A ternary model of decompression sickness in rats
© 2014 Elsevier Ltd. Background: Decompression sickness (DCS) in rats is commonly modelled as a binary outcome. The present study aimed to develop a ternary model of predicting probability of DCS in rats, (as no-DCS, survivable-DCS or death), based upon the compression/decompression profile and phys...
| Main Authors: | , , , , , , , |
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| Format: | Journal Article |
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Elsevier
2014
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| Online Access: | http://hdl.handle.net/20.500.11937/72494 |
| _version_ | 1848762764901744640 |
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| author | Buzzacott, Peter Lambrechts, K. Mazur, A. Wang, Q. Papadopoulou, V. Theron, M. Balestra, C. Guerrero, F. |
| author_facet | Buzzacott, Peter Lambrechts, K. Mazur, A. Wang, Q. Papadopoulou, V. Theron, M. Balestra, C. Guerrero, F. |
| author_sort | Buzzacott, Peter |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | © 2014 Elsevier Ltd. Background: Decompression sickness (DCS) in rats is commonly modelled as a binary outcome. The present study aimed to develop a ternary model of predicting probability of DCS in rats, (as no-DCS, survivable-DCS or death), based upon the compression/decompression profile and physiological characteristics of each rat. Methods: A literature search identified dive profiles with outcomes no-DCS, survivable-DCS or death by DCS. Inclusion criteria were that at least one rat was represented in each DCS status, not treated with drugs or simulated ascent to altitude, that strain, sex, breathing gases and compression/decompression profile were described and that weight was reported. A dataset was compiled (. n=1602 rats) from 15 studies using 22 dive profiles and two strains of both sexes. Inert gas pressures in five compartments were estimated. Using ordinal logistic regression, model-fit of the calibration dataset was optimised by maximum log likelihood. Two validation datasets assessed model robustness. Results: In the interpolation dataset the model predicted 10/15 cases of nDCS, 3/3 sDCS and 2/2 dDCS, totalling 15/20 (75% accuracy) and 18.5/20 (92.5%) were within 95% confidence intervals. Mean weight in the extrapolation dataset was more than 2. SD outside of the calibration dataset and the probability of each outcome was not predictable. Discussion: This model is reliable for the prediction of DCS status providing the dive profile and rat characteristics are within the range of parameters used to optimise the model. The addition of data with a wider range of parameters should improve the applicability of the model. |
| first_indexed | 2025-11-14T10:52:45Z |
| format | Journal Article |
| id | curtin-20.500.11937-72494 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T10:52:45Z |
| publishDate | 2014 |
| publisher | Elsevier |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-724942018-12-13T09:31:55Z A ternary model of decompression sickness in rats Buzzacott, Peter Lambrechts, K. Mazur, A. Wang, Q. Papadopoulou, V. Theron, M. Balestra, C. Guerrero, F. © 2014 Elsevier Ltd. Background: Decompression sickness (DCS) in rats is commonly modelled as a binary outcome. The present study aimed to develop a ternary model of predicting probability of DCS in rats, (as no-DCS, survivable-DCS or death), based upon the compression/decompression profile and physiological characteristics of each rat. Methods: A literature search identified dive profiles with outcomes no-DCS, survivable-DCS or death by DCS. Inclusion criteria were that at least one rat was represented in each DCS status, not treated with drugs or simulated ascent to altitude, that strain, sex, breathing gases and compression/decompression profile were described and that weight was reported. A dataset was compiled (. n=1602 rats) from 15 studies using 22 dive profiles and two strains of both sexes. Inert gas pressures in five compartments were estimated. Using ordinal logistic regression, model-fit of the calibration dataset was optimised by maximum log likelihood. Two validation datasets assessed model robustness. Results: In the interpolation dataset the model predicted 10/15 cases of nDCS, 3/3 sDCS and 2/2 dDCS, totalling 15/20 (75% accuracy) and 18.5/20 (92.5%) were within 95% confidence intervals. Mean weight in the extrapolation dataset was more than 2. SD outside of the calibration dataset and the probability of each outcome was not predictable. Discussion: This model is reliable for the prediction of DCS status providing the dive profile and rat characteristics are within the range of parameters used to optimise the model. The addition of data with a wider range of parameters should improve the applicability of the model. 2014 Journal Article http://hdl.handle.net/20.500.11937/72494 10.1016/j.compbiomed.2014.10.012 Elsevier restricted |
| spellingShingle | Buzzacott, Peter Lambrechts, K. Mazur, A. Wang, Q. Papadopoulou, V. Theron, M. Balestra, C. Guerrero, F. A ternary model of decompression sickness in rats |
| title | A ternary model of decompression sickness in rats |
| title_full | A ternary model of decompression sickness in rats |
| title_fullStr | A ternary model of decompression sickness in rats |
| title_full_unstemmed | A ternary model of decompression sickness in rats |
| title_short | A ternary model of decompression sickness in rats |
| title_sort | ternary model of decompression sickness in rats |
| url | http://hdl.handle.net/20.500.11937/72494 |