Actors influencing cancer-related fatigue and the construction of a risk prediction model in lung cancer patients
PurposeThe paper aims to investigate the factors influencing cancer-related fatigue (CRF) in lung cancer patients and construct a CRF risk prediction model, providing effective intervention strategies for clinical medical staff.MethodsThis paper employs convenience sampling to select 400 lung cancer...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-01-01
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| Series: | Frontiers in Oncology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fonc.2024.1485317/full |
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| author | Mei-Ning Zhang Yi-Chen Zhou Zhu Zeng Cun-Liang Zeng Bo-Tao Hou Gui-Rong Wu Qiao Jiao Dai-Yuan Ma |
| author_facet | Mei-Ning Zhang Yi-Chen Zhou Zhu Zeng Cun-Liang Zeng Bo-Tao Hou Gui-Rong Wu Qiao Jiao Dai-Yuan Ma |
| author_sort | Mei-Ning Zhang |
| collection | DOAJ |
| description | PurposeThe paper aims to investigate the factors influencing cancer-related fatigue (CRF) in lung cancer patients and construct a CRF risk prediction model, providing effective intervention strategies for clinical medical staff.MethodsThis paper employs convenience sampling to select 400 lung cancer patients who visited a tertiary hospital in Dazhou, Sichuan Province, from January 2021 to January 2022. A questionnaire survey was conducted using the Revised Piper Fatigue Scale (PFS-R), Pittsburgh Sleep Quality Index (PSQI), and Hospital Anxiety and Depression Scale (HADS) to collect data on patient demographics and sociological characteristics, disease-related information, physiological indicators, sleep quality, mental health, and other relevant factors. To explore the factors influencing CRF in lung cancer patients, single-factor analysis and multiple logistic regression analysis were performed. A CRF risk prediction model was then established, with its predictive performance and calibration evaluated using ROC curves.FindingsThe results of multivariate logistic regression analysis showed that gender, age, education level, living status, daily exercise, clinical stage, course of disease, treatment mode, chronic disease, BMI, hemoglobin, serum albumin, blood glucose, potassium concentration, magnesium concentration, PSQI score and HAD score were the influencing factors of CRF in lung cancer patients (P<0.05). The AUC of the model construction group and the model validation group were 0.863 and 0.838, respectively, and the results of Hosmer-Lemeshow fit test showed that χ2 = 7.540, P=0.378>0.05 of the model construction group and χ2 = 8.120, P=0.320>0.05 of the model validation group indicated that the model had high prediction accuracy.Originality/valueThe risk prediction model for CRF holds significant clinical value. It can help medical staff to promptly identify high-risk patients, develop personalized intervention strategies, alleviate fatigue symptoms, and improve overall patient quality of life. |
| format | Article |
| id | doaj-art-29bd445915fd4c98b43f925cfbc3bf74 |
| institution | Kabale University |
| issn | 2234-943X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Oncology |
| spelling | doaj-art-29bd445915fd4c98b43f925cfbc3bf742025-01-24T05:21:23ZengFrontiers Media S.A.Frontiers in Oncology2234-943X2025-01-011410.3389/fonc.2024.14853171485317Actors influencing cancer-related fatigue and the construction of a risk prediction model in lung cancer patientsMei-Ning Zhang0Yi-Chen Zhou1Zhu Zeng2Cun-Liang Zeng3Bo-Tao Hou4Gui-Rong Wu5Qiao Jiao6Dai-Yuan Ma7Nursing Department, Dazhou Central Hospital, Dazhou, Sichuan, ChinaDepartment of Oncology, Dazhou Central Hospital, Dazhou, Sichuan, ChinaNursing Department, Dazhou Central Hospital, Dazhou, Sichuan, ChinaCardiac Vascular Surgery, Dazhou Central Hospital, Dazhou, Sichuan, ChinaDepartment of Oncology, Dazhou Central Hospital, Dazhou, Sichuan, ChinaDepartment of Oncology, Dazhou Central Hospital, Dazhou, Sichuan, ChinaDepartment of Oncology, Dazhou Integrated Traditional Chinese Medicine (TCM) and Western Medicine Hospital, Dazhou, Sichuan, ChinaDepartment of Oncology, Affiliated Hospital of North Sichuan Medical College, Nanchong, Sichuan, ChinaPurposeThe paper aims to investigate the factors influencing cancer-related fatigue (CRF) in lung cancer patients and construct a CRF risk prediction model, providing effective intervention strategies for clinical medical staff.MethodsThis paper employs convenience sampling to select 400 lung cancer patients who visited a tertiary hospital in Dazhou, Sichuan Province, from January 2021 to January 2022. A questionnaire survey was conducted using the Revised Piper Fatigue Scale (PFS-R), Pittsburgh Sleep Quality Index (PSQI), and Hospital Anxiety and Depression Scale (HADS) to collect data on patient demographics and sociological characteristics, disease-related information, physiological indicators, sleep quality, mental health, and other relevant factors. To explore the factors influencing CRF in lung cancer patients, single-factor analysis and multiple logistic regression analysis were performed. A CRF risk prediction model was then established, with its predictive performance and calibration evaluated using ROC curves.FindingsThe results of multivariate logistic regression analysis showed that gender, age, education level, living status, daily exercise, clinical stage, course of disease, treatment mode, chronic disease, BMI, hemoglobin, serum albumin, blood glucose, potassium concentration, magnesium concentration, PSQI score and HAD score were the influencing factors of CRF in lung cancer patients (P<0.05). The AUC of the model construction group and the model validation group were 0.863 and 0.838, respectively, and the results of Hosmer-Lemeshow fit test showed that χ2 = 7.540, P=0.378>0.05 of the model construction group and χ2 = 8.120, P=0.320>0.05 of the model validation group indicated that the model had high prediction accuracy.Originality/valueThe risk prediction model for CRF holds significant clinical value. It can help medical staff to promptly identify high-risk patients, develop personalized intervention strategies, alleviate fatigue symptoms, and improve overall patient quality of life.https://www.frontiersin.org/articles/10.3389/fonc.2024.1485317/fulllung cancercancer-related fatiguesleep qualityanxietydepressionrisk prediction model |
| spellingShingle | Mei-Ning Zhang Yi-Chen Zhou Zhu Zeng Cun-Liang Zeng Bo-Tao Hou Gui-Rong Wu Qiao Jiao Dai-Yuan Ma Actors influencing cancer-related fatigue and the construction of a risk prediction model in lung cancer patients Frontiers in Oncology lung cancer cancer-related fatigue sleep quality anxiety depression risk prediction model |
| title | Actors influencing cancer-related fatigue and the construction of a risk prediction model in lung cancer patients |
| title_full | Actors influencing cancer-related fatigue and the construction of a risk prediction model in lung cancer patients |
| title_fullStr | Actors influencing cancer-related fatigue and the construction of a risk prediction model in lung cancer patients |
| title_full_unstemmed | Actors influencing cancer-related fatigue and the construction of a risk prediction model in lung cancer patients |
| title_short | Actors influencing cancer-related fatigue and the construction of a risk prediction model in lung cancer patients |
| title_sort | actors influencing cancer related fatigue and the construction of a risk prediction model in lung cancer patients |
| topic | lung cancer cancer-related fatigue sleep quality anxiety depression risk prediction model |
| url | https://www.frontiersin.org/articles/10.3389/fonc.2024.1485317/full |
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