DECOMPRESSION SICKNESS (DCS) is caused by. Decompression Sickness Risk Model: Development and Validation by 150 Prospective Hypobaric Exposures

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1 RESEARCH ARTICLE Decompression Sickness Risk Model: Development and Validation by 150 Prospective Hypobaric Exposures Andrew A. Pilmanis, Lambros J. Petropoulos, Nandini Kannan, and James T. Webb PILMANIS AA, PETROPOULOS LJ, KANNAN N, WEBB JT. Decompression sickness risk model: development and validation by 150 prospective hypobaric exposures. Aviat Space Environ Med 2004; 75: Introduction: High altitude exposure has an inherent risk of altitude decompression sickness (DCS). A predictive DCS model was needed to reduce operational risk. To be operationally acceptable, such a theoretical model would need to be validated in the laboratory using human subjects. Methods: The Air Force Research Laboratory (AFRL) has conducted numerous studies on human subjects exposed to simulated altitudes in hypobaric chambers. The database from those studies was used to develop a statistical altitude DCS model. In addition, a bubble growth model was developed using a finite difference method to solve for bubble radius as a function of time. The bubble growth model, integrated with the statistical model, constitutes the AFRL DCS Risk Assessment Model. Validation of the model was accomplished by comparing computer predictions of DCS risk with results from subsequent prospective human subject exposures. There were five exposure profiles, not previously found in the database, covering a wide parameter of ranges of altitude (18,000 35,000 ft), exposure time ( min), prebreathe time (0 90 min), and activity level (rest-strenuous) that were used. The subjects were monitored for DCS symptoms and venous gas emboli. Results: There were 30 subjects who were exposed to each of the 5 altitude profiles. The DCS incidence onset curves predicted by the model were not significantly different from the experimental values for all scenarios tested and were generally within 5% of the actual values. Conclusion: A predictive altitude DCS model was successfully developed and validated. Keywords: bubble growth, model, altitude, DCS, emboli, decompression sickness, hypobaric, decompression, prebreathe, preoxygenation, venous gas emboli. DECOMPRESSION SICKNESS (DCS) is caused by evolved gas bubbles which form when living tissues are subjected to large reductions in environmental pressure. Such reductions in pressure can occur during decompression from compressed gas diving or pressurized tunnel work, ascent in the atmosphere, decompression during preparation for extravehicular activity in space, or decompression during training or research in hyperbaric or hypobaric chambers. Before decompression, nitrogen is dissolved in the tissues and fluids at a concentration related to the level of nitrogen in the breathing mixture prior to decompression. Thus, the tissues and fluids are saturated with nitrogen. Nitrogen supersaturation occurs in the body during decompression as the environmental pressure is reduced more rapidly than the tissue pressure of nitrogen. When the magnitude and rate of environmental pressure reduction are sufficient, the supersaturation with nitrogen can lead to the transition of nitrogen from the dissolved phase into the gas phase as gas emboli. The various clinical symptoms of altitude DCS are thought to result mainly from the physical interactions of gas bubbles with tissues and fluids. The size and location of these bubbles are thought to have a significant effect on the resulting DCS symptoms. The gas bubbles may interact with nerve endings or block microcirculation. by Ingenta The resulting to symptoms include pain, Delivered AEROMEDICAL sensory deprivation, LIBRARY skin manifestations, (cid 13432) and respiratory (cid distress ) The risk of DCS increases with extended webb IP: altitude exposure times, very high altitudes (45), and greater physical activity during the exposure (29). The risk decreases with preoxygenation (43). Formal reports of DCS from the field are rare. Research indicates that the incidence of DCS during high altitude missions is actually quite high, but reports are suppressed for fear of being grounded (3), and accurate evaluation of DCS incidence is significantly hindered by inconsistent classification of symptoms (14). When coupled with the extensive data from altitude chamber studies showing high rates of DCS incidence for simulated operational flight profiles, these findings indicate that DCS may continue to be an operational problem. With proper procedures (e.g., preoxygenation, suit/ cabin pressurization, etc.), the risk of DCS can be significantly reduced. Countermeasures for preventing DCS are thus not the problem. Rather, the problem facing aircrews is how to quantify the risk of DCS and then select an appropriate combination of available From the Air Force Research Laboratory, AFRL/HEPR, Brooks City-Base (A. A. Pilmanis); the Air Force Research Laboratory, Wyle Laboratories - Life Sciences, Systems, and Services at Brooks City- Base (L. J. Petropoulos, J. T. Webb); and the Department of Management Science and Statistics, University of Texas at San Antonio (N. Kannan); San Antonio, TX. This manuscript was received for review in June It was accepted for publication in June Address reprint requests to: Andrew A. Pilmanis, Ph.D., Air Force Research Laboratory, AFRL/HEPR, 2485 Gillingham Drive, Brooks City-Base, TX ; andrew.pilmanis@brooks.af.mil. Reprint & Copyright by Aerospace Medical Association, Alexandria, VA. 749

