progression. However, inflammation-related markers have not been well studied in the risk stratification of unruptured IAs. The purpose of this study was to investigate the predictive value of serum inflammatory markers in the unstable progression of small saccular intracranial aneurysms (SIAs). This study retrospectively included 275 patients with small SIAs (aneurysm diameter less than or equal to 7 mm), to compare the level difference of serum inflammatory complex marker systemic immune-inflammatory index (SII), white blood cell to platelet ratio (WPR), and homocysteine (Hcy) in patients with stable (asymptomatic unruptured) and unstable (symptomatic unruptured, ruptured) small SIAs. 187 patients (68%) had aneurysm-related compression symptoms and rupture outcomes. In the multivariate logistic regression after adjusting for baseline differences, SII, WPR, and Hcy were independent risk factors for the instability of small SIAs, the prediction model combined with other risk factors (previous stroke history, aneurysm irregularity) showed good predictive ability for the instability of small SIAs, with an area under the curve of 0.905. In addition, correlation analysis showed that SII, WPR, and Hcy also had significant differences in patients with symptomatic unruptured and ruptured small SIAs, and higher inflammation levels often promoted the disease progression of small SIAs. Higher levels of SII, WPR and Hcy can be used as independent predictors of instability of small SIAs. As an economical and convenient biomarker, it is crucial for clinical treatment strategies of stable small SIAs.

Clinical features and in-hospital mortality predictors of concurrent cardio-cerebral infarction: insights from a dual-center retrospective study

OBJECTIVE

This study aimed to enhance the understanding of cardio-cerebral infarction (CCI) clinical features and identify key prognostic factors, thereby providing an empirical foundation for advancing prevention and treatment strategies and ultimately improving clinical outcomes for CCI patients.

METHODS

We retrospectively analyzed 17,645 AIS and 7,584 AMI patients admitted to two hospitals from 2014 to 2023. Univariate analysis, Spearman correlation, and multivariate logistic regression were performed to identify independent risk factors. Receiver operating characteristic (ROC) curves were used to determine optimal cutoff values.

RESULTS

This study enrolled 85 patients with CCI, representing an overall CCI incidence of approximately 0.34%. Males comprised 64.71% of the cohort. ST-segment elevation myocardial infarction and cardiogenic cerebral infarction were the most predominant subtypes. The in-hospital mortality rate was 30.59%, with 65.38% of deaths attributed to cardiac causes. Multivariate logistic regression analysis identified three independent risk factors for in-hospital mortality: elevated neutrophil-to-lymphocyte ratio (NLR), decreased serum albumin, and increased peak N-terminal pro-B-type natriuretic peptide levels (NT-proBNP). ROC curve analysis demonstrated that the area under the curve (AUC) for the NLR, albumin concentration and peak NT-proBNP concentration were 0.863, 0.723, and 0.824, respectively. The optimal cutoff values were 6.914 for NLR, 33.80 g/L for albumin, and 9474.50 pg/mL for peak NT-proBNP. The AUC of the combined diagnostic model reached 0.959, significantly outperforming the individual indicators.

CONCLUSION

Elevated NLR, decreased serum albumin, and increased peak NT-proBNP levels independently predict in-hospital mortality in CCI patients. Combining these biomarkers enhances predictive capability for adverse outcomes.

Systemic Inflammatory Response Index and the Short-Term Functional Outcome of Patients with Acute Ischemic Stroke: A Meta-analysis

INTRODUCTION

The systemic inflammatory response index (SIRI) is a novel indicator of systemic inflammation derived from the absolute counts of neutrophils, monocytes, and lymphocytes. The aim of this meta-analysis was to evaluate the association between SIRI and functional outcome in patients with acute ischemic stroke (AIS).

METHODS

The Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines were followed in this meta-analysis. Relevant cohort studies were retrieved by a search of electronic databases including PubMed, Web of Science, Embase, Wanfang, and China National Knowledge Infrastructure from database inception to February 9, 2024. A poor functional outcome was defined as a modified Rankin Scale ≥ 3 within 3 months after disease onset. A random-effects model was used to combine the data by incorporating the influence of between-study heterogeneity. The protocol of the meta-analysis was not prospectively registered in PROSPERO.

