恶病质前期的检测对于恶病质的预防和治疗具有重要意义。然而，如何识别恶病质前期仍然是一个挑战。本研究旨在用简单的方法检测癌症前期恶病质，并区分恶病质前期和恶病质的不同特征。这项研究共纳入了3896名参与者。我们使用所有基线特征作为输入变量并训练机器学习（ML）模型来计算变量的重要性。根据重要性过滤变量后，对模型进行了重新训练。根据接收器操作特征值选择最佳模型。随后，我们使用相同的方法和流程在非恶病质人群中识别出恶病质前期患者。这项研究的参与者包括 2228 名男性（57.2%）和 1668 名女性（42.8%），其中 471 人被诊断为恶病质前期患者。恶病质前期，1178 患有恶病质，其余为非恶病质。恶病质最重要的特征是饮食变化、臂围、高密度脂蛋白（HDL）水平和C反应蛋白白蛋白比率（CAR）。区分恶病前期的最重要特征是饮食变化、血清肌酐、HDL、握力和 CAR。用于筛查恶病质和诊断恶病质前期的两种逻辑回归模型的曲线下面积值最高，分别为 0.830 和 0.701。校准和决策曲线表明该模型具有良好的准确性。我们开发了两种用于识别恶病质前期和恶病质的模型，这将有助于临床医生检测和诊断恶病质前期。© 2024。作者。

Detection of precachexia is important for the prevention and treatment of cachexia. However, how to identify precachexia is still a challenge.This study aimed to detect cancer precachexia using a simple method and distinguish the different characteristics of precachexia and cachexia.We included 3896 participants in this study. We used all baseline characteristics as input variables and trained machine learning (ML) models to calculate the importance of the variables. After filtering the variables based on their importance, the models were retrained. The best model was selected based on the receiver operating characteristic value. Subsequently, we used the same method and process to identify patients with precachexia in a noncachexia population using the same method and process.Participants in this study included 2228 men (57.2%) and 1668 women (42.8%), of whom 471 were diagnosed with precachexia, 1178 with cachexia, and the remainder with noncachexia. The most important characteristics of cachexia were eating changes, arm circumference, high-density lipoprotein (HDL) level, and C-reactive protein albumin ratio (CAR). The most important features distinguishing precachexia were eating changes, serum creatinine, HDL, handgrip strength, and CAR. The two logistic regression models for screening for cachexia and diagnosing precachexia had the highest area under the curve values of 0.830 and 0.701, respectively. Calibration and decision curves showed that the models had good accuracy.We developed two models for identifying precachexia and cachexia, which will help clinicians detect and diagnose precachexia.© 2024. The Author(s).