1. Construction of a predictive model for cognitive impairment risk in patients with advanced cancer.
- Author
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Xinran, Zhu, Shumei, Zhuang, Xueying, Zhou, Linan, Wang, Ying, Guo, Peng, Wang, Yahong, Hou, Longting, Ma, and Jing, Wang
- Subjects
COGNITION disorders diagnosis ,COGNITION disorder risk factors ,STATISTICS ,GLUCOCORTICOIDS ,CROSS-sectional method ,MULTIVARIATE analysis ,TERTIARY care ,ANXIETY testing ,SELF-report inventories ,RISK assessment ,TUMOR classification ,CANCER patients ,NEUROPSYCHOLOGICAL tests ,COMPARATIVE studies ,T-test (Statistics) ,QUALITY of life ,AGING ,QUESTIONNAIRES ,DESCRIPTIVE statistics ,RESEARCH funding ,TUMORS ,PREDICTION models ,LOGISTIC regression analysis ,STATISTICAL models ,ARTIFICIAL neural networks ,ANXIETY ,DATA analysis software ,RECEIVER operating characteristic curves ,DISEASE complications - Abstract
Aims: The purpose of this study was to identify risk factors for cognitive impairment in advanced cancer patients and to develop predictive models based on these risk factors. Background: Cancer‐related cognitive impairment seriously affects the quality of life of advanced cancer patients. However, neural network models of cognitive impairment in patients with advanced cancer have not yet been identified. Design: A cross‐sectional design was used. Methods: This study collected 494 questionnaires between January and June 2022. Statistically significant clinical indicators were selected by univariate analysis, and the artificial neural network model and logistic regression model were used for multivariate analysis. The predicted value of the model was estimated using the area under the subject's working characteristic curve. Result: The artificial neural network and the logistic regression models suggested that cancer course, anxiety and age were the major risk factors for cognitive impairment in advanced cancer patients. All the indexes of artificial neural network model constructed in this study are better than those of the logistic model. Conclusion: The artificial neural network model can better predict the risk factors of cognitive impairment in patients with advanced cancer. Better prediction will enable nurses and other healthcare professionals to provide better targeted and timely support. Summary statement: What is already known about this topic?: Cancer‐related cognitive impairment has significantly affected the quality of life of patients with advanced cancer.Risk factors associated with cognitive impairment in patients with advanced cancer have not been adequately documented, and neural network models have not been identified. What this paper adds?: This study found that long a cancer course, anxiety and ageing were the most important risk factors for cognitive impairment in patients with advanced cancer.The artificial neural network showed good predictive indicators, indicating that it can be used to predict cognitive impairment in patients with advanced cancer. The implications of this paper: Neural network models can help nurses and other clinicians predict cognitive impairment in advanced cancer patients early which will allow provision of timely intervention. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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