Supine Bicycle Stress Echocardiography at Low Altitude for Identification of Susceptibility to Acute Mountain Sickness.

J Am Soc Echocardiogr

Key Laboratory of Ultrasound in Cardiac Electrophysiology and Biomechanics of Sichuan Province, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China. Electronic address:

Published: December 2024

Background: Exposure to high altitude may unpredictably lead to acute mountain sickness (AMS). The purpose of this study was to identify the predictors of AMS at low altitude using exercise stress echocardiography (ESE).

Methods: A total of 40 healthy adults were enrolled and underwent comprehensive supine bicycle ESE at low altitude, including pulmonary vascular resistance (PVR), right ventricular area index at the end of diastole, B-lines, and inferior vena cava (IVC) diameter. All subjects ascended to 3,600 m within 24 hours. The risk factors for AMS were screened using least absolute shrinkage and selection operator regression analysis. A novel nomogram model was then established using multivariable logistic regression analysis, and a clinical impact curve was constructed.

Results: At the altitude of 3,600 m, 20 of 40 subjects had AMS (AMS group). On least absolute shrinkage and selection operator regression analyses, PVR, IVC, and B-lines at peak exercise were all independent factors influencing AMS. The nomogram built on the basis of these factors predicted AMS with sensitivity of 0.950 and specificity of 0.804, which outperformed the individual predictive C indexes of each indicator (nomogram: cutoff, 59.3; area under the curve [AUC], 0.90 [95% CI, 0.80-1.00]; PVR at peak exercise: cutoff, 1.55; AUC, 0.81 [95% CI, 0.70-0.91]; B-lines at peak exercise: cutoff, 1; AUC, 0.78 [95% CI, 0.69-0.92]; IVC at peak exercise: cutoff, 13.8; AUC, 0.74 [95% CI, 0.65-0.87]). The established model was validated by plotting the clinical decision curve analysis and clinical impact curve.

Conclusions: Supine bicycle ESE is a useful technique to identify subjects susceptible to AMS. This study established a nomogram to predict the development to AMS with high discrimination and accuracy.

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http://dx.doi.org/10.1016/j.echo.2024.12.007DOI Listing

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