Comprehensive Prognostic Model: The Study Introduces a Novel Prognostic Model That Incorporates a Wide Range of Clinical and Laboratory Variables, Providing a More Holistic Approach to Predicting Overall Survival in HCC Patients

NCT06550739 | Completed DMC

• 1 other identifier: CZLS2023343-A
• Study Type: observational
• Target: 1,053
• Locations: 1 country
• Sites: 1

Brief Summary

Background: Hepatocellular carcinoma (HCC) is a significant health problem in China, with high incidence and mortality rates. This study aimed to identify the prognostic factors of elderly HCC patients in southwest China and construct a new prognostic model for predicting overall survival (OS). Methods: This retrospective cohort study collected clinical data from 958 patients with liver cancer on January 1, 2019, and December 12, 2020. The Cox regression model was used to test the significance of all available variables as prognostic factors of OS. Independent prognostic factors were identified based on multivariable analysis to model nomograms. The concordance index (C-index), the area under the receiver operating characteristic (AUC), the Time-dependent C-index, the Time-dependent AUC, the calibration curve, and decision curve analysis (DCA) were measured to assess the model performance of the nomogram. Conclusions: The comprehensive risk prediction model for OS in HCC patients developed through this retrospective cohort study offers a promising avenue for improving clinical outcomes and patient care.

Conditions

Hepatocellular Carcinoma

Outcome Measures

Primary Outcomes (1)

• Survival status

  Patient survival or death

  April 31, 2024

Study Arms (2)

training set

Patients were randomly divided into two groups: a training group of 671 individuals (approximately 70% of the data) and a validation group of 287 individuals (approximately 30% of the data).The nomogram model was developed using the training cohort.

Other: Randomization

validation set

Validate the effectiveness of the model

Other: Randomization

Interventions

Randomization OTHER

Randomly divide the data into a training set and a validation set in a 7:3 ratio

training set; validation set

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

The data used in the retrospective cohort study were obtained from 958 patients with HCC from the Chongqing University Cancer Hospital tumor database between Jan 1, 2019, and Dec 12, 2020.

You may qualify if:

• (1) age ≥ 18 years; (2) had pathologically confirmed primary liver cancer; (3) received the main treatment in our hospital; (4) provided baseline clinical information and follow-up information; (5) completed the entire course of radiotherapy, chemotherapy and targeted therapy.

You may not qualify if:

• no follow-up records and a history of cancer treatments.

Sponsors & Collaborators

• Haike Lei: lead

Study Sites (1)

Chongqing University Cancer Hospital

Chongqing, China

Location

MeSH Terms

Conditions

Carcinoma, Hepatocellular

Interventions

Random Allocation

Condition Hierarchy (Ancestors)

Adenocarcinoma; Carcinoma; Neoplasms, Glandular and Epithelial; Neoplasms by Histologic Type; Neoplasms; Liver Neoplasms; Digestive System Neoplasms; Neoplasms by Site; Digestive System Diseases; Liver Diseases

Intervention Hierarchy (Ancestors)

Epidemiologic Research Design; Epidemiologic Methods; Investigative Techniques; Research Design; Methods; Health Care Evaluation Mechanisms; Quality of Health Care; Health Care Quality, Access, and Evaluation; Public Health; Environment and Public Health

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR INVESTIGATOR
• PI Title: Director

Study Record Dates

• First Submitted: August 7, 2024
• First Posted: August 13, 2024
• Study Start: January 1, 2019
• Primary Completion: December 31, 2020
• Study Completion: April 30, 2024
• Last Updated: August 13, 2024

Record last verified: 2024-08

Data Sharing

• IPD Sharing: Will not share

Locations

CN (1)