Evidence map›Paper›PMID 30187693›Full record

ArticlePhysiological reports2018

The Smell of Hypoxia: using an electronic nose at altitude and proof of concept of its role in the prediction and diagnosis of acute mountain sickness.

Jonathan R N Lacey, Carlos Kidel, Jildou M van der Kaaij, Paul Brinkman, Edward T Gilbert-Kawai, Michael P W Grocott, Michael G Mythen, Daniel S Martin, Xtreme Everest 2 Research Group

Open access · goldAbstract read
In one paragraph

Article in Physiological reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
0.7field-weighted citation impact, top 32% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed, 13 citations in OpenAlex.

  1. Review
  2. Observational
  3. Review
  4. Article
  5. Review
  6. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors at 4 institutions in 2 countries.

Jonathan R N LaceyUniversity College London Centre for Altitude Space and Extreme Environment (CASE) Medicine, UCLH NIHR Biomedical Research Centre, Institute of Sport and Exercise Health, London, United Kingdom.
Carlos KidelRoyal Free London NHS Foundation Trust, London, United Kingdom.
Jildou M van der KaaijUniversity College London Centre for Altitude Space and Extreme Environment (CASE) Medicine, UCLH NIHR Biomedical Research Centre, Institute of Sport and Exercise Health, London, United Kingdom.
Paul BrinkmanRespiratory Medicine, AMC, University of Amsterdam, Amsterdam, Netherlands.
Edward T Gilbert-KawaiUniversity College London Centre for Altitude Space and Extreme Environment (CASE) Medicine, UCLH NIHR Biomedical Research Centre, Institute of Sport and Exercise Health, London, United Kingdom.
Michael P W GrocottUniversity College London Centre for Altitude Space and Extreme Environment (CASE) Medicine, UCLH NIHR Biomedical Research Centre, Institute of Sport and Exercise Health, London, United Kingdom.
Michael G MythenUniversity College London Centre for Altitude Space and Extreme Environment (CASE) Medicine, UCLH NIHR Biomedical Research Centre, Institute of Sport and Exercise Health, London, United Kingdom.
Daniel S MartinUniversity College London Centre for Altitude Space and Extreme Environment (CASE) Medicine, UCLH NIHR Biomedical Research Centre, Institute of Sport and Exercise Health, London, United Kingdom.
Xtreme Everest 2 Research Group
National Institute for Health Research · GBUniversity College London · GBRoyal Free London NHS Foundation Trust · GBUniversity of Amsterdam · NL

Funding

Department of Health
6 · The paper itself

Abstract

Electronic nose (e-nose) devices may be used to identify volatile organic compounds (VOCs) in exhaled breath. VOCs generated via metabolic processes are candidate biomarkers of (patho)physiological pathways. We explored the feasibility of using an e-nose to generate human "breathprints" at high altitude. Furthermore, we explored the hypothesis that pathophysiological processes involved in the development of acute mountain sickness (AMS) would manifest as altered VOC profiles. Breath analysis was performed on Sherpa and lowlander trekkers at high altitude (3500 m). The Lake Louise Scoring (LLS) system was used to diagnose AMS. Raw data were reduced by principal component (PC) analysis (PCA). Cross validated linear discriminant analysis (CV-LDA) and receiver-operating characteristic area under curve (ROC-AUC) assessed discriminative function. Breathprints suitable for analysis were obtained from 58% (37/64) of samples. PCA showed significant differences between breathprints from participants with, and without, AMS; CV-LDA showed correct classification of 83.8%, ROC-AUC 0.86; PC 1 correlated with AMS severity. There were significant differences between breathprints of participants who remained AMS negative and those whom later developed AMS (CV-LDA 68.8%, ROC-AUC 0.76). PCA demonstrated discrimination between Sherpas and lowlanders (CV-LDA 89.2%, ROC-AUC 0.936). This study demonstrated the feasibility of breath analysis for VOCs using an e-nose at high altitude. Furthermore, it provided proof-of-concept data supporting e-nose utility as an objective tool in the prediction and diagnosis of AMS. E-nose technology may have substantial utility both in altitude medicine and under other circumstances where (mal)adaptation to hypoxia may be important (e.g., critically ill patients).

Indexed as

AdultAltitude SicknessBreath TestsElectronic NoseFemaleHumansMaleProof of Concept StudySensitivity and SpecificityVolatile Organic CompoundsVolatile Organic CompoundsAltitude sicknessbreath testse-Nosehypoxiavolatile organic compounds

Identifiers

PMID30187693
PMCPMC6125242
OpenAlexW2890551138

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.