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This study will collect patient PRO (physical strength, pain, defecation, appetite, weight, etc.) data through the APP, use corpus collection cards, facial photography and other technologies to collect PGHD characteristic phenotypes, and then combine artificial intelligence technology to train and cultivate agents (agents) to carry out joint offline routine follow-up of patients after radical pancreatic cancer resection to evaluate the feasibility of nutritional risk assessment intervention. Thus, the feasibility of artificial intelligence prediction of health status is verified, and an efficient follow-up tool and nutritional support evaluation plan are provided for the management of pancreatic cancer patients throughout the course of the disease, so as to improve the treatment prognosis and quality of life of pancreatic cancer patients.
This study is a prospective randomized controlled exploratory clinical trial to recruit 200 patients after radical pancreatic cancer resection.
Screening eligible subjects will be randomly assigned to the test group and the control group, and the randomization stratification factors include: age, gender, TNM stage, ECOG score, baseline BMI.
The experimental group and the control group received offline routine follow-up combined with PGHD-AI's APP management and routine outpatient follow-up, respectively, to compare the prediction of nutritional status risk and intervention response.
The study period is 1 year.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Intervention group | Experimental | Patients use the APP to check in daily and record PGHD data, combined with offline routine follow-up, nutritional assessment and intervention. |
|
| Control group | No Intervention | Patients received routine outpatient follow-up and nutritional assessment by investigator. |
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| PGHD's AI intelligent model(APP) | Other | To collect patient PRO (physical strength, pain, defecation, appetite, weight, etc.) data through the PGHD's AI intelligent model(APP), and use corpus collection cards, facial photography and other technologies to collect PGHD characteristic phenotypes. |
| Measure | Description | Time Frame |
|---|---|---|
| The incidence and/or improvement rate | 12 months after enrollment | |
| The progression free survival | 12 months after enrollment | |
| The overall survival time | 12 months after enrollment |
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Inclusion Criteria:
Exclusion Criteria:
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| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| Long Jiang, MD | Contact | 18017317460 | jiang.long@shgh.cn |
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| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Shanghai General Hospital | Recruiting | Shanghai | Shanghai Municipality | 200080 | China |
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|
| ID | Term |
|---|---|
| D010190 | Pancreatic Neoplasms |
| ID | Term |
|---|---|
| D004067 | Digestive System Neoplasms |
| D009371 | Neoplasms by Site |
| D009369 | Neoplasms |
| D004701 | Endocrine Gland Neoplasms |
| D004066 | Digestive System Diseases |
| D010182 | Pancreatic Diseases |
| D004700 | Endocrine System Diseases |
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