Chen Chen, Wang Zehua, Qin Yanru
Department of Oncology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, People's Republic of China.
J Inflamm Res. 2023 Jul 12;16:2929-2946. doi: 10.2147/JIR.S417798. eCollection 2023.
This study aims to construct a novel hematological inflammation-nutrition score (HINS) and investigate its prognostic value in patients with advanced gastric cancer (AGC). We investigated the risk stratification performance of HINS and developed a HINS-based nomogram model to predict overall survival by combining traditional predictors.
We conducted a retrospective study on 812 AGC patients who received first-line platinum- or fluoropyrimidine-containing chemotherapy at The First Affiliated Hospital of Zhengzhou University Hospital between 2014 and 2019. Patients were randomly divided into a training cohort (N=609) and a validation cohort (N=203). HINS (0-2) was constructed based on a pre-chemotherapy systemic immune-inflammation index (SII) and albumin (ALB). Prognostic factors were screened by univariate and multivariate COX proportional regression models. Significant factors were used to construct a nomogram model. Internal validation was performed by calibration curves, time-dependent receiver operating characteristics (ROC) curves, and decision curve analysis (DCA), evaluating its prediction consistency, discrimination ability, and clinical net benefit.
HINS was constructed based on SII and ALB. HINS showed a better stratification ability than JCOG prognostic index, with significant differences between groups. Multivariate analysis showed that ECOG ≥1 (HR: 1.379; P=0.005), Stage IV (HR: 1.581; P <0.001), diffuse-type histology (HR: 1.586; P <0.001), number of metastases ≥2 (HR: 1.274; P=0.038), without prior gastrectomy (HR: 1.830; P <0.001), ALP ≥ULN (HR: 1.335; P=0.034), HINS (P <0.001) were independent factors of OS. We successfully established a HINS-based nomogram model that showed a strong discriminative ability, accuracy, and clinical utility in training and validation cohorts.
HINS shows a superior risk stratification ability, which might be a potential prognostic biomarker for AGC patients receiving palliative first-line palliative chemotherapy. The HINS-based nomogram model is a convenient and efficient tool for managing prognosis and follow-up treatments.
本研究旨在构建一种新型血液学炎症 - 营养评分（HINS），并探讨其在晚期胃癌（AGC）患者中的预后价值。我们研究了HINS的风险分层性能，并通过结合传统预测因素开发了一种基于HINS的列线图模型来预测总生存期。
我们对2014年至2019年期间在郑州大学第一附属医院接受含铂或氟嘧啶一线化疗的812例AGC患者进行了回顾性研究。患者被随机分为训练队列（N = 609）和验证队列（N = 203）。HINS（0 - 2）基于化疗前的全身免疫炎症指数（SII）和白蛋白（ALB）构建。通过单因素和多因素COX比例回归模型筛选预后因素。使用显著因素构建列线图模型。通过校准曲线、时间依赖性受试者工作特征（ROC）曲线和决策曲线分析（DCA）进行内部验证，评估其预测一致性、辨别能力和临床净效益。
HINS基于SII和ALB构建。HINS显示出比日本临床肿瘤学会（JCOG）预后指数更好的分层能力，组间存在显著差异。多因素分析显示，美国东部肿瘤协作组（ECOG）≥1（HR：1.379；P = 0.005）、IV期（HR：1.581；P < 0.001）、弥漫型组织学（HR：1.586；P < 0.001）、转移灶数量≥2（HR：1.274；P = 0.038）、未行过胃切除术（HR：1.830；P < 0.001）、碱性磷酸酶（ALP）≥正常上限（ULN）（HR：1.335；P = 0.034）、HINS（P < 0.001）是总生存期的独立因素。我们成功建立了一种基于HINS的列线图模型，该模型在训练队列和验证队列中显示出强大的辨别能力、准确性和临床实用性。
HINS显示出卓越的风险分层能力，可能是接受一线姑息化疗的AGC患者的潜在预后生物标志物。基于HINS的列线图模型是管理预后和后续治疗的便捷有效工具。
