Diving decompression models and bubble metrics: Modern computer syntheses

Physiology of repeated mixed gas 100-m wreck dives using a closed-circuit rebreather: a field bubble study

PURPOSE

Data regarding decompression stress after deep closed-circuit rebreather (CCR) dives are scarce. This study aimed to monitor technical divers during a wreck diving expedition and provide an insight in venous gas emboli (VGE) dynamics.

METHODS

Diving practices of ten technical divers were observed. They performed a series of three consecutive daily dives around 100 m. VGE counts were measured 30 and 60 min after surfacing by both cardiac echography and subclavian Doppler graded according to categorical scores (Eftedal-Brubakk and Spencer scale, respectively) that were converted to simplified bubble grading system (BGS) for the purpose of analysis. Total body weight and fluids shift using bioimpedancemetry were also collected pre- and post-dive.

RESULTS

Depth-time profiles of the 30 recorded man-dives were 97.3 ± 26.4 msw [range: 54-136] with a runtime of 160 ± 65 min [range: 59-270]. No clinical decompression sickness (DCS) was detected. The echographic frame-based bubble count par cardiac cycle was 14 ± 13 at 30 min and 13 ± 13 at 60 min. There is no statistical difference neither between dives, nor between time of measurements (P = 0.07). However, regardless of the level of conservatism used, a high incidence of high-grade VGE was detected. Doppler recordings with the O'dive were highly correlated with echographic recordings (Spearman r of 0.81, P = 0.008).

CONCLUSION

Although preliminary, the present observation related to real CCR deep dives questions the precedence of decompression algorithm over individual risk factors and pleads for an individual approach of decompression.

A biophysical vascular bubble model for devising decompression procedures

Vascular bubble models, which present a realistic biophysical approach, hold great promise for devising suitable diver decompression procedures. Nanobubbles were found to nucleate on a flat hydrophobic surface, expanding to form bubbles after decompression. Such active hydrophobic spots (AHS) were formed from lung surfactants on the luminal aspect of ovine blood vessels. Many of the phenomena observed in these bubbling vessels correlated with those known to occur in diving. On the basis of our previous studies, which proposed a new model for the formation of arterial bubbles, we now suggest the biophysical model presented herein. There are two phases of bubble expansion after decompression. The first is an extended initiation phase, during which nanobubbles are transformed into gas micronuclei and begin to expand. The second, shorter phase is one of simple diffusion‐driven growth, the inert gas tension in the blood remaining almost constant during bubble expansion. Detachment of the bubble occurs when its buoyancy exceeds the intermembrane force. Three mechanisms underlying the appearance of arterial bubbles should be considered: patent foramen ovale, intrapulmonary arteriovenous anastomoses, and the evolution of bubbles in the distal arteries with preference for the spinal cord. Other parameters that may be quantified include age, acclimation, distribution of bubble volume, AHS, individual sensitivity, and frequency of bubble formation. We believe that the vascular bubble model we propose adheres more closely to proven physiological processes. Its predictability may therefore be higher than other models, with appropriate adjustments for decompression illness (DCI) data.

Nanobubbles Form at Active Hydrophobic Spots on the Luminal Aspect of Blood Vessels: Consequences for Decompression Illness in Diving and Possible Implications for Autoimmune Disease-An Overview

Decompression illness (DCI) occurs following a reduction in ambient pressure. Decompression bubbles can expand and develop only from pre-existing gas micronuclei. The different hypotheses hitherto proposed regarding the nucleation and stabilization of gas micronuclei have never been validated. It is known that nanobubbles form spontaneously when a smooth hydrophobic surface is submerged in water containing dissolved gas. These nanobubbles may be the long sought-after gas micronuclei underlying decompression bubbles and DCI. We exposed hydrophobic and hydrophilic silicon wafers under water to hyperbaric pressure. After decompression, bubbles appeared on the hydrophobic but not the hydrophilic wafers. In a further series of experiments, we placed large ovine blood vessels in a cooled high pressure chamber at 1,000 kPa for about 20 h. Bubbles evolved at definite spots in all the types of blood vessels. These bubble-producing spots stained positive for lipids, and were henceforth termed “active hydrophobic spots” (AHS). The lung surfactant dipalmitoylphosphatidylcholine (DPPC), was found both in the plasma of the sheep and at the AHS. Bubbles detached from the blood vessel in pulsatile flow after reaching a mean diameter of ~1.0 mm. Bubble expansion was bi-phasic—a slow initiation phase which peaked 45 min after decompression, followed by fast diffusion-controlled growth. Many features of decompression from diving correlate with this finding of AHS on the blood vessels. (1) Variability between bubblers and non-bubblers. (2) An age-related effect and adaptation. (3) The increased risk of DCI on a second dive. (4) Symptoms of neurologic decompression sickness. (5) Preconditioning before a dive. (6) A bi-phasic mechanism of bubble expansion. (7) Increased bubble formation with depth. (8) Endothelial injury. (9) The presence of endothelial microparticles. Finally, constant contact between nanobubbles and plasma may result in distortion of proteins and their transformation into autoantigens.

Diving Into Research of Biomedical Engineering in Scuba Diving

The physiologic response of the human body to different environments is a complex phenomenon to ensure survival. Immersion and compressed gas diving, together, trigger a set of responses. Monitoring those responses in real time may increase our understanding of them and help us to develop safety procedures and equipment. This review outlines diving physiology and diseases and identifies physiological parameters worthy of monitoring. Subsequently, we have investigated technological approaches matched to those in order to evaluated their capability for underwater application. We focused on wearable biomedical monitoring technologies, or those which could be transformed to wearables. We have also reviewed current safety devices, including dive computers and their underlying decompression models and algorithms. The review outlines the necessity for biomedical monitoring in scuba diving and should encourage research and development of new methods to increase diving safety.

