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Related Experiment Video

Updated: Mar 6, 2026

Surfactant Depletion Combined with Injurious Ventilation Results in a Reproducible Model of the Acute Respiratory Distress Syndrome ARDS
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The probability and severity of decompression sickness.

Laurens E Howle1,2,3, Paul W Weber1,3, Ethan A Hada2

  • 1Department of Mechanical Engineering and Materials Science, Hudson Hall, Research Drive, Duke University, Durham, NC United States of America.

Plos One
|March 16, 2017
PubMed
Summary
This summary is machine-generated.

This study developed a new probabilistic model for decompression sickness (DCS) in divers. The model better predicts mild and serious DCS, potentially adjusting dive limits for improved safety.

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Area of Science:

  • Hyperbaric Medicine and Physiology
  • Risk Assessment and Predictive Modeling

Background:

  • Decompression sickness (DCS) is a risk for individuals exposed to pressurized environments, including scuba divers and aviators.
  • Current risk assessment models for DCS may not fully capture the nuances of mild versus serious manifestations.
  • Existing U.S. Navy guidelines set specific probability limits for mild and serious DCS.

Purpose of the Study:

  • To develop and validate a hierarchical trinomial probabilistic model for predicting both mild and serious DCS simultaneously.
  • To compare the predictive accuracy of the trinomial model against traditional binomial models using different DCS severity classifications.
  • To evaluate the impact of revised DCS risk probabilities on allowable no-decompression limits (NDLs) for air dives.

Main Methods:

  • Retrospective analysis of 3322 air and N2-O2 dives with 190 recorded DCS events.
  • Scoring of DCS outcomes into categories of mild (pain, constitutional) and serious (neurological, cardiopulmonary).
  • Development of a 3-state probabilistic model to predict mild and serious DCS concurrently, compared to a 2-state model.

Main Results:

  • The trinomial model demonstrated a significant improvement in fit over the binomial model for both Type I/II and Type A/B DCS classifications (p << 0.01).
  • Using the Type I/II definition, the model predicted longer allowable bottom times for mild DCS but significantly decreased times for serious DCS.
  • The Type A/B classification allowed longer NDLs compared to Type I/II, with better agreement to current air NDLs when allowing a 0.2% risk of serious DCS.

Conclusions:

  • A hierarchical trinomial model offers a more accurate probabilistic prediction of DCS severity than traditional binomial models.
  • The choice of DCS classification (Type I/II vs. Type A/B) significantly impacts the predicted dive limits and risk assessment.
  • The proposed model, particularly with Type A/B scoring and adjusted risk tolerance, may refine safety guidelines for diving and other pressurized exposures.