Abstract：
BACKGROUND: Acute nutrition-related adverse outcomes are common in head and neck cancer patients undergoing radiotherapy. Predictive models can assist in identifying high-risk patients to enable targeted intervention. We aimed to systematically evaluate predictive models for predicting severe acute nutritional symptoms, insufficient intake, tube feeding, sarcopenia, and weight loss.
METHODS: We searched PubMed, Web of Science, EBSCO, Embase, WanFang, CNKI, and SinoMed. We selected studies developing predictive models for the aforementioned outcomes. Data were extracted using a predefined checklist. Risk of bias and applicability assessment were assessed using the Prediction model Risk of Bias Assessment Tool. A narrative synthesis was conducted to summarize the model characteristics, risk of bias, and performance.
RESULTS: A total of 2941 studies were retrieved and 19 were included. Study outcome measure were different symptoms (n = 11), weight loss (n = 5), tube feeding (n = 3), and symptom or tube feeding (n = 1). Predictive factors mainly encompassed sociodemographic data, disease-related data, and treatment-related data. Seventeen studies reported area under the curve or C-index values ranging from 0.610 to 0.96, indicating moderate to good predictive performance. However, candidate predictors were incomplete, outcome measures were diverse, and the risk of bias was high. Most of them used traditional model development methods, and only two used machine learning.
CONCLUSIONS: Most current models showed moderate to good predictive performance. However, predictors are incomplete, outcome are inconsistent, and the risk of bias is high. Clinicians could carefully select the models with better model performance from the available models according to their actual conditions. Future research should include comprehensive and modifiable indicators and prioritize well-designed and reported studies for model development.
METHODS: We searched PubMed, Web of Science, EBSCO, Embase, WanFang, CNKI, and SinoMed. We selected studies developing predictive models for the aforementioned outcomes. Data were extracted using a predefined checklist. Risk of bias and applicability assessment were assessed using the Prediction model Risk of Bias Assessment Tool. A narrative synthesis was conducted to summarize the model characteristics, risk of bias, and performance.
RESULTS: A total of 2941 studies were retrieved and 19 were included. Study outcome measure were different symptoms (n = 11), weight loss (n = 5), tube feeding (n = 3), and symptom or tube feeding (n = 1). Predictive factors mainly encompassed sociodemographic data, disease-related data, and treatment-related data. Seventeen studies reported area under the curve or C-index values ranging from 0.610 to 0.96, indicating moderate to good predictive performance. However, candidate predictors were incomplete, outcome measures were diverse, and the risk of bias was high. Most of them used traditional model development methods, and only two used machine learning.
CONCLUSIONS: Most current models showed moderate to good predictive performance. However, predictors are incomplete, outcome are inconsistent, and the risk of bias is high. Clinicians could carefully select the models with better model performance from the available models according to their actual conditions. Future research should include comprehensive and modifiable indicators and prioritize well-designed and reported studies for model development.
摘要：
背景：急性营养相关的不良结局在接受放疗的头颈部癌症患者中很常见。预测模型可以帮助识别高风险患者，以实现有针对性的干预。我们旨在系统地评估预测严重急性营养症状的预测模型，摄入量不足,管进料,少肌症,和减肥。
方法：我们搜索了PubMed，WebofScience,EBSCO,Embase,万方,CNKI,还有SinoMed.我们选择了为上述结果开发预测模型的研究。使用预定义的检查表提取数据。使用预测模型偏差风险评估工具评估偏差风险和适用性评估。进行了叙事综合，总结了模型特征，偏见的风险，和性能。
结果：共检索到2941项研究，纳入19项。研究结果测量是不同的症状(n=11)，体重减轻（n=5），管进料（n=3），和症状或管饲（n=1）。预测因素主要包括社会人口统计数据，疾病相关数据，和治疗相关数据。17项研究报告了曲线下面积或C指数值范围为0.610至0.96，表明中等至良好的预测性能。然而,候选预测因子不完整，结果衡量标准多种多样，偏见的风险很高。他们大多采用传统的模型开发方法,只有两个人使用机器学习。
结论：当前大多数模型显示中等到良好的预测性能。然而,预测因子是不完整的，结果不一致，偏见的风险很高。临床医生可以根据自己的实际情况，从现有模型中仔细选择模型性能更好的模型。未来的研究应包括全面和可修改的指标，并优先考虑精心设计和报告的模型开发研究。
方法：我们搜索了PubMed，WebofScience,EBSCO,Embase,万方,CNKI,还有SinoMed.我们选择了为上述结果开发预测模型的研究。使用预定义的检查表提取数据。使用预测模型偏差风险评估工具评估偏差风险和适用性评估。进行了叙事综合，总结了模型特征，偏见的风险，和性能。
结果：共检索到2941项研究，纳入19项。研究结果测量是不同的症状(n=11)，体重减轻（n=5），管进料（n=3），和症状或管饲（n=1）。预测因素主要包括社会人口统计数据，疾病相关数据，和治疗相关数据。17项研究报告了曲线下面积或C指数值范围为0.610至0.96，表明中等至良好的预测性能。然而,候选预测因子不完整，结果衡量标准多种多样，偏见的风险很高。他们大多采用传统的模型开发方法,只有两个人使用机器学习。
结论：当前大多数模型显示中等到良好的预测性能。然而,预测因子是不完整的，结果不一致，偏见的风险很高。临床医生可以根据自己的实际情况，从现有模型中仔细选择模型性能更好的模型。未来的研究应包括全面和可修改的指标，并优先考虑精心设计和报告的模型开发研究。
