Systemic inflammation and immune index predicting outcomes in patients with intracerebral hemorrhage

Chemokines-like factor 1 as a serological biomarker in relation to disease severity and 90-day prognosis after spontaneous intracerebral hemorrhage: a prospective cohort study

Chemokines-like factor 1 (CKLF1) possesses chemokine-related biological functions, and overproduction of CKLF1 may exacerbate inflammatory response, thereby worsening neurological impairments after acute brain injury diseases. The aim of this study was to determine its ability to predict early neurological deterioration (END) and 90-day poor prognosis after spontaneous intracerebral hemorrhage (sICH). A total of 129 sICH patients participated in this prospective cohort study. Serum CKLF1 levels were measured at admission in all patients and 129 healthy controls. Meanwhile, serum of CKLF1 levels of 50 patients were also measured at days 1, 2, 3, 5 and 7 after admission. Glasgow coma scale (GCS) scores and hematoma volumes were used to assess hemorrhage severity. END and postinjury 90-day poor prognosis [modified Rankin scale (mRS) scores 3-6] were observed as outcome variables. Multifactorial analyses were performed to determine the correlation of serum CKLF1 levels with disease severity and the association of neurofunctional prognosis. Serum CKLF1 levels in patients with sICH were elevated at admission, peaked on day 2, then gradually decreased and were significantly higher than those in healthy controls up to 7 days after sICH. Serum CKLF1 levels were independently correlated with hematoma volume, GCS scores and mRS scores. Meanwhile, serum CKLF1 levels were linearly correlated with the likelihood of not only developing END but also 90-days poor prognosis after sICH, and showed a strong discriminatory efficiency under the receiver operating characteristic curve. Serum CKLF1 levels, coupled with GCS scores and hematoma volumes were the independent predictors of END and 90-day poor prognosis. The three independent predictors were consolidated to establish two models for END and poor prognosis separately, which were visually delineated by nomograms. A series of statistical methods, such as calibration curve, decision curve, and ROC curve, were applied for validating the models' stability, clinical fit, and discriminatory efficiency. Serum CKLF1 levels are prone to increase significantly shortly after sICH. Serum CKLF1 levels are independently associated with disease severity, occurrence of END, and development of 90-day poor prognosis after stroke, suggesting that serum CKLF1 may be a potential biomarker of sICH. Clinical trial number Not applicable.

The predictive role of composite inflammatory ratio parameters in the conscious awareness recovery after severe acute ischemic stroke: a retrospective cohort study

BACKGROUND

Inflammatory mechanisms play a significant role in ischemic stroke. Peripheral neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), systemic immune-inflammation index (SII), systemic inflammation response index (SIRI), which are indicators capable of reflecting the magnitude of the inflammatory response, have been research hotspots. However, few research findings evaluate the prediction significance of these biomarkers in the recovery of conscious awareness following severe acute ischemic stroke.

METHODS

This was a retrospective cohort study of 142 patients with consciousness disorders after acute ischemic stroke (GCS score ≤ 8) treated from January 2022 to May 2024. The cases were divided into three groups according to the GCS score at discharge as died/ vegetative state (GCS ≤ 3),moderate/ severe coma(GCS = 4-11) and mild coma/ normal (GCS:12-15). Demographic and clinical assessment data were reviewed and abstracted. NLR, PLR, SII and SIRI were calculated based on the peripheral blood tests at admission. The study investigated the correlation between changes in GCS scores from admission to discharge (calculated as the GCS score at discharge minus the baseline GCS score, where a negative value indicates worsening and a positive value indicates improvement) and the levels of NLR, PLR, SII, and SIRI.

RESULTS

The level of NLR, PLR, SII and SIRI in died/ vegetative state group were significantly higher than those in moderate/ severe coma group (p = 0.0429, p = 0.0215, p = 0.0288, p = 0.026, respectively) and mild coma/ normal group (p = 0.0085, p = 0.0079, p = 0.0019, p = 0.0017, respectively). The area under the curve (AUC) values of NLR, PLR, SII, and SIRI to prognosis were 0.670, 0.661, 0.677, and 0.609, respectively. Spearman correlation analysis indicated NLR, PLR and SII were negatively correlated with GCS scores increase during hospitalization (r =- 0.317, p<0.0001 for NLR, r = -0.285, p = 0.001 for PLR, r = -0.3331, p < 0.0001 for SII, r= -0.199,p = 0.018 for SIRI).However, ordinal logistic regression analyses failed to indicate that NLR, PLR, SII and SIRI were independent predictors of poor consciousness response for severe acute ischemic stroke coma patients after adjusting for other confounders.

