Influencing Factors and a Predictive Nomogram of Frailty in Chinese Patients with Cancer: A Single-Center Retrospective Study

• Published in: European journal of cancer care Vol. 2024
• Main Authors: Yang, Zhihui, Luo, Yuanyuan, Luo, Jiahui, Fang, Qinghong, Miao, Jingxia, Zhang, Lili
• Format: Journal Article
• Language: English
• Published: Oxford John Wiley & Sons, Inc 23.09.2024; Hindawi Limited
• Subjects: Analysis; Calibration; Cancer; Cancer patients; Cancer therapies; Care and treatment; Chemotherapy; Chronic illnesses; Coping; Diet; Disease; Exercise; Frailty; Measuring instruments; Medical personnel; Medical records; Mortality; Multivariate analysis; Nomograms; Nutrient deficiency; Oncology, Experimental; Parenteral nutrition; Patients; Physical activity; Psychological stress; Quality of life; Questionnaires; Risk factors; Risk groups; Social interactions; Social support; Sociodemographics; Stress (Psychology); Surgery; Validity; Vegetarian diet
• ISSN: 0961-5423; 1365-2354
• DOI: 10.1155/2024/3194941

Objective. The number of cancer survivors is increasing, and the high prevalence of frailty not only reduces quality of life but also affects the treatment of cancer patients. This study aimed to identify the prevalence and risk factors of frailty in cancer patients and to construct a nomogram to predict the probability of frailty. Methods. Nine hundred fifty-eight cancer patients were included in this retrospective study, randomly divided into a development set (n=680) and a validation set (n=278). Frailty was assessed using the Tilburg frailty indicator (TFI). Social support, medical coping styles, and psychological distress were assessed by the Social Support Self-Rating Scale (SSRS), Medical Coping Modes Questionnaire (MCMQ), and distress thermometer (DT), respectively. Results. The prevalence of frailty in cancer patients was 45.93%. Cancer patients who exercised regularly, ate a balanced diet, and actively coped with diseases were less likely to become frail. The risk factors for frailty identified by a multivariate analysis were parenteral nutrition, advanced TNM staging, vegetarian diet, unemployment, psychological distress ≥4, low physical activity, and negative coping styles. These risk factors were used to construct a nomogram, and the C-index, calibration curve, and decision curve analysis (DCA) were used to assess the performance of the nomogram. The C-index was 0.762, and the calibration curve showed satisfactory coherence. The net benefit of the nomogram was better between threshold probabilities of 17%-96% in DCA. Conclusion. Special focus needs to be placed on frail cancer patients due to their high prevalence and severe outcomes. Clinical medical workers could use this nomogram to identify high-risk patients and intervene early to prevent frailty.