开发和验证名录和机器学习模型,以预测慢性病患者的肉症
Yan Zhao1, Zongyue Hu2, Huaize Dong3
1Department of Prevention & Healthcare, The First Affiliated Hospital, Army Medical University, Chongqing, 400038, China.
Scientific reports
|November 27, 2025
概括
这项研究开发了一种Nomogram,用于预测慢性病 (CKD) 的中国患者的肉症风险. 该模型使用了八个关键因素,显示了高准确度,有助于早期检测和干预CKD相关的sarcopenia.
科学领域:
- 腎臟病學 (nephrology) 是一種醫學.
- 老年学是一门学科.
- 公共卫生 公共卫生
背景情况:
- 慢性病 (CKD) 是一个全球性健康问题,患病率不断增加.
- Sarcopenia 是 CKD 的常见和严重并发症,但缺乏特定的预测工具.
- 在CKD患者中早期识别肉类症风险对于及时干预至关重要.
研究的目的:
- 为了建立一个预测模型 (Nomogram) 对中国CKD患者的肉症风险.
- 在这个人群中确定与肉症相关的关键预测因素.
- 为高风险个体提供早期查和干预的工具.
主要方法:
- 利用了2015年中国健康与退休长度研究 (CHARLS) 的数据.
- 用于特征选择的使用最小绝对收缩和选择运算符 (LASSO) 回归.
- 构建了一个多变量逻辑回归模型,并使用内部和外部数据集进行验证.
- 使用ROC曲线,AUC,校准曲线和决策曲线分析 (DCA) 评估模型性能.
主要成果:
- 鉴定了八个独立的萨尔科佩尼亚预测因素:年龄,性别,癌症史,吸烟,腰围,睡眠,白细胞计数和疼痛.
- 开发的Nomogram表现出高精度,AUC值为0.867 (训练),0.828 (验证) 和0.818 (外部验证).
- 模型校准和临床实用性通过Hosmer-Lemeshow测试,校准曲线和DCA.证实.
结论:
- 诺米克图是评估中国CKD患者的肉症风险的有效工具.
- 早期识别高风险患者可以促进个性化干预.
- 这项研究强调了机器学习模型在预测CKD中肉症风险方面的潜力.
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