RESULTS

Fourteen cohort studies were included. Pooled results showed that a high SIRI at admission was associated with increased risk of poor functional outcome within 3 months (odds ratio [OR]: 1.57, 95% confidence interval: 1.39 to 1.78, p < 0.001; I2 = 0%). Results of the meta-regression analysis suggested that the cutoff for defining a high SIRI was positively related to the OR for the association between SIRI and the risk of poor functional outcome (coefficient = 0.13, p = 0.03), while other variables including sample size, mean age, severity of stroke at admission, percentage of men, current smokers, or patients with diabetes did not significantly modify the results. Subgroup analyses according to study design, main treatments, and study quality scores showed similar results.

CONCLUSION

A high SIRI may be associated with a poor functional outcome in patients after AIS.

The Systemic Inflammatory Response Index as a Novel Diagnostic Indicator for Bell's Palsy

Aims/Background The systemic inflammatory response index (SIRI), an emerging hematological marker of inflammation, has shown promise as a promising biomarker for a variety of inflammatory conditions. This study aims to explore the diagnostic role of SIRI in Bell's palsy (BP). Methods For this retrospective study, 73 people diagnosed with BP between January 2021 and December 2023 were recruited, along with 73 healthy controls who were age- and sex-matched. The SIRI and other blood inflammatory markers, including the systemic immune-inflammation index (SII), neutrophil-to-lymphocyte ratio (NLR), and platelet-to-lymphocyte ratio (PLR), were determined for all participants, by enumerating their peripheral blood cell counts. Facial nerve function was assessed upon admission and after one month of treatment using the House-Brackmann Facial Nerve Grading System (H-B). According to this system, patients with an H-B grade of 1-2 are considered recovered, while those with an H-B grade of 3-6 are regarded as not recovered. Results The SIRI (0.94 vs 0.48, p < 0.001), SII (480.3 vs 329.12, p < 0.001), NLR (2.42 vs 1.41, p < 0.001), and PLR (141.05 vs 117.28, p = 0.001) showed a significant increase in the BP group compared to the control group. The receiver operating characteristic (ROC) curve analysis revealed that the area under the curve (AUC) for SIRI was higher than those for SII, NLR, and PLR, respectively. Upon one-month follow-up, significant differences in the values of SIRI, SII, and NLR were observed between the favorable prognosis group and the poor prognosis group (SIRI: 1.07 vs 0.87, p = 0.011; SII: 647.85 vs 422.11, p = 0.005; NLR: 3.31 vs 2.11, p = 0.013). The AUC of ROC curve for SIRI was found to be lower than that of SII but higher than that of NLR. Conclusion The SIRI has the potential to be an important BP diagnostic and prognostic marker.

Non-contrast CT radiomics-clinical machine learning model for futile recanalization after endovascular treatment in anterior circulation acute ischemic stroke

OBJECTIVE

To establish a machine learning model based on radiomics and clinical features derived from non-contrast CT to predict futile recanalization (FR) in patients with anterior circulation acute ischemic stroke (AIS) undergoing endovascular treatment.

METHODS

A retrospective analysis was conducted on 174 patients who underwent endovascular treatment for acute anterior circulation ischemic stroke between January 2020 and December 2023. FR was defined as successful recanalization but poor prognosis at 90 days (modified Rankin Scale, mRS 4-6). Radiomic features were extracted from non-contrast CT and selected using the least absolute shrinkage and selection operator (LASSO) regression method. Logistic regression (LR) model was used to build models based on radiomic and clinical features. A radiomics-clinical nomogram model was developed, and the predictive performance of the models was evaluated using area under the curve (AUC), accuracy, sensitivity, and specificity.

RESULTS

A total of 174 patients were included. 2016 radiomic features were extracted from non-contrast CT, and 9 features were selected to build the radiomics model. Univariate and stepwise multivariate analyses identified admission NIHSS score, hemorrhagic transformation, NLR, and admission blood glucose as independent factors for building the clinical model. The AUC of the radiomics-clinical nomogram model in the training and testing cohorts were 0.860 (95%CI 0.801-0.919) and 0.775 (95%CI 0.605-0.945), respectively.

CONCLUSION

The radiomics-clinical nomogram model based on non-contrast CT demonstrated satisfactory performance in predicting futile recanalization in patients with anterior circulation acute ischemic stroke.

The characteristic and prognostic role of blood inflammatory markers in patients with Huntington's disease from China

Objectives

This study aims to elucidate the role of peripheral inflammation in Huntington's disease (HD) by examining the correlation of peripheral inflammatory markers with clinical manifestations and disease prognosis.

