Paperdoi:10.3389/fphys.2026.1939874
Prediction of acute mountain sickness from overnight pulse oximetry during ascent to Everest Base Camp: a field-based machine learning study
Frontiers in Physiology (published 30 Sep 2026)
(opens in a new tab)Helen Abbott et al., "Prediction of acute mountain sickness from overnight pulse oximetry during ascent to Everest Base Camp: a field-based machine learning study", doi:10.3389/fphys.2026.1939874, CC BY 4.0
TL;DR
Field ML study: healthy lowlanders (n≈139–144 across datasets) overnight pulse oximetry at Namche (3,440 m) and Dingboche-level (~4,371–4,730 m) during Everest Base Camp ascent; predict physician-diagnosed AMS at Lobuche (~4,940 m). Hardware: Nonin WristOx2 Model 3150 wrist unit + flexible finger PureLight sensor (Adult 8000J); 1 Hz SpO₂ + HR; 125 biomarkers. Biomarker-only logistic regression mean AUC 0.74 at both altitudes; at Dingboche-level, biomarkers + metadata AUC 0.84 ± 0.08 (sens 0.73 ± 0.23).
- Form factor: finger
Abstract
Background
Acute mountain sickness (AMS) affects a large proportion of individuals ascending to high altitude every year. While AMS is not life threatening, if left untreated it can progress to serious illness (e.g., high altitude cerebral oedema). Current assessment tools rely on subjective symptom reporting and do not enable prospective prediction. Objective, field-deployable methods for early risk stratification remain limited. This study evaluated whether physiological biomarkers derived from overnight pulse oximetry can predict subsequent AMS during ascent to Everest Base Camp (EBC).
Methods
A field study was conducted in the Khumbu region of Nepal. Healthy lowlanders (n = 139–144 across datasets) underwent overnight pulse oximetry recordings at Namche Bazaar (3,440 m) and Dingboche-level (4,371-4,730 m). A total of 125 physiological features were extracted from continuous, overnight oxygen saturation (SpO 2 ) and heart rate recordings. Supervised machine-learning models were trained to classify physician-diagnosed AMS at Lobuche (~4,940 m) using: 1) physiological biomarkers alone and 2) biomarkers combined with clinical and contextual metadata. Model performances were assessed using repeated stratified k-fold cross-validation, with area under the receiver operating characteristic curve (AUC) as the primary metric.
Results
Biomarker-only models demonstrated consistent above-chance discrimination at both altitudes (mean AUC 0.74 at both Namche and Dingboche-level). At lower altitude, predictive performance was achieved using a small set of physiological features, and inclusion of metadata did not improve model performance. At higher altitude, combining biomarkers with contextual variables improved discrimination (AUC 0.84 ± 0.08; sensitivity 0.73 ± 0.23). Selected features were primarily related to nocturnal oxygenation burden, desaturation dynamics, and heart rate variability.
Conclusion
Overnight pulse oximetry captures physiological signals associated with subsequent AMS development prior to symptom onset. These findings support the feasibility of objective, non-invasive risk stratification during early ascent. Integration of wearable monitoring with computational modelling may provide a complementary tool to support preventative decision-making along high-altitude trekking routes.
Verbatim from the publisher's DOI record (opens in a new tab) · Publisher licence: https://creativecommons.org/licenses/by/4.0/
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Cite this paper
APA
Abbott, H., Cutts, L. R., Stanley, B., Kansakar, S., Broughton, S. E., Scott, T., Davis, S., Clarke, S., Clark, T., Simpkins, C., Goves, J., Howes, E., Pattinson, K. T., Garcia, A., Trender, W., Bradwell, A. R., Baniya, S., Edsell, M., Ashdown, K., & Joyce, K. E. (2026). Prediction of acute mountain sickness from overnight pulse oximetry during ascent to Everest Base Camp: a field-based machine learning study. Frontiers in Physiology. https://doi.org/10.3389/fphys.2026.1939874
BibTeX
@article{abbott2026prediction,
title = {{Prediction of acute mountain sickness from overnight pulse oximetry during ascent to Everest Base Camp: a field-based machine learning study}},
author = {Abbott, Helen and Cutts, Luke R. and Stanley, Ben and Kansakar, Suniya and Broughton, Sophie E. and Scott, Taea and Davis, Sam and Clarke, Sarah and Clark, Tim and Simpkins, Ciaran and Goves, Johnathan and Howes, Ewein and Pattinson, Kyle T. and Garcia, Adrian and Trender, William and Bradwell, Arthur R. and Baniya, Santosh and Edsell, Mark and Ashdown, Kimberly and Joyce, Kelsey E.},
year = {2026},
journal = {Frontiers in Physiology},
doi = {10.3389/fphys.2026.1939874}
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