Department of Clinical Nutrition, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences - Peking Union Medical College, Beijing, China; Department of Clinical Nutrition, Shengli Clinical Medical College of Fujian Medical University, Fujian Key Laboratory of Geriatrics Diseases, Fujian Provincial Center for Geriatrics, Fujian Provincial Hospital, Fuzhou University Affiliated Provincial Hospital, Fuzhou, China.
Department of Clinical Nutrition, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences - Peking Union Medical College, Beijing, China.
Clin Nutr. 2024 Oct;43(10):2327-2335. doi: 10.1016/j.clnu.2024.08.030. Epub 2024 Aug 30.
BACKGROUND & AIMS: Malnutrition is prevalent among hospitalised patients, and increases the morbidity, mortality, and medical costs; yet nutritional assessments on admission are not routine. This study assessed the clinical and economic benefits of using an artificial intelligence (AI)-based rapid nutritional diagnostic system for routine nutritional screening of hospitalised patients.
A nationwide multicentre randomised controlled trial was conducted at 11 centres in 10 provinces. Hospitalised patients were randomised to either receive an assessment using an AI-based rapid nutritional diagnostic system as part of routine care (experimental group), or not (control group). The overall medical resource costs were calculated for each participant and a decision-tree was generated based on an intention-to-treat analysis to analyse the cost-effectiveness of various treatment modalities. Subgroup analyses were performed according to clinical characteristics and a probabilistic sensitivity analysis was performed to evaluate the influence of parameter variations on the incremental cost-effectiveness ratio (ICER).
In total, 5763 patients participated in the study, 2830 in the experimental arm and 2933 in the control arm. The experimental arm had a significantly higher cure rate than the control arm (23.24% versus 20.18%; p = 0.005). The experimental arm incurred an incremental cost of 276.52 CNY, leading to an additional 3.06 cures, yielding an ICER of 90.37 CNY. Sensitivity analysis revealed that the decision-tree model was relatively stable.
The integration of the AI-based rapid nutritional diagnostic system into routine inpatient care substantially enhanced the cure rate among hospitalised patients and was cost-effective.
NCT04776070 (https://clinicaltrials.gov/study/NCT04776070).
营养不良在住院患者中很常见，会增加发病率、死亡率和医疗费用；然而，入院时的营养评估并非常规。本研究评估了使用基于人工智能（AI）的快速营养诊断系统对住院患者进行常规营养筛查的临床和经济效益。
在 10 个省的 11 个中心进行了一项全国性多中心随机对照试验。将住院患者随机分为接受 AI 快速营养诊断系统评估（实验组）或不接受评估（对照组）。对每位参与者计算了总体医疗资源成本，并根据意向治疗分析生成决策树，以分析各种治疗方式的成本效益。根据临床特征进行亚组分析，并进行概率敏感性分析，以评估参数变化对增量成本效益比（ICER）的影响。
共有 5763 名患者参与了这项研究，其中 2830 名患者在实验组，2933 名患者在对照组。实验组的治愈率明显高于对照组（23.24%比 20.18%；p=0.005）。实验组的增量成本为 276.52 元，额外治愈 3.06 人，增量成本效益比为 90.37 元。敏感性分析表明，决策树模型相对稳定。
将基于 AI 的快速营养诊断系统整合到常规住院患者护理中，显著提高了住院患者的治愈率，具有成本效益。
NCT04776070（https://clinicaltrials.gov/study/NCT04776070）。
