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Establishing a Competing Risk Regression Nomogram Model for Survival Data
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Trinomial decompression sickness model using full, marginal, and non-event outcomes.

Amy E King1, Nicholas R Andriano1, Laurens E Howle2

  • 1Department of Mechanical Engineering and Materials Science, Duke University, Durham, NC, USA.

Computers in Biology and Medicine
|March 17, 2020
PubMed
Summary
This summary is machine-generated.

This study developed a new probabilistic model to predict decompression sickness (DCS) risk in divers, improving safety by analyzing full, marginal, and no DCS outcomes. The LE1 model offers a better understanding of decompression schedules than previous methods.

Keywords:
Decompression illnessDecompression sicknessModelingProbabilitySeverity

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Area of Science:

  • Physiology
  • Diving Medicine
  • Biostatistics

Background:

  • Decompression sickness (DCS) risk assessment is crucial for safe diving and altitude exposure.
  • Current models often simplify DCS outcomes, limiting predictive accuracy.

Purpose of the Study:

  • To develop a probabilistic model for trinomial DCS outcomes (full, marginal, no DCS).
  • To improve the understanding and prediction of DCS risk from dive exposures.

Main Methods:

  • Optimized six exponential-exponential (EE) and linear-exponential (LE) decompression models.
  • Utilized data from 3322 exposures in the BIG292 empirical human dive trial.
  • Employed log likelihood difference tests to determine the best model fit.

Main Results:

  • The LE1 trinomial marginal model demonstrated the best fit for the dive outcome data.
  • This model treats full and marginal DCS as distinct, weighted events, enhancing analysis.
  • It expands upon binomial models by providing a more nuanced approach to DCS outcomes.

Conclusions:

  • The LE1 trinomial marginal model offers improved insights into decompression schedules.
  • This probabilistic approach can enhance the safety of diving and altitude exposure protocols.
  • Future research may explore tetranomial models for even finer DCS outcome categorization.