A Simple Probabilistic Model for Estimating the Risk of Standard Air Dives
Abstract
Statistical fitting of an algorithm to "calibration data" gives parameter values for a "probabilistic decompression model." Some previous probabilistic models prescribe long times at decompression stops for standard air dives. Here we present a simple model, based on premises different from those used previously, to test whether long decompression times are necessary and to enable risk of decompression sickness (UCS) to be estimated in air dives. Using logistic regression, we focus on the total times spent at decompression stops For calibration data, we use carefully controlled experimental dives recorded in the U.S. Navy Decompression Database in the range of standard air dives, but we exclude saturation and repetitive dives. To evaluate the model, we display its prescriptions for total decompression time along with individual dive-outcome points from the calibration data. Chi-square analyses and graphs of predicted versus observed DOS incidence as functions of depth, bottom time, and time at decompression stops show that our model agrees well with the data For most depths. the model's prescriptions avoid the experimental DCS cases, and its prescriptions for 2% probability of DCS are close to those of a deterministic model based on the VVal-18 Algorithm. Our model indicates that the long times at decompression stops mandated by some probabilistic models are not warranted and that these other models' estimates of DCS risk are incorrect. Our model. which cannot be used operationally because it cannot calculate depths and times at decompression stops. indicates that the VVal-l9 Algorithm is acceptable for computing the decompression requirements of single-level. non-repetitive air dives.
Document Details
- Document Type
- Technical Report
- Publication Date
- Dec 01, 2004
- Accession Number
- ADA442655
Entities
People
- E. T. Flynn
- H. D. Van Liew
Organizations
- United States Navy Experimental Diving Unit