Predicting mortality from burns: the need for age-group specific models

Sandra L Taylor1, MaryBeth Lawless2, Terese Curri1

  • 1University of California Davis, Sacramento, CA, United States.

Insights

Age-specific models for burn mortality are more accurate than all-ages models, especially for children and seniors. Burn outcomes and mortality risk vary significantly by age group.

Area of Science:

  • Trauma surgery
  • Burn care research
  • Biostatistics in medicine

Background:

  • Traditional burn mortality prediction models often use aggregated age data.
  • Existing models may not accurately reflect age-specific variations in burn injury outcomes.
  • Hypothesis: Age significantly influences burn mortality, necessitating age-specific predictive models.

Purpose of the Study:

  • To develop and compare age-specific burn mortality models (children, adults, seniors) against an all-ages model.
  • To assess the predictive accuracy of these models using data from the National Burn Repository.
  • To identify how age, burn size (TBSA), and inhalation injury impact mortality across different age groups.

Main Methods:

  • Utilized data from the American Burn Association National Burn Repository (2000-2009).
  • Employed mixed-effect logistic regression to build mortality models for all ages and specific age groups (<18, 18-60, >60).
  • Evaluated model performance using the area under the receiver operating curve (AUC).

Main Results:

  • Overall mortality was 4%, with significant variation by age (seniors 17%, children <1%).
  • Age, TBSA burn, and inhalation injury were significant predictors across all models.
  • Age-specific models showed distinct differences, particularly for children and seniors, with seniors exhibiting higher mortality risk per age and burn size increase.

Conclusions:

  • A 'one-size-fits-all' approach to burn mortality prediction is inadequate.
  • Age-specific models, especially for pediatric and geriatric populations, offer improved accuracy.
  • Recommendations for age-tailored burn outcome prediction models are warranted.

Related Concept Videos

Actuarial Approach01:20

Actuarial Approach

The actuarial approach, a statistical method originally developed for life insurance risk assessment, is widely used to calculate survival rates in clinical and population studies. This method accounts for participants lost to follow-up or those who die from causes unrelated to the study, ensuring a more accurate representation of survival probabilities.
Consider the example of a high-risk surgical procedure with significant early-stage mortality. A two-year clinical study is conducted,...
384
Burn Injuries01:22

Burn Injuries

Burn injuries occur when the skin and underlying tissues are damaged due to exposure to heat, electricity, chemicals, radiation, or friction. They can vary in severity, from minor superficial burns to severe deep burns that can be life-threatening.
The damage results in the death of skin cells, which can lead to a massive loss of fluid. Dehydration, electrolyte imbalance, and renal and circulatory failure follow, which can be fatal. Burn patients are treated with intravenous fluids to offset...
4.4K
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...
930
Parametric Survival Analysis: Weibull and Exponential Methods01:14

Parametric Survival Analysis: Weibull and Exponential Methods

Parametric survival analysis models survival data by assuming a specific probability distribution for the time until an event occurs. The Weibull and exponential distributions are two of the most commonly used methods in this context, due to their versatility and relatively straightforward application.
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...
1.3K
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.
491
Life Tables01:22

Life Tables

A life table is a statistical tool that summarizes the mortality and survival patterns of a population, providing detailed insights into the likelihood of survival or death across different age intervals within a cohort. By organizing data on survival probabilities and mortality rates, life tables offer a clear snapshot of population dynamics over time. They are extensively used in demography, public health, actuarial science, and ecology to analyze life expectancy, design health interventions,...
669