CONCLUSION

Patients with poorer consciousness outcomes exhibited a tendency towards elevated NLR, PLR, SII, and SIRI levels which were inversely correlated with GCS scores increase during hospitalization. However, the four indexes did not exhibit sufficient promise to be the valuable predictors for the prognosis of recovery from consciousness following severe acute ischemic stroke.

Predictive value of the dynamic systemic immune-inflammation index in the prognosis of patients with intracerebral hemorrhage: a 10-year retrospective analysis

Background and purpose

The systemic immune-inflammation index (SII) is a novel immune inflammatory marker which has been proven to have excellent predictive value for many diseases. The aim of this study was to investigate the predictive value of SII at different time points after admission for functional outcome at discharge in patients with intracerebral hemorrhage (ICH).

Methods

The clinical data of patients with ICH who were treated at a medical center for neurological diseases in China between October 2012 and April 2022 were analyzed in this retrospective study. The SII was calculated based on neutrophil×platelet/lymphocyte counts and collected within the first 14 days after admission to analyze the dynamic changes. Adverse outcome was defined as a modified Rankin Scale (mRS) score of 4-6 at discharge. The correlation between the SII and the outcome was assessed using univariate and multivariate logistic regression analyses. The ability of SII to predict outcome was evaluated by the area under the receiver operating characteristic (ROC) curve (AUC).

Results

A total of 1,569 patients with ICH were included, of whom 790 had adverse outcome (50.35%). The Univariate logistic regression analysis showed that SII at any time point within 14 days after admission was significantly associated with adverse outcome. In the multivariate logistic regression analysis, the SII within 7 days after admission was found to be an independent predictor of adverse functional outcome in ICH patients at discharge. The ROC curve demonstrated that compared to other time points, the SII on day 2 after admission exhibited stronger predictive power for the functional outcome of patients with ICH at discharge (AUC:0.733, 95%CI = 0.679-0.787) (sensitivity 47.09%, specificity 87.02%) (OR 1.074, 95%CI = 1.033-1.126, p = 0.001).

Conclusion

SII within 7 days after admission, especially on day 2, is independently associated with adverse functional outcome in ICH patients at discharge. This association could be utilized in clinical practice and warrants further investigation.

The clinical value of inflammation index in predicting ICU mortality of critically ill patients with intracerebral hemorrhage

Background

The inflammatory response holds paramount significance in the context of intracerebral hemorrhage (ICH) and exhibits a robust correlation with mortality rates. Biological markers such as the neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), lymphocyte-to-monocyte ratio (LMR), systemic immune inflammation index (SII), and systemic inflammatory response index (SIRI) play crucial roles in influencing the systemic inflammatory response following ICH. This study aims to compare the predictive efficacy of NLR, PLR, LMR, SII, and SIRI concerning the risk of mortality in the intensive care unit (ICU) among critically ill patients with ICH. Such a comparison seeks to elucidate their early warning capabilities in the management and treatment of ICH.

Methods

Patients with severe ICH requiring admission to the ICU were screened from the Medical Information Marketplace for Intensive Care (MIMIC-IV) database. The outcomes studied included ICU mortality and 30 day ICU hospitalization rates, based on tertiles of the NLR index level. To explore the relationship between the NLR index and clinical outcomes in critically ill patients with ICH, we utilized receiver operating characteristic (ROC) analysis, decision curve analysis (DCA), and multivariate logistic regression analysis.

Results

A total of 869 patients (51.9% male) were included in the study, with an ICU mortality rate of 22.9% and a 30 day ICU hospitalization rate of 98.4%. Among the five indicators examined, both the ROC curve and DCA indicated that NLR (AUC: 0.660, 95%CI: 0.617-0.703) had the highest predictive ability for ICU mortality. Moreover, this association remained significant even after adjusting for other confounding factors during multivariate analysis (HR: 3.520, 95%CI: 2.039-6.077). Based on the results of the multivariate analysis, incorporating age, albumin, lactic acid, NLR, and GCS score as variables, we developed a nomogram to predict ICU mortality in critically ill patients with ICH.

Conclusion

NLR emerges as the most effective predictor of ICU mortality risk among critically ill patients grappling with ICH when compared to the other four indicators. Furthermore, the integration of albumin and lactic acid indicators into the NLR nomogram enhances the ability to promptly identify ICU mortality in individuals facing severe ICH.

