Heat stroke management during the COVID-19 pandemic: Recommendations from the experts in Japan (2nd edition).

Both coronavirus disease 2019 (COVID-19) and heat stroke have symptoms of fever or hyperthermia and the difficulty in distinguishing them could lead to a strain on emergency medical care. To mitigate the potential confusion that could arise from actions for preventing both COVID-19 spread and heat stroke, particularly in the context of record-breaking summer season temperatures, this work offers new knowledge and evidence that address concerns regarding indoor ventilation and indoor temperatures, mask wearing and heat stroke risk, and the isolation of older adults. Specifically, the current work is the second edition to the previously published guidance for handling heat stroke during the COVