PDF(Pubmed)
Abstract：
Malnutrition in patients is associated with reduced tolerance to treatment-related side effects and higher risks of complications, directly impacting patient prognosis. Consequently, a pressing requirement exists for the development of uncomplicated yet efficient screening methods to detect patients at heightened nutritional risk. The aim of this study was to formulate a concise nutritional risk prediction model for prompt assessment by oncology medical personnel, facilitating the effective identification of hepatocellular carcinoma patients at an elevated nutritional risk. Retrospective cohort data were collected from hepatocellular carcinoma patients who met the study\'s inclusion and exclusion criteria between March 2021 and April 2022. The patients were categorized into two groups: a normal nutrition group and a malnutrition group based on body composition assessments. Subsequently, the collected data were analyzed, and predictive models were constructed, followed by simplification. A total of 220 hepatocellular carcinoma patients were included in this study, and the final model incorporated four predictive factors: age, tumor diameter, TNM stage, and anemia. The area under the ROC curve for the short-term nutritional risk prediction model was 0.990 [95% CI (0.966-0.998)]. Further simplification of the scoring rule resulted in an area under the ROC curve of 0.986 [95% CI (0.961, 0.997)]. The developed model provides a rapid and efficient approach to assess the short-term nutritional risk of hepatocellular carcinoma patients. With easily accessible and swift indicators, the model can identify patients with potential nutritional risk more effectively and timely.
摘要：
患者的营养不良与对治疗相关副作用的耐受性降低和并发症的风险增加有关。直接影响患者预后。因此,迫切需要开发简单而有效的筛查方法，以检测营养风险升高的患者。这项研究的目的是制定一个简明的营养风险预测模型，以便肿瘤科医务人员迅速评估，有助于有效识别营养风险升高的肝细胞癌患者。回顾性队列数据收集自2021年3月至2022年4月符合研究纳入和排除标准的肝细胞癌患者。根据身体成分评估，将患者分为两组：正常营养组和营养不良组。随后，对收集的数据进行了分析，并构建了预测模型，其次是简化。本研究共纳入220例肝细胞癌患者，最终的模型包含了四个预测因素：年龄，肿瘤直径,TNM阶段,和贫血。短期营养风险预测模型的ROC曲线下面积为0.990[95%CI(0.966-0.998)]。评分规则的进一步简化导致ROC曲线下面积为0.986[95%CI(0.961,0.997)]。开发的模型提供了一种快速有效的方法来评估肝细胞癌患者的短期营养风险。具有易于访问和快速的指标，该模型能更有效、及时地识别潜在营养风险患者。
