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CompletedNCT04776070Updated Mar 29, 2023

Establishing Malnutrition Diagnosis System by Using Artificial-intelligence Technology

An observational study in Disease-related Malnutrition, sponsored by Peking Union Medical College Hospital. Completed at 1 site in China. Open to participants aged 18 Years to 100 Years. Per ClinicalTrials.gov, last updated 2023-03-29.

Sponsored by Peking Union Medical College Hospital · Observational

Study type
Observational
Model
Case-only
Time perspective
Cross-sectional
Enrollment
500
Ages
18 Years to 100 Years
Sex
All
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Study 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.

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Conditions studied

  • Disease-related Malnutrition
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In context

Lead sponsor

Peking Union Medical College Hospital is the lead sponsor of 1,148 studies on the registry; 473 are open to participants now.

Of its 5 completed or terminated interventional studies of FDA-regulated products, 0 (0%) have results posted.

Counted across the registry records on this site, refreshed daily.

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Who can participate

Ages eligible
18 Years to 100 Years
Sexes eligible
All
Accepts healthy volunteers
No
Sampling method
Non-probability sample

Study population

Hospitalized patients in Peking Union Medical College

Go through the criteria one by one — yes, no or not sure — and take the list to your doctor or the study team. Kept in this browser only.

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

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
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Study design

Observational model
Case-only
Time perspective
Cross-sectional
Enrollment
500 participants (actual)
Patient registry
No
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What researchers measure

Primary outcomes

  1. malnutrition diagnosis

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

    Time frame: Within 48 hours of admission

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Study locations

1 site
  • Dongcheng district,Peking union medical college hospital
    Beijing, Beijing Municipality 100010, China
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References and documents

Publications

  • 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. PubMed 39232261 ↗
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Updates

Tracking since Sep 25, 2026
No changes since tracking began. The registry record was last updated on Mar 29, 2023, before this site started recording changes on Sep 25, 2026. Its history is on ClinicalTrials.gov ↗
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Registry details

Key details

Study ID
NCT04776070
Collaborators
Sichuan Academy of Medical Sciences, Peking Union Medical College
Responsible party
Sponsor
First posted
Mar 1, 2021
Start date
Aug 13, 2021
Primary completion
Apr 21, 2022
Completion
May 21, 2022
Last update
Mar 29, 2023

Oversight

FDA-regulated drug
No
FDA-regulated device
No
View the source record on ClinicalTrials.gov ↗

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This study is completed, as verified in Feb 2021. You cannot join it, but the record below documents what was studied.

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