Physiologically driven, altitude-adaptive model for the interpretation of pediatric oxygen saturation at altitudes above 2,000 m a.s.l.

Physiologically driven, altitude-adaptive model for the interpretation of pediatric oxygen saturation at altitudes above 2,000 m a.s.l. Tüshaus, L., Moreo, M., Zhang, J., Hartinger, S., M., Mäusezahl, D., & Karlen, W. Journal of Applied Physiology, 127(3):847-57, 2019.

Paper

Physiologically driven, altitude-adaptive model for the interpretation of pediatric oxygen saturation at altitudes above 2,000 m a.s.l. [link]

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Measuring peripheral oxygen saturation (SpO2) with pulse oximeters at the point of care is widely established. However, since [Formula: see text] is dependent on ambient atmospheric pressure, the distribution of SpO2 values in populations living above 2000 m a.s.l. is largely unknown. Here, we propose and evaluate a computer model to predict SpO2 values for pediatric permanent residents living between 0 and 4,000 m a.s.l. Based on a sensitivity analysis of oxygen transport parameters, we created an altitude-adaptive SpO2 model that takes physiological adaptation of permanent residents into account. From this model, we derived an altitude-adaptive abnormal SpO2 threshold using patient parameters from literature. We compared the obtained model and threshold against a previously proposed threshold derived statistically from data and two empirical data sets independently recorded from Peruvian children living at altitudes up to 4,100 m a.s.l. Our model followed the trends of empirical data, with the empirical data having a narrower healthy SpO2 range below 2,000 m a.s.l. but the medians never differed more than 2.3% across all altitudes. Our threshold estimated abnormal [Formula: see text] in only 17 out of 5,981 (0.3%) healthy recordings, whereas the statistical threshold returned 95 (1.6%) recordings outside the healthy range. The strength of our parametrized model is that it is rooted in physiology-derived equations and enables customization. Furthermore, as it provides a reference SpO2, it could assist practitioners in interpreting SpO2 values for diagnosis, prognosis, and oxygen administration at higher altitudes.

@article{
 title = {Physiologically driven, altitude-adaptive model for the interpretation of pediatric oxygen saturation at altitudes above 2,000 m a.s.l.},
 type = {article},
 year = {2019},
 keywords = {altitude,child health,hypoxemia,model,oxygen saturation,physiological adaptation,pneumonia},
 pages = {847-57},
 volume = {127},
 websites = {http://www.ncbi.nlm.nih.gov/pubmed/31525318,https://www.physiology.org/doi/10.1152/japplphysiol.00478.2018},
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 created = {2019-07-14T11:35:24.538Z},
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 last_modified = {2022-09-04T18:12:16.438Z},
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 citation_key = {Tushaus2019},
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 abstract = {Measuring peripheral oxygen saturation (SpO2) with pulse oximeters at the point of care is widely established. However, since [Formula: see text] is dependent on ambient atmospheric pressure, the distribution of SpO2 values in populations living above 2000 m a.s.l. is largely unknown. Here, we propose and evaluate a computer model to predict SpO2 values for pediatric permanent residents living between 0 and 4,000 m a.s.l. Based on a sensitivity analysis of oxygen transport parameters, we created an altitude-adaptive SpO2 model that takes physiological adaptation of permanent residents into account. From this model, we derived an altitude-adaptive abnormal SpO2 threshold using patient parameters from literature. We compared the obtained model and threshold against a previously proposed threshold derived statistically from data and two empirical data sets independently recorded from Peruvian children living at altitudes up to 4,100 m a.s.l. Our model followed the trends of empirical data, with the empirical data having a narrower healthy SpO2 range below 2,000 m a.s.l. but the medians never differed more than 2.3% across all altitudes. Our threshold estimated abnormal [Formula: see text] in only 17 out of 5,981 (0.3%) healthy recordings, whereas the statistical threshold returned 95 (1.6%) recordings outside the healthy range. The strength of our parametrized model is that it is rooted in physiology-derived equations and enables customization. Furthermore, as it provides a reference SpO2, it could assist practitioners in interpreting SpO2 values for diagnosis, prognosis, and oxygen administration at higher altitudes.},
 bibtype = {article},
 author = {Tüshaus, Laura and Moreo, Monica and Zhang, Jia and Hartinger, Stella Maria and Mäusezahl, Daniel and Karlen, Walter},
 doi = {10.1152/japplphysiol.00478.2018},
 journal = {Journal of Applied Physiology},
 number = {3}
}

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