Survival prediction in patients with chronic limb-threatening ischemia who undergo infrainguinal revascularization

Jessica P Simons1, Andres Schanzer1, Julie M Flahive1

  • 1Division of Vascular and Endovascular Surgery, University of Massachusetts Medical School, Worcester, Mass.

Insights

Validated survival models for chronic limb-threatening ischemia (CLTI) patients are lacking. New models predict survival accurately, aiding clinical decision-making for revascularization strategies.

Area of Science:

  • Vascular Surgery
  • Cardiovascular Medicine
  • Health Outcomes Research

Background:

  • Accurate survival prediction is crucial for managing patients with chronic limb-threatening ischemia (CLTI).
  • Existing survival models for CLTI patients lack validation.
  • The Bypass versus Angioplasty in Severe Ischaemia of the Leg (BASIL) trial highlighted long-term benefits of bypass over endovascular intervention.

Purpose of the Study:

  • To develop and validate predictive survival models for patients with CLTI undergoing revascularization.
  • To stratify CLTI patients into distinct risk groups (low, medium, high) based on predicted survival.
  • To inform evidence-based revascularization recommendations aligned with current guidelines.

Main Methods:

  • Utilized the Vascular Quality Initiative database (2003-2017) for patients with CLTI undergoing infrainguinal bypass or endovascular intervention.
  • Developed Cox survival models using only preoperative variables to predict survival at 30 days, 2 years, and 5 years.
  • Defined risk groups based on predicted 30-day and 2-year survival rates.

Main Results:

  • Analysis included 38,470 CLTI patients; 63% received endovascular intervention, 37% infrainguinal bypass.
  • Overall survival rates were 98% (30 days), 81% (2 years), and 69% (5 years).
  • Identified independent predictors of mortality including advanced age, COPD, stage 5 CKD, and bedbound status. Procedure type did not significantly impact survival predictions (C-indices: 0.76, 0.72, 0.71 for 30-day, 2-year, 5-year models).

Conclusions:

  • Developed survival prediction models for CLTI patients demonstrate good performance and require external validation.
  • Most CLTI patients undergoing revascularization are at average risk and predicted to survive beyond two years.
  • These models effectively stratify patients, supporting evidence-based revascularization choices.
Abstract

Related Concept Videos

Predicting Molecular Geometry02:27

Predicting Molecular Geometry

VSEPR Theory for Determination of Electron Pair Geometries
45.5K
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...
407
Survival Curves01:18

Survival Curves

Survival curves are graphical representations that depict the survival experience of a population over time, offering an intuitive way to track the proportion of individuals who remain event-free at each time point. These curves are widely used in fields such as medicine, public health, and reliability engineering to visualize and compare survival probabilities across different groups or conditions.
The Kaplan-Meier estimator is the most common method for constructing survival curves. This...
684
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...
766
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...
579
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...
599