Department of Anesthesia, Chongqing Medical University, Chongqing, China.
Department of Pharmacy Practice, College of Pharmacy and Health Sciences, Western New England University, Springfield, United States; Department of Pharmacy, Xiangya Hospital Central South University, Changsha, Hunan, China.
Bosn J Basic Med Sci. 2021 Oct 1;21(5):632-641. doi: 10.17305/bjbms.2020.5420.
There is a lack of predictive models to determine the prognosis of elderly patients diagnosed with Stage I small-cell lung cancer (SCLC). The purpose of this study was to establish a useful nomogram to predict cancer-specific survival (CSS) in the elderly patient population. Based on the Surveillance, Epidemiology, and End Results registry database, patients aged ≥ 65 years with pathological AJCC (American Joint Committee on Cancer) Stage I SCLC from 2004 to 2014 were identified. The CSS was evaluated by the Kaplan-Meier method. Patients were randomly split into training and validation sets. In the training cohort, univariate analysis and multivariate analysis using the Cox regression identified risk factors that affected CSS, and the results were utilized to construct a nomogram for prediction of the 1-, 3-, and 5-year CSS rates of elderly patients with Stage I SCLC. The effectiveness of the nomogram was validated internally and externally by the bootstrap method. The clinical practicability and accuracy of the nomogram were evaluated by the concordance index (C-index), calibration curve, receiver operating characteristic curve, and decision curve analysis. In total, we extracted 1,623 elderly patients with Stage I SCLC. The median CSS was 34 months, and the 5-year CSS was 41%. Multivariate analysis revealed that histologic type, tumor size, age, and AJCC Stage were significant predictors of CSS. A nomogram was constructed according to the results of multivariate COX analysis. The C-indices of the nomogram for training and validation sets were 0.68 and 0.62, indicating that the nomogram demonstrated a favorable level of discrimination. The calibration curves exhibited satisfactory agreement between the actual observation and nomogram prediction. The net benefit of the nomogram was better than the AJCC TNM staging. A practical nomogram to predict the CSS of elderly patients with Stage I SCLC is constructed. The predictive tool is helpful for patient counseling and treatment decision-making.
对于诊断为 I 期小细胞肺癌（SCLC）的老年患者，目前缺乏预测预后的模型。本研究旨在建立一个有用的列线图，以预测老年患者人群的癌症特异性生存（CSS）。基于监测、流行病学和最终结果（SEER）登记数据库，确定了 2004 年至 2014 年间年龄≥65 岁、具有病理 AJCC（美国癌症联合委员会）I 期 SCLC 的患者。通过 Kaplan-Meier 方法评估 CSS。患者被随机分为训练集和验证集。在训练队列中，使用 Cox 回归进行单因素和多因素分析，确定影响 CSS 的危险因素，并利用这些结果构建预测 I 期 SCLC 老年患者 1、3 和 5 年 CSS 率的列线图。通过自举法对内外部验证了该列线图的有效性。通过一致性指数（C-index）、校准曲线、接收者操作特征曲线和决策曲线分析评估了列线图的临床实用性和准确性。总共提取了 1623 例 I 期 SCLC 老年患者。中位 CSS 为 34 个月，5 年 CSS 为 41%。多因素分析显示组织学类型、肿瘤大小、年龄和 AJCC 分期是 CSS 的显著预测因素。根据多因素 COX 分析的结果构建了一个列线图。训练集和验证集的列线图 C-index 分别为 0.68 和 0.62，表明列线图具有良好的区分度。校准曲线显示实际观察值与列线图预测值之间具有良好的一致性。列线图的净效益优于 AJCC TNM 分期。构建了一个预测 I 期 SCLC 老年患者 CSS 的实用列线图。该预测工具有助于为患者提供咨询和治疗决策。
