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Construction and validation of a risk-prediction model for chemotherapy-related cognitive impairment in patients with breast cancer.
Journal of Cancer Survivorship : Research and Practice 2024 March 22
PURPOSE: To identify risk factors of chemotherapy-related cognitive impairment (CRCI) and construct and validate a visual prediction model of such for patients with breast cancer.
METHODS: A multicenter, descriptive, and cross-sectional design was adopted. Data were collected from ten public tertiary hospitals in China. Cognitive function was assessed by using Functional Assessment of Cancer Therapy-cognitive function. Socio-demographic, clinical, psychological, and physical indicators were also assessed. The logistic prediction model was constructed by fivefold cross-validation. Then, a nomogram was utilized to visualize the prediction model, which was also evaluated via discrimination, calibration, and decision curve analysis.
RESULTS: A total of 71 breast cancer patients had CRCI with a prevalence of 9.58%. This visual prediction model was constructed based on education background, exercise frequency, chemotherapy times, and fatigue and demonstrated good discrimination, with an area under the receiver operating characteristic curve of 0.882. The calibration curve indicated good agreement between experimental and projected values, and the decision curve proved good clinical applicability.
CONCLUSION: Education background, exercise frequency, chemotherapy times, and fatigue were associated with high incidence of CRCI. The prediction model exhibits superior performance and has promise as a useful instrument for assessing the likelihood of CRCI in breast cancer patients.
IMPLICATIONS FOR CANCER SURVIVORS: Our findings could provide breast cancer survivors with risk screening based on CRCI predictors to implement prevention and early intervention, and help patients integrate into society and achieve comprehensive recovery.
METHODS: A multicenter, descriptive, and cross-sectional design was adopted. Data were collected from ten public tertiary hospitals in China. Cognitive function was assessed by using Functional Assessment of Cancer Therapy-cognitive function. Socio-demographic, clinical, psychological, and physical indicators were also assessed. The logistic prediction model was constructed by fivefold cross-validation. Then, a nomogram was utilized to visualize the prediction model, which was also evaluated via discrimination, calibration, and decision curve analysis.
RESULTS: A total of 71 breast cancer patients had CRCI with a prevalence of 9.58%. This visual prediction model was constructed based on education background, exercise frequency, chemotherapy times, and fatigue and demonstrated good discrimination, with an area under the receiver operating characteristic curve of 0.882. The calibration curve indicated good agreement between experimental and projected values, and the decision curve proved good clinical applicability.
CONCLUSION: Education background, exercise frequency, chemotherapy times, and fatigue were associated with high incidence of CRCI. The prediction model exhibits superior performance and has promise as a useful instrument for assessing the likelihood of CRCI in breast cancer patients.
IMPLICATIONS FOR CANCER SURVIVORS: Our findings could provide breast cancer survivors with risk screening based on CRCI predictors to implement prevention and early intervention, and help patients integrate into society and achieve comprehensive recovery.
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