Related Experiment Video

Updated: May 8, 2026

Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

Within-patient temporal variance in MELD score and impact on survival prediction after TIPS creation

Ron C Gaba1, Kruti D Shah, Patrick M Couture

  • 1Department of Radiology, Section of Interventional Radiology.

Annals of Hepatology
|September 11, 2013
PubMed
Abstract

Insights

Model for End Stage Liver Disease (MELD) scores exhibit significant temporal variability in patients undergoing transjugular intrahepatic portosystemic shunt (TIPS) procedures. Scores obtained on the day of TIPS best predict patient mortality risk.

Area of Science:

  • Hepatology
  • Interventional Radiology
  • Medical Statistics

Background:

  • Assessing temporal variability in Model for End Stage Liver Disease (MELD) scores is crucial for accurate prognostication after transjugular intrahepatic portosystemic shunt (TIPS) creation.
  • Understanding MELD score fluctuations pre-TIPS can refine patient selection and risk stratification.

Purpose of the Study:

  • To evaluate the within-patient temporal variability of MELD scores prior to TIPS.
  • To determine the impact of MELD score variability on predicting 30- and 90-day post-TIPS clinical outcomes.

Main Methods:

  • Retrospective analysis of MELD scores from 68 patients at four pre-TIPS time points (1, 2-6, 7-14, 15-35 days).
  • Repeated measures analysis of variance used to assess score variability.
  • Area under the receiver operating characteristic (AUROC) curves compared to evaluate predictive accuracy of MELD scores at different time points for mortality.

Main Results:

  • Significant within-subject variance in pre-TIPS MELD scores (approaching ±18.5%, P=0.032).
  • Higher MELD scores exhibited greater sequential variability.
  • MELD scores obtained on the day of TIPS demonstrated superior predictive capacity for 30-day (AUROC=0.876) and 90-day (AUROC=0.805) mortality compared to earlier scores.

Conclusions:

  • MELD scores display significant within-patient variability over time.
  • MELD scores calculated on the day of TIPS are the most accurate predictors of post-TIPS mortality.
  • Utilizing same-day MELD scores is recommended for optimal patient selection and counseling before TIPS.

More Related Videos

Establishing a Competing Risk Regression Nomogram Model for Survival Data04:57

Establishing a Competing Risk Regression Nomogram Model for Survival Data

Published on: October 23, 2020

Related Concept Videos

Survival Tree01:19

Survival Tree

Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a survival tree begins...
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and Cox...
Truncation in Survival Analysis01:09

Truncation in Survival Analysis

Truncation in survival analysis refers to the exclusion of individuals or events from the dataset based on specific criteria related to the time of the event. This exclusion can happen in two primary forms: left truncation and right truncation.
Left truncation occurs when individuals who experienced the event of interest before a certain time are not included in the study. This is often due to a "delayed entry" into the study where only those who survive until a certain entry point are observed.
Cancer Survival Analysis01:21

Cancer Survival Analysis

Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...
Assumptions of Survival Analysis01:15

Assumptions of Survival Analysis

Survival models analyze the time until one or more events occur, such as death in biological organisms or failure in mechanical systems. These models are widely used across fields like medicine, biology, engineering, and public health to study time-to-event phenomena. To ensure accurate results, survival analysis relies on key assumptions and careful study design.
Introduction To Survival Analysis01:18

Introduction To Survival Analysis

Survival analysis is a statistical method used to study time-to-event data, where the "event" might represent outcomes like death, disease relapse, system failure, or recovery. A unique feature of survival data is censoring, which occurs when the event of interest has not been observed for some individuals during the study period. This requires specialized techniques to handle incomplete data effectively.
The primary goal of survival analysis is to estimate survival time—the time until a...