2 countermeasures compatible with the constraints of a given mission. Protection from DCS depends on reducing the potential for nitrogen supersaturation and limiting the factors associated with bubble formation and growth. Adequate cabin pressurization will prevent DCS if unexpected loss of pressurization is followed quickly by a timely descent to below 10,000 ft, where the ambient pressure is sufficient to greatly reduce formation and growth of gas emboli. If the degree of planned pressure reduction cannot be avoided, the risk of DCS can be minimized or prevented with sufficient denitrogenation by prebreathing pure oxygen (preoxygenation) before such exposures. The duration of preoxygenation is directly related to its effectiveness over the practical range of preoxygenation times; i.e., up to 4 h (43). To determine the need for preoxygenation, the risk of DCS must be assessed. There are several methods of assessing DCS risk. The simplest is to find the answer in the literature. However, data on a specific exposure profile is not usually available. The next obvious approach is to conduct an altitude chamber study to determine the DCS risk for that specific profile. However, such studies are expensive and time consuming. Finally, people with considerable experience in the field may extrapolate from available data and make a best guess. A best guess is more intuitive than scientific and not likely to produce repeatable results. The scientifically sound approach to DCS risk assessment is to develop and validate an altitude DCS model that can accurately predict the DCS risk for a wide range of exposure profiles. There are many applications for an altitude DCS risk assessment computer-based model: mission planning, systems design, education and research, real-time risk prediction in a cockpit, pressure suit control, and cabin pressurization control. The ability to predict DCS risk, real-time in a cockpit, as well as for mission planning, is an operational need for both military and civilian aerospace applications (26). The ability to predict the onset of DCS during a planned exposure scenario would allow modification of the primary factors which determine DCS risk (duration of preoxygenation, exposure altitude, duration of exposure, and level of activity while decompressed) and enable operators to plan a safer mission. Early altitude DCS modeling efforts involved modification of existing diving decompression models, ignoring the fundamental differences that exist between altitude and diving exposures (27). The lists below illustrate the significant differences between altitude and diving decompression. Altitude 1. Decompression starts from a ground level tissue N 2 saturated state. 2. Breathing gas is usually high in O 2 to prevent hypoxia and promote denitrogenation. 3. The time of decompressed exposure to altitude is limited. 4. Pre-mission denitrogenation (preoxygenation) reduces DCS risk. 5. DCS usually occurs during the mission. 6. Symptoms are usually mild and limited to joint pain. 7. Recompression to ground level is therapeutic and universal. 8. Tissue PN 2 decreases with altitude exposure to very low levels. 9. Metabolic gases become progressively more important as altitude increases. 10. There are very few documented chronic sequelae. Diving 1. Upward excursions from saturation diving are rare. 2. Breathing gas mixtures are usually high in inert gas due to oxygen toxicity concerns. 3. The time at surface pressure following decompression is not limited. 4. The concept of preoxygenation is generally not applicable. 5. DCS risk is usually greatest after mission completion. 6. Neurological symptoms are common. 7. Therapeutic chamber recompression is time limited and sometimes hazardous. 8. Tissue PN 2 increases with hyperbaric exposure to very high levels. 9. Inert gases dominate. Delivered 10. Chronic by Ingenta bone necrosis to and neurological damage AEROMEDICAL have been documented. LIBRARY (cid 13432) webb (cid ) IP: These differences illustrate the limited applicability of diving models in assessing and predicting DCS risk for altitude exposures. In recent years, a few articles have appeared that have proposed models specifically for altitude DCS. However, a standardized approach is not available and none of these altitude models have been validated with actual experimental data from hypobaric exposures with the primary factors which determine DCS risk as variables. The purpose of this paper is to describe the evolution of an altitude DCS model developed at the Air Force Research Laboratory (AFRL), Brooks Air Force Base, TX, and to present the results of the validation of this model with human subject altitude exposures. Altitude DCS Model Development Van Liew et al. (38) developed a probabilistic, doseresponse model of altitude DCS that related several independent variables accounting for decompression stress to the probability of occurrence of symptoms. The mechanistic principles used in their model were based on the premise that the risk of DCS is related to the number of bubbles and the volume of gas that can be liberated from a unit volume of tissue. Several competing models were tested and evaluated on the basis of how well they fit the data on human exposures from several different sources. The three independent variables used in the analysis were duration of breathing 100% oxygen at ground level (preoxygenation), exposure pressure, and exposure duration. The major assumption the authors made is that DCS incidence can 750

3 be predicted from the characteristics of bubbles. In their conclusions (38), the authors recognize that symptoms may be secondary to bubbles and suggest that their mechanistic premises be scrutinized and investigated further. Gerth and Vann (8) developed an extensive model of bubble dynamics to provide an assessment of DCS. The equations considered in the paper describing bubble growth and resolution are similar to those in Van Liew et al. (38), being dose-response models with the percentage of symptomatic individuals as the response variable. The unknown parameters in the model were estimated using maximum likelihood methods. The authors acknowledge the need for inclusion of symptom onset times for better assessment of DCS risk. These models focused on mathematical relationships describing bubble growth during a defined decompression and are called approximate models because they make certain assumptions (approximations). The approximations used did not account for the influence of any initial conditions due to their assumption that a quasi-steady state, or equilibrium, exists in the tissue surrounding a bubble nucleus. They also rely on only one or two factors that affect DCS risk, assuming the others are not as critical. In addition, the approaches outlined in these articles rely heavily on the premise that DCS incidence can be predicted from the characteristics of bubbles. The relationship between bubble formation and DCS is not well understood. It is well