Development and validation of a machine learning-based predictive model for assessing the 90-day prognostic outcome of patients with spontaneous intracerebral hemorrhage

BACKGROUND

Spontaneous intracerebral hemorrhage (sICH) is associated with significant mortality and morbidity. Predicting the prognosis of patients with sICH remains an important issue, which significantly affects treatment decisions. Utilizing readily available clinical parameters to anticipate the unfavorable prognosis of sICH patients holds notable clinical significance. This study employs five machine learning algorithms to establish a practical platform for the prediction of short-term prognostic outcomes in individuals afflicted with sICH.

METHODS

Within the framework of this retrospective analysis, the model underwent training utilizing data gleaned from 413 cases from the training center, with subsequent validation employing data from external validation center. Comprehensive clinical information, laboratory analysis results, and imaging features pertaining to sICH patients were harnessed as training features for machine learning. We developed and validated the model efficacy using all the selected features of the patients using five models: Support Vector Machine (SVM), Logistic Regression (LR), Random Forest (RF), XGboost and LightGBM, respectively. The process of Recursive Feature Elimination (RFE) was executed for optimal feature screening. An internal five-fold cross-validation was employed to pinpoint the most suitable hyperparameters for the model, while an external five-fold cross-validation was implemented to discern the machine learning model demonstrating the superior average performance. Finally, the machine learning model with the best average performance is selected as our final model while using it for external validation. Evaluation of the machine learning model's performance was comprehensively conducted through the utilization of the ROC curve, accuracy, and other relevant indicators. The SHAP diagram was utilized to elucidate the variable importance within the model, culminating in the amalgamation of the above metrics to discern the most succinct features and establish a practical prognostic prediction platform.

RESULTS

A total of 413 patients with sICH patients were collected in the training center, of which 180 were patients with poor prognosis. A total of 74 patients with sICH were collected in the external validation center, of which 26 were patients with poor prognosis. Within the training set, the test set AUC values for SVM, LR, RF, XGBoost, and LightGBM models were recorded as 0.87, 0.896, 0.916, 0.885, and 0.912, respectively. The best average performance of the machine learning models in the training set was the RF model (average AUC: 0.906 ± 0.029, P < 0.01). The model still maintains a good performance in the external validation center, with an AUC of 0.817 (95% CI 0.705-0.928). Pertaining to feature importance for short-term prognostic attributes of sICH patients, the NIHSS score reigned supreme, succeeded by AST, Age, white blood cell, and hematoma volume, among others. In culmination, guided by the RF model's variable importance weight and the model's ROC curve insights, the NIHSS score, AST, Age, white blood cell, and hematoma volume were integrated to forge a short-term prognostic prediction platform tailored for sICH patients.

CONCLUSION

We constructed a prediction model based on the results of the RF model incorporating five clinically accessible predictors with reliable predictive efficacy for the short-term prognosis of sICH patients. Meanwhile, the performance of the external validation set was also more stable, which can be used for accurate prediction of short-term prognosis of sICH patients.

Relationship between SIRI and android and gynoid fat related to obesity and cardiovascular risk: a cross-sectional study of NHANES 2017-2018

OBJECTIVE

Inflammation has a crucial role in several metabolic diseases, such as obesity. The author aimed to investigate the relationship between the system inflammation response index (SIRI) and android fat composition and distribution.

METHODS

Data for individuals aged 8-59 years, SIRI, android percent fat, and android-to-gynoid ratio from the 2017 to 2018 National Health and Nutrition Examination Survey were used. Weighted multiple linear regression and smooth curve fitting were used to test for linear and nonlinear associations. Additional subgroup analyses were performed. Threshold effect analysis was performed using a two-linear regression model.

RESULTS

Multiple linear regression showed a positive correlation between SIRI and android percent fat (β 0.92, 95% confidence interval [CI] 0.25-1.59) and between SIRI and the android-to-gynoid ratio (β 0.01, 95% CI 0.00-0.03) in 3783 Americans aged 8-59 years. The results showed that the effect of factors, other than smoking status, on the relationship between SIRI and android percent fat and android-to-gynoid ratio was not significant. There was a nonlinear relationship between SIRI and both android percent fat and android-to-gynoid ratio.

CONCLUSIONS

Elevated SIRI levels were associated with an increased android percent fat and android-to-gynoid ratio. Larger prospective studies are needed to validate the findings.

Neutrophil-to-lymphocyte ratio, white blood cell, and C-reactive protein predicts poor outcome and increased mortality in intracerebral hemorrhage patients: a meta-analysis

Objective

Inflammation participates in the pathology and progression of secondary brain injury after intracerebral hemorrhage (ICH). This meta-analysis intended to explore the prognostic role of inflammatory indexes, including neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), white blood cell (WBC), and C-reactive protein (CRP) in ICH patients.

