Predisposing Factors for Exertional Heat Illness

Pryor, J. Luke; Périard, Julien D.; Pryor, Riana R.

doi:10.1007/978-3-030-27805-2_3

Cited by 10 publications

(10 citation statements)

References 130 publications

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“…It is also clear that the nature and cause of EHI are not fully understood, and most recently, the human EHI literature has turned to the role of intrinsic risk factors as an explanation for its continued incidence despite the establishment of significant regulatory and preventative strategies. [26][27][28][29][30] In this study, R 2 was only 43% for all weather elements in PEHI and 53% in EHIRS. These apparently low values agree with the human experience in athletic, military and other occupational fields, 26 the residual variation being possibly attributable to individual intrinsic risk factors and/or to environmental factors other than the weather.…”

Section: Discussioncontrasting

confidence: 56%

An investigation into environmental variables influencing post‐race exertional heat illness in thoroughbred racehorses in temperate eastern Australia

Brownlow

Brotherhood

2021

Aust Veterinary J

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Exertional heat illness (EHI) is a syndrome that occurs when metabolic heat production from muscular contraction exceeds the rate at which it can be dissipated. Core body temperature rises to critical levels, causing hyperthermia and central nervous system dysfunction. Best practice for the prevention of EHI centres around early detection, rapid response and aggressive cooling. Advance planning enables risk mitigation measures. The more that is known about EHI in horses, the better prepared those in the positions of responsibility can be to anticipate events in which the risk of EHI may be elevated. This prospective, observational study investigated environmental risk factors associated with the occurrence of EHI. From 2014 to 2018, the number of horses exhibiting the symptoms of post-race EHI was recorded at 73 convenience sampled race meetings. Of the 4809 starters, the signs of EHI were recorded for 457. Thermal environmental data were measured and included ambient temperature, radiant heat, vapor pressure (humidity) and wind speed (WS). Mixed linear regression models were computed to assess the associations between the occurrence and incidence of post-race EHI and the four thermal environmental variables. The analysis showed that vapor pressure and WS had the largest effects on the occurrence of post-race EHI. The major limitations were that the race meetings selected were convenience sampled according to attendance by the first author and individual horse data were not available. EHI is influenced by a complex interaction of independently acting environmental variables, but warm, windless, humid days are most likely to result in the cases of EHI.Keywords ambient temperature; environmental variables; exertional heat illness; radiant heat; thoroughbred racehorses; vapour pressure; wet-bulb globe temperature; wind speed Abbreviations DB, Tdb ( C), dry-bulb air temperature; EHI, exertional heat illness; EHIRS, exertional heat illness race status (i.e. did at least one horse show post-race symptoms of EHI? [yes/no]); MRT ( C), mean radiant temperature; PEHI, percentage of horses showing the signs of post-race EHI; Tg ( C), globe temperature; Twb ( C), wet-bulb temperature; VP, vapour pressure (also referred to as absolute humidity, kPa); WBGT ( C), wet-bulb globe temperature; WS, wind speed (m/s)

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Section: Discussioncontrasting

confidence: 56%

An investigation into environmental variables influencing post‐race exertional heat illness in thoroughbred racehorses in temperate eastern Australia

Brownlow

Brotherhood

2021

Aust Veterinary J

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“… Major Reference Pertaining to each Priority for Table 2 :‐ (1) Pryor et al. (2020); (2) Brownlow and Smith (2021); (3) Casa et al. (2012); (4) Proulx et al .…”

Section: The Treatment Of Ehi – Early Detection Rapid Assessment and ...mentioning

confidence: 99%

Exertional heat illness in Thoroughbred racehorses – Pathophysiology, case definition and treatment rationale

