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
{"title":"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","authors":"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","doi":"10.1016/j.clnu.2024.08.030","DOIUrl":null,"url":null,"abstract":"<div><h3>Background & aims</h3><p>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.</p></div><div><h3>Methods</h3><p>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).</p></div><div><h3>Results</h3><p>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.</p></div><div><h3>Conclusion</h3><p>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.</p></div><div><h3>Registration</h3><p>NCT04776070 (<span><span>https://clinicaltrials.gov/study/NCT04776070</span><svg><path></path></svg></span>).</p></div>","PeriodicalId":10517,"journal":{"name":"Clinical nutrition","volume":"43 10","pages":"Pages 2327-2335"},"PeriodicalIF":6.6000,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Health economic evaluation of an artificial intelligence (AI)-based rapid nutritional diagnostic system for hospitalised patients: A multicentre, randomised controlled trial\",\"authors\":\"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\",\"doi\":\"10.1016/j.clnu.2024.08.030\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><h3>Background & aims</h3><p>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.</p></div><div><h3>Methods</h3><p>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).</p></div><div><h3>Results</h3><p>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.</p></div><div><h3>Conclusion</h3><p>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.</p></div><div><h3>Registration</h3><p>NCT04776070 (<span><span>https://clinicaltrials.gov/study/NCT04776070</span><svg><path></path></svg></span>).</p></div>\",\"PeriodicalId\":10517,\"journal\":{\"name\":\"Clinical nutrition\",\"volume\":\"43 10\",\"pages\":\"Pages 2327-2335\"},\"PeriodicalIF\":6.6000,\"publicationDate\":\"2024-08-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Clinical nutrition\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S026156142400308X\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"NUTRITION & DIETETICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Clinical nutrition","FirstCategoryId":"3","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S026156142400308X","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"NUTRITION & DIETETICS","Score":null,"Total":0}
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.
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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