Methods

This investigation involved 92 HD patients and 92 matched healthy controls (HCs). We quantified various peripheral inflammatory markers and calculated their derived metrics including neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), lymphocyte-to-monocyte ratio (LMR), and systemic immune-inflammation index (SII). Clinical assessments spanning cognitive, motor, and disease severity were administered. Comparative analysis of inflammatory markers and clinical correlations between HD and controls was performed. Kaplan-Meier survival analysis and Cox regression model were used to assess the effect of inflammatory markers on survival.

Results

The study revealed that HD patients had significantly reduced lymphocyte counts, and LMR. Conversely, NLR, PLR, and SII were elevated compared to HCs. Lymphocyte levels inversely correlated with the age of onset and monocyte levels inversely correlated with the UHDRS-total functional capacity (TFC) scores. After adjusting for age, sex, and CAG repeat length, lymphocyte count, NLR, PLR, and SII were significantly correlated with the progression rate of TFC scores. Elevated levels of white blood cells and monocytes were associated with an increased risk of disability and mortality in the HD cohort.

Conclusion

Our findings indicate that HD patients display a distinct peripheral inflammatory profile with increased NLR, PLR, and SII levels compared to HCs. The peripheral inflammation appears to be linked with accelerated disease progression and decreased survival in HD.

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 Potential Value of Systemic Inflammation Response Index on Delirium After Hip Arthroplasty Surgery in Older Patients: A Retrospective Study

Purpose

To explore the relationship between the systemic inflammation response index (SIRI) and postoperative delirium (POD) in older patients with hip arthroplasty surgery.

Patients and Methods

Older patients who underwent elective hip arthroplasty surgery were included in this retrospective study. SIRI, neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR) were collected from blood routine examination at admission. Binary logistic regression analysis was performed to evaluate the association between SIRI levels and POD was analyzed.

Results

Ultimately, 116 older patients who met the inclusion criteria were assessed. Thirty-four (29%) of 116 patients diagnosed with POD were defined as the POD group, and the rest consisted of the Non-POD group. Compared with non-POD patients, POD patients showed significantly higher levels of SIRI (P < 0.001) and NLR (P = 0.002) at admission. There was no significance in the levels of PLR between two groups. SIRI was independently associated with the occurrence of POD in multivariate logistic regression analysis [odds ratio (OR) = 3.34, 95% confidence interval (95% CI) = 1.26-8.85, P = 0.016]. Receiver operating characteristic curve analysis indicated that SIRI with an optimal cutoff value of 0.987 predicted the POD with a sensitivity of 88.2% and specificity of 74.4%, and the area under the curve was 0.82 (95% CI, 0.74-0.90, P < 0.01).

Conclusion

Preoperative SIRI and NLR levels in the blood are associated with the occurrence of POD. Moreover, preoperative SIRI level is a useful candidate biomarker to identify delirium after elective hip arthroplasty surgery in older patients.

Systemic Inflammation Response Index as a Predictor of Stroke Risk in Elderly Patients with Hypertension: A Cohort Study

Objective

This study aimed to evaluate the relationship between the systemic inflammation response index (SIRI) and the risk of stroke and its subtypes in elderly patients with hypertension and to explore its predictive accuracy and any potential effect modifiers.

Methods

The study included 4749 participants with no history of stroke at baseline. Cox regression was used to estimate adjusted hazard ratios (HR) and 95% confidence intervals (CIs). Interaction tests and subgroup analyses were conducted. The predictive performance of various inflammatory indicators for stroke was compared using the area under the curve (AUC), continuous net reclassification improvement (NRI), and integrated discrimination improvement (IDI).

Results

During a median follow-up period of 3.2 years, 640 strokes were recorded, of which 526 were ischemic and the remainder hemorrhagic. After adjustment for confounders, compared to the reference group, the HRs (95% CI) of stroke were 1.28 (95% CI, 1.01-1.64) and 1.46 (95% CI, 1.14-1.88) for participants in the second and third tertiles, respectively. We observed interactions between SIRI and homocysteine levels (< 15 vs. ≥ 15 μmol/L) (p for interaction = 0.014) on ischemic stroke risk. Furthermore, the AUC, NRI, and IDI analyses demonstrated that SIRI exhibited better predictive value for stroke risk when compared to other indicators. Similar results were observed for both ischemic and hemorrhagic strokes.

Conclusion

Elevated SIRI levels were significantly associated with the risk of stroke and its subtypes in elderly patients with hypertension, suggesting its potential as a promising indicator for stroke risk in this population. However, larger prospective studies are needed to confirm these findings.