医学
内科学
肿瘤科
围手术期
危险分层
腺癌
肺
总体生存率
索引(排版)
比例危险模型
预测模型
风险评估
生存分析
性能状态
术前护理
作者
Wei Yuanpu,Wei‐Ming Chen,Hao Chen,Wenjie Yuan,Wei Zheng,Bin Zheng,Chun Chen,Yang Zhang
出处
期刊:Maturitas
[Elsevier BV]
日期:2025-09-17
卷期号:202: 108735-108735
标识
DOI:10.1016/j.maturitas.2025.108735
摘要
INTRODUCTION: The clinical utility of preoperative inflammatory and nutritional indices remains unclear in older patients undergoing surgery for resectable invasive lung adenocarcinoma (IAC). This study evaluates the prognostic significance of these indices and develops a predictive model specifically for this vulnerable population. METHODS: We retrospectively analyzed 376 patients aged ≥70 years who underwent surgical resection for IAC. Eight preoperative inflammatory and nutritional indices were evaluated. Optimal cutoff values were identified using X-tile software. Prognostic implications for overall survival (OS) and progression-free survival (PFS) were assessed through Cox proportional hazards models and Kaplan-Meier analysis. A prognostic nomogram was developed and validated. RESULTS: Prognostic nutritional index (PNI) emerged as an independent prognostic factor for both OS (p = 0.038) and PFS (p = 0.008). Patients with low PNI (≤ 51.90) were significantly older (p = 0.006), predominantly male (p = 0.048), and had a history of smoking (p = 0.037). Kaplan-Meier analysis demonstrated significantly lower 5-year OS (82.5 % vs. 91.4 %, p = 0.007; HR = 0.356, 95 % CI 0.162-0.782) and PFS (83.5 % vs. 92.4 %, p = 0.006; HR = 0.366, 95 % CI 0.173-0.733) in the low PNI group compared with high PNI group. The developed nomogram incorporating PNI and other clinical variables demonstrated strong predictive capability, with area under the curve (AUC) values of 0.818, 0.834, and 0.811 for predicting 1-, 3-, and 5-year OS, respectively. CONCLUSIONS: PNI serves as a robust independent prognostic marker in older patients with resectable IAC, suggesting potential utility for preoperative risk stratification and individualized perioperative management aimed at enhancing patient outcomes.
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