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ORIGINAL RESEARCH article

Front. Oncol.
Sec. Cancer Epidemiology and Prevention
Volume 14 - 2024 | doi: 10.3389/fonc.2024.1485317

Actors Influencing Cancer-Related Fatigue and the Construction of a Risk Prediction Model in Lung Cancer Patients

Provisionally accepted
Mei-Ning Zhang Mei-Ning Zhang 1Yi-Chen Zhou Yi-Chen Zhou 1Zhu Zeng Zhu Zeng 1Cun - Liang Cun - Liang 1Bo-Tao Hou Bo-Tao Hou 1Gui- Rong Wu Gui- Rong Wu 1Qiao Jiao Qiao Jiao 2Dai-Yuan Ma Dai-Yuan Ma 3*
  • 1 Dazhou Central Hospital, DaZhou, China
  • 2 Dazhou Hospital of Integrated Traditional Chinese and Western Medicine, DaZhou, China
  • 3 Affiliated Hospital of North Sichuan Medical College, Nanchong, Sichuan Province, China

The final, formatted version of the article will be published soon.

    Purpose: The 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. Methods: This 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. Findings: The 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, haemoglobin, 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/value: The 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.

    Keywords: lung cancer, Cancer-related fatigue, sleep quality, Anxiety, Depression, Risk prediction model, Logistic regression analysis

    Received: 23 Aug 2024; Accepted: 27 Dec 2024.

    Copyright: © 2024 Zhang, Zhou, Zeng, Liang, Hou, Wu, Jiao and Ma. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Dai-Yuan Ma, Affiliated Hospital of North Sichuan Medical College, Nanchong, Sichuan Province, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.