■本研究旨在开发和验证生存预测模型和列线图,以预测接受抗程序性细胞死亡1受体(PD-1)治疗的晚期胃或胃食管交界(G/GEJ)腺癌患者的生存。该模型结合了免疫相关的不良事件(irAEs)以及常见的临床特征作为预测因子。
■收集了一个包含255名诊断为晚期G/GEJ腺癌的成年患者的数据集。识别并整合影响总生存期(OS)的irAE作为候选变量,以及其他12个候选变量。这些包括性别,年龄,东部肿瘤协作组表现状况(ECOGPS)评分,肿瘤分期,人表皮生长因子受体2(HER2)表达状态,存在腹膜和肝转移,抗PD-1治疗的年份和线,中性粒细胞与淋巴细胞比率(NLR),控制营养状况(CONUT)评分,和Charlson合并症指数(CCI)。为了减轻与ERP相关的时序偏差,采用了里程碑分析。使用最小绝对收缩和选择算子(LASSO)回归进行变量选择,以确定重要的预测因子,并应用方差膨胀因子来解决多重共线性问题。随后,利用前向似然比方法进行Cox回归分析,以建立生存预测模型,排除不满足比例风险(PH)假设的变量。该模型是使用整个数据集开发的,然后通过引导重新采样进行内部验证,并与另一家医院的队列进行外部验证。此外,创建列线图来描绘预测模型.
■将来自皮肤和内分泌系统的irAE整合为单一的保护性irAE类别并应用界标分析后,我们对预后预测模型进行了变量选择以及其他候选变量.最终的模型包括七个变量:ECOGPS得分,肿瘤分期,HER2在肿瘤组织中的表达状态,一线抗PD-1治疗,腹膜转移,CONUT得分,和保护性IRAE。模型的总体一致性指数为0.66。校准分析验证了模型在将预测结果与实际结果对齐方面的准确性。临床决策曲线分析表明,利用该模型进行治疗决策可以提高患者1年和2年生存率的净收益。
■这项研究通过整合irAEs和G/GEJ腺癌的共同临床特征,建立了预后预测模型。该模型具有良好的临床实用性,对晚期G/GEJ腺癌患者的总生存期具有准确的预测能力。
UNASSIGNED: This study aimed to develop and validate a survival prediction model and nomogram to predict survival in patients with advanced gastric or gastroesophageal junction (G/GEJ) adenocarcinoma undergoing treatment with anti-programmed cell death 1 receptor (PD-1). This model incorporates immune-related adverse events (irAEs) alongside common clinical characteristics as predictive factors.
UNASSIGNED: A dataset comprising 255 adult patients diagnosed with advanced G/GEJ adenocarcinoma was assembled. The irAEs affecting overall survival (OS) to a significant degree were identified and integrated as a candidate variable, together with 12 other candidate variables. These included gender, age, Eastern cooperative oncology group performance status (ECOG PS) score, tumor stage, human epidermal growth factor receptor 2 (HER2) expression status, presence of peritoneal and liver metastases, year and line of anti-PD-1 treatment, neutrophil-to-lymphocyte ratio (NLR), controlling nutritional status (CONUT) score, and Charlson comorbidity index (CCI). To mitigate timing bias related to irAEs, landmark analysis was employed. Variable selection was performed using the least absolute shrinkage and selection operator (LASSO) regression to pinpoint significant predictors, and the variance inflation factor was applied to address multicollinearity. Subsequently, a Cox regression analysis utilizing the forward likelihood ratio method was conducted to develop a survival prediction model, excluding variables that failed to satisfy the proportional hazards (PH) assumption. The model was developed using the entire dataset, then internally validated through bootstrap resampling and externally validated with a cohort from another Hospital. Furthermore, a nomogram was created to delineate the predictive model.
UNASSIGNED: After consolidating irAEs from the skin and endocrine systems into a single protective irAE category and applying landmark analysis, variable selection was conducted for the prognostic prediction model along with other candidate variables. The finalized model comprised seven variables: ECOG PS score, tumor stage, HER2 expression status in tumor tissue, first-line anti-PD-1 treatment, peritoneal metastasis, CONUT score, and protective irAE. The overall concordance index for the model was 0.66. Calibration analysis verified the model\'s accuracy in aligning predicted outcomes with actual results. Clinical decision curve analysis indicated that utilizing this model for treatment decisions could enhance the net benefit regarding 1- and 2-year survival rates for patients.
UNASSIGNED: This study developed a prognostic prediction model by integrating common clinical characteristics of irAEs and G/GEJ adenocarcinoma. This model exhibits good clinical practicality and possesses accurate predictive ability for overall survival OS in patients with advanced G/GEJ adenocarcinoma.