A comparative evaluation of two decompression procedures for technical diving using inflammatory responses: compartmental versus ratio deco

INTRODUCTION

The aim of this study was to compare two decompression procedures commonly adopted by technical divers: the ZH-L16 algorithm modified by 30/85 gradient factors (compartmental decompression model, CDM) versus the 'ratio decompression strategy' (RDS). The comparison was based on an analysis of changes in diver circulating inflammatory profiles caused by decompression from a single dive.

METHODS

Fifty-one technical divers performed a single trimix dive to 50 metres' sea water (msw) for 25 minutes followed by enriched air (EAN50) and oxygen decompression. Twenty-three divers decompressed according to a CDM schedule and 28 divers decompressed according to a RDS schedule. Peripheral blood for detection of inflammatory markers was collected before and 90 min after diving. Venous gas emboli were measured 30 min after diving using 2D echocardiography. Matched groups of 23 recreational divers (dive to 30 msw; 25 min) and 25 swimmers were also enrolled as control groups to assess the effects of decompression from a standard air dive or of exercise alone on the inflammatory profile.

RESULTS

Echocardiography at the single 30 min observation post dive showed no significant differences between the two decompression procedures. Divers adopting the RDS showed a worsening of post-dive inflammatory profile compared to the CDM group, with significant increases in circulating chemokines CCL2 (P = 0.001) and CCL5 (P = 0.006) levels. There was no increase in chemokines following the CDM decompression. The air scuba group also showed a statistically significant increase in CCL2 (P < 0.001) and CCL5 (P = 0.003) levels post dive. No cases of decompression sickness occurred.

CONCLUSION

The ratio deco strategy did not confer any benefit in terms of bubbles but showed the disadvantage of increased decompression-associated secretion of inflammatory chemokines involved in the development of vascular damage.

Pre-dive Whole-Body Vibration Better Reduces Decompression-Induced Vascular Gas Emboli than Oxygenation or a Combination of Both

Purpose: Since non-provocative dive profiles are no guarantor of protection against decompression sickness, novel means including pre-dive "preconditioning" interventions, are proposed for its prevention. This study investigated and compared the effect of pre-dive oxygenation, pre-dive whole body vibration or a combination of both on post-dive bubble formation. Methods: Six healthy volunteers performed 6 no-decompression dives each, to a depth of 33 mfw for 20 min (3 control dives without preconditioning and 1 of each preconditioning protocol) with a minimum interval of 1 week between each dive. Post-dive bubbles were counted in the precordium by two-dimensional echocardiography, 30 and 90 min after the dive, with and without knee flexing. Each diver served as his own control. Results: Vascular gas emboli (VGE) were systematically observed before and after knee flexing at each post-dive measurement. Compared to the control dives, we observed a decrease in VGE count of 23.8 ± 7.4% after oxygen breathing (p < 0.05), 84.1 ± 5.6% after vibration (p < 0.001), and 55.1 ± 9.6% after vibration combined with oxygen (p < 0.001). The difference between all preconditioning methods was statistically significant. Conclusions: The precise mechanism that induces the decrease in post-dive VGE and thus makes the diver more resistant to decompression stress is still not known. However, it seems that a pre-dive mechanical reduction of existing gas nuclei might best explain the beneficial effects of this strategy. The apparent non-synergic effect of oxygen and vibration has probably to be understood because of different mechanisms involved.

Statistics of acoustically induced bubble-nucleation events in in vitro blood: a feasibility study

Bubbles can form in biological tissues through ultrasonic activation of natural gas nuclei. The damaging aftereffects raise safety concerns. However, the population of nuclei is currently unknown, and bubble nucleation is stochastic and thus unpredictable. This study investigates the statistical behavior of bubble nucleation experimentally and introduces a model-based analysis to determine the distribution of nuclei in biological samples-two pig blood samples in vitro. Combined ultra-fast passive and active cavitation detection with a linear array was used to detect nucleation from pulsed ultrasound excitations at 660 kHz. Single nucleation events were detected at peak rarefaction pressures from -3.6 to -24 MPa, and the nucleation probability over the range 0 to 1 was estimated from more than 330 independent acquisitions per sample. Model fitting of the experimental probability revealed that the distribution of nuclei is most likely continuous, and nuclei are rare in comparison to blood cells.

Computer validation and statistical correlations of a modern decompression diving algorithm

A diving algorithm is a safe combination of model and data to efficiently stage diver ascents following arbitrary underwater exposures. To that end, we detail a modern one, the LANL reduced gradient bubble model (RGBM), dynamical principles, and correlations with the LANL Data Bank data. Table, profile, and meter fit and risk parameters are obtained in statistical likelihood analysis from decompression exposure data. The RGBM algorithm enjoys extensive and utilitarian application in mixed gas diving, both in recreational and technical sectors, and forms the bases for released tables, software, and decompression meters used by scientific, commercial, and research divers. The LANL Data Bank is described, and the methods used to deduce risk are detailed. Risk functions for dissolved gas and bubbles are summarized. Parameters that can be used to estimate profile risk are tallied. To fit data, a modified Levenberg-Marquardt routine is employed. The LANL Data Bank presently contains 2879 profiles with 20 cases of DCS across nitrox, trimix, and heliox deep and decompression diving. This work establishes needed correlation between global mixed gas diving, specific bubble model, and deep stop data. Our objective is operational diving, not clinical science. The fit of bubble model to deep stop data is chi squared significant to 93%, using the logarithmic likelihood ratio of null set (actual set) to fit set. The RGBM algorithm is thus validated within the LANL Data Bank. Extensive and safe utilization of the model reported in field user statistics for tables, meters, and software also suggests real world validation, that is, one without noted nor reported DCS spikes in the field.