established that there is a positive correlation between increasing circulating bubbles such as those monitored by ultrasound methods and the occurrence of DCS symptoms. However, this does not imply causation, nor is prediction of symptoms possible from venous gas emboli (VGE) scores (2,22,28). Most of these models involve equations describing bubble growth based on certain physiological assumptions. It is extremely difficult, in fact virtually impossible, to verify these assumptions with real data. The only data on bubble characteristics is from Doppler ultrasound of VGE in the heart and not of the tissues bubbles that presumably cause the vast majority of the symptoms. Most of these models also only use the percentage of symptomatic individuals and ignore the symptom onset time. Finally, information from the large majority of subjects, those who remain asymptomatic throughout the exposures, is not adequately incorporated into these previous models. Development of the Bubble Growth Model Once the determining factors that effect DCS risk are defined, a critical step in assessing DCS risk is modeling the growth of a bubble in the extravascular tissues based on those factors (see Appendix A for model equations and definition of symbols). The numerical methods portion of the model and most of the mathematical formulation assumptions were based on a paper by Arefmanesh et al. (1). The diffusion equation is defined within a hypothetical tissue shell surrounding the bubble. This shell has a finite thickness. In situations where a large number of bubbles nucleate and grow simultaneously in close proximity, the amount of tissue immediately surrounding the bubble is finite as is the amount of dissolved gas. The use of a tissue shell enables us to account for different bubble population densities by varying the shell/bubble radius ratio. The convection-diffusion equation (Eq.1) is slightly modified from the one used by Arefmanesh (1). Since blood leaving the capillaries removes nitrogen gas from the system, especially when the breathing mixture contains a low percentage of nitrogen, a sink term (33) is added to the right-hand side of the equation. This new addition accounts for tissue nitrogen loss due to capillary-tissue gas exchanges. The gas inside the bubble is assumed to be an ideal gas whose pressure is related to the gas concentration at the interface through Henry s law. The initial gas concentration used by the model is the concentration value just prior to ascent. Altitude decompressions are considered decompressions from a saturated condition. A single tissue denitrogenation rate is used during preoxygenation. During bubble growth, the variable perfusion parameters account for the different exercise levels which, in turn, effect the perfusion rate. Prior to decompression, the dissolved inert gas is assumed to be uniformly distributed throughout the tissue shell [C(r,0) C0]. The reduced internal pressure at altitude (Boyle s law) causes bubble expansion and induces a concentration gradient in the tissue shell. Internal pressure is related to the concentration in the Delivered by Ingenta to tissue layer adjacent to the bubble through Henry s law. AEROMEDICAL LIBRARY (cid 13432) This initiates bubble growth and is governed by Eqs. webb (cid ) 1 3. Eq. 1 is a convection-diffusion equation with a term IP: to account for capillary removal of nitrogen. It describes the rate of gas diffusion in relation to the movement of the gas-tissue interface. Eq. 2 relates the gas pressure, bubble radius, and rate of gas diffusion through the interface. Eq. 3 relates the movement of the gas-tissue interface to the gas pressure inside the bubble (dots denote differentiation with respect to time ). Since there are other bubbles growing in close proximity, the amount of dissolved gas in the tissue available for each bubble is finite. The concentration gradient at the outer boundary of the tissue shell is assumed zero at all times (no flux through the outer boundary of the shell). Nevertheless, the amount of gas in the tissue and the bubble is not constant, due to the sink term in the diffusion equation, which accounts for the perfusion effect on bubble growth. Hence the boundary conditions for Eq. 1 are given in Eqs. 4 and 5. In the past, a number of studies have described bubble growth in supersaturated liquids (e.g., decompression) and bubble dissolution in undersaturated liquids (e.g., breathing 100% oxygen after bubble formation during altitude exposure) (9,15,23,35 37,39). In all of these studies, it has been assumed that the growth rate of the concentration boundary layer thickness around a dissolving bubble is fast compared with the rate of gas-tissue interface movement. This assumption provided the basis for approximate solutions of bubble dissolution. Epstein and Plesset (7) and Hlastala and Van Liew (9) obtained approximate quasi-stationary solutions by neglecting convective transport and by solving the resulting diffusion equation for the case 751

4 where the bubble surface is considered stationary. The mass flux at the interface determined from the solution of the standard diffusion equation was then used to establish the motion of the bubble surface. Further, it was assumed by Hlastala and Van Liew (9) that the interior of the bubble was uniform, inertial, and viscous, and surface tension effects were excluded. Under these assumptions, the equations of motion for the tissue phase allowed the internal bubble pressure, Pg, to be set equal to the constant ambient pressure with negligible error. Consequently, the growth or dissolution of the bubble in those models was controlled strictly by the diffusion process in the tissue. In more recent work (36,37) based on Van Liew and Hlastala (39), diffusion was assumed to quickly reach a steady state ( in these models, concentration outside the bubble was governed by Laplace s equation). This additional approximation could be referred to as a quasisteady state approximation. In the quasi-steady state model the ratio of the difference between the pressure of nitrogen in the bubble and in the tissue divided by the pressure of nitrogen in the bubble [(PbubN 2 PtisN 2 )/ PbubN 2 ]defines the level of tissue supersaturation, and provides a measure of the driving force behind