基于机器学习的老年营养不良患者辅助诊断模型的开发和评估：队列研究。

Development and Assessment of Assisted Diagnosis Models Using Machine Learning for Identifying Elderly Patients With Malnutrition: Cohort Study.

机构信息

Department of Clinical Nutrition, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.

Institute of Medical Information, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.

出版信息

J Med Internet Res. 2023 Mar 14;25:e42435. doi: 10.2196/42435.

BACKGROUND

Older patients are at an increased risk of malnutrition due to many factors related to poor clinical outcomes.

OBJECTIVE

This study aims to develop an assisted diagnosis model using machine learning (ML) for identifying older patients with malnutrition and providing the focus of individualized treatment.

METHODS

We reanalyzed a multicenter, observational cohort study including 2660 older patients. Baseline malnutrition was defined using the global leadership initiative on malnutrition (GLIM) criteria, and the study population was randomly divided into a derivation group (2128/2660, 80%) and a validation group (532/2660, 20%). We applied 5 ML algorithms and further explored the relationship between features and the risk of malnutrition by using the Shapley additive explanations visualization method.

RESULTS

The proposed ML models were capable to identify older patients with malnutrition. In the external validation cohort, the top 3 models by the area under the receiver operating characteristic curve were light gradient boosting machine (92.1%), extreme gradient boosting (91.9%), and the random forest model (91.5%). Additionally, the analysis of the importance of features revealed that BMI, weight loss, and calf circumference were the strongest predictors to affect GLIM. A BMI of below 21 kg/m2 was associated with a higher risk of GLIM in older people.

CONCLUSIONS

We developed ML models for assisting diagnosis of malnutrition based on the GLIM criteria. The cutoff values of laboratory tests generated by Shapley additive explanations could provide references for the identification of malnutrition.

TRIAL REGISTRATION

Chinese Clinical Trial Registry ChiCTR-EPC-14005253; https://www.chictr.org.cn/showproj.aspx?proj=9542.

背景

由于与不良临床结局相关的多种因素，老年患者存在营养不良的风险增加。

目的

本研究旨在使用机器学习（ML）开发一种辅助诊断模型，以识别营养不良的老年患者，并提供个体化治疗的重点。

方法

我们重新分析了一项多中心、观察性队列研究，共纳入 2660 名老年患者。使用全球营养不良领导倡议（GLIM）标准定义基线营养不良，将研究人群随机分为推导组（2128/2660，80%）和验证组（532/2660，20%）。我们应用了 5 种 ML 算法，并进一步使用 Shapley 加法解释可视化方法探讨了特征与营养不良风险之间的关系。

结果

所提出的 ML 模型能够识别营养不良的老年患者。在外部验证队列中，曲线下面积最高的前 3 个模型分别是轻梯度提升机（92.1%）、极端梯度提升机（91.9%）和随机森林模型（91.5%）。此外，特征重要性分析表明，BMI、体重减轻和小腿围是影响 GLIM 的最强预测因素。BMI 低于 21 kg/m2 与老年人 GLIM 风险增加相关。