Experimental Physiology· 2026Q2
Prediction of acute mountain sickness occurring at 4554 m using overnight pulse oximetry from lower altitude: A pilot study
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- Q2SCImago
- 2026year
Short summary
Acute mountain sickness (AMS) at 4554 m can be predicted using overnight pulse oximetry data collected at lower altitudes (2600 m and 3647 m) with machine learning models, achieving 100% accuracy from 2600 m data.
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Key points
- Overnight pulse oximetry data collected at 2600 m predicted AMS at 4554 m with 100% accuracy using k-nearest neighbour and support vector machine models.
- Data collected at 3647 m predicted AMS at 4554 m with 80% accuracy and a 90.9% true positive rate.
- Limited differences were observed in overnight oximetry biomarkers between individuals who developed AMS and those who did not during ascent.
- This pilot study demonstrates the potential of machine learning applied to overnight oximetry for AMS prediction.
AI-generated from the title and abstract; the full text is not read.
Abstract
Abstract Acute mountain sickness (AMS) affects many individuals ascending to high altitude annually, carrying potential morbidity and mortality risks (e.g., high‐altitude cerebral oedema). The objective of this pilot study was to record overnight pulse oximetry during ascent to 4554 m and determine whether: (1) extracted overnight biomarkers had any relationship to AMS; and (2) AMS at 4554 m could be predicted by oximetry biomarkers using classification models trained/validated on overnight data from lower altitudes. Twenty lowlanders (five females, 36.8 ± 18.5 years old) completed a 4 day ascent to 4554 m, where AMS status was determined by clinical examination. Oximetry was recorded continuously each night, with >40 biomarkers extracted and compared between AMS and non‐AMS. Exploratory classification models (5‐fold cross‐validation) were tested for nightly data with a systematic approach to feature selection. Model performances were evaluated based on predictions of AMS at 4554 m. A significant effect of ascent was observed for many overnight biomarkers; however, no effect of AMS status was evident, nor were any differences observed between AMS and non‐AMS for any overnight biomarkers during ascent. AMS at 4554 m was most accurately predicted from overnight recordings collected at 2600 m (accuracy, 100%; true positive rate, 100%) and 3647 m (accuracy, 80%; true positive rate, 90.9%) using k ‐nearest neighbour and support vector machine classification models. Biomarkers were extracted from overnight oximetry recordings, with limited differences observed between AMS and non‐AMS. In conclusion, AMS at 4554 m can be predicted from overnight oximetry using machine learning; however, these preliminary findings need confirmation in a larger cohort, with additional investigation into the most clinically relevant biomarkers.
The authors' abstract, as published at the source. Experimental Physiology, 2026 · DOI ↗
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Field: Genetics (Biochemistry, Genetics and Molecular Biology)
GeneticsBiochemistry, Genetics and Molecular Biology