Systemic Inflammatory Response Syndrome on Admission and Clinical Outcomes After Intracerebral Hemorrhage

Liu, Lijun; Wang, Anxin; Wang, Dandan; Guo, Jiahuan; Zhang, Xiaoli; Wang, Wenjuan

doi:10.2147/jir.s394635

Cited by 1 publication

(1 citation statement)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The interaction between the brain and the heart is currently a research hotspot in the field of stroke. After intracerebral hemorrhage, the disruption of the blood-brain barrier leads to the activation of the hypothalamic-pituitary-adrenal axis and the sympathetic nervous system, triggering a systemic inflammatory response ( 14 ). The release of inflammatory mediators leads to vasoconstriction in peripheral blood vessels and an increase in heart rate, resulting in elevated rate-pressure product (RPP) index, undoubtedly significantly increasing the burden on the heart.…”

Section: Discussionmentioning

confidence: 99%

The rate-pressure product combined model within 24 h on admission predicts the 30-day mortality rate in conservatively treated patients with intracerebral hemorrhage

Zheng,

Tang,

Zhou

et al. 2024

Front. Neurol.

View full text Add to dashboard Cite

Background and objectivesRecently, some literature has proposed new indicators such as rate-pressure product, platelet-to-lymphocyte ratio, neutrophil-to-lymphocyte ratio, etc. However, there has been no literature that has utilized these new indicators to establish a predictive model for assessing the risk of mortality in patients within 24 h on admission. Therefore, this study aims to build a predictive model that can rapidly assess the likelihood of mortality in patients within 24 h of admission.MethodsThe datasets used in this study are available from the corresponding author upon reasonable request. Patients were randomly assigned to the training or validation cohort based on a ratio of 7:3, which was implemented as internal validations for the final predictive models. In the training set, least absolute shrinkage and selection operator (LASSO) regression was employed to select predictive factors, followed by both univariate and subsequent multivariate analysis. The predictive ability was assessed by the area under the receiver operating characteristic (ROC) curve.ResultsA total of 428 patients were included in our research. The final model included 4 independent predictors (Glasgow Coma Scale, hematoma volume, rate-pressure product, c-reactive protein) and was developed as a simple-to-use nomogram. The training set and internal validation set model’s C-index are 0.933 and 0.954, demonstrating moderate predictive ability with regard to risks of mortality. Compared to ICH score (AUC: 0.910 and 0.925), the net reclassification index (NRI) is 0.298 (CI = −0.105 to 0.701, p: 0.147) and integrated discrimination improvement (IDI) is 0.089 (CI = −0.049 to 0.228, p: 0.209). Our model is equally excellent as the classic ICH score model.ConclusionWe developed a model with four independent risk factors to predict the mortality of ICH patients. Our predictive model is effective in assessing the risk of mortality in patients within 24 h on admission, which might be worth considering in clinical settings after further external validation.

show abstract

Section: Discussionmentioning

confidence: 99%