A Machine Learning-based Estimated Survival Model

Zhao Siyao

Status: Active, not recruiting

Conditions: Advanced Solid Tumor

Study type: Observational

Funder types: Other

Identifiers

NCT06432283

2024 Review (807)

Details and patient eligibility

About

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.

Full 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.

Enrollment: 1,000 estimated patients

Sex: All

Ages: 60+ years old

Volunteers: No Healthy Volunteers

Inclusion and exclusion criteria

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.

Trial design

1,000 participants in 1 patient group

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

Description:

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.