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[1]刘志静,马 婧,王利颖,等.终末期肺癌患者癌因性疲乏及影响因素分析[J].中华肺部疾病杂志,2025,(02):310-314.[doi:10.3877/cma.j.issn.1674-6902.2025.02.020]
　Liu Zhijing,Ma Jing,Wang Liying,et al.Status and influencing factors of cancer-related fatigue in patients with end-stage lung cancer[J].,2025,(02):310-314.[doi:10.3877/cma.j.issn.1674-6902.2025.02.020]
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终末期肺癌患者癌因性疲乏及影响因素分析(PDF)

《中华肺部疾病杂志》[ISSN:1006-6977/CN:61-1281/TN]

卷:
期数:
2025年02期
页码:
310-314
栏目:
论著
出版日期:
2025-04-25

文章信息/Info

Title:
Status and influencing factors of cancer-related fatigue in patients with end-stage lung cancer
作者:
刘志静1; 马 婧1; 王利颖1; 李 朋1; 张亚楠1; 陈童真2
061000 沧州,沧州市人民医院肿瘤内科三区(安宁疗护)1；061000 沧州,沧州市人民医院甲状腺乳腺外科2
Author(s):
Liu Zhijing1;  Ma Jing1;  Wang Liying1;  Li Peng1;  Zhang Yanan1;  Chen Tongzhen2.
1Oncology Department of Cangzhou People's Hospital, Zone 3(Palliative Care), Cangzhou 061000, China; 2Thyroid and Breast Surgery Department of Cangzhou People's Hospital, Cangzhou 061000, China
关键词:
终末期肺癌;  癌因性疲乏;  影响因素
Keywords:
End-stage lung cancer;  Cancer-related fatigue;  Current situation;  Influencing factors
分类号:
R734.2
DOI:
10.3877/cma.j.issn.1674-6902.2025.02.020
摘要:
目的 分析终末期肺癌患者癌因性疲乏情况及影响因素。方法 选取2021年8月至2023年8月我院收治的75例终末期肺癌患者为对象,根据Piper疲乏修正量表(revised Piper fatigue scale, RPFS)将癌因性疲乏32例分为观察组,无癌因性疲乏43例为对照组,分析RPFS评分,比较两组临床资料,采用Logistic回归分析终末期肺癌患者癌因性疲乏影响因素,建立列线图模型。结果 观察组躯体、行为、认知、情感疲乏评分分别为(5.09±1.17)分、(5.17±1.01)分、(4.08±0.89)分、(4.12±0.95)分,平均总分(4.58±1.12)分。观察组视觉模拟量表(visual analog scale, VAS)(3.50±0.70)分高于对照组(2.65±0.68)分(P<0.05),观察组卡式评分(Karnofsky, KPS)(75.06±5.48)分、社会支持评定量表(social support rating scale, SSRS)(26.09±3.06)分低于对照组(82.31±6.45)分、(29.97±3.31)分(P<0.05)。Logistic结果显示,VAS评分(OR=25.507,95%CI:5.523～117.802)是癌因性疲乏危险因素,KPS评分(OR=0.725,95%CI:0.585～0.900)、SSRS评分(OR=0.679,95%CI:0.492～0.936)是癌因性疲乏保护因素(P<0.05)。75例按7︰3拆分训练集与验证集,训练集52例,验证集23例,训练集和验证集预测癌因性疲乏受试验者特征曲线(receiver operating characteristic, ROC)曲线下面积(area under curve, AUC)(95%CI)分别为0.98(0.96～1.00)和0.93(0.80～1.00)。校准曲线结果显示,列线图模型预测癌因性疲乏校正曲线趋近于理想曲线(P=0.907、0.871)。决策分析曲线(decision curve analysis, DCA)可见列线图模型概率阈值20%～90%正向净收益高。结论 终末期肺癌患者癌因性疲乏发生率高,VAS评分、KPS评分、SSRS评分为终末期肺癌癌因性疲乏影响因素,针对性干预降低疲乏发生率。
Abstract:
Objective To analyze the status and influencing factors of cancer-related fatigue in patients with end-stage lung cancer. Methods All of 75 patients with end-stage lung cancer admitted to our hospital from August 2021 to August 2023 were selected as subjects. According to the revised Piper fatigue scale(RPFS), 32 cases with cancer-related fatigue were divided into observation group and 43 cases without cancer-related fatigue were divided into control group. The RPFS score was analyzed, the clinical data of the two groups were compared, and the influencing factors of cancer-related fatigue in end-stage lung cancer patients were analyzed by Logistic regression, and a nomogram model was established. Results The scores of physical, behavioral, cognitive and emotional fatigue in the observation group were(5.09±1.17),(5.17±1.01),(4.08±0.89)and(4.12±0.95), respectively. The average total score was(4.58±1.12). The visual analog scale(VAS)score of the observation group(3.50±0.70)was higher than that of the control group(2.65±0.68)(P<0.05), and the Karnofsky(KPS)score(75.06±5.48)and SSRS(26.09±3.06)of the observation group were lower than those of control group KPS(82.31±6.45)and SSRS(29.97±3.31)(P<0.05). Logistic results showed that VAS score(OR=25.507, 95%CI: 5.523～117.802)was a risk factor for cancer-related fatigue, KPS score(OR=0.725, 95%CI: 0.585～0.900), SSRS score(OR=0.679, 95%CI: 0.492～0.936)was a protective factor for cancer-related fatigue(P<0.05). 75 cases by 7︰3 split the training set and the verification set, the training set of 52 cases, the verification set of 23 cases, the training set and the verification set predicted the receiver operating characteristic(ROC)curve(area under curve, AUC)(95%CI)were 0.98(0.96～1.00)and 0.93(0.80～1.00), respectively. Calibration curve results showed that the calibration curve predicted by the nomogram model was close to the ideal curve(P=0.907, 0.871). The decision curve analysis(DCA)shows that the probability threshold of the nomogram model was 20%～90% and the net return was higher. Conclusion The incidence of cancer-related fatigue is high in patients with end-stage lung cancer. VAS score, KPS score and SSRS score are influential factors of cancer-related fatigue in end-stage lung cancer. Targeted intervention can reduce the incidence of fatigue.

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备注/Memo

备注/Memo:
通信作者: 刘志静, Email: liu.zhi.jing123@163.com
更新日期/Last Update: 2025-04-25