Ignite Creation Date:
2024-06-16 @ 11:49 AM
Last Modification Date:
2024-10-26 @ 3:30 PM
Study NCT ID:
NCT06432283
Status:
ACTIVE_NOT_RECRUITING
Last Update Posted:
2024-05-29
First Post:
2024-05-15
Brief Title:
A Machine Learning-based Estimated Survival Model
Sponsor:
Zhao Siyao
Organization:
West China Hospital
Study Overview
Official Title:
Construction and Validation of a Machine Learning-based Estimated Survival Model for Elderly Patients With Advanced Malignancy
Status:
ACTIVE_NOT_RECRUITING
Status Verified Date:
2024-05
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
False
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
None
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
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
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 Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
False
Is a FDA Regulated Device?:
False
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?:
None