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

Changsha Medical University

Status: Completed

Conditions: Cancer, Carcinoma; Nutrition; Artificial Intelligence (AI)

Treatments: Other: group 1: Use AI to assist nutrition management; Other: group 2：Traditional nutrition management（through the hospital dietitian）

Study type: Interventional

Funder types: Other

Identifiers

NCT06603909

AI-nutrition to NPC patients

Details and patient eligibility

About

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.

Enrollment: 500 patients

Sex: All

Volunteers: No Healthy Volunteers

Inclusion criteria

• Malignant tumor patient

Exclusion criteria

• no

Trial design

Primary purpose: Health Services Research

Allocation: Non-Randomized

Interventional model: Parallel Assignment

Masking: None (Open label)

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500 participants in 3 patient groups

group 1: Use AI to assist nutrition management

Experimental group

Description:

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.

Treatment:

Other: group 1: Use AI to assist nutrition management

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

Experimental group

Description:

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

Treatment:

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

group 3: Do not adopt the two management methods.

No Intervention group

Description:

blank control group