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

Haike Lei

Status: Completed

Conditions: Hepatocellular Carcinoma

Treatments: Other: Randomization

Study type: Observational

Funder types: Other

Identifiers

NCT06550739

CZLS2023343-A

Details and patient eligibility

About

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.

Enrollment: 1,053 patients

Sex: All

Ages: 18+ years old

Volunteers: No Healthy Volunteers

Inclusion criteria

• (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.

Exclusion criteria

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

Trial design

1,053 participants in 2 patient groups

training set

Description:

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.

Treatment:

Other: Randomization

validation set

Description:

Validate the effectiveness of the model

Treatment:

Other: Randomization