识别准备从免疫检查点抑制剂(ICI)疗法中获益的个体是定制医疗保健领域的关键要素。程序性死亡配体1(PD-L1)的表达水平与ICI治疗的反应有关,但是它的评估通常需要大量的肿瘤组织,这可能是具有挑战性的。相比之下,血液样本更适合临床应用。已经提出了许多有希望的外周生物标志物来克服这一障碍。这项研究旨在评估白蛋白与乳酸脱氢酶比值(LAR)的预后效用,泛免疫炎症值(PIV),和预后营养指数(PNI)预测晚期非小细胞肺癌(NSCLC)患者对ICI治疗的反应。此外,本研究旨在构建包含这些标记物的预测列线图,以便于选择更有可能从ICI治疗获益的患者.一项研究计划仔细检查了江西两个医疗中心接受ICI治疗的157例晚期NSCLC患者的治疗记录。来自江西省人民医院的队列(包括108名患者)用于训练数据集,而江西省肿瘤医院的特遣队(49例患者)为验证目的服务。分层是基于既定的LAR,PIV,和PNI基准,以探索与DCR和ORR指标的关联。通过单变量和多变量Cox回归分析可以识别对ICI治疗成功的因素影响。随后,a设计了列线图来预测结果,其精度由ROC和校准曲线衡量,DCA分析,和跨机构验证。在训练组中,LAR的最佳阈值,PIV,和PNI分别为5.205、297.49和44.6。基于这些阈值,LAR,PIV,和PNI分为高(≥截止)和低(<截止)组。低LAR(L-LAR)患者,低PIV(L-PIV),高PNI(H-PNI)的疾病控制率(DCR)更高(P<0.05),中位无进展生存期(PFS)更长(P<0.05)。Cox多变量分析表明,PS,恶性胸腔积液,肝转移,高PIV(H-PIV),低PNI(L-PNI)是影响免疫治疗疗效的危险因素(P<0.05)。列线图模型预测的一致性指数(C指数)为0.78(95%CI:0.73-0.84)。训练组6、9、12个月的ROC曲线下面积(AUC)分别为0.900、0.869、0.866,而外部验证组在同一时间点的AUC分别为0.800,0.886和0.801.在整个免疫疗法中,PIV和PNI可以作为预测NSCLC患者治疗成功的前瞻性指标。而设计的列线图模型对患者预后具有很强的预测性能。
Identifying individuals poised to gain from immune checkpoint inhibitor (ICI) therapies is a pivotal element in the realm of tailored healthcare. The expression level of Programmed Death Ligand 1 (PD-L1) has been linked to the response to ICI therapy, but its assessment typically requires substantial tumor tissue, which can be challenging to obtain. In contrast, blood samples are more feasible for clinical application. A number of promising peripheral biomarkers have been proposed to overcome this hurdle. This research aims to evaluate the prognostic utility of the albumin-to-lactate dehydrogenase ratio (
LAR), the Pan-immune-inflammation Value (PIV), and the Prognostic Nutritional Index (PNI) in predicting the response to ICI therapy in individuals with advanced non-small cell lung cancer (NSCLC). Furthermore, the study seeks to construct a predictive nomogram that includes these markers to facilitate the selection of patients with a higher likelihood of benefiting from ICI therapy. A research initiative scrutinized the treatment records of 157 advanced NSCLC patients who received ICI therapy across two Jiangxi medical centers. The cohort from Jiangxi Provincial People\'s Hospital (comprising 108 patients) was utilized for the training dataset, while the contingent from Jiangxi Cancer Hospital (49 patients) served for validation purposes. Stratification was based on established
LAR, PIV, and PNI benchmarks to explore associations with DCR and ORR metrics. Factorial influences on ICI treatment success were discerned through univariate and multivariate Cox regression analysis. Subsequently, a Nomogram was devised to forecast outcomes, its precision gauged by ROC and calibration curves, DCA analysis, and cross-institutional validation. In the training group, the optimal threshold values for
LAR, PIV, and PNI were identified as 5.205, 297.49, and 44.6, respectively. Based on these thresholds,
LAR, PIV, and PNI were categorized into high (≥ Cut-off) and low (< Cut-off) groups. Patients with low
LAR (L-
LAR), low PIV (L-PIV), and high PNI (H-PNI) exhibited a higher disease control rate (DCR) (P < 0.05) and longer median progression-free survival (PFS) (P < 0.05). Cox multivariate analysis indicated that PS, malignant pleural effusion, liver metastasis, high PIV (H-PIV), and low PNI (L-PNI) were risk factors adversely affecting the efficacy of immunotherapy (P < 0.05). The Nomogram model predicted a concordance index (C-index) of 0.78 (95% CI: 0.73-0.84). The areas under the ROC curve (AUC) for the training group at 6, 9, and 12 months were 0.900, 0.869, and 0.866, respectively, while the AUCs for the external validation group at the same time points were 0.800, 0.886, and 0.801, respectively. Throughout immunotherapy, PIV and PNI could act as prospective indicators for forecasting treatment success in NSCLC patients, while the devised Nomogram model exhibits strong predictive performance for patient prognoses.