dissolution or growth. When the tissue nitrogen pressure is higher than the bubble gas partial pressure, this ratio is negative (the tissue is supersaturated) and the bubble grows. Conversely, if the partial pressure of nitrogen is higher in the bubble than in the tissue, the ratio is positive and the bubble will reduce in size as nitrogen diffuses from the bubble to the tissue shell. During chamber or operational exposures, the bubbles grow to a maximum radius and then lose their nitrogen to the ever-denitrogenating tissue shells if the person continues to breathe 100% oxygen. If diffusion rapidly approaches equilibrium, the concentration gradient would only vary with respect to the spatial coordinate. By definition, a steady-state model cannot show timedependence in the concentration field or gradient. The initial sign of the concentration gradient will be maintained. The bubble will either grow or dissolve, but it cannot do both. More recently, Nikolarv (24) took a step beyond Van Liew s efforts and used the steady state solutions of the diffusion equation with a discontinuity on the diffusion coefficient. He assumed two shells surrounding the bubble identifying the second one as a massive layer of cells with different diffusivity. His assumption that D1 (10 3)D2 is very similar to our no-flux outer boundary condition. However, instead of using approximate solutions as he did, we chose to solve the full system of equations, following Arefmanesh s (1) numerical methods (25). Development of the Statistical Model For over two decades, AFRL has conducted human subject research in altitude chambers. The results of over 3,000 experiments, with both male and female subjects, have been deposited into the AFRL DCS Database. A number of articles have appeared that deal in detail with specific studies from the database (16,30,34,41,43,45,46). An examination of this database (40) reveals certain interesting characteristics of altitude DCS and offers some insight into an improved approach for statistical modeling of DCS: Not all individuals exposed to identical experimental conditions exhibit symptoms of DCS. The severity and breadth of symptoms for individuals who do experience DCS is extremely variable, ranging from mild knee pain to severe respiratory problems. The time of onset of symptoms varies significantly from individual to individual. An individual subject is sometimes required to perform identical exposures in successive weeks, resulting in the same reaction or something totally different, i.e., there is very little certainty about an individual response. These observations reveal a stochastic nature of DCS, i.e., the onset of symptoms is not fixed, but a random variable. This necessitates the use of survival analysis/ reliability techniques to adequately model the incidence of DCS (10). In a series of papers, Kumar et al. (18 21) recognized that survival analysis techniques work well to model DCS risk. They developed logistic and loglinear models to predict DCS as a function of tissue ratio, a measure of tissue nitrogen pressure in relation to total ambient Delivered pressure. by Maximum Ingenta likelihood to techniques were used to AEROMEDICAL estimate the parameters LIBRARY of(cid the13432) models, the response webb variable (cid being ) the logarithm of symptom onset time. In IP: a recent paper, Conkin et al. (6) developed loglogistic survival models using data from 66 NASA and USAF hypobaric chamber tests. The Conkin et al. (6) model also examined the effects of tissue ratio on the risk of DCS, but included exercise status (yes, no) and some of the mechanistic principles of bubble growth. The articles in this second group (5,6) were certainly a step in the right direction in terms of development of models for predicting altitude DCS risk. However, they are limited to examining the effects of one or two risk factors on the probability of DCS and supporting experimental data was extremely limited. In addition, these previous models did not account for the varying number of subjects in different protocols and the large variation in exposure times. Experimental data from the database shows that the primary risk factors affecting DCS include altitude, preoxygenation duration, exposure duration, and level of exercise performed during the exposure. Several other factors including gender, height, and body mass were also examined, but the p-values for these risk factors were not significant. In other words, these factors do not add significantly to our ability to model DCS onset time in the presence of the primary risk factors. In order to develop a comprehensive model that accurately describes altitude DCS, it is essential to include all of the primary factors. With this goal in mind, Kannan et al. (11,12) developed a model based on the loglogistic distribution incorporating several risk factors. We know from empirical evidence that the instantaneous risk of developing symptoms increases with increased exposure duration. However, because of deni- 752

5 trogenation, the risk starts to drop after it reaches a maximum. This information on the shape of the risk function indicated that the lognormal and loglogistic distributions were appropriate for these data. The model was fit to data from the database. The authors examined the data on preoxygenation times, pressure, and time of exposure. The data seemed to indicate that subjects with longer preoxygenation times were more likely to have longer exposure durations at higher altitudes. To adjust for this, they suggested the inclusion of a risk factor by taking the ratio of preoxygenation time to exposure time. The risk factors that were highly significant (p-value ) were final pressure (altitude), ratio of preoxygenation to exposure time, and exercise level. The model also accounted for the large dispersion in exposure times by assigning weights to the observations. These weights were chosen to be inversely proportional to the variances of onset times. Maximum likelihood estimators of the unknown parameters were obtained using the LIFEREG procedure of the statistical package SAS (SAS Institute Inc, Cary, NC). The estimated cumulative distribution function was used to predict the probability of DCS for a variety of exposure profiles. Validation and cross validation