Predictive model for acute radiation esophagitis in esophageal carcinoma based on prognostic nutritional index and systemic inflammatory index and its application

被引:1
作者
Wu, Lijun [1 ,2 ]
Li, Wen [1 ]
Ma, Xuanxuan [1 ]
Yuan, Mengmeng [1 ]
Wang, Yichun [2 ]
Li, Shuwen [1 ]
机构
[1] Anhui Med Univ, Sch Nursing, 81 Meishan Rd, Hefei 230022, Anhui, Peoples R China
[2] Anhui Med Univ, Dept Oncol, Affiliated Hosp 1, Hefei 230022, Anhui, Peoples R China
关键词
esophageal cancer; acute radiation esophagitis; prediction model; CANCER; TOXICITY; THERAPY;
D O I
10.3892/ol.2024.14730
中图分类号
R73 [肿瘤学];
学科分类号
100214 ;
摘要
Acute radiation esophagitis (ARE) is a common complication in patients with esophageal cancer undergoing radiotherapy. Therefore, it is important to construct an effective ARE risk-prediction model for clinical treatment. The present study performed a retrospective analysis of 225 patients with esophageal cancer who received radiotherapy at the First Affiliated Hospital of Anhui Medical University (Hefei, China) from January 2018 to December 2022. Univariate and logistic regression analyses were performed to screen patients with esophageal cancer after radiotherapy. The results revealed that 147 patients developed radiation esophagitis. Logistic regression analysis results demonstrated that the prognostic nutritional index [odds ratio (OR), 0.864; 95% confidence interval (CI), 0.809-0.924], neutrophil to lymphocyte ratio (OR, 1.795; 95% CI, 1.209-2.667) and platelet to lymphocyte ratio (OR, 1.011; 95% CI, 1.000-1.022) were independent predictors of ARE in patients receiving intensity-modulated conformal radiotherapy for esophagus cancer (P<0.05). A nomogram model for predicting the occurrence of ARE was established based on the three risk factors. The decision curve suggested a high net benefit value when the threshold probability was within 0.25-1.0. External verification confirmed the reproducibility and generalizability of the nomogram model. In general, the calibration curve of this model was close to the ideal curve and had excellent prediction accuracy. Therefore, it may be used as a new tool for early prediction of the ARE risk.
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页数:8
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