Predictive Capacity of Pulmonary Function Tests for Acute Mountain Sickness

Small, Elan; Juul, Nicholas H.; Pomeranz, David; Burns, Patrick R.; Phillips, Caleb; Cheffers, Mary; Lipman, Grant S.

doi:10.1089/ham.2020.0150

Cited by 7 publications

(3 citation statements)

References 40 publications

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“…The results showed that non-invasive measurement of SaO 2 after 30 min of hypoxic exposure was simple, practical, and capable of identifying AMS-susceptible individuals with high sensitivity; moreover, combining it with respiratory rate measurements further improved the success rate in predicting AMS. Despite limited data and inconsistent results on the association between lung function tests and AMS, Small et al from Stanford University School of Medicine ( Small et al, 2021 ) explored the predictive capacity of pulmonary function tests through a double-blind, prospective observational study. They compared acetazolamide, budesonide, and placebo in preventing AMS in a randomized controlled trial at White Mountain, California.…”

Section: Research On Predictive Indicators and Model Construction For...mentioning

confidence: 99%

Recent advances in predicting acute mountain sickness: from multidimensional cohort studies to cutting-edge model applications

Wang,

Chen,

Song

et al. 2024

Front. Physiol.

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High-altitude illnesses, encompassing a spectrum of health threats including Acute Mountain Sickness (AMS), pose significant challenges to individuals exposed to high altitude environments, necessitating effective prophylaxis and immediate management. Given the variability in individual responses to these conditions, accurate prediction of high-altitude illnesses onset is of paramount importance. This review systematically consolidates recent advancements in research on predicting AMS by evaluating existing cohort data, predictive models, and methodologies, while also delving into the application of emerging technologies. Through a thorough analysis of scholarly literature, we discuss traditional prediction methods anchored in physiological parameters (e.g., heart rate, respiratory frequency, blood pressure) and biochemical markers, as well as the integration and utility of novel technologies such as biosensors, genetic testing, and artificial intelligence within high-altitude prediction research. While conventional pre-diction techniques have been extensively used, they are often constrained by limitations in accuracy, reliability, and multifactorial influences. The advent of these innovative technologies holds promise for more precise individual risk assessments and personalized preventive and therapeutic strategies across various forms of AMS. Future research endeavors must pivot decisively towards the meticulous identification and stringent validation of innovative predictive biomarkers and models. This strategic re-direction should catalyze intensified interdisciplinary cooperation to significantly deepen our mechanistic insights into the pathogenesis of AMS while refining existing prediction methodologies. These groundbreaking advancements harbor the potential to fundamentally transform preventive and therapeutic frameworks for high-altitude illnesses, ultimately securing augmented safety standards and wellbeing for individuals operating at elevated altitudes with far-reaching global implications.

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Section: Research On Predictive Indicators and Model Construction For...mentioning

confidence: 99%

Recent advances in predicting acute mountain sickness: from multidimensional cohort studies to cutting-edge model applications

Wang,

Chen,

Song

et al. 2024

Front. Physiol.

View full text Add to dashboard Cite

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“…Additionally, statistically significant differences usually require large-scale and/or randomized controlled trials, which are currently almost impossible to complete under high-altitude circumstances. In addition, pulmonary function testing 12 , cardiopulmonary exercise testing 13 , and hypoxic exercise testing 14 have been used to assess the risk of hypoxemia, but the applicability of these measurements to high-altitude exposure has not been fully established 2 .…”

Section: Introductionmentioning

confidence: 99%

Establishing a prediction model of severe acute mountain sickness using machine learning of support vector machine recursive feature elimination

Yang¹,

Wu²,

Yang³

et al. 2023

Sci Rep

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Severe acute mountain sickness (sAMS) can be life-threatening, but little is known about its genetic basis. The study was aimed to explore the genetic susceptibility of sAMS for the purpose of prediction, using microarray data from 112 peripheral blood mononuclear cell (PBMC) samples of 21 subjects, who were exposed to very high altitude (5260 m), low barometric pressure (406 mmHg), and hypobaric hypoxia (VLH) at various timepoints. We found that exposure to VLH activated gene expression in leukocytes, resulting in an inverted CD4/CD8 ratio that interacted with other phenotypic risk factors at the genetic level. A total of 2286 underlying risk genes were input into the support vector machine recursive feature elimination (SVM-RFE) system for machine learning, and a model with satisfactory predictive accuracy and clinical applicability was established for sAMS screening using ten featured genes with significant predictive power. Five featured genes (EPHB3, DIP2B, RHEBL1, GALNT13, and SLC8A2) were identified upstream of hypoxia- and/or inflammation-related pathways mediated by microRNAs as potential biomarkers for sAMS. The established prediction model of sAMS holds promise for clinical application as a genetic screening tool for sAMS.

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“…Also, statistically signi cant differences usually require large scale and/ or randomized controlled trials, which are currently almost impossible to complete under high-altitude circumstance. Beside, pulmonary-function testing [12], cardiopulmonary exercise testing [13], and hypoxic exercise testing [14] have been used to assess the risk of hypoxemia, but the applicability of these measurements to high-altitude exposure has not been fully established [2].…”

mentioning

confidence: 99%

Establishing a prediction model of severe acute mountain sickness using deep learning of support vector machine recursive feature elimination

Yang¹,

Wu²,

Yang³

et al. 2023

Preprint

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Background Severe acute mountain sickness (sAMS) can be life-threatening, but little is known about its genetic basis. Using microarray genotype data and phenotype data for deep learning, we aimed to explore the genetic susceptibility of sAMS for the purpose of prediction. Methods The study was based on microarray data from 112 peripheral blood mononuclear cell (PBMC) samples of 21 subjects, who were exposed to very high altitude (5260 m), low barometric pressure (406 mmHg), and hypobaric hypoxia (VLH) at various timepoints. Subjects were investigated for the interplay effects between multiple phenotypic risk factors, and the underlying risk genes were identified to establish the prediction model of sAMS using the support vector machine recursive feature elimination (SVM-RFE) method. Results Exposure to VLH activated the gene expression in leukocytes, resulting in inverted CD4/CD8 ratio which interplayed with other phenotypic risk factors at the genetic level (P < 0.001). 2291 underlying risk genes were input to SVM-RFE system for deep learning, and a prediction model was established with satisfactory predictive accuracy (C-index = 1), and clinical applicability for sAMS using ten featured genes with significant predictive power (P < 0.05). Five featured genes (EPHB3, DIP2B, RHEBL1, GALNT13, and SLC8A2) were identified as the upstream of hypoxia and/ or inflammation-related pathways mediated by micorRNAs as potential biomarkers for sAMS. Conclusions The established prediction model of sAMS holds promise to be clinically applied as a genetic screening tool for sAMS. More studies are needed to establish the role of the featured genes as biomarker for sAMS.

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Predictive Capacity of Pulmonary Function Tests for Acute Mountain Sickness

Cited by 7 publications

References 40 publications

Recent advances in predicting acute mountain sickness: from multidimensional cohort studies to cutting-edge model applications

Recent advances in predicting acute mountain sickness: from multidimensional cohort studies to cutting-edge model applications

Establishing a prediction model of severe acute mountain sickness using machine learning of support vector machine recursive feature elimination

Establishing a prediction model of severe acute mountain sickness using deep learning of support vector machine recursive feature elimination

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