A Machine Learning-based Estimated Survival Model

NCT06432283 | Active Not Recruiting

• 1 other identifier: 2024 Review (807)
• Study Type: observational
• Target: 1,000
• Locations: 1 country
• Sites: 1

Brief 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.

Conditions

Advanced Solid Tumor

Outcome Measures

Primary Outcomes (1)

• A model

  Build a survival estimation model for elderly late-stage cancer patients.

  2026-12-31

Study Arms (1)

advanced cancer (stage III and IV) patients aged 60 years and above.

advanced cancer (stage III and IV) patients aged 60 years and above who are receiving treatment at the mentioned institution. The research subjects voluntarily participate and sign informed consent forms.

Eligibility Criteria

• Age: 60 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Probability Sample

Study Population

Advanced cancer （stage III and IV） malignant tumor patients aged 60 years and above.

You may qualify if:

• Clinical diagnosis of advanced malignant tumor: TNM stage III or IV
• "Surprise question": If this patient were to die within the next 6 months, it would not be surprising to you.
• Karnofsky performance status (KPS) score ≤ 50
• Palliative Performance Scale (PPS) ≤ 50%

You may not qualify if:

• Patients who refuse to participate in the study;
• Patients who, for various reasons, are unable to cooperate and complete the questionnaire survey;
• Patients who, for various reasons, are unable to cooperate and complete the follow-up.

Sponsors & Collaborators

• Zhao Siyao: lead

Study Sites (1)

Siyao Zhao

Chengdu, Sichuan, 610041, China

Location

Study Officials

• Siyao Zhao, postgraduate

  West China Hospital

  PRINCIPAL INVESTIGATOR

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR INVESTIGATOR
• PI Title: Sponser-Investigator

Study Record Dates

• First Submitted: May 15, 2024
• First Posted: May 29, 2024
• Study Start: May 1, 2024
• Primary Completion: December 31, 2025
• Study Completion (Estimated): December 31, 2026
• Last Updated: May 29, 2024

Record last verified: 2024-05

Locations

CN (1)