A Machine Learning-based Estimated Survival Model

Advanced Solid Tumor Clinical Trial

Official title:

Construction and Validation of a Machine Learning-based Estimated Survival Model for Elderly Patients With Advanced Malignancy

Clinical Trial Details — Status: Active, not recruiting

Administrative data

• NCT number: NCT06432283
• Other study ID #: 2024 Review (807)
• Status: Active, not recruiting
• Start date: May 1, 2024
• Est. completion date: December 31, 2026

Study information

• Verified date: May 2024
• Source: West China Hospital
• Contact: n/a
• Is FDA regulated: No
• Study type: Observational

Clinical Trial Summary

Malignant tumors are the leading cause of death in elderly patients, and palliative care can improve the quality of life for elderly advanced cancer patients. One of the main reasons why these patients are not included in palliative care is the lack of accurate estimation of their survival period by patients, family members, and doctors. Both doctors and patients tend to be overly optimistic about the survival period of elderly advanced cancer patients, leading to overtreatment. Therefore, assessing the risk of death for these patients and further establishing a survival period estimation model can improve the accuracy of doctors' clinical predictions of patient survival, facilitate early referral to palliative care, and promote rationalization of medical decision-making.

Description:

1. By searching the literature, conducting systematic reviews, and meta-analyses, we aim to uncover the prognostic factors related to death in elderly advanced cancer patients.

2. Based on evidence-based data and considering the clinical conditions of elderly advanced cancer patients in China, we will establish relevant entries for a risk assessment scale for death in elderly advanced cancer patients. By using the Delphi expert consultation evaluation method, we will finalize the assessment scale framework, laying the theoretical foundation for the establishment and validation of a death risk prediction model for elderly advanced cancer patients in China.

3. Develop a survival estimation model for elderly advanced cancer patients; through metabolomics studies and other research methods, we will investigate metabolic biomarkers related to predicting the survival period of elderly advanced cancer patients.

Recruitment information / eligibility

• Status: Active, not recruiting
• Enrollment: 1000
• Est. completion date: December 31, 2026
• Est. primary completion date: December 31, 2025
• Accepts healthy volunteers: No
• Gender: All
• Age group: 60 Years and older

Eligibility

Inclusion Criteria:Inclusion criteria for late-stage malignant tumor patients: Must meet Condition 1) and also meet either Condition 2), 3), or 4):

1. Clinical diagnosis of advanced malignant tumor: TNM stage III or IV

2. "Surprise question": If this patient were to die within the next 6 months, it would not be surprising to you.

3. Karnofsky performance status (KPS) score = 50

4. Palliative Performance Scale (PPS) = 50%

Exclusion Criteria:

1. Patients who refuse to participate in the study;

2. Patients who, for various reasons, are unable to cooperate and complete the questionnaire survey;

3. Patients who, for various reasons, are unable to cooperate and complete the follow-up.

Related Conditions & MeSH terms

• Advanced Solid Tumor

Locations

Country	Name	City	State
China	Siyao Zhao	Chengdu	Sichuan

Sponsors (1)

Lead Sponsor	Collaborator
Zhao Siyao

Country where clinical trial is conducted

China, 

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	A model	Build a survival estimation model for elderly late-stage cancer patients.	2026-12-31