Methods

Embase, PubMed, Web of Science, and Cochrane Library were searched until June 2023. Two outcomes, including poor outcome and mortality were extracted and measured. Odds ratio (OR) and 95% confidence interval (CI) were presented for outcome assessment.

Results

Forty-six studies with 25,928 patients were included in this meta-analysis. The high level of NLR [OR (95% CI): 1.20 (1.13-1.27), p < 0.001], WBC [OR (95% CI): 1.11 (1.02-1.21), p = 0.013], and CRP [OR (95% CI): 1.29 (1.08-1.54), p = 0.005] were related to poor outcome in ICH patients. Additionally, the high level of NLR [OR (95% CI): 1.06 (1.02-1.10), p = 0.001], WBC [OR (95% CI): 1.39 (1.16-1.66), p < 0.001], and CRP [OR (95% CI): 1.02 (1.01-1.04), p = 0.009] were correlated with increased mortality in ICH patients. Nevertheless, PLR was not associated with poor outcome [OR (95% CI): 1.00 (0.99-1.01), p = 0.749] or mortality [OR (95% CI): 1.00 (0.99-1.01), p = 0.750] in ICH patients. The total score of risk of bias assessed by Newcastle-Ottawa Scale criteria ranged from 7-9, which indicated the low risk of bias in the included studies. Publication bias was low, and stability assessed by sensitivity analysis was good.

Conclusion

This meta-analysis summarizes that the high level of NLR, WBC, and CRP estimates poor outcome and higher mortality in ICH patients.

Systemic inflammation response index as a clinical outcome evaluating tool and prognostic indicator for hospitalized stroke patients: a systematic review and meta-analysis

BACKGROUND

Stroke, which is the main element of cerebrovascular disease (CVD), has become the foremost reason for death and disability on a global scale. The systemic inflammation response index (SIRI), a newly developed and comprehensive indicator, has demonstrated promise in forecasting clinical results for diverse ailments. Nevertheless, the uncertainty surrounding the assessment and prediction of clinical outcomes for stroke patients by SIRI persists, and the conflicting findings from the limited studies conducted on this matter further complicate the situation. Consequently, we performed a thorough systematic review and meta-analysis to explore the correlation between SIRI and the clinical results in individuals suffering from stroke.

METHODS

This research was registered in PROSPERO and carried out following the PRISMA guidelines. A thorough investigation was carried out on PubMed, Embase, the Cochrane Library, Web of Science, and Scopus databases. Furthermore, we conducted a manual search in Chinese databases, such as China national Knowledge Infrastructure (CNKI), WanFang, VIP, and China Biology Medicine (CBM). We assessed the potential for bias in the studies included by utilizing the Newcastle-Ottawa Scale (NOS) tool. Adverse clinical outcomes were the main focus of the study, with secondary endpoints including mortality, the predictive value of SIRI, SIRI values across various endpoints, and clinical parameters associated with subarachnoid hemorrhage (SAH) in relation to low and high SIRI group.

RESULTS

Following rigorous evaluation, a grand total of 22 investigations, encompassing a populace of 12,737 individuals, were considered suitable for incorporation in the final analysis. The findings from our meta-analysis indicate a strong and consistent correlation between elevated SIRI levels and adverse functional outcomes, irrespective of the method used to evaluate unfavorable outcomes. Furthermore, increased SIRI values have a strong correlation with mortality rates in both the short and long term. Besides, SIRI is a useful indicator of the severity of SAH. SIRI demonstrates strong predictive ability in identifying unfavorable outcomes and stroke-related pneumonia (SAP), as higher SIRI values are typically linked to negative endpoints. Nevertheless, the meta-analysis indicated that there was no significant increase in the risk of early neurological deterioration (END) and acute hydrocephalus (AHC) in high SIRI group when comparing to low SIRI.

CONCLUSION

This study could potentially pave the way for groundbreaking insights into the relationship between SIRI and stroke patient outcomes, as it appears to be the first meta-analysis to explore this association. Given the critical role of the inflammatory response in stroke recovery, closely monitoring patients with high SIRI levels could represent a promising strategy for mitigating brain damage post-stroke. Thus, further investigation into SIRI and its impact on clinical outcomes is essential. While our initial findings offer valuable insights into this area, continued research is necessary to fully elucidate the potential of SIRI, ideally through dynamic monitoring and large-scale, multi-center studies. Ultimately, this research has the potential to inform clinical decision-making and improve patient outcomes following stroke.

SYSTEMATIC REVIEW REGISTRATION

https://www.crd.york.ac.uk/prospero/ ; Identifier CRD42023405221.