Establishing Malnutrition Diagnosis System by Using Artificial-intelligence Technology

Establishing Malnutrition Diagnosis System by Using Artificial-intelligence Technology to Improve the Application of Clinical Pathway

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.

Study Overview

• Status: Completed
• Conditions: Disease-related Malnutrition

• Study Type: Observational
• Enrollment (Actual): 500

Contacts and Locations

• Study Contact: Name: Wei Chen; Phone Number: +8669154095; Email: chenw@pumch.cn

Study Locations

• China
  • Beijing
    • Beijing, Beijing, China, 100010
      • Dongcheng district，Peking union medical college hospital

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 18 years to 100 years (Adult, Older Adult)
• Accepts Healthy Volunteers: No
• Genders Eligible for Study: All

• Sampling Method: Non-Probability Sample

Study Population

Hospitalized patients in Peking Union Medical College

Description

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

Study Plan

How is the study designed?

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
malnutrition diagnosis	Using Global Leadership Initiative on Malnutrition(GLIM) to diagnose malnutrition among hospitalized patients	Within 48 hours of admission

Collaborators and Investigators

• Sponsor: Peking Union Medical College Hospital
• Collaborators: Peking Union Medical College; Sichuan Academy of Medical Sciences

Study record dates

Study Major Dates

• Study Start (Actual): August 13, 2021
• Primary Completion (Actual): April 21, 2022
• Study Completion (Actual): May 21, 2022

Study Registration Dates

• First Submitted: February 25, 2021
• First Submitted That Met QC Criteria: February 25, 2021
• First Posted (Actual): March 1, 2021

Study Record Updates

• Last Update Posted (Actual): March 29, 2023
• Last Update Submitted That Met QC Criteria: March 28, 2023
• Last Verified: February 1, 2021

More Information

Terms related to this study

• Additional Relevant MeSH Terms: Nutrition Disorders; Malnutrition
• Other Study ID Numbers: JS-2768

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No