Biomedical subjects

S S Survanshi

Publications and source records attributed to S S Survanshi.

9 recordsLinked to original sources

PubMed1998

Probabilistic models of the role of oxygen in human decompression sickness.

Probabilistic models of human decompression sickness (DCS) have been successful in describing DCS risk observed across a wide variety of N2-O2 dives but have failed to account for the observed DCS incidence in dives with high PO2 during decompression. Our most successful previous model, calibrated with 3,322 N2-O2 dives, predicts only 40% of the observed incidence in dives with 100% O2 breathing during decompression. We added 1,013 O2 decompression dives to the calibration data. Fitting the prior model to this expanded data set resulted in only a modest improvement in DCS prediction of O2 data. Therefore, two O2-specific modifications were proposed: PO2-based alteration of inert gas kinetics (model 1) and PO2 contribution to total inert gas (model 2). Both modifications statistically significantly improved the fit, and each predicts 90% of the observed DCS incidence in O2 dives. The success of models 1 and 2 in improving prediction of DCS occurrence suggests that elevated PO2 levels contribute to DCS risk, although less than the equivalent amount of N2. Both models allow rational optimization of O2 use in accelerating decompression procedures.

Air Pressure↗

PubMed1997

Improved probabilistic decompression model risk predictions using linear-exponential kinetics.

Using a data base of 2,383 air and nitrogen-oxygen dives resulting in 131 cases of decompression sickness (DCS), risk functions were developed for a set of probabilistic decompression models according to survival analysis techniques. Parameters were optimized using the method of maximum likelihood Gas kinetics were either traditional exponential uptake and elimination, or an exponential uptake followed by linear elimination (LE kinetics) when calculated supersaturation was excessive. Risk functions either used the calculated relative gas supersaturation directly, or a delayed risk using a time integral of prior supersaturation. The most successful model (considering both incidence and time of onset of DCS) used supersaturation risk, and LE kinetics (in only 1 of 3 parallel compartments). Several methods of explicitly incorporating metabolic gases in physiologically plausible functions were usually found in lumped threshold terms and did not explicitly affect the overall data fit. The role of physiologic fidelity vs. empirical data fitting ability in accounting for model success is discussed.

Decompression Sickness↗

PubMed1995

A model for predicting central nervous system oxygen toxicity from hyperbaric oxygen exposures in humans.

Under certain circumstances, Navy divers breathe 100% O2 when working underwater. Serious symptoms of central nervous system (CNS) O2 toxicity can develop from hyperbaric O2 exposure; immersion and exercise are also known to exacerbate toxicity. We developed risk models for quantitative prediction of the probability of developing symptoms using a large set of human data in which occupational exposure conditions were simulated. Exposures were 5 to 265 min at PO2 levels from 20 to 50 feet of sea water (fsw) (1 fsw = 3.06 kPa). Approximately half of the exposures were to a single PO2, while the remainder were more complicated consisting of exposures to multiple levels of hyperbaric O2. In 688 trials, there were 42 exposure-stopping symptoms. We used maximum likelihood to estimate parameters, likelihood ratios to compare model fits, and chi 2 tests to judge goodness-of-fit of model predictions to observations. The modeling shows that risk has a steep PO2 dependence. A model with autocatalytic features fits the data as well as a simpler model: when PO2 is elevated beyond 34 fsw, risk accumulates rapidly without bound while accumulating toward an asymptote at lower PO2 levels. This autocatalytic feature of risk accumulation implies a testable hypothesis that substantial protection from human CNS O2 toxicity can be obtained from intermittent exposure (periodic exposure to lower PO2). The models predict that the probability of O2 toxicity is less than 7% with current Navy limits while breathing 95% O2. Probability of symptoms is < 1% if FIO2 is maintained at the United States Navy recommended level of 75%.

Central Nervous System↗

PubMed1994

A test for variations in individual sensitivity to hyperbaric oxygen toxicity.

It has been suggested that some individuals have above-average sensitivity to hyperbaric oxygen toxicity. An extensive human study completed at the Naval Experimental Diving Unit (NEDU) tested human tolerance to HBO and raised the possibility of assessing this hypothesis. In a group of 113 subjects given multiple exposures, some developed no symptoms of O2 toxicity while others developed symptoms on several occasions. The subjects in this study received unequal numbers of exposures of different depths and durations however, and it was not obvious how to determine unusual sensitivity. To assess the influences of chance vs. differences in sensitivity on the outcome of this experimental series, we performed a Monte Carlo simulation in which the experimental design was duplicated and the sensitivity hypothesis was evaluated statistically. The number of subjects giving rise to any symptoms and the distribution of individuals having symptoms on multiple occasions were evaluated. The simulation showed that the NEDU results were not unusual: nearly one quarter of the time the observed pattern of multiple symptoms could have been expected due to chance alone. The power of this simulation would have permitted detection of sensitivity factors 10 times (or greater) normal in 20% of the subjects at least half of the time.

Diving↗

PubMed1993

Contribution of tissue lipid to long xenon residence times in muscle.