techniques were used to evaluate the predictions from the model. The predictions from this model agreed closely with empirical data from the database for a variety of different exposure profiles. With additional data becoming available from ongoing studies, it was evident that this model needed modification. It was noticed that, at very high altitude, the effects of exercise are far more pronounced than at lower altitudes. To address the interaction of exercise and altitude, stratified models were developed by Kannan et al. (13). These authors also addressed the possibility of DCS thresholds at both higher and lower altitudes. Webb et al. (42) described an abrupt increase in DCS symptoms with zero preoxygenation above 21,200 ft. To account for these thresholds, a stratified model was developed and the predictions improved dramatically over the previous loglogistic model. The data contained in the Database include the time to onset of DCS symptoms, amount of time spent in preoxygenation, pressure/altitude, time at maximum altitude, and exercise code. During the experiments, subjects were monitored continuously for DCS and VGE. The experiment either lasted the entire exposure period or was terminated if the individual had DCS symptoms. For individuals reporting no symptoms, their onset time was replaced by their corresponding time at maximum altitude. A censoring variable was created to indicate the status of each individual: 1 DCS and 0 No DCS (censored). The altitude levels used in the analysis ranged from 11,500 ft (493 mmhg) to 35,000 ft (179 mmhg). The preoxygenation times ranged from 0 to 240 min. The database protocols used to develop the model contained a variety of exercise types while at altitude. The exercise was not continuous. Periods of exercise were separated by periods of rest. These exercise types were divided into three categories by the mean level of oxygen consumption during the entire exposure as expressed by the percent of peak V O2. The categories were rest, mild exercise, or heavy exercise. These three exercise categories were chosen with operational simplicity in mind for the projected model software. Rest was defined as a mean of about 5% of V O2 peak, mild exercise as a mean of 8 20% of V O2 peak, and heavy exercise as a mean of over 20% of V O2 peak. Detailed descriptions of the exercise types can be found in previous publications (17,41,44,45). All exposures in this database used the same ascent rate and the same breathing gas mixture. For some protocols, the same individuals performed as many as five exposures of the same profiles. To avoid any possibility of data contamination, we decided to use only the results of the first exposures of these individuals. Even though this resulted in a smaller dataset, we felt that this would reduce the effects of susceptible or resistant individuals. We also deleted data from studies where the breathing mixture was different from 100% O 2. With these deletions, the reduced dataset contained 1015 observations of which 522 (51%) were censored (non-symptomatic). The earlier model developed by Kannan et al. (12) considered three risk factors: pressure, ratio of preoxygenation to exposure time, and exercise. Validation and cross-validation techniques were used to conclude that predictions from this model agreed closely with empirical data for most exposure profiles. The model, however, did not perform satisfactorily for low preoxygen- Delivered by Ingenta to AEROMEDICAL LIBRARY (cid 13432) ation times, and at high altitudes. webb (cid ) The classification of exercise as rest, mild, and heavy IP: did not seem adequate for all altitudes. At very high altitudes ( 30,000 ft), the effects of even mild exercise were very pronounced. At 30,000 ft with mild exercise, almost 80% of subjects developed DCS. For the resting profile at 30,000 ft, 53% of subjects reported symptoms. This suggested either a strong interaction effect between altitude and exercise or some sort of threshold at 30,000 ft. It has been observed in the literature that there is a lower altitude threshold for DCS without preoxygenation (21,000 ft), below which very few cases of DCS occur (42). The risk of DCS increases sharply at altitudes above this threshold. Based on this, it seemed reasonable to assume such a threshold might exist at the higher altitudes. Kannan and Pilmanis (13) proposed a stratified or weighted model based on three strata. The first stratum consisted of altitude levels in the range between 22,500 ft (314 mmhg) and 25,000 ft (282 mmhg). The second stratum consisted of altitude levels in the range between 25,000 ft and 30,000 ft (226 mmhg). The final stratum consisted of altitudes at or above 30,000 ft. For each stratum, the exercise variable was defined appropriately and another risk factor measuring the interaction between pressure and exercise was added to the model. Table I provides the results of this analysis. We fitted the model to the data based on the loglogistic distribution using three risk factors: pressure in mmhg, preoxygenation time, and the interaction between exercise and pressure. The coefficients associated with the risk factors are estimated from the data using the LIFEREG procedure in SAS (parameter estimates), and approximately reflect the importance of 753

6 TABLE I. PARAMETER ESTIMATES FOR THE STRATIFIED LOGLOGISTIC MODEL. Estimate SE Chi-Squared p-value Stratum 1: Altitudes below 7620 m (25,000 ft) Intercept Pressure Preox Expres Scale Stratum 2: Altitudes above 7620 m (25,000 ft) and below 9,144 m (30,000 ft) Intercept Pressure Preox Expres Scale Stratum 3: Altitudes above and including 9,144 m (30,000 ft) Intercept Pressure Preox Expres Scale Preox preoxygenation; Expres the interaction between exercise and pressure. the different risk factors. The Chi-squared and p-values are used to test the significance of the risk factors. For example, the p-value for the interaction between exercise and pressure in Table I is , indicating that in this stratum, the interaction between altitude and exercise has the most effect on the probability of DCS. The scale parameter provides a measure of the steepness of the incidence curve. Since the scale parameter is less than one for all three strata, the risk function increases to a maximum and then decreases