Development and validation of a nomogram for predicting cancer-related fatigue in patients with glioma: a multicenter study.

BACKGROUND

Cancer-related fatigue (CRF) is one of the most prevalent symptoms which drastically affect patient health and quality of life. This study aimed to construct and validate a nomogram to accurately predict the occurrence of cancer-related fatigue in patients with glioma.

METHODS

This cross-sectional study included 470 glioma patients from two hospitals (training cohort: n=284; validation cohort: n=186). All patients were categorized into two groups based on their Numerical Rating Scale scores of cancer-related fatigue: a no or mild fatigue group (scores 0-3) and a moderate to severe fatigue group (scores 4-10). LASSO model and multivariable logistic regression analyses were used to determine the significant risk factors contributing to the occurrence of cancer-related fatigue in glioma patients. A nomogram was constructed and its predictive accuracy and conformity was validated by ROC curves, calibration curves and decision curve analysis.

RESULTS

Combining LASSO algorithm and multivariable logistic regression analyses, the cancer stage (p=0.014), and the scores of Perceived Social Support Scale (PSSS) (p<0.001), physical functioning (PF) (p<0.001), bodily pain (BP) (p=0.031), general health (GH) (p<0.001), and mental health (MH) (p=0.009) were the independent risk factors for cancer-related fatigue of glioma patients. A clinically quantitative predictive model nomogram was developed based on these extracted risk factors. The concordance-index of nomogram was 0.964 (0.935-0.993). The AUC values of nomogram were 0.916 (CI: 0.879-0.953) in the training cohort and 0.885 (CI: 0.829-0.941) in the validation cohort. The calibration curves of this nomogram exhibited a notable concordance with the ideal diagonal line. The decision curve analyses illuminated that this nomogram achieved high clinical net benefit.

CONCLUSION

The nomogram, incorporating the cancer stage of glioma, perceived social support, and quality of life of patients, demonstrated good accuracy and clinical practicality. It can serve as a valuable prediction and evaluation tool for anticipating the occurrence of cancer-related fatigue in patients with glioma.