Experiments demonstrate that the mean residence time of an inert gas in tissue is longer than that predicted by a single-compartment model of gas exchange. Also the relative dispersion (RD, the standard deviation of residence times divided by the mean) is 1 according to this model, but RDs in real tissues are closer to 2, suggesting that a multiple-compartment model might be more accurate. The residence time of a gas is proportional to its solubility in the tissue. Although the noble gases in particular are 10 times more soluble in lipid than in nonlipid tissues, models of gas exchange generally do not incorporate measurements of the lipid in tissue, which may lead to error in the predicted gas residence times. Could a multiple-compartment model that accounts for the lipid in tissue more accurately predict the mean and RD of gas residence times? In this study, we determined the mean and RD of Xe residence times in intact and surgically isolated muscles in a canine model. We then determined the lipid content and the perfusion heterogeneity in each tissue, and we used these measurements with a multiple-compartment model of gas exchange to predict the longest physiologically plausible Xe residence times. Even so, we found the observed Xe mean residence times to be twice as long as those predicted by the model. However, the predicted RDs were considerably larger than the observed RDs. We conclude that lipid alone cannot account for the residence times of Xe in tissue and that a multiple-compartment model is not an accurate representation of inert gas exchange in tissue.(ABSTRACT TRUNCATED AT 250 WORDS)

Animals↗

PubMed1992

Predicting the time of occurrence of decompression sickness.

Probabilistic models and maximum likelihood estimation have been used to predict the occurrence of decompression sickness (DCS). We indicate a means of extending the maximum likelihood parameter estimation procedure to make use of knowledge of the time at which DCS occurs. Two models were compared in fitting a data set of nearly 1,000 exposures, in which greater than 50 cases of DCS have known times of symptom onset. The additional information provided by the time at which DCS occurred gave us better estimates of model parameters. It was also possible to discriminate between good models, which predict both the occurrence of DCS and the time at which symptoms occur, and poorer models, which may predict only the overall occurrence. The refined models may be useful in new applications for customizing decompression strategies during complex dives involving various times at several different depths. Conditional probabilities of DCS for such dives may be reckoned as the dive is taking place and the decompression strategy adjusted to circumstance. Some of the mechanistic implications and the assumptions needed for safe application of decompression strategies on the basis of conditional probabilities are discussed.

Decompression Sickness↗

PubMed1990

Xenon kinetics in muscle are not explained by a model of parallel perfusion-limited compartments.

Experimental tissue gas kinetics do not follow the prediction for a single stirred perfusion-limited compartment. One hypothesis proposes that the kinetics might be explained by considering the tissue as a collection of parallel compartments, each with its own flow, reflecting the tissue microcirculatory flow heterogeneity. In this study, observed tissue gas kinetics were compared with the kinetics predicted by a model of multiple parallel compartments. Gas exchange curves were generated by recording the time course of tissue radioactivity in the intact calf muscles of anesthetized ventilated dogs exposed to step function changes of 133Xe in the inspired air for 5-h periods. Microcirculatory flow heterogeneity in the same tissue was determined by the radioactive microsphere method. Observed mean tissue transit times were on average longer than predicted by a factor of 6.7. Observed means averaged 52.1 min compared with 8.3 min predicted by the perfusion-limited model. Relative dispersions of tissue transit times were also uniformly larger than predicted. We conclude that Xe gas kinetics in intact canine skeletal muscle are not explained by a model of multiple parallel perfusion-limited compartments. Countercurrent exchange of gas between vessels is a possible explanation.

Animals↗

PubMed1990

Relative decompression risk of dry and wet chamber air dives.

The difference in risk of decompression sickness (DCS) between dry chamber subjects and wet, working divers is unknown and a direct test of the difference would be large and expensive. We used probabilistic models and maximum likelihood estimation to examine 797 dry (and generally resting and comfortable) and 244 wet (and generally working and cold) chamber dives from the Defence and Civil Institute of Environmental Medicine, supplemented with 483 wet (working, cold) dives from the Navy Experimental Diving Unit. Several analyses considered whether dry and wet data were distinguishable using several models, whether models obtained from one set of exposure conditions would correctly predict the occurrence of DCS in the other condition, and whether a single wet-dry risk difference parameter was different from zero. Although the two conditions may not produce identical risks, immersion appears to change relative risk of DCS by less than 30% and certainly involves less than a doubling of DCS risk. Uncontrolled differences in exercise and temperature stresses unavoidably complicate interpretation. Several methods are presented to extrapolate results from dry-test subjects in decompression trials to expected at-sea performance.

Data Interpretation, Statistical↗

PubMed1988

The modulation of oxygen toxicity by intermittent exposure.

Intermittent delivery of hyperbaric O2 protects animals from pulmonary and central nervous system toxicity: more total O2 time can be tolerated if interrupted by short periods of low O2. Little is known about the mechanisms or optimization of systematically varied intermittency. Survival time was recorded in groups of 16 awake guinea pigs (239 +/- 23(SD) g) exposed to continuous O2 at 2.8 ATA or to one of six different schedules of O2 delivered with periodic air (PO2 = 0.588 ATA) interruptions. The survival curves had a lag time (11-14 hr of O2 time depending on the intermittency schedule) with a rapid loss of animals thereafter. Data were analyzed with risk models linking the probability of death to the accumulation of a putative toxic substance, X1. A model in which X1 accumulated in proportion to the PO2 and disappeared by first-order decay during periods of low O2 exposure was modified to include an effective rate constant for changes in X1: dX1/dt = a.PO2 + K1.(PO2 - Os).X1. First-order kinetics operated when PO2 was below the oxygen set point (Os), but the rate constant reversed sign to become a self-amplifying system when PO2 was above Os. This model achieved an excellent fit as judged by goodness-of-fit statistics while a simpler one did not. Our analysis suggests that the accumulation of toxicity does not correspond to a stable linear toxic process, but requires one in which a toxic process grows autocatalytically.

Animals↗