Icahn School of Medicine at Mount Sinai, New York, New York, USA.
Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
J Hum Nutr Diet. 2024 Jun;37(3):622-632. doi: 10.1111/jhn.13286. Epub 2024 Feb 13.
Malnutrition is associated with increased morbidity, mortality, and healthcare costs. Early detection is important for timely intervention. This paper assesses the ability of a machine learning screening tool (MUST-Plus) implemented in registered dietitian (RD) workflow to identify malnourished patients early in the hospital stay and to improve the diagnosis and documentation rate of malnutrition.
This retrospective cohort study was conducted in a large, urban health system in New York City comprising six hospitals serving a diverse patient population. The study included all patients aged ≥ 18 years, who were not admitted for COVID-19 and had a length of stay of ≤ 30 days.
Of the 7736 hospitalisations that met the inclusion criteria, 1947 (25.2%) were identified as being malnourished by MUST-Plus-assisted RD evaluations. The lag between admission and diagnosis improved with MUST-Plus implementation. The usability of the tool output by RDs exceeded 90%, showing good acceptance by users. When compared pre-/post-implementation, the rate of both diagnoses and documentation of malnutrition showed improvement.
MUST-Plus, a machine learning-based screening tool, shows great promise as a malnutrition screening tool for hospitalised patients when used in conjunction with adequate RD staffing and training about the tool. It performed well across multiple measures and settings. Other health systems can use their electronic health record data to develop, test and implement similar machine learning-based processes to improve malnutrition screening and facilitate timely intervention.
营养不良与发病率、死亡率和医疗保健成本增加有关。早期发现对于及时干预很重要。本文评估了一种在注册营养师（RD）工作流程中实施的机器学习筛查工具（MUST-Plus），以早期识别住院患者中的营养不良患者，并提高营养不良的诊断和记录率。
这项回顾性队列研究在纽约市的一个大型城市卫生系统中进行，该系统由六家医院组成，服务于不同的患者群体。研究包括所有年龄≥18 岁、非因 COVID-19 入院且住院时间≤30 天的患者。
在符合纳入标准的 7736 例住院治疗中，有 1947 例（25.2%）通过 MUST-Plus 辅助 RD 评估被确定为营养不良。入院和诊断之间的时间滞后随着 MUST-Plus 的实施而改善。RD 生成的工具输出的可用性超过 90%，表明用户接受度良好。与实施前后相比，营养不良的诊断和记录率均有所提高。
当与充足的 RD 人员配备和有关该工具的培训一起使用时，基于机器学习的筛查工具 MUST-Plus 有望成为住院患者的营养不良筛查工具。它在多项措施和环境中表现良好。其他医疗系统可以利用其电子健康记录数据来开发、测试和实施类似的基于机器学习的流程，以改善营养不良的筛查并促进及时干预。
