Actors influencing cancer-related fatigue and the construction of a risk prediction model in lung cancer patients - Zhang, Mei-Ning; Zhou, Yi-Chen; Zeng, Zhu; Zeng, Cun-Liang; Hou, Bo-Tao; Wu, Gui-Rong; Jiao, Qiao; Ma, Dai-Yuan (Frontiers in Oncology, 2025)
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.