A prediction model for moderate to severe cancer-related fatigue in colorectal cancer after chemotherapy: a prospective case‒control study

Abstract

Aims: To develop a model to predict the risk of moderate to severe cancer-related fatigue (CRF) in colorectal cancer patients after chemotherapy. Methods: The study population was colorectal cancer patients who received chemotherapy from September 2021 to June 2022 in a grade 3 and rst-class hospital. Demographic, clinical, physiological, psychological, and socioeconomic factors were collected 1 to 2 days before chemotherapy. Patients were followed for 1 to 2 days after chemotherapy to assess fatigue using the Piper Fatigue Scale. A random sampling method was used to select 181 patients with moderate to severe CRF as the case group. The risk set sampling method was used to select 181 patients with mild or no CRF as the control group.Logistic regression, back-propagation arti cial neural network (BP-ANN) and decision tree models were constructed and compared.Results: A total of 362 patients consisting of 241 derivation samples and 121 validation samples were enrolled. Comparing the three models, the prediction effect of BP-ANN was the best, with a receiver operating characteristic curve (ROC) of 0.83. Internal and external veri cation indicated the accuracy of prediction was 70.4% and 80.8%, respectively. Signi cant predictors identi ed were surgery, complications, hypokalaemia, albumin, neutrophil percentage, pain (VAS score), Activities of Daily Living (ADL) score, sleep quality (PSQI score), anxiety (HAD-A score), depression (HAD-D score) and nutrition (PG-SGA score).Conclusions: BP-ANN was the best model, offering theoretical guidance for clinicians to formulate a tool to identify patients at high risk of moderate to severe CRF. Impact• A prediction model can be developed to predict the risk of moderate to severe cancer-related fatigue in colorectal cancer patients after chemotherapy.• The BP-ANN model offers theoretical guidance for a clinically predictable tool to assist nurses in identifying and supporting patients at high risk of moderate to severe CRF.• There are 11 risk factors for moderate to severe CRF in patients with colorectal cancer after chemotherapy, and the BP-ANN is the best prediction model with strong predictive performance.

Development and validation of a nomogram for predicting sever cancer-related fatigue in patients with cervical cancer

“…Lee et al also constructed a random forest regression model for CRF in patients with breast cancer, and found a subset of genes with more predictive significance, such as peroxygenase-5, connector protein, and the accuracy of the model was high [ 18 ]. Huang et al constructed a back-propagation artificial neural network model to predict the risk of moderate to severe CRF in colorectal cancer patients and found surgery, complications, hypokalaemia, albumin, neutrophil percentage, pain, sleep quality, anxiety, depression and nutrition were predictive factors [ 19 ].…”