with increased exposure. For Stratum 1, the preoxygenation variable was not significant. This is not surprising since most profiles in this range had zero preoxygenation times. For Stratum 1, the interaction between exercise and pressure was the only significant risk factor. Pressure by itself was not significant. This was expected since the stratum contained lower altitudes. The intercept was large because at the lower altitudes, the baseline for DCS risk was fairly high (i.e., onset times are very large). This indicates that there is a substantial lag before symptoms occur. This is consistent with the belief that DCS is caused by increasing levels of nitrogen in the tissues relative to ambient, so at the lower altitudes, the supersaturation of tissues may take longer. For Stratum 2, the interaction between pressure and exercise was again very significant. Preoxygenation time was also significant. For Stratum 3, all the risk factors are highly significant. The most significant is the interaction between exercise and altitude; even mild exercise at such high altitudes presumably increases the formation of bubbles and results in very short DCS onset times. This effect has also been observed in the paper by Webb et al. (45). The results for all three strata indicate the relative importance of the three risk factors. The interaction between exercise and altitude clearly has the greatest effect on increasing the risk of DCS. The resulting loglogistic model is defined by Eq. 6, where the parameter is related to the risk factors through Eq. 7. The coefficients in Eq. 7 are estimated using SAS. Clearly, the parameter is always positive. At t 0, the probability of DCS is 0, and the probability of DCS increases over time. The coefficients 0, 1, 2, and 3 are estimated using SAS and reflect the importance of the different risk factors. A large positive intercept 0 indicates a higher median DCS onset time (the time by which we expect 50% of the population to exhibit symptoms). On the other hand, a large negative intercept indicates a much shorter median onset time. Looking at Table I for Stratum 3, we can see that for the higher altitudes the intercept is indeed negative we expect the onset times to be shorter. Combined Model It is generally agreed that the symptoms of DCS are caused by the growth of nitrogen bubbles in the tissues. The size and locations of these bubbles are believed to have a significant effect on the manifestation of symptoms. However, it is still not clear where these bubbles originate, and how exactly they affect the onset of symptoms. The database contains quantitative information on the VGE found during all exposures to altitude. Echocardiography was used to monitor the subjects at regular intervals. The extent of these circulating bubbles is graded using Spencer s scale (32), which ranges from Delivered Grade 0 by indicating Ingenta no to bubbles, to Grade 4 indicating AEROMEDICAL numerous bubbles LIBRARY that obscure (cid the 13432) heart sounds or fill webb the right (cid ) heart image. The data recorded include the IP: bubble grade and the time of onset. The bubbles that are observed are circulating bubbles and it is clear that these are not directly the cause of most symptoms, such as joint pain. It is not clear whether the presence of these bubbles is somehow correlated to the size or growth of the extravascular bubbles that are thought to cause most symptoms. However, it is not possible to obtain measurements on the size or location of the bubbles that directly impact symptoms, and VGE are the only currently observable measure of bubble activity in human subjects. Using the database, Kannan and Raychaudhuri (11) showed that in individuals who have at least bubbles of Grade 2 or more, the time at which the bubble grade is maximum is highly correlated with symptom onset time. A loglogistic model was fit to the data adding maximum bubble grade as a risk factor. The results indicated that maximum bubble grade is highly significant, dampening the effects of the other risk factors. This loglogistic model with maximum bubble grade was used to predict the probability of DCS for several profiles. For profiles with low preoxygenation times, the predictions were significantly closer to the database values compared with the model that did not include bubble data. However, the problem with considering maximum bubble grade as a risk factor is that, unlike the other risk factors, it is not fixed by the profile but varies from individual to individual. An estimate of maximum bubble grade must be provided before the model can be used for prediction. The mathematical model (bubble growth model) described above provides an estimate of the time at which the bubble 754

7 TABLE II. PROFILE DESCRIPTIONS. Database Profile Pre-Breathe, min Exposure Duration, min Altitude, ft Exercise A ,000 Moderate B ,000 Heavy C ,500 Heavy D ,000 Heavy E ,000 Rest reaches its maximum size. This maximum bubble size time combines with the other risk factors in the loglogistic model. In summary, inputs to the combined model were the following risk factors: 1) altitude/pressure; 2) exposure time; 3) preoxygenation time; 4) level of exercise; and 5) time to maximum bubble grade (bubble growth model). Thus, the statistical and deterministic (bubble growth) models described above were combined to provide a single output expressed as the percent of DCS predicted. Model Validation Theoretical decompression models abound in the literature. Scientific discourse regarding pros and cons is a valuable academic process. However, such a theoretical model should not be transitioned to the operational setting until there is evidence of its efficacy. Thus, before this combined model can be transitioned to operational application, it must be validated in the laboratory. Such prospective human trials for validation of decompression schedules are rare because of time, expense, and potential sequelae in the subjects, especially with hyperbaric exposures (31). This study prospectively exposed human subjects to altitude scenarios not previously used in the development of the model. The incidence of DCS in these exposures was then compared with that predicted by the model. This validation process is crucial in determining that a given model is indeed