Why is knowledge from mountain medicine valuable even in countries where high mountain ranges are not readily available? What attracts medical students to this field, and what are the latest scientific insights? All this was presented to MeDitorial by MUDr. Lenka Horáková, Ph.D., from the Department of Anesthesiology, Intensive and Emergency Medicine of the Third Faculty of Medicine, Charles University, and Bulovka University Hospital in Prague.
According to Dr. Horáková, mountain medicine is characterized by the combination of extreme physiology and the mountain environment, where solutions often have to be found without modern hospital facilities. She herself focuses on what she considers one of the most important areas within mountain medicine — the physiology and pathology of hypoxia, which typically occurs with decreasing atmospheric pressure at increasing altitude (hypobaric hypoxia), but can also be simulated using specialized equipment (normobaric hypoxia).
So-called hypoxic training, developed on the basis of this knowledge, is used not only by elite athletes but also, for example, by patients treated for cardiological conditions. These insights also help to better understand the physiology of critically ill patients in intensive care units.
Mountain medicine also has a distinct Czech footprint. Several research teams in the Czech Republic focus on topics in this field. Dr. Horáková is a member of a team led by Prof. Ing. Karel Roubík, Ph.D., from the Faculty of Biomedical Engineering at the Czech Technical University in Prague. His research focuses on physiological changes in individuals buried by avalanche snow and breathing into it, for which experimental simulations are being developed.
Motivation for research in this area also comes from findings of a related questionnaire survey, showing that people buried in avalanches recall various guidelines and try to apply them. “It therefore makes sense to pass on recommendations, because even in these crisis situations people are able to recall them,” the physician adds.
Dr. Horáková considers it her mission to bring mountain medicine back to academia as an independent discipline. She has strong support from the leadership of her home institution, where teaching of medical students began last academic year in an elective course titled Mountain Medicine. Student interest was so high that the course’s time allocation was expanded and it became a two-semester subject.
According to Dr. Horáková, knowledge from mountain medicine is also useful for physicians working under local conditions, as Czech travelers frequently head to high-altitude regions and may appreciate consultation on the health aspects of their journey — whether before, after, or remotely during the trip.
Mountain medicine also led Dr. Horáková to the conviction that anesthesiology, emergency medicine, and intensive care medicine need to be more closely integrated. “We can observe, for example, that many physicians working in emergency medicine began their professional careers in anesthesiology and intensive care. Unfortunately, they often had to combine work in these two fields in a rather complicated way,” she says.
In conclusion, she highlights another step that could change the rules of the game in this field: greater use of the potential of machine learning applied to medical data.
“In intensive care medicine, we have patients who, throughout their illness, are connected to a wide range of sophisticated devices that measure their vital functions and record them with remarkable precision — up to 300 data points per second. However, we mostly do not use these data at all, often we do not even record them, and yet we already know from partial studies that they contain a great deal of important information,” Dr. Horáková explains, adding:
“In further research projects, I work, for example, with data from patients with acute brain injury, whether due to trauma or following resuscitation after cardiac arrest. Here, machine learning may in the future help us individualize treatment parameters so that patient outcomes are as good as possible. For machine learning, however, it is absolutely essential that models have a sufficient amount of high-quality data. For me, this is one of the key challenges of the coming years — ensuring these high-quality input data for machine learning models.”
Editorial Team, Medscope.pro
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