Artificial intelligence driven malnutrition diagnostic model for patients with acute abdomen based on GLIM criteria: a cross-sectional research protocol
Background Patients with acute abdomen often experience reduced voluntary intake and a hypermetabolic process, leading to a high occurrence of malnutrition. The Global Leadership Initiative on Malnutrition (GLIM) criteria have rapidly developed into a principal methodological tool for nutritional di...
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BMJ Publishing Group
2024-03-01
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| Series: | BMJ Open |
| Online Access: | https://bmjopen.bmj.com/content/14/3/e077734.full |
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| author | Yu Wang Lu Wang Hua Jiang Wei Ma Bin Cai Ming-Wei Sun Charles Damien Lu |
| author_facet | Yu Wang Lu Wang Hua Jiang Wei Ma Bin Cai Ming-Wei Sun Charles Damien Lu |
| author_sort | Yu Wang |
| collection | DOAJ |
| description | Background Patients with acute abdomen often experience reduced voluntary intake and a hypermetabolic process, leading to a high occurrence of malnutrition. The Global Leadership Initiative on Malnutrition (GLIM) criteria have rapidly developed into a principal methodological tool for nutritional diagnosis. Additionally, machine learning is emerging to establish artificial intelligent-enabled diagnostic models, but the accuracy and robustness need to be verified. We aimed to establish an intelligence-enabled malnutrition diagnosis model based on GLIM for patients with acute abdomen.Method This study is a single-centre, cross-sectional observational investigation into the prevalence of malnutrition in patients with acute abdomen using the GLIM criteria. Data collection occurs on the day of admission, at 3 and 7 days post-admission, including biochemical analysis, body composition indicators, disease severity scoring, nutritional risk screening, malnutrition diagnosis and nutritional support information. The occurrence rate of malnutrition in patients with acute abdomen is analysed with the GLIM criteria based on the Nutritional Risk Screening 2002 and the Mini Nutritional Assessment Short-Form to investigate the sensitivity and accuracy of the GLIM criteria. After data cleansing and preprocessing, a machine learning approach is employed to establish a predictive model for malnutrition diagnosis in patients with acute abdomen based on the GLIM criteria.Ethics and dissemination This study has obtained ethical approval from the Ethics Committee of the Sichuan Academy of Medical Sciences and Sichuan Provincial People’s Hospital on 28 November 2022 (Yan-2022–442). The results of this study will be disseminated in peer-reviewed journals, at scientific conferences and directly to study participants.Trial registration number ChiCTR2200067044. |
| format | Article |
| id | doaj-art-be41817e62d14dc2a34bfd0468de06b1 |
| institution | DOAJ |
| issn | 2044-6055 |
| language | English |
| publishDate | 2024-03-01 |
| publisher | BMJ Publishing Group |
| record_format | Article |
| series | BMJ Open |
| spelling | doaj-art-be41817e62d14dc2a34bfd0468de06b12025-08-20T03:12:41ZengBMJ Publishing GroupBMJ Open2044-60552024-03-0114310.1136/bmjopen-2023-077734Artificial intelligence driven malnutrition diagnostic model for patients with acute abdomen based on GLIM criteria: a cross-sectional research protocolYu Wang0Lu Wang1Hua Jiang2Wei Ma3Bin Cai4Ming-Wei Sun5Charles Damien Lu62 Jinan University First Affiliated Hospital, Guangzhou, Guangdong, ChinaFriedman School of Nutrition Science and Policy, Tufts University, Boston, MA, USASchool of Medicine, Sichuan Provincial People`s Hospital, Sichuan Provincial Clinical Research Center for Emergency and Critical Care Medicine, University of Electronic Science and Technology of China, Chengdu, ChinaSchool of Medicine, Sichuan Provincial People`s Hospital, Department of Emergency Medicine, University of Electronic Science and Technology of China, Chengdu, ChinaSchool of Medicine and Life Sciences, Chengdu University of Traditional Chinese Medicine, Chengdu, ChinaSchool of Medicine, Sichuan Provincial People`s Hospital, Department of Emergency Medicine, University of Electronic Science and Technology of China, Chengdu, China1 Institute for Emergency and Disaster Medicine, Sichuan Academy of Medical Science, Sichuan Provincial People`s Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, ChinaBackground Patients with acute abdomen often experience reduced voluntary intake and a hypermetabolic process, leading to a high occurrence of malnutrition. The Global Leadership Initiative on Malnutrition (GLIM) criteria have rapidly developed into a principal methodological tool for nutritional diagnosis. Additionally, machine learning is emerging to establish artificial intelligent-enabled diagnostic models, but the accuracy and robustness need to be verified. We aimed to establish an intelligence-enabled malnutrition diagnosis model based on GLIM for patients with acute abdomen.Method This study is a single-centre, cross-sectional observational investigation into the prevalence of malnutrition in patients with acute abdomen using the GLIM criteria. Data collection occurs on the day of admission, at 3 and 7 days post-admission, including biochemical analysis, body composition indicators, disease severity scoring, nutritional risk screening, malnutrition diagnosis and nutritional support information. The occurrence rate of malnutrition in patients with acute abdomen is analysed with the GLIM criteria based on the Nutritional Risk Screening 2002 and the Mini Nutritional Assessment Short-Form to investigate the sensitivity and accuracy of the GLIM criteria. After data cleansing and preprocessing, a machine learning approach is employed to establish a predictive model for malnutrition diagnosis in patients with acute abdomen based on the GLIM criteria.Ethics and dissemination This study has obtained ethical approval from the Ethics Committee of the Sichuan Academy of Medical Sciences and Sichuan Provincial People’s Hospital on 28 November 2022 (Yan-2022–442). The results of this study will be disseminated in peer-reviewed journals, at scientific conferences and directly to study participants.Trial registration number ChiCTR2200067044.https://bmjopen.bmj.com/content/14/3/e077734.full |
| spellingShingle | Yu Wang Lu Wang Hua Jiang Wei Ma Bin Cai Ming-Wei Sun Charles Damien Lu Artificial intelligence driven malnutrition diagnostic model for patients with acute abdomen based on GLIM criteria: a cross-sectional research protocol BMJ Open |
| title | Artificial intelligence driven malnutrition diagnostic model for patients with acute abdomen based on GLIM criteria: a cross-sectional research protocol |
| title_full | Artificial intelligence driven malnutrition diagnostic model for patients with acute abdomen based on GLIM criteria: a cross-sectional research protocol |
| title_fullStr | Artificial intelligence driven malnutrition diagnostic model for patients with acute abdomen based on GLIM criteria: a cross-sectional research protocol |
| title_full_unstemmed | Artificial intelligence driven malnutrition diagnostic model for patients with acute abdomen based on GLIM criteria: a cross-sectional research protocol |
| title_short | Artificial intelligence driven malnutrition diagnostic model for patients with acute abdomen based on GLIM criteria: a cross-sectional research protocol |
| title_sort | artificial intelligence driven malnutrition diagnostic model for patients with acute abdomen based on glim criteria a cross sectional research protocol |
| url | https://bmjopen.bmj.com/content/14/3/e077734.full |
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