Predictive Ability of Systemic Inflammation Response Index for the Risk of Pneumonia in Patients with Acute Ischemic Stroke

Duan, Yan; Dai, Caijun; Xu, Ruoting; Huang, Qiqi; Ren, Wenwei

doi:10.1159/000524759

Cited by 17 publications

(22 citation statements)

References 46 publications

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“…Consistent with other reports (16,37), the hospital stay length was prolonged in the SAP group. Studies have shown that stroke-related infections, especially pneumonia, are independently associated with poor functional prognosis after stroke.…”

Section: Discussionsupporting

confidence: 92%

“…It was revealed that elevated SIRI is an independent indicator of poor prognosis in stroke (24)(25)(26), aneurysmal subarachnoid hemorrhage (28), and some tumors (29)(30)(31). Yan et al (16) found that higher SIRI was a significant risk factor for pneumonia in patients with acute ischemic stroke. A SIRI threshold of ≥2.74 was correlated with an increased incidence of SAP in patients with AIS (OR: 5.82, 95% CI: 4.54, 7.49, P < 0.001).…”

Section: Discussionmentioning

confidence: 99%

“…The pathophysiological mechanisms of SICH and acute ischemic stroke (AIS) are very different. Previous studies have shown that high NLR and SIRI predict SAP in patients with AIS ( 15 , 16 ). However, the clinical significance of these inflammatory factors for SAP after SICH remains questionable.…”

Section: Introductionmentioning

confidence: 92%

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Inflammatory response biomarkers nomogram for predicting pneumonia in patients with spontaneous intracerebral hemorrhage

Liu

et al. 2023

Front. Neurol.

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ObjectivesInflammatory response biomarkers are promising prognostic factors to improve the prognosis of stroke-associated pneumonia (SAP) after ischemic stroke. This study aimed to investigate the prognostic significance of inflammatory response biomarkers on admission in SAP after spontaneous intracerebral hemorrhage (SICH) and establish a corresponding nomogram.MethodsThe data of 378 patients with SICH receiving conservative treatment from January 2019 to December 2021 at Taizhou People's Hospital were selected. All eligible patients were randomized into the training (70%, 265) and validation cohorts (30%, 113). In the training cohort, multivariate logistic regression analysis was used to establish an optimal nomogram, including inflammatory response biomarkers and clinical risk factors. The area under the receiver operating characteristic (ROC) curve (AUC), calibration curve, and decision curve analysis (DCA) were used to evaluate the nomogram's discrimination, calibration, and performance, respectively. Moreover, this model was further validated in a validation cohort.ResultsA logistic regression analysis showed that intraventricular hemorrhage (IVH), hypertension, dysphagia, Glasgow Coma Scale (GCS), National Institute of Health Stroke Scale (NIHSS), systemic inflammation response index (SIRI), and platelet/lymphocyte ratio (PLR) were correlated with SAP after SICH (P < 0.05). The nomogram was composed of all these statistically significant factors. The inflammatory marker-based nomogram showed strong prognostic power compared with the conventional factors, with an AUC of 0.886 (95% CI: 0.841–0.921) and 0.848 (95% CI: 0.799–0.899). The calibration curves demonstrated good homogeneity between the predicted risks and the observed outcomes. In addition, the model has a significant net benefit for SAP, according to DCA. Also, internal validation demonstrated the reliability of the prediction nomogram. The length of hospital stay was shorter in the non-SAP group than in the SAP group. At the 3-month follow-up, clinical outcomes were worse in the SAP group (P < 0.001).ConclusionSIRI and PLR at admission can be utilized as prognostic inflammatory biomarkers in patients with SICH in the upper brain treated with SAP. A nomogram covering SIRI and PLR can more accurately predict SAP in patients' supratentorial SICH. SAP can influence the length of hospital stay and the clinical outcome.

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Section: Discussionsupporting

confidence: 92%

Section: Discussionmentioning

confidence: 99%

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Inflammatory response biomarkers nomogram for predicting pneumonia in patients with spontaneous intracerebral hemorrhage

Liu

et al. 2023

Front. Neurol.

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“…The inflammatory response increases rapidly after stroke onset and is strongly linked to the occurrence of SAP ( 18 ). Inflammatory biomarkers such as the NLR, SII, SIRI and PLR can reflect the state of systemic inflammation and the immune response ( 10 ). Neurological injury induced by the inflammatory response and the release of cytokines from immune cells results in the production of anti-inflammatory signals and inhibits cytokine production, ultimately inhibiting infection and preventing disease progression ( 19 ).…”

Section: Discussionmentioning

confidence: 99%

“…It has been used to predict poor outcomes following ICH and clinical outcomes of delayed cerebral vasospasm in patients with aneurysmal SAH ( 7 , 8 ). The systemic inflammation response index (SIRI) indicates the balance between the inflammatory response and immune status ( 9 ) and has good predictive power for SAP in acute ischemic stroke patients ( 10 ). There is evidence that the SIRI can predict aneurismal subarachnoid hemorrhage and tumors ( 11 ).…”

Section: Introductionmentioning

confidence: 99%

The clinical value of neutrophil-to-lymphocyte ratio (NLR), systemic immune-inflammation index (SII), platelet-to-lymphocyte ratio (PLR) and systemic inflammation response index (SIRI) for predicting the occurrence and severity of pneumonia in patients with intracerebral hemorrhage

Wang

Jiang

et al. 2023

Front. Immunol.

