Prediction of acute mountain sickness from overnight pulse oximetry during ascent to Everest Base Camp: a field-based machine learning study.

@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 = {Helen Abbott and Luke R Cutts and Ben Stanley and Suniva Kansakar and Sophie E Broughton and Taea Scott and Sam Davis and Sarah Clarke and Tim Clark and Ciaran Simpkins and Johnathan Goves and Ewein Howes and Kyle T Pattinson and Adrian Garcia and William Trender and Arthur R Bradwell and Santosh Baniya and Mark Edsell and Kimberly Ashdown and Kelsey E Joyce},
  journal = {Frontiers in physiology},
  year = {2026},
  month = sep,
  doi = {10.3389/fphys.2026.1939874},
  url = {https://europepmc.org/article/MED/42903699},
  note = {CC BY},
}

TY  - JOUR
TI  - Prediction of acute mountain sickness from overnight pulse oximetry during ascent to Everest Base Camp: a field-based machine learning study.
AU  - Helen Abbott
AU  - Luke R Cutts
AU  - Ben Stanley
AU  - Suniva Kansakar
AU  - Sophie E Broughton
AU  - Taea Scott
AU  - Sam Davis
AU  - Sarah Clarke
AU  - Tim Clark
AU  - Ciaran Simpkins
AU  - Johnathan Goves
AU  - Ewein Howes
AU  - Kyle T Pattinson
AU  - Adrian Garcia
AU  - William Trender
AU  - Arthur R Bradwell
AU  - Santosh Baniya
AU  - Mark Edsell
AU  - Kimberly Ashdown
AU  - Kelsey E Joyce
PY  - 2026
DA  - 2026/09/30
JO  - Frontiers in physiology
DO  - 10.3389/fphys.2026.1939874
UR  - https://europepmc.org/article/MED/42903699
AB  - 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 (SpO₂) 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.
SN  - 1664-042X
ER  -