Establishing Malnutrition Diagnosis System by Using Artificial-intelligence Technology

NCT04776070 | Completed

• 1 other identifier: JS-2768
• Study Type: observational
• Target: 500
• Locations: 1 country
• Sites: 1

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

Outcome Measures

Primary Outcomes (1)

• malnutrition diagnosis

  Using Global Leadership Initiative on Malnutrition(GLIM) to diagnose malnutrition among hospitalized patients

  Within 48 hours of admission

Eligibility Criteria

• Age: 18 Years - 100 Years
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Hospitalized patients in Peking Union Medical College

You may qualify if:

• 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.

You may not qualify if:

• 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

Sponsors & Collaborators

• Peking Union Medical College Hospital: lead
• Sichuan Academy of Medical Sciences: collaborator
• Peking Union Medical College: collaborator

Study Sites (1)

Dongcheng district，Peking union medical college hospital

Beijing, Beijing Municipality, 100010, China

Location

Related Publications (1)

• 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.

  PMID: 39232261 DERIVED

Study Design

• Study Type: observational
• Observational Model: CASE ONLY
• Time Perspective: CROSS SECTIONAL
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: February 25, 2021
• First Posted: March 1, 2021
• Study Start: August 13, 2021
• Primary Completion: April 21, 2022
• Study Completion: May 21, 2022
• Last Updated: March 29, 2023

Record last verified: 2021-02

Locations

CN (1)