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

TL;DR

This study developed a tool to predict cancer-related fatigue in glioma patients using factors like cancer stage and quality of life.

Contribution

A new nomogram was developed and validated for predicting cancer-related fatigue in glioma patients.

Findings

The nomogram achieved a concordance-index of 0.964 and AUC values of 0.916 in the training cohort and 0.885 in the validation cohort.

Cancer stage, perceived social support, and quality of life were identified as key predictors of cancer-related fatigue.

Calibration curves and decision curve analysis confirmed the nomogram's accuracy and clinical utility.

Abstract

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.

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.

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.

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.

Figures