目的 探讨衰弱在老年肺癌患者中的发生现状及其影响因素,构建衰弱预测模型并完成验证。 方法 便利抽样选取 海安市人民医院于 2022 年 1 月至 2024 年 3 月收治的 500 例肺癌患者作为研究对象,分析衰弱现状及其存在的影响因素,利 用回归分析构建衰弱风险预测模型,使用列线图展示模型并完成验证。 结果 建模组纳入 350 例老年肺癌患者,年龄 61 ~ 79 岁,平均(68. 22±7. 25)岁;女性 121 例(34. 57%),男性 229 例(65. 43%)。 纳入的 350 例老年肺癌患者中,有 123 例发生衰 弱,发生率为 35. 14%。 单因素分析结果:年龄、Charson 共病指数、营养风险、病程时间、体质指数(BMI)、高血红蛋白、D-二聚 体、白蛋白、抑郁及癌性疲乏成为老年肺癌患者衰弱的独立危险因素(P <0. 05)。 多因素分析结果:年龄、Charson 共病指数、 病程时间、D-二聚体、抑郁及癌性疲乏成为老年肺癌患者衰弱的危险因素(P<0. 05),血红蛋白、白蛋白、BMI、无营养风险成为 老年肺癌患者衰弱的保护性因素(P<0. 05)。 验证组 150 例老年肺癌患者衰弱曲线下面积(AUC)为 0. 842(95%CI = 0. 802 ~ 0. 886),敏感度为 83. 5%,特异度为 73. 8%。 结论 老年肺癌患者衰弱发生受到年龄、Charson 共病指数、营养风险、病程时间、D-二聚体、白蛋白、抑郁及癌性疲乏等的影响。 未来应开展轨迹研究,对老年肺癌患者衰弱预测模型进行优化,建立动态列线图模型,为临床动态预测提供依据,以期减少衰弱和改善患者生存质量。

Objective To investigate the occurrence and influencing factors of frailty in elderly lung cancer patients establish a frailty prediction model and verify it. Method In this study 500 patients with lung cancer admitted to our hospital during the period from January 2022 to March 2024 were selected as research objects. The frailty status and influencing factors were analyzed statistically. Regression analysis was used to construct frailty risk prediction model and the model was displayed and verified by column graph. Result In the modeling group of this study 350 elderly lung cancer patients aged 61-79 years old with an average age of 68. 22 ± 7. 25 years old including 121 females 34. 57% and 229 males 65. 43% participated. Among 350 elderly patients with lung cancer 123 had frailty 35. 14% . Univariate analysis showed that age Charson comorbidity index nutritional status duration of disease body mass index BMI hemoglobin D - dimer albumin depression and cancer fatigue were the independent risk factors for fadility in elderly lung cancer patients P < 0. 05 . Result of multi - factor analysis Age Charson comorbidity index duration of disease D-dimer depression and cancer fatigue were the risk factors for frailty in elderly lung cancer patients P < 0. 05 hemoglobin albumin BMI nutritional status were the protective factors for frailty in elderly lung cancer patients P < 0. 05 . In the verification group AUC was 0. 842 95%CI = 0. 802-0. 886 sensitivity was 83. 5% and specificity was 73. 8%. Conclusion This study established a risk prediction model for frailty in elderly lung cancer patients. Frailty in elderly lung cancer patients is affected by age Charson comorbidity index nutritional status duration of disease D-dimer albumin depression and cancerous fatigue. In the future trajectory studies should be carried out to optimize the prediction model and establish a dynamic nomogram model to provide a reliable tool for clinical nurses to dynamically predict the frailty of elderly patients with lung cancer with a view to reducing frailty.