Zhang Peng, Zhu Haiyang, Li Xinbo, Qian Yiwei, Zhu Yehao, Zhang Weizhong, Yan Zhiyuan, Ni Haoqi, Lin Zhongxiao, Lin Xiao, Li Zequn, Zhuge Qichuan, Zeng Bo
Department of Neurosurgery, the First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, People's Republic of China.
Zhejiang Provincial Key Laboratory of Aging and Neurological Disorder Research, the First Affiliated Hospital of Wenzhou Medical University, Wenzhou, 325000, People's Republic of China.
J Inflamm Res. 2024 Dec 14;17:11073-11085. doi: 10.2147/JIR.S481066. eCollection 2024.
To identify biomarkers and develop an inflammatory score based on proper integration to improve risk prediction of delayed cerebral ischemia (DCI) and poor outcome in patients with aneurysmal subarachnoid hemorrhage (aSAH). We also further explore the mediation and interaction of DCI within the chain of events using the four-way effect decomposition.
Machine learning algorithms are used for biomarker selection and constructed the inflammatory score. Multivariate logistic regression was performed to identify the association of inflammatory score with DCI and poor outcome. Next, we employed a four-way decomposition to assess the extent to which the inflammation effect on the risk of poor outcome is mediated by or interacts with DCI. Finally, the additive value of inflammatory score was measured using the area under the curve (AUC), net reclassification improvement (NRI) and integrated discrimination improvement (IDI).
In total, 368 aSAH patients were included. The inflammatory score was calculated with the combination of lymphocyte, pan-immune-inflammation value (PIV), red blood cell distribution width (RDW), and lactate dehydrogenase (LDH). Multivariate analysis identified that inflammatory score was independently associated with DCI and poor outcome. The effect of high inflammatory score on poor outcome may be partly explained by DCI, where there is both pure mediation and mediated interaction. With DCI as a potential mediator, the excess relative risk could be decomposed into 30.86% controlled direct effect, 3.60% mediation only, 26.64% interaction only, and 38.89% mediated interaction. Adding the inflammatory score to the predictive model improved the AUC from 0.772 to 0.822, with an NRI of 5.3% and IDI of 6.9%.
The inflammatory score was significantly associated with DCI and poor outcome in patients with aSAH. Not only may be a potential synergistic interaction between high inflammatory score and DCI on the risk of poor outcome but also where DCI is an important mediating mechanism.
识别生物标志物并基于合理整合开发炎症评分，以改善动脉瘤性蛛网膜下腔出血（aSAH）患者延迟性脑缺血（DCI）和不良预后的风险预测。我们还使用四向效应分解进一步探讨DCI在事件链中的中介作用和相互作用。
使用机器学习算法进行生物标志物选择并构建炎症评分。进行多因素逻辑回归以确定炎症评分与DCI和不良预后的关联。接下来，我们采用四向分解来评估炎症对不良预后风险的影响在多大程度上由DCI介导或与DCI相互作用。最后，使用曲线下面积（AUC）、净重新分类改善（NRI）和综合鉴别改善（IDI）来衡量炎症评分的附加值。
总共纳入了368例aSAH患者。炎症评分通过淋巴细胞、全免疫炎症值（PIV）、红细胞分布宽度（RDW）和乳酸脱氢酶（LDH）的组合计算得出。多因素分析确定炎症评分与DCI和不良预后独立相关。高炎症评分对不良预后的影响可能部分由DCI解释，其中存在纯中介作用和介导的相互作用。以DCI作为潜在中介，超额相对风险可分解为30.86%的受控直接效应、3.60%的仅中介作用、26.64%的仅相互作用以及38.89%的介导相互作用。将炎症评分添加到预测模型中可使AUC从0.772提高到0.822，NRI为5.3%，IDI为6.9%。
炎症评分与aSAH患者的DCI和不良预后显著相关。高炎症评分与DCI在不良预后风险上不仅可能存在潜在的协同相互作用，而且DCI是重要的中介机制。