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BackgroundInflammatory mechanisms play important roles in intracerebral hemorrhage (ICH) and have been linked to the development of stroke-associated pneumonia (SAP). The neutrophil-to-lymphocyte ratio (NLR), systemic immune-inflammation index (SII), platelet-to-lymphocyte ratio (PLR) and systemic inflammation response index (SIRI) are inflammatory indexes that influence systemic inflammatory responses after stroke. In this study, we aimed to compare the predictive value of the NLR, SII, SIRI and PLR for SAP in patients with ICH to determine their application potential in the early identification of the severity of pneumonia.MethodsPatients with ICH in four hospitals were prospectively enrolled. SAP was defined according to the modified Centers for Disease Control and Prevention criteria. Data on the NLR, SII, SIRI and PLR were collected at admission, and the correlation between these factors and the clinical pulmonary infection score (CPIS) was assessed through Spearman’s analysis.ResultsA total of 320 patients were enrolled in this study, among whom 126 (39.4%) developed SAP. The results of the receiver operating characteristic (ROC) analysis revealed that the NLR had the best predictive value for SAP (AUC: 0.748, 95% CI: 0.695-0.801), and this outcome remained significant after adjusting for other confounders in multivariable analysis (RR=1.090, 95% CI: 1.029-1.155). Among the four indexes, Spearman’s analysis showed that the NLR was the most highly correlated with the CPIS (r=0.537, 95% CI: 0.395-0.654). The NLR could effectively predict ICU admission (AUC: 0.732, 95% CI: 0.671-0.786), and this finding remained significant in the multivariable analysis (RR=1.049, 95% CI: 1.009-1.089, P=0.036). Nomograms were created to predict the probability of SAP occurrence and ICU admission. Furthermore, the NLR could predict a good outcome at discharge (AUC: 0.761, 95% CI: 0.707-0.8147).ConclusionsAmong the four indexes, the NLR was the best predictor for SAP occurrence and a poor outcome at discharge in ICH patients. It can therefore be used for the early identification of severe SAP and to predict ICU admission.

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Evaluation of peripheral blood inflammatory biomarkers in sickle cell disease with and without retinopathy

Özer,

Doğan,

Baysal

et al. 2024

Graefes Arch Clin Exp Ophthalmol

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Background The aim of this study was to evaluate the clinical significance of blood-cell associated inflammation markers in patients with sickle cell disease (SCD) and sickle cell retinopathy (SCR). Methods Neutrophil to lymphocyte ratio (NLR), platelet to lymphocyte ratio (PLR), monocyte to lymphocyte ratio (MLR), systemic immune inflammation index (SIII), systemic inflammation response index (SIRI), systemic inflammation modulation index (SIMI) and aggregate systemic inflammation index (AISI) were calculated. This study included 45 healthy controls (Group 1) and 100 SCD (Group 2). Patients in Group 2 were then divided into two groups: without SCR (Group 3) and with SCR (Group 4), and patients with SCR (Group 4) were further divided into two groups: non-proliferative sickle cell retinopathy (NPSCR) (Group 5) and proliferative sickle cell retinopathy (PSCR) (Group 6). Results The mean values for NLR, PLR, SIII, SIRI, AISI, and SIMI were significantly higher in Group 2 compared to Group 1 (p = 0.011 for NLR, p = 0.004 for SIII, and p < 0.001 for others). Furthermore, AISI and SIMI parameters demonstrated statistically significant discriminatory power to distinguish Group 5 from Group 6 (p = 0.0016 and p = 0.0006, respectively). Conclusion Given the critical role of inflammatory mechanisms in the pathogenesis of SCD and its related complications, the assessment of blood-cell-associated inflammatory markers may present a pragmatic and advantageous approach to the clinical oversight and therapeutic intervention of SCD.

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Predictive Ability of Systemic Inflammation Response Index for the Risk of Pneumonia in Patients with Acute Ischemic Stroke

Cited by 17 publications

References 46 publications

Inflammatory response biomarkers nomogram for predicting pneumonia in patients with spontaneous intracerebral hemorrhage

Inflammatory response biomarkers nomogram for predicting pneumonia in patients with spontaneous intracerebral hemorrhage

The clinical value of neutrophil-to-lymphocyte ratio (NLR), systemic immune-inflammation index (SII), platelet-to-lymphocyte ratio (PLR) and systemic inflammation response index (SIRI) for predicting the occurrence and severity of pneumonia in patients with intracerebral hemorrhage

Evaluation of peripheral blood inflammatory biomarkers in sickle cell disease with and without retinopathy

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