American journal of translational research · 2025
Risk factors for low intake dehydration in malignant tumor patients and predictive model construction.
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Zhang Lili, Chen Min, Liu Nana, Zhou Liumin
Аннотация
OBJECTIVE: To investigate the factors influencing low intake dehydration in patients with malignant tumors and to construct a nomogram model for predicting its risk.
METHODS: A total of 383 patients with malignant tumors were enrolled in this study. Based on the presence of low intake dehydration at admission, patients were classified into a dehydration group (119 patients) and a non-dehydration group (264 patients). Multiple logistic regression analysis was used to identify factors associated with low intake dehydration. A nomogram model was subsequently constructed, and its predictive performance was validated.
RESULTS: Significant differences were observed between the dehydration and non-dehydration groups in age (χ2 = 6.988, P = 0.008), serum albumin (t = 3.158, P = 0.002), tumor duration (t = 4.257, P < 0.001), swallowing disorders (χ2 = 8.753, P = 0.003), Cancer Patient Appetite Questionnaire (CASQ) score (t = 3.373, P = 0.001), and Barthel Index (BI) score (t = 2.768, P = 0.006). Logistic regression analysis revealed that age ≥ 60 years, serum albumin level, tumor duration, dysphagia, CASQ score, and BI score were significant factors associated with low intake dehydration in cancer patients. The constructed nomogram model showed no overfitting (P > 0.05 in the goodness-of-fit test), with an area under the ROC curve of 0.861. Decision curve analysis confirmed the model's practical value. Ten-fold cross-validation demonstrated the robustness of the model.
CONCLUSIONS: The risk of low intake dehydration in cancer patients is influenced by age, albumin level, tumor duration, dysphagia, CASQ score, and BI score. The nomogram model effectively assesses the risk of low intake dehydration in these patients.
METHODS: A total of 383 patients with malignant tumors were enrolled in this study. Based on the presence of low intake dehydration at admission, patients were classified into a dehydration group (119 patients) and a non-dehydration group (264 patients). Multiple logistic regression analysis was used to identify factors associated with low intake dehydration. A nomogram model was subsequently constructed, and its predictive performance was validated.
RESULTS: Significant differences were observed between the dehydration and non-dehydration groups in age (χ2 = 6.988, P = 0.008), serum albumin (t = 3.158, P = 0.002), tumor duration (t = 4.257, P < 0.001), swallowing disorders (χ2 = 8.753, P = 0.003), Cancer Patient Appetite Questionnaire (CASQ) score (t = 3.373, P = 0.001), and Barthel Index (BI) score (t = 2.768, P = 0.006). Logistic regression analysis revealed that age ≥ 60 years, serum albumin level, tumor duration, dysphagia, CASQ score, and BI score were significant factors associated with low intake dehydration in cancer patients. The constructed nomogram model showed no overfitting (P > 0.05 in the goodness-of-fit test), with an area under the ROC curve of 0.861. Decision curve analysis confirmed the model's practical value. Ten-fold cross-validation demonstrated the robustness of the model.
CONCLUSIONS: The risk of low intake dehydration in cancer patients is influenced by age, albumin level, tumor duration, dysphagia, CASQ score, and BI score. The nomogram model effectively assesses the risk of low intake dehydration in these patients.