AUTHOR=Lan Yunxia , Wang Yongting , Jia Tiantian , Cheng Qingyun , Han Siyu , Mi Yanzhi , Ding Mi 

TITLE=Development and validation of a multivariate predictive model for cancer-related fatigue in esophageal carcinoma: a prospective cohort study integrating biomarkers and psychosocial factors

JOURNAL=Frontiers in Oncology

VOLUME=Volume 15 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2025.1674710

DOI=10.3389/fonc.2025.1674710

ISSN=2234-943X

ABSTRACT=BackgroundTo develop and validate a predictive model for cancer-related fatigue (CRF) in patients with esophageal cancer.MethodsA convenience sample comprising patients diagnosed with esophageal cancer and admitted to the Department of Thoracic Surgery at a tertiary hospital in Henan Province, China, between June 2024 and May 2025, was enrolled. Data were collected using a general information questionnaire, the Chinese version of the revised Piper Fatigue Scale, the Hospital Anxiety and Depression Scale, the Pittsburgh Sleep Quality Index, the Nutrition Risk Screening 2002, and a visual analogue scale. Then, univariate and multivariate logistic regression analyses were conducted to identify risk factors and construct the predictive model. Lastly, a nomogram was developed, and its performance was evaluated through internal and external validation.ResultsThe incidence of CRF among patients with esophageal cancer was 70.67%. Multivariate logistic regression identified preoperative hemoglobin concentration, postoperative day-1 serum potassium level, neutrophil ratio, nutritional impairment, anxiety, depression, and sleep disturbance as independent risk factors (all p < 0.05). The model demonstrated satisfactory discriminatory power, with a sensitivity of 90.60% and specificity of 93.44%.Additionally, the Hosmer-Lemeshow test indicated favorable calibration (χ² = 7.048; p = 0.531). In the validation cohort, the area under the receiver operating characteristic curve was 0.887 (95% CI 0.802-0.944), with an optimal cut-off value of 0.797, yielding a sensitivity of 82.54% and specificity of 81.48%. Finally, calibration plots revealed excelling agreement between predicted and observed outcomes, and decision curve analysis suggested favorable clinical utility.ConclusionThe proposed model reliably predicts the risk of cancer-related fatigue in patients with esophageal cancer and may assist in the early identification of high-risk individuals, thereby enabling timely and targeted interventions.