Establishing Malnutrition Diagnosis System by Using Artificial-intelligence Technology

NCT ID: NCT04776070

Last Updated: 2023-03-29

Basic Information

• Recruitment Status: COMPLETED
• Total Enrollment: 500 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2021-08-13
• Study Completion Date: 2022-05-21

Brief Summary

The prevalence of malnutrition is estimated at 30-50% of hospitalized patients in China. Disease-related malnutrition increases the risk of infection, mortality, length of hospitalization as well as the economic burden. National Nutrition Plan proposed to reduce malnutrition, but a clear, effective roadmap and protocol has not existed yet. Several factors impede to resolve the above challenges. They include :1) the low efficiency of current malnutrition diagnosis methods; 2) the lack of dynamic, standard method that can evaluate nutritional status in quantitative way. To this end, the investigators aim to establish an artificial-intelligence malnutrition diagnosis system to improve the application of malnutrition Clinical Pathway. Firstly, the investigators will establish a multidimensional malnutrition large data set, based on our previously built national hospital nutrition screening data set.

It will contain deep 3D facial images, semi-structured and structured electronic medical record. Then, the investigators will use ensemble learning algorithm to establish a fully automatic, artificial-intelligence malnutrition diagnosis model that includes both etiological and phenotypic diagnosis.

Conditions

Disease-related Malnutrition

Study Design

• Observational Model Type: CASE_ONLY
• Study Time Perspective: CROSS_SECTIONAL

Eligibility Criteria

Inclusion Criteria

• Adults (≥18 years old);
• Within 48 hours of admission;
• Inpatients at high risk of malnutrition, such as malignant tumors, chronic obstructive pulmonary disease, etc;
• Han nationality;
• Able to given informed consent.

Exclusion Criteria

• Patients with artificial facial changes (such as plastic surgery , head and neck radiotherapy , head and neck trauma);
• Diseases with special facial changes (such as acromegaly);
• High dose glucocorticoid users;
• Patients with facial edema;
• Emergency admission with an expected length of stay of less than 3 days;
• Other conditions researchers thought could not be included

• Minimum Eligible Age: 18 Years
• Maximum Eligible Age: 100 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

Sichuan Academy of Medical Sciences

OTHER

Sponsor Role: collaborator

Peking Union Medical College

OTHER

Sponsor Role: collaborator

Peking Union Medical College Hospital

OTHER

Sponsor Role: lead

Responsible Party

Responsibility Role: SPONSOR

Locations

Dongcheng district，Peking union medical college hospital

Beijing, Beijing Municipality, China

Countries

China

References

Sun MY, Wang Y, Zheng T, Wang X, Lin F, Zheng LY, Wang MY, Zhang PH, Chen LY, Yao Y, Sun J, Li ZN, Hu HY, Jiang H, Yue HY, Zhao Q, Wang HY, Han L, Ma X, Ji MT, Xu HX, Luo SY, Liu YH, Zhang Y, Han T, Li YS, Hou PP, Chen W. Health economic evaluation of an artificial intelligence (AI)-based rapid nutritional diagnostic system for hospitalised patients: A multicentre, randomised controlled trial. Clin Nutr. 2024 Oct;43(10):2327-2335. doi: 10.1016/j.clnu.2024.08.030. Epub 2024 Aug 30.

Reference Type: DERIVED

PMID: 39232261 (View on PubMed)

Other Identifiers

JS-2768

Identifier Type: -

Identifier Source: org_study_id