近年来，一些研究表明，全身免疫炎症指数（PIV）与癌症预后相关。然而，在食管鳞状细胞癌（ESCC）中，PIV的预后影响尚不清楚。本研究旨在调查术前PIV对ESCC根治术预后的影响。回顾性分析了294例接受根治术的ESCC患者的数据。通过分析PIV与癌症特异性生存（CSS）之间的非线性关系，使用受限制的立方样条（RCS）模型计算了PIV的最佳切点值。采用Cox比例风险回归分析来确定预后因素。采用递归分区分析（RPA）建立危险分层模型。运用决策曲线分析（DCA）和受试者工作特征（ROC）评估RPA模型的性能。RCS可视化了PIV与CSS之间的非线性关系（P < 0.0001）。然后根据最佳阈值308.2将患者分为高组和低组。高PIV组的5年CSS（17.7% vs 48.3%，P < 0.001）明显较低。亚组分析证实，在不同病理肿瘤分期（pTNM）阶段，低PIV组的患者5年生存率更高（pTNM I：P = 0.022；pTNM II：P = 0.001；pTNM III：P = 0.011）。PIV作为CSS的独立预后因素（风险比=1.983，P < 0.001）。基于pTNM和PIV的RPA算法开发出涉及三个风险组的新分期，其CSS显著不同于pTNM分期。与pTNM分类相比，基于RPA的模型在预测预后方面表现出显著优越性。本研究证实了ESCC在根治术治疗中PIV对预后的影响。PIV与肿瘤分期和预后相关，可能有助于ESCC术前评估。版权所有 © 2023. Elsevier B.V.出版。

Several researches have shown that pan-immune-inflammation value (PIV) is related to cancer prognosis in recent years. In esophageal squamous cell carcinoma (ESCC), nevertheless, the prognostic impact of PIV remains unclear. The present study sought to investigate the prognostic impact of preoperative PIV in ESCC with radical resection.The data of 294 ESCC patients who received radical resection were retrospectively analyzed. Based on analyzing the non-linear relationship between PIV and cancer-specific survival (CSS), the optimal cutoff value for PIV was calculated by the restricted cubic spline (RCS) model. Cox proportional hazards regression was carried out to identify the prognostic factors. A risk stratification model was established by recursive partitioning analysis (RPA). The performance of the RPA-based model was assessed by the decision curve analysis (DCA) and receiver operating characteristic (ROC).The RCS visualized the non-linear relationship between PIV and CSS (P < 0.0001). Then patients were then divided into high and low groups based on the optimal threshold of 308.2. The 5-year CSS (17.7 % vs. 48.3 %, P < 0.001) was significantly worse in patients with high PIV than those in the low group. Subgroup analyses confirmed that patients with low PIV also achieved better 5-year survival at different pathological tumor node metastasis (pTNM) stages (pTNM I: P = 0.022; pTNM II: P = 0.001; pTNM III: P = 0.011). PIV served as an independent prognostic factor of CSS (hazard ratio = 1.983, P < 0.001). A new staging involving three risk groups with significantly different CSS was developed using RPA algorithms based on pTNM and PIV. Compared with the pTNM classification, the RPA-based model exhibited significantly superior performance for prognostication.The present study confirmed the prognostic impact of PIV in ESCC who treated with radical resection. PIV was associated with the tumor stage and prognosis, which might be useful in the preoperative assessment of ESCC.Copyright © 2023. Published by Elsevier B.V.