accurate. This can only be assured by using the model to predict DCS risk during exposures where at least one of the primary factors affecting DCS risk is substantially at variance with the database used to develop the model. Human subject exposures are then accomplished under the same conditions used to develop the model prediction. Comparison of results from the model predictions and actual exposures, with identical primary factors affecting DCS risk, will determine the ability of the model to accurately predict DCS risk. Altitude profiles were selected for this validation study that represented holes in the database and would, therefore, provide the best test of the model. METHODS The voluntary, fully informed written consent of the subjects used in this research was obtained and the protocol was approved by the Brooks Institutional Review Board and the USAF Surgeon General soffice. All subjects passed an appropriate physical examination and were representative of the USAF rated aircrew population. The general procedures and precautions for altitude exposure of subjects were as in previously published AFRL studies (46). Breathing gas during preoxygenation, while decompressed, during descent, and during post-breathing was 100% oxygen (aviator s breathing oxygen; normal analysis % oxygen). A neck-seal respirator made by Intertechnique (Plaisir Cedex, France) was used to deliver oxygen. This mask provided a slight (2-cm of water) positive pressure, which reduced the opportunity for inboard leaks of nitrogen from the atmosphere and was more comfortable than the standard aviator s mask. Subjects were not allowed to participate in scuba diving, hyperbaric exposures, or flying for at least 72 h before each scheduled altitude exposure. The five exposure profiles are described in Table II. For profiles B through E, chamber ascent was made to an altitude not higher than 30,000 ft at a rate not greater than 5000 ft min 1. For profile A, chamber ascent was at a rate of 5000 ft min 1 to 20,000 ft, and then to 35,000 ft at a rate of 10,000 ft min 1. Each subject accomplished two of the five exposure Delivered profiles shown by Ingenta in Table to II. Each profile had 30 exposures. Subjects were LIBRARY randomly (cid 13432) assigned to exposure AEROMEDICAL webb profiles. (cid ) IP: At 16-min intervals during the exposure, the subjects were monitored for VGE using a SONOS 1000 Doppler/Echo-Imaging System (Hewlett Packard, Houston, TX). This system permits both audio and visual monitoring and recording of gas emboli in all four chambers of the heart and allows for easier and more accurate determination of emboli presence than Doppler alone. Detection of any left ventricular gas emboli was made possible due to these echo-imaging sessions and was cause for immediate recompression to avoid potentially serious symptoms resulting from arterial gas emboli. VGE were graded using a modified Spencer scale (32). At altitude, the subjects rested or performed moderate to heavy exercise (Table II). Moderate exercise consisted of a cycle of three exercises designed to simulate extravehicular activities performed by NASA shuttle crewmembers and described in more detail in Webb et al. (42). The subjects exercised at each station for 4 min each cycle. At one station, the subjects turned a Monarch 868 cycle ergometer (Monarch, Varberg, Sweden) set up as a hand crank at 24 rpm (4 N), alternating arms each two revolutions; each 5 s. At a second station, the subjects applied force with a torque wrench, set at 25 ft-lbs, to bolt-like projections mounted on a wall for 5 s in each position alternately with each arm. At the third station, the subjects pulled on a handgrip attached to a pulley system set to 17 lbs (8.5 kg) of resistance. The subject pulled from arms reach at head level to their waist once each 5 s, alternating left, right, then both arms. Heavy exercise consisted of cycle ergometry. Subjects continuously alternated between exercising for a 755

8 TABLE III. RESULTS OF VALIDATION. Profile N Preox (min) Alt (ft) Exercise Duration PDCS (%) ADCS (%)* AVGE (%) A 7F, 23M 90 35,000 Mild / 9 83 B 9F, 21M 30 25,000 Heavy C 4F, 26M 15 22,500 Heavy D 5F, 25M 0 18,000 Heavy E 9F, 21M 75 30,000 Rest NOTE: 30 subjects were exposed to each of the 5 profiles; the N is divided into male (M) and female (F) subjects. PDCS is the DCS predicted by the model; ADCS and AVGE are the actual DCS and VGE incidences found experimentally. *The margin of error was computed using 95% confidence intervals for the Kaplan-Meier estimates. maximum of 3 min and resting for a minimum of 7 min. Exercise intensity and/or duration was adjusted for each subject so that each subject was capable of performing the heavy exercise for the duration of the exposure. Exercise intensity was followed by monitoring the subject s heart rate response to the exercise and/or by the subject s perception of exercise intensity using the Borg scale (4). The target exercise intensity was a heart rate of 60 80% of maximal predicted heart rate at the completion of each exercise cycle and/or a rating of on the Borg scale of Each flight profile was separated by at least 72 h, most often by 1 mo. All chamber exposures had a medical monitor and an aerospace physiologist available to ensure subject safety. Endpoints (test termination criteria) of the exposures were as follows: 1) completion of the scheduled exposure; 2) development of any DCS signs or symptoms including respiratory symptoms, neurologic symptoms, paresthesia, and constant pain; 3) detection of left ventricular gas emboli. Any subject who developed symptoms of DCS was immediately brought to ground level. Subjects experiencing DCS, other than joint pain which resolved on descent, were referred immediately to Hyperbaric Medicine for evaluation and treatment. Subjects experiencing joint pain DCS symptoms which resolved on descent and subjects who completed the full exposure profile breathed 100% oxygen at ground level for 2 h to minimize the probability of recurring or delayed symptoms (post-breathing). The incidence and onset times of DCS from these exposures were compared with those predicted by the model. To assess the goodness of fit of the model, we used the 95% confidence bands and a Chi-squared test comparing the predicted DCS