基于人工智能(AI)的住院病人快速营养诊断系统的卫生经济评估:多中心随机对照试验

IF 6.6 2区 医学 Q1 NUTRITION & DIETETICS Clinical nutrition Pub Date : 2024-08-30 DOI:10.1016/j.clnu.2024.08.030
Ming-Yao Sun , Yu Wang , Tian Zheng , Xue Wang , Fan Lin , Lu-Yan Zheng , Mao-Yue Wang , Pian-Hong Zhang , Lu-Ying Chen , Ying Yao , Jie Sun , Zeng-Ning Li , Huan-Yu Hu , Hua Jiang , Han-Yang Yue , Qian Zhao , Hai-Yan Wang , Lei Han , Xuan Ma , Meng-Ting Ji , Wei Chen
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引用次数: 0

摘要

背景&amp; 目的营养不良在住院病人中很普遍,会增加发病率、死亡率和医疗费用;但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。方法 在 10 个省的 11 个中心开展了一项全国多中心随机对照试验。住院病人被随机分为两组,一组接受人工智能快速营养诊断系统的评估,作为常规护理的一部分(实验组),另一组不接受评估(对照组)。我们计算了每位参与者的总体医疗资源成本,并根据意向治疗分析法生成了决策树,以分析各种治疗方式的成本效益。根据临床特征进行了分组分析,并进行了概率敏感性分析,以评估参数变化对增量成本效益比(ICER)的影响。实验组的治愈率明显高于对照组(23.24% 对 20.18%;P = 0.005)。实验组的增量成本为 276.52 元人民币,额外治愈 3.06 例,ICER 为 90.37 元人民币。敏感性分析显示,决策树模型相对稳定。结论将基于人工智能的快速营养诊断系统纳入常规住院治疗可大幅提高住院患者的治愈率,且具有成本效益。RegistrationNCT04776070 (https://clinicaltrials.gov/study/NCT04776070)。
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Health economic evaluation of an artificial intelligence (AI)-based rapid nutritional diagnostic system for hospitalised patients: A multicentre, randomised controlled trial

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.

Methods

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).

Results

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.

Conclusion

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.

Registration

NCT04776070 (https://clinicaltrials.gov/study/NCT04776070).

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来源期刊
Clinical nutrition
Clinical nutrition 医学-营养学
CiteScore
14.10
自引率
6.30%
发文量
356
审稿时长
28 days
期刊介绍: Clinical Nutrition, the official journal of ESPEN, The European Society for Clinical Nutrition and Metabolism, is an international journal providing essential scientific information on nutritional and metabolic care and the relationship between nutrition and disease both in the setting of basic science and clinical practice. Published bi-monthly, each issue combines original articles and reviews providing an invaluable reference for any specialist concerned with these fields.
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