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Wang Y, Tian L, Liu X, Zhang H, Tang Y, Zhang H, Nie W, Wang L. Multidimensional Predictors of Cancer-Related Fatigue Based on the Predisposing, Precipitating, and Perpetuating (3P) Model: A Systematic Review. Cancers (Basel) 2023; 15:5879. [PMID: 38136423 PMCID: PMC10741552 DOI: 10.3390/cancers15245879] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Revised: 10/22/2023] [Accepted: 12/15/2023] [Indexed: 12/24/2023] Open
Abstract
Cancer-related fatigue (CRF) is a widespread symptom with high prevalence in cancer patients, seriously affecting their quality of life. In the context of precision care, constructing machine learning-based prediction models for early screening and assessment of CRF is beneficial to this situation. To further understand the predictors of CRF for model construction, we conducted a comprehensive search in PubMed, Web of Science, Embase, and Scopus databases, combining CRF with predictor-related terms. A total of 27 papers met the inclusion criteria. We evaluated the above studies into three subgroups following the predisposing, precipitating, and perpetuating (3P) factor model. (1) Predisposing factors-baseline fatigue, demographic characteristics, clinical characteristics, psychosocial traits and physical symptoms. (2) Precipitating factors-type and stage of chemotherapy, inflammatory factors, laboratory indicators and metabolic changes. (3) Perpetuating factors-a low level of physical activity and poorer nutritional status. Future research should prioritize large-scale prospective studies with emerging technologies to identify accurate predictors of CRF. The assessment and management of CRF should also focus on the above factors, especially the controllable precipitating factors, to improve the quality of life of cancer survivors.
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Affiliation(s)
- Yiming Wang
- School of Nursing, Jilin University, No. 965 Xinjiang Street, Changchun 130021, China; (Y.W.); (L.T.)
| | - Lv Tian
- School of Nursing, Jilin University, No. 965 Xinjiang Street, Changchun 130021, China; (Y.W.); (L.T.)
| | - Xia Liu
- Senior Department of Hematology, The Fifth Medical Center of PLA General Hospital, Beijing 100071, China; (X.L.); (Y.T.); (H.Z.)
| | - Hao Zhang
- Yanda Medical Research Institute, Hebei Yanda Hospital, Sanhe 065201, China;
| | - Yongchun Tang
- Senior Department of Hematology, The Fifth Medical Center of PLA General Hospital, Beijing 100071, China; (X.L.); (Y.T.); (H.Z.)
| | - Hong Zhang
- Senior Department of Hematology, The Fifth Medical Center of PLA General Hospital, Beijing 100071, China; (X.L.); (Y.T.); (H.Z.)
| | - Wenbo Nie
- School of Nursing, Jilin University, No. 965 Xinjiang Street, Changchun 130021, China; (Y.W.); (L.T.)
| | - Lisheng Wang
- School of Nursing, Jilin University, No. 965 Xinjiang Street, Changchun 130021, China; (Y.W.); (L.T.)
- Yanda Medical Research Institute, Hebei Yanda Hospital, Sanhe 065201, China;
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