onset curves and those obtained from the data. RESULTS The model-predicted incidence of DCS (PDCS) and actual experimental DCS incidence (ADCS) from this prospective validation study are shown in Table III. The ADCS column also shows the margin of error for the actual DCS incidence. The actual incidence of any VGE (grade 1 or higher) at the end of the exposures (AVGE) is also shown in Table III. Shapes of the DCS and VGE onset curves representing cumulative DCS and VGE incidence vs. time for each of the model validation study profiles are shown in Fig. 1, 2, 3, 4 and 5. The 95% confidence intervals for the profiles curves encompass the respective predicted onset curves from the model. The confidence bands are the criteria used here. A statistical test indicated the predicted and actual DCS curves were not significantly different (p 0.05). In order to evaluate the predictions from the model, we use the estimates of the coefficients from the Tables in the expression for the loglogistic distribution obtained from SAS to predict the probability of DCS over time for several exposure profiles (Fig. 1 5). The thick solid lines in Fig. 1 5 are the predicted probability of DCS over time. The thinner lines with dots represent the actual DCS results from the validation. The thin lines are the 95% confidence intervals (CI). Both the predicted curves and the actual curves are sigmoidal in shape. It is apparent that most of the predicted values Delivered are within by the Ingenta 95% intervals. to Although as a whole the AEROMEDICAL predicted and the LIBRARY actual DCS (cid curves 13432) were not significantly (cid different, ) portions of the predictive curves in Fig. webb IP: 1, , and 3 are outside of the 95% CI. In Fig. 4 and 5, the predictive curves are entirely within the 95% CI. In Fig. 1, the predicted DCS is higher than the actual DCS early in the exposure duration, but the two curves are virtually indistinguishable at the end. The 35,000-ft exposures have provided valuable data about the effects of high altitude combined with exercise which will be used to update the model and further improve the predictions. In Fig. 2, between min of exposure time, the predictive curve slightly underpredicts the DCS risk. As previously mentioned, there were no profiles that incorporated heavy exercise in the database. The addition of these data to the database will provide for greater accuracy in future model modifications. Fig. 1. Cumulative DCS and any VGE vs. exposure time for Profile A (35,000 ft); predicted vs. actual % DCS incidence and 95% confidence intervals. 756

9 Fig. 2. Cumulative DCS and any VGE vs. exposure time for Profile B (25,000 ft); predicted vs. actual % DCS incidence and 95% confidence intervals. DISCUSSION An altitude DCS predictive model has been developed at AFRL and the predictive ability and accuracy of this model have been validated with prospective human subject exposures. Each of the five profiles in the model validation study was under different conditions than those used to develop the model. The chamber profiles resulted in symptom onset curves which were generally within 5% of the predicted curves. The predicted values for the end of the exposures were not significantly different from the actual experimental values found. After this validation process, the data from the five experimental exposures of this study will be added to the database and the model will be modified to improve its prediction accuracy. Results from these and other studies at AFRL required the addition of new stratified models to adjust for interactions between pressure and exercise (13). The database used to develop the initial model contained no information on heavy exercise, whereas three of the five validation profiles used heavy exercise. These data on heavy exercise helped to better evaluate and understand the effects of various exercise levels on DCS symptoms. The data at 35,000 ft helped to separate the effects of rest, mild exercise, and heavy exercise, and led to a better definition of the risk factors. Although the model does not predict VGE incidence, the VGE data for the actual exposures are included in Fig. 1 5 and Table III. The data include any VGE at grade 1 or higher. In Table III, the actual VGE incidences at the end of the exposures do not show much Fig. 4. Cumulative DCS and any VGE vs. exposure time for Profile D (18,000 ft); predicted vs. actual % DCS incidence and 95% confidence intervals. difference between the five profiles, even though the altitudes range from 18,000 ft to 35,000 ft. Furthermore, from Fig. 1 5 it is clear that the VGE incidence onset curves level off at approximately 90 min into the exposures for all five profiles. These data further support the concept that DCS cannot be predicted from VGE monitoring (28,2). Therefore, modeling VGE data for the purpose of predicting DCS would not be fruitful. It is interesting to note that at the lower altitudes, VGE onset curves are widely separated from the much lower DCS curves. Progressively, these VGE and DCS curves move closer together as the altitude increases. In Profile A, at Delivered 35,000 ft, by theingenta DCS curve to increases above the VGE curve AEROMEDICAL at approximatelylibrary 110 min into (cid the 13432) exposure. After this webb time, (cid DCS ) symptoms would occur before VGE are detected IP: CONCLUSION A predictive altitude DCS model has been developed at AFRL. The predictive ability and accuracy of this model have been validated by a total of five profiles using human subjects exposed in an altitude chamber. Although there have been attempts in the past to validate altitude DCS models (5), this study represents the first time an altitude DCS model has been successfully validated using subsequent human subject exposures and rigorous experimental/statistical techniques over a wide range of exposure profiles. It should be remembered that this model is based on DCS data from exposure of military subjects and application to the civilian Fig. 3. Cumulative DCS and any VGE vs. exposure time for Profile C (22,500 ft); predicted vs. actual % DCS incidence and 95% confidence intervals. Fig. 5. Cumulative DCS and any VGE vs. exposure time for Profile E (30,000 ft); predicted vs. actual % DCS incidence and 95% confidence intervals. 757