Disease-related MalnutritionNCT04776070
Establishing Malnutrition Diagnosis System by Using Artificial-intelligence Technology
An observational study of Disease-related Malnutrition, run by Peking Union Medical College Hospital at 1 site in China.
Trial 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. Read more about this study
- Who is it for?
- Ages 18 to 100 years
- Conditions the study is for
- Disease-related Malnutrition
- How many people
- 500 participants
- Where
- 1 siteChina
- Study dates
- 13 Aug 2021to 21 May 2022
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 to 100 years
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: Hospitalized patients in Peking Union Medical College
Who may be able to join
- 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.
Who cannot join
- 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
Trial locations and contacts
1 site in 1 country.
China1 site
Dongcheng district，Peking union medical college hospital
Beijing, Beijing Municipality, 100010
About this study
Official title: 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.
- Conditions
- Disease-related Malnutrition
What is being measured
The primary outcome is the main question the study is designed to answer.
malnutrition diagnosis
Using Global Leadership Initiative on Malnutrition(GLIM) to diagnose malnutrition among hospitalized patients
Measured: Within 48 hours of admission
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted1 Mar 2021
- Study start13 Aug 2021
- Primary completion21 Apr 2022
- Study completion21 May 2022
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (1)Papers the sponsor has linked to this study.
- Other trials for Disease-related MalnutritionStudies of the same condition, open and under way.
- More trials run by Peking Union Medical College HospitalThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Case only
- Time perspective
- Cross-sectional
- Enrolment
- 500 participants (actual)
Sponsor and investigators
- Lead sponsor
- Peking Union Medical College Hospital
- Sponsor type
- University, hospital or other
- Collaborators
- Sichuan Academy of Medical Sciences, Peking Union Medical College
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT04776070
- Sponsor's protocol ID
- JS-2768
- Status last verified
- Feb 2021
- First submitted
- 25 Feb 2021
- Last updated
- 29 Mar 2023
- Results posted
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
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
Study at a glance
From the registry record, last updated by the sponsor on 29 Mar 2023. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Establishing Malnutrition Diagnosis System by Using Artificial-intelligence Technology to Improve the Application of Clinical Pathway
- ClinicalTrials.gov ID
- NCT04776070
- Acronym / sponsor ID
- JS-2768
- Conditions
- Disease-related Malnutrition
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 500 participants (actual)
- Lead sponsor
- Peking Union Medical College HospitalUniversity, hospital or other
- Collaborators
- Sichuan Academy of Medical Sciences, Peking Union Medical College
- Study start
- 13 Aug 2021 (actual)
- Primary completion
- 21 Apr 2022 (actual)
- Study completion
- 21 May 2022 (actual)
- First posted
- 1 Mar 2021 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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 design
- Observational model
- Case only
- Time perspective
- Cross-sectional
Who may qualify
- Ages
- 18 to 100 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- Hospitalized patients in Peking Union Medical College
Key inclusion criteria (5 listed)
- 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.
Key exclusion criteria (6 listed)
- 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;
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
malnutrition diagnosis
Time frame: Within 48 hours of admission
Using Global Leadership Initiative on Malnutrition(GLIM) to diagnose malnutrition among hospitalized patients
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 29 Mar 2023.
- Derived from this study: PubMed 39232261Sun 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.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
The record lists no contact. Use the official record to reach the sponsor.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Disease-related Malnutrition, run by Peking Union Medical College Hospital at 1 site in China.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 5 inclusion and 6 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 29 Mar 2023. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in China. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
malnutrition diagnosis (time frame: Within 48 hours of admission). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT04776070, which also lists 1 linked publication.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 29 Mar 2023.