Brownlow

Mizzi²

2021

Equine Veterinary Education

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The combination of extreme heat, humidity and strenuous exercise is potentially challenging to the thermoregulatory system of a Thoroughbred racehorse, and under certain circumstances can cause a condition called exertional heat illness (EHI). It is difficult to predict its occurrence in an individual animal due to the combination on race day of extrinsic and intrinsic risk factors. Education regarding EHI is essential to its prevention, and lack of understanding amongst those responsible for the horse's care may in fact be the most important risk factor. The pathophysiology of EHI is similar across elite athletic species and is directly related to an elevation in core body temperature that exceeds the 'critical thermal maximum', causing widespread destruction of cells. It is not only the high core temperature but also its duration that will dictate outcome. Treatment strategies should decrease the hyperthermia to near normal levels within 30 min of onset, which will minimise adverse consequences.This review article outlines the pathophysiological changes as EHI progresses, and the case definition at each emergent level. Pharmacological treatment strategies and their rationale are presented. Aggressive cooling, however, is the key treatment and requires a technique which is both practical and effective in rapidly reducing core body temperature. The treatment paradigm is early detection, rapid assessment enabling prioritisation of individuals and aggressive cooling. Each stage is critically discussed.

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“…Additionally, the rates of heat accumulation and dissipation and, in turn, individual CBT have shown to be affected by individual variables, including gender, age, level of physical fitness, skin surface to body mass ratio, hydration status, sleep deprivation, and phase of the menstrual cycle [ 36 ]. Studies have also shown that the chances of HRI are increased by up to 40% in those with previous episodes of HRI; skin conditions increasing heat storage; cardiac or thyroid conditions preventing proper thermoregulation; those experiencing alcohol or opioid withdrawal; and those taking medications that affect the body’s ability to thermoregulate, including anticholinergics, antipsychotics, antihistamines, and stimulants [ 9 , 10 , 28 , 35 ]. However, the subject pools in the reported studies did not show much variability, with the average subject age and mass ranging from 21.6 to 31.1 years and 72.1 to 81.0 kg, only a third of the reviewed algorithms including female subjects in addition to male and most studies standardizing the results by excluding participants with medical conditions or taking medication.…”

Section: Discussionmentioning

confidence: 99%

Wearable Sensor Technology to Predict Core Body Temperature: A Systematic Review

Dolson

Harlow

Phelan

et al. 2022

Sensors

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Heat-related illnesses, which range from heat exhaustion to heatstroke, affect thousands of individuals worldwide every year and are characterized by extreme hyperthermia with the core body temperature (CBT) usually > 40 °C, decline in physical and athletic performance, CNS dysfunction, and, eventually, multiorgan failure. The measurement of CBT has been shown to predict heat-related illness and its severity, but the current measurement methods are not practical for use in high acuity and high motion settings due to their invasive and obstructive nature or excessive costs. Noninvasive predictions of CBT using wearable technology and predictive algorithms offer the potential for continuous CBT monitoring and early intervention to prevent HRI in athletic, military, and intense work environments. Thus far, there has been a lack of peer-reviewed literature assessing the efficacy of wearable devices and predictive analytics to predict CBT to mitigate heat-related illness. This systematic review identified 20 studies representing a total of 25 distinct algorithms to predict the core body temperature using wearable technology. While a high accuracy in prediction was noted, with 17 out of 18 algorithms meeting the clinical validity standards. few algorithms incorporated individual and environmental data into their core body temperature prediction algorithms, despite the known impact of individual health and situational and environmental factors on CBT. Robust machine learning methods offer the ability to develop more accurate, reliable, and personalized CBT prediction algorithms using wearable devices by including additional data on user characteristics, workout intensity, and the surrounding environment. The integration and interoperability of CBT prediction algorithms with existing heat-related illness prevention and treatment tools, including heat indices such as the WBGT, athlete management systems, and electronic medical records, will further prevent HRI and increase the availability and speed of data access during critical heat events, improving the clinical decision-making process for athletic trainers and physicians, sports scientists, employers, and military officers.

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Predisposing Factors for Exertional Heat Illness

Cited by 10 publications

References 130 publications

An investigation into environmental variables influencing post‐race exertional heat illness in thoroughbred racehorses in temperate eastern Australia

An investigation into environmental variables influencing post‐race exertional heat illness in thoroughbred racehorses in temperate eastern Australia

Exertional heat illness in Thoroughbred racehorses – Pathophysiology, case definition and treatment rationale

Wearable Sensor Technology to Predict Core Body Temperature: A Systematic Review

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