Health Outcomes of Nasopharyngeal Carcinoma Patients Three Years After Treatment by the AI-assisted Home Enteral Nutrition Management

NCT06603909 | Completed

• 1 other identifier: AI-nutrition to NPC patients
• Study Type: interventional
• Target: 500
• Locations: 1 country
• Sites: 1

Brief Summary

You have completed the current stage of cancer treatment, after which you need to check regularly and pay attention to nutrition. So we started this study to see if AI could help with nutrition management. Main options: Option 1: Use AI to assist nutrition management; Option 2: Contact the nutritionist team of the hospital for nutrition management according to your own situation. Option 3: Do not adopt the first two management methods. Special Statement: Please choose; Cancer screening data is used for statistical analysis, but does not reveal any personal privacy.

Conditions

Cancer, Carcinoma; Artificial Intelligence (AI); Nutrition

Outcome Measures

Primary Outcomes (2)

• Survival Rate

  Survival Rate

  three years after treatment

• Local Recurrence Rate

  Local Recurrence Rate

  three years after treatment

Secondary Outcomes (2)

• DNA positive rate of Epstein-Barr(EB) virus

  three years after treatment

• Body mass index

  three years after treatment

Study Arms (3)

group 1: Use AI to assist nutrition management

EXPERIMENTAL

The first step: Preliminary clinical nutrition screening and evaluation. Information of patients' age, stage of nasopharyngeal cancer, treatment stage (radiotherapy, chemotherapy stage) and other information were collected, and the management plan was determined. The second step: AI-assisted HEN management mode. Regular nutritional monitoring and follow-up of patients were conducted by means of intelligent computer, intelligent App body fat device and mobile communication network collection, and basic signs, nutritional status, nutritional risks and implementation of support programs of patients were managed. Nutritional analysis model and index model are used to start the intelligent daily monitoring management and acute attack early warning mechanism. The third step: Monitor and alert. The AI system popularized the basic knowledge of nutrition to patients through the App platform.

Other: group 1: Use AI to assist nutrition management

group 2: Contact the nutritionist team of the hospital for nutrition management

EXPERIMENTAL

Contact the nutritionist team of the hospital for nutrition management according to patients' own situation

Other: group 2：Traditional nutrition management（through the hospital dietitian）

group 3: Do not adopt the two management methods.

NO INTERVENTION

blank control group

Interventions

group 1: Use AI to assist nutrition management OTHER

group1：AI-assisted nutrition management. group 2：Traditional nutrition management（through the hospital dietitian）.group 3:control.

group 1: Use AI to assist nutrition management

group 2：Traditional nutrition management（through the hospital dietitian） OTHER

group 2：Traditional nutrition management（through the hospital dietitian）

group 2: Contact the nutritionist team of the hospital for nutrition management

Eligibility Criteria

• Sex: all
• Healthy Volunteers: No
• Age Groups: Child (0-17), Adult (18-64), Older Adult (65+)

You may qualify if:

• Malignant tumor patient

Sponsors & Collaborators

• Changsha Medical University: lead

Study Sites (1)

First Affiliated Hospital of Zhengzhou University

Zhengzhou, China

Location

Related Publications (5)

• Kulkarni S, Seneviratne N, Baig MS, Khan AHA. Artificial Intelligence in Medicine: Where Are We Now? Acad Radiol. 2020 Jan;27(1):62-70. doi: 10.1016/j.acra.2019.10.001. Epub 2019 Oct 19.

  PMID: 31636002 BACKGROUND

• Mundi MS, Mohamed Elfadil O, Olson DA, Pattinson AK, Epp LM, Miller LD, Seegmiller SL, Schneckloth JM, Baker MR, Abdelmagid MG, Patel A, Wescott BA, Elder LS, Hagenbrock MC, Sefried LE, Hurt RT. Home enteral nutrition: A descriptive study. JPEN J Parenter Enteral Nutr. 2023 May;47(4):550-562. doi: 10.1002/jpen.2498. Epub 2023 Apr 12.

  PMID: 36912121 BACKGROUND

• Bischoff SC, Austin P, Boeykens K, Chourdakis M, Cuerda C, Jonkers-Schuitema C, Lichota M, Nyulasi I, Schneider SM, Stanga Z, Pironi L. ESPEN practical guideline: Home enteral nutrition. Clin Nutr. 2022 Feb;41(2):468-488. doi: 10.1016/j.clnu.2021.10.018. Epub 2021 Nov 24.

  PMID: 35007816 BACKGROUND

• Muscaritoli M, Arends J, Bachmann P, Baracos V, Barthelemy N, Bertz H, Bozzetti F, Hutterer E, Isenring E, Kaasa S, Krznaric Z, Laird B, Larsson M, Laviano A, Muhlebach S, Oldervoll L, Ravasco P, Solheim TS, Strasser F, de van der Schueren M, Preiser JC, Bischoff SC. ESPEN practical guideline: Clinical Nutrition in cancer. Clin Nutr. 2021 May;40(5):2898-2913. doi: 10.1016/j.clnu.2021.02.005. Epub 2021 Mar 15.

  PMID: 33946039 BACKGROUND

• Liu J, Wang X, Ye X, Chen D. Improved health outcomes of nasopharyngeal carcinoma patients 3 years after treatment by the AI-assisted home enteral nutrition management. Front Nutr. 2025 Jan 7;11:1481073. doi: 10.3389/fnut.2024.1481073. eCollection 2024.

  PMID: 39839291 DERIVED

MeSH Terms

Conditions

Neoplasms; Carcinoma

Condition Hierarchy (Ancestors)

Neoplasms, Glandular and Epithelial; Neoplasms by Histologic Type

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: NON RANDOMIZED
• Masking: NONE
• Purpose: HEALTH SERVICES RESEARCH
• Intervention Model: PARALLEL
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Doctor

Study Record Dates

• First Submitted: September 11, 2024
• First Posted: September 19, 2024
• Study Start: January 1, 2021
• Primary Completion: June 30, 2024
• Study Completion: June 30, 2024
• Last Updated: September 19, 2024

Record last verified: 2024-09

Data Sharing

• IPD Sharing: Will share
• Shared Documents: STUDY PROTOCOL

Locations

CN (1)