The prognostic value of systemic immune-inflammation index in patients with aneurysmal subarachnoid hemorrhage: a systematic review

Dynamic changes of systemic inflammation response index and systemic immune-inflammation index are associated with delayed cerebral ischemia after aneurysmal subarachnoid hemorrhage

OBJECTIVES

Delayed cerebral ischemia (DCI) is a factor contributing to poor outcome of aneurysmal subarachnoid hemorrhage (aSAH). Serial inflammatory response is known to affect the occurrence of DCI. The aim of this study was to evaluate associations of dynamic changes of various inflammatory markers with occurrence of DCI after aSAH.

METHODS

A total of 279 patients with interventional treatment for aSAH were enrolled, and dichotomized according to the occurrence of DCI. Various inflammatory markers, including systemic immune-inflammation index (SII), systemic inflammation response index (SIRI), and their dynamic changes were analyzed at four different time points. Receiver operating characteristic (ROC) curve analysis with area under the curve (AUC) and univariate, multivariate Cox regression analyses with hazard ratio (HR) and 95 % confidence interval (CI) were performed to identify predictors for DCI.

RESULTS

Differences of SII and SIRI values between DCI (+) and DCI (-) group were significantly higher at 5-7 days than at other time points (P < 0.001 and P < 0.001, respectively). SII and SIRI had higher predicting values for DCI occurrence than other inflammatory markers (AUC: 0.862, 95 % CI: 0.786-0.928; P < 0.001 and AUC: 0.851, 95 % CI: 0.769-0.913; P < 0.001, respectively). SII at 5-7 days (HR: 1.74, 95 % CI: 1.38-3.22, P = 0.020) and SIRI at 5-7 days (HR: 1.62, 95 % CI: 1.28-2.84, P = 0.035) were associated with occurrence of DCI.

CONCLUSIONS

Dynamic changes of SII and SII might be predictors of DCI occurrence in patients with aSAH.

A nomogram model based on the systemic immune-inflammation index to predict the risk of venous thromboembolism in elderly patients after hip fracture: A retrospective cohort study

Background and objectives

Venous thromboembolism (VTE), encompassing deep vein thrombosis (DVT) and secondary pulmonary embolism (PE), represents a significant complication post-hip fracture in the elderly. It is a prevalent cause of VTE-related complications, prolonged hospitalization, and mortality. This study aimed to investigate the potential of the systemic immune-inflammation index (SII) as a predictive marker for VTE in older patients following hip fracture.

Methods

The study was structured as an observational, analytical, retrospective cohort analysis. A total of 346 elderly patients diagnosed with hip fracture were included. We retrospectively collated clinical and laboratory data for these patients. Using the bootstrap method, the patients were divided in a 7:3 ratio into a training cohort (DVT group = 170 patients; no-DVT group = 72 patients) and an internal validation cohort (DVT group = 81 patients; no-DVT group = 23 patients). In the training cohort, relevant indices were initially identified using univariate analysis. Subsequently, least absolute shrinkage and selection operator logistic analysis was employed to determine significant potential independent risk factors (P < 0.05). A dynamic online diagnostic nomogram was developed, with its discriminative ability assessed using the area under the receiver operating characteristic curve (AUC). The nomogram's accuracy was further appraised using calibration plots. The clinical utility of the nomogram was evaluated through decision curve analysis (DCA) and corroborated by internal validation within the training set.

Results

SII emerged as the sole independent risk factor identified from the multivariate logistic analysis of the training cohort and was incorporated into the VTE diagnostic nomogram for older patients' post-hip fracture. The nomogram demonstrated AUC values of 0.648 in the training cohort and 0.545 in the internal testing cohort. Calibration curves corroborated the close alignment of the nomogram's predicted outcomes with the ideal curve, indicating consistency between predicted and actual outcomes. The DCA curve suggested that all patients could derive benefit from this model. These findings were also validated in the validation cohort.

Conclusion

The systemic immune-inflammation index is a robust predictor of venous thromboembolism in elderly patients following hip fracture, underscoring its potential as a valuable tool in clinical practice.

Pan-immune-inflammation value predict delayed cerebral ischemia in patients with aneurysmal subarachnoid hemorrhage

Inflammatory reaction and immune dysregulation are known as components contributing to delayed cerebral ischemia (DCI) in patients with following aneurysmal subarachnoid hemorrhage (aSAH). The objective of this study was to investigate the role of pan-immune-inflammation value (PIV) as a novel comprehensive inflammatory marker in predicting the DCI development following aSAH. A total of 1028 participants with aSAH were enrolled. There were 296 patients with DCI and 732 patients without DCI. Various inflammatory markers were analyzed using peripheral blood sample obtained at admission. Receiver operating characteristic (ROC) analysis was performed to identify the optimal cutoff value of PIV for distinguishing DCI. Multivariate analysis was used to determine independent predictors for DCI. Mean PIV was significantly higher in the DCI (+) group than in the DCI (-) group (437.6 ± 214.7 vs 242.1 ± 154.7, P = 0.007). In ROC analysis, the optimal cutoff value of PIV was 356.7 for predicting DCI (area under the curve [AUC] 0.772, 95 % confidence interval [CI] 0.718-0.816; P < 0.001). Multivariate analysis showed that high Hunt-Hess grade (odds ratio [OR] 1.70, 95 % CI 1.38-2.22; P = 0.007), thick SAH (OR 1.82, 95 % CI 1.44-2.32; P = 0.005), and elevated PIV (≥356.7) (OR 1.42, 95 % CI 1.10-1.74; P = 0.013) were independent predictors of DCI after aSAH. PIV is a potent predictor of DCI in patients with aSAH. Elevated PIV is associated with more DCI development. Thus, PIV has predictive value for DCI development.