Statistical Bubble Dynamics Algorithms for Assessment of Altitude Decompression Sickness Incidence.
Abstract
Air Force personnel are routinely exposed to atmospheric decompressions that often incur significant risk of decompression sickness (DCS). Management of these risks requires analytic methods able to: (a) define risk/hazard envelopes for all routine and emergency decompressions, (b) assess the DCS risks included or introduced in the contemplation or design of new operational procedures and equipment, and; (c) support real-time monitoring of DCS risk incurred by personnel during various chamber and aircraft operations. Present work contributed to meeting these requirements through development and application of methods by which DCS risks during decompression profiles are determined from statistical/biophysical models of in vivo gas exchange and bubble growth and resolution using maximum likelihood, both logistic and survival models were fit to DCS incidence data from the USAF Armstrong Laboratory (USAFAL) for a wide variety of decompression profiles. The models were incorporated into software that operates on personal computers. System software, including a data transcription routine to serve as a software interface between the USAFAL Hypobaric Decompression Sickness Database and the present modeling system, was delivered for use and evaluation of USAFAL personnel.
Document Details
- Document Type
- Technical Report
- Publication Date
- Jul 01, 1995
- Accession Number
- ADA297456
Entities
People
- Richard D. Vann
- Wayne A. Gerth
Organizations
- Duke University Hospital