Massachusetts General Hospital, Boston, MA, United States.
Department of Medicine, Harvard Medical School, Boston, MA, United States.
Cancer Med. 2022 Aug;11(15):3009-3022. doi: 10.1002/cam4.4658. Epub 2022 Mar 26.
Older patients with non-small cell lung cancer (NSCLC) are a heterogeneous population with varying degrees of frailty. An electronic frailty index such as the Veterans Affairs Frailty Index (VA-FI) can potentially help identify vulnerable patients at high risk of poor outcomes.
NSCLC patients ≥65 years old and diagnosed in 2002-2017 were identified using the VA Central Cancer Registry. The VA-FI was calculated using administrative codes from VA electronic health records data linked with Medicare and Medicaid data. We assessed associations between the VA-FI and times to mortality, hospitalization, and emergency room (ER) visit following diagnosis by Kaplan-Meier analysis and multivariable stratified Cox models. We also evaluated the change in discrimination and calibration of reference prognostic models after adding VA-FI.
We identified a cohort of 42,204 older NSCLC VA patients, in which 55.5% were classified as frail (VA-FI >0.2). After adjustment, there was a strong association between VA-FI and the risk of mortality (HR = 1.23 for an increase of four deficits or, equivalently, an increase of 0.129 on VA-FI, p < 0.001), hospitalization (HR = 1.16 for four deficits, p < 0.001), and ER visit (HR = 1.18 for four deficits, p < 0.001). Adding VA-FI to baseline prognostic models led to statistically significant improvements in time-dependent area under curves and did not have a strong impact on calibration.
Older NSCLC patients with higher VA-FI have significantly elevated risks of mortality, hospitalizations, and ER visits following diagnosis. An electronic frailty index can serve as an accessible tool to identify patients with vulnerabilities to inform clinical care and research.
老年非小细胞肺癌（NSCLC）患者是一个异质性人群，其衰弱程度不一。电子衰弱指数，如退伍军人事务部衰弱指数（VA-FI），可以帮助识别出脆弱、预后不良风险高的患者。
使用退伍军人事务部中央癌症登记处，确定了 2002-2017 年间年龄≥65 岁且诊断为 NSCLC 的患者。VA-FI 是根据退伍军人事务部电子病历数据中的行政代码计算得出的，这些数据与医疗保险和医疗补助数据相链接。我们通过 Kaplan-Meier 分析和多变量分层 Cox 模型评估 VA-FI 与诊断后死亡率、住院率和急诊室（ER）就诊率之间的关系。我们还评估了在添加 VA-FI 后，参考预后模型的区分度和校准度的变化。
我们确定了一个由 42204 名老年 NSCLC 退伍军人事务部患者组成的队列，其中 55.5%的患者被归类为衰弱（VA-FI>0.2）。调整后，VA-FI 与死亡率风险（每增加四个缺陷，即 VA-FI 增加 0.129，HR=1.23，p<0.001）、住院率（每增加四个缺陷，HR=1.16，p<0.001）和 ER 就诊率（每增加四个缺陷，HR=1.18，p<0.001）之间存在很强的关联。将 VA-FI 添加到基线预后模型中可显著提高时间依赖性曲线下面积，且对校准影响不大。
VA-FI 较高的老年 NSCLC 患者在诊断后，其死亡率、住院率和 ER 就诊率显著升高。电子衰弱指数可以作为一种便捷的工具，用于识别脆弱患者，为临床护理和研究提供信息。
