Construction and analysis of adverse reaction prediction model for liver cancer patients with radiotherapy and chemotherapy
containing nutritional indicators
Ma Dongbo, Wang Zhong
Department of Clinical Nutrition, Lianyungang First People's Hospital, Lianyungang Hospital Affiliated to Xuzhou Medical University,
Lianyungang 222000, Jiangsu, China
Abstract：Objective To explore the construction of a predictive model for adverse reactions of liver cancer patients with radiotherapy
and chemotherapy containing nutritional indicators. Method Prospectively selected 231 patients with liver cancer radiotherapy and
chemotherapy admitted to Lianyungang First People's Hospital from January 2019 to March 2021 as the research objects. Randomly selected
70% (162 cases) of cases as the modeling set, and 30% (69 cases) as the test set. Compared the relevant data of the modeling set after
radiotherapy and chemotherapy, and apply multi⁃factor Logistic regression to screen the relevant factors, construct the prediction model,
use the consistency index (C⁃index) to quantify and calibrate the curve, evaluate the performance of the model, draw the decision curve
to analyze and evaluate the clinical net of the nutritional index model. At the same time, external verification of the model is performed
on the test set. Result After one course of radiotherapy and chemotherapy, 74 cases (45.68%) in the modeling set had adverse reactions
and were classified as the adverse reaction group, and 88 cases (54.32%) without adverse reactions were classified as the non⁃adverse
reaction group. In modeling set two groups, tumor diameter, controlling nutritional status (CONUT) score, alpha⁃fetoprotein (AFP), alkaline
phosphatase (ALP), γ⁃glutamyl transferase (GGT), des⁃γ⁃carboxy prothrombin (DCP), and prognostic nutritional index (PNI) had
statistically significant differences (P<0.05). Multivariate Logistic analysis showed that tumor diameter (OR=1.699, 95%CI=1.117-2.583),
CONUT score (OR=2.396, 95%CI=1.205-4.763), AFP (OR=1.068, 95%CI=1.020-1.118), DCP (OR=1.013, 95%CI=1.000-1.025), GGT
(OR=1.090, 95%CI=1.037-1.144), ALP (OR=1.013, 95%CI=1.003-1.023) in liver cancer are independent risk factors for adverse reactions
and chemotherapy. PNI (OR=0.913, 95%CI=0.875-0.953) is a protective factor for adverse reactions (all P<0.05). The Nomogram model
predicts the occurrence of adverse reactions with a C⁃index of 0.867 (95%CI=0.815-0.920). The decision curve shows that when the
predicted value of the model with CONUT score and PNI nutrition⁃related index is in the interval (0-0.6), additional clinical benefits can
be provided. External validation showed that among the 69 patients in the validation set, 31 (44.93%) had adverse reactions and were
classified as the adverse reaction group, and 38 (55.07%) without adverse reactions were classified as the non⁃adverse reaction group. The prediction sensitivity of the model was 90.32% and the specificity was 91.67%. Conclusion The prediction model constructed with CONUT
score and PNI nutritional indicators can improve the accuracy of predicting the occurrence of adverse reactions in patients with liver cancer
radiotherapy and chemotherapy.
马东波，王仲. 含营养指标的肝癌放化疗患者不良反应预测模型构建
分析[J]. 肿瘤代谢与营养电子杂志, 2022, 9(2): 200-206.
Ma Dongbo, Wang Zhong. Construction and analysis of adverse reaction prediction model for liver cancer patients with radiotherapy and chemotherapy
containing nutritional indicators. Electron J Metab Nutr Cancer, 2022, 9(2): 200-206.
