When Should a Heat-Tolerance Test Be Scheduled After Clinical Recovery From an Exertional Heat Illness?

OBJECTIVE: Researchers have produced a hypothesis of transient heat intolerance (HI) after exertional heat stroke (EHS). Based on this hypothesis, heat-tolerance testing (HTT) has been postponed until weeks 6 to 8 after EHS and other types of exertional heat illness (EHI). We compared the HTT results of participants after either EHS or other EHI who were tested earlier (≤6-week group) versus those who were tested later (>6-week group) to verify the hypothesis. DESIGN: Cohort study. SETTING: Data obtained from records of military athletes who experienced EHS or EHI. PATIENTS OR OTHER PARTICIPANTS: All participants who underwent HTT after EHI or EHS experienced between 2014 and 2018 and for whom complete data regarding the severity of the event (rectal temperature, neurologic symptoms, and laboratory results) and HTT results were available were included. Participants with suspected EHS and those with other EHIs were evaluated separately. MAIN OUTCOME MEASURE(S): The percentages of participants with HI and mean probability of heat tolerance were compared between those tested within 6 weeks of the event and those tested later. RESULTS: A total of 186 participants were included in this study (EHS: 12 in the <6-week group, 9 in the >6-week group; EHI: 94 in the <6-week group, 71 in the >6-week group). In the EHS group, the percentages with HI (33% versus 44%, P = .67) and mean probability of heat tolerance (0.82 versus 0.82, P = .98) did not differ. In the EHI group, participants who were tested after 6 weeks had a greater chance of being diagnosed with HI (38% versus 21.3%, P < .02). CONCLUSIONS: The HTT results were similar between participants with EHS who were tested early (<6 weeks) and those tested late (>6 weeks). Further investigation of heat-tolerance changes in larger cohorts of patients after EHS is required to verify the theory of transient HI.

Probability of Heat Intolerance: Standardized Interpretation of Heat-Tolerance Testing Results Versus Specialist Judgment.

CONTEXT: The heat-tolerance test (HTT) is a screening tool for secondary prevention of exertional heat illness by the Israel Defense Forces. To discern participant tolerance, recruits are exposed to intermediate environmental and exercise stresses, and their physiological responses, core temperature, and heart rate are monitored. When their physiological measures rise at a higher rate or exceed the upper levels of absolute values compared with other participants, heat intolerance (HI) is diagnosed. OBJECTIVE: To develop a mathematical model to interpret HTT results and provide a quantitative estimate of the probability of heat tolerance (PHT). DESIGN: Cross-sectional study. SETTING: Warrior Health Research Institute. PATIENTS OR OTHER PARTICIPANTS: The HTT results of 175 random individuals tested after an episode of exertional heat illness were classified qualitatively and then divided into training (n = 112) and testing (n = 63) datasets. All individuals were male soldiers (age range = 18-22 years) who had sustained an episode of definitive or suspected exertional heat stroke. MAIN OUTCOME MEASURE(S): Based on the decision algorithm used by the Israel Defense Forces for manual interpretation of the HTT, we designed a logistic regression model to predict the heat-tolerance state. The model used a time series of physiological measures (core temperature and heart rate) of individuals to predict the manually assigned diagnosis of HT or HI. It was initially fitted and then tested on 2 separate, random datasets. The model produced a single value, the PHT, and its predictive ability was demonstrated by prediction-density plots, receiver operating characteristic curve, contingency tables, and conventional screening test evaluation measures. RESULTS: According to prediction-density plots of the testing set, all HT patients had a PHT of 0.7 to 1. The receiver operating characteristic curve plot showed that PHT was an excellent predictor of the manual HT interpretations (area under the curve = 0.973). Using a cutoff probability of 0.5 for the diagnosis of HI, we found that PHT had sensitivity, specificity, and accuracy of 100%, 90%, and 92.06%, respectively. CONCLUSIONS: The PHT has the potential to be substituted for manual interpretation of the HTT and to serve in a variety of clinical and research applications.