A Machine Learning-based Estimated Survival Model

May 22, 2024 updated by: Zhao Siyao

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

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.

Study Overview

Status

Active, not recruiting

Conditions

Detailed 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.

Study Type

Observational

Enrollment (Estimated)

1000

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Locations

  • China
    • Sichuan
      • Chengdu, Sichuan, China, 610041
        • Siyao Zhao

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • Adult
  • Older Adult

Accepts Healthy Volunteers

No

Sampling Method

Probability Sample

Study Population

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

Description

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.

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

Cohorts and Interventions

Group / Cohort
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.

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
A model
Time Frame: 2026-12-31
Build a survival estimation model for elderly late-stage cancer patients.
2026-12-31

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Zhao Siyao

Investigators

  • Principal Investigator: Siyao Zhao, postgraduate, West China Hospital

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

May 1, 2024

Primary Completion (Estimated)

December 31, 2025

Study Completion (Estimated)

December 31, 2026

Study Registration Dates

First Submitted

May 15, 2024

First Submitted That Met QC Criteria

May 22, 2024

First Posted (Actual)

May 29, 2024

Study Record Updates

Last Update Posted (Actual)

May 29, 2024

Last Update Submitted That Met QC Criteria

May 22, 2024

Last Verified

May 1, 2024

More Information

Terms related to this study

Other Study ID Numbers

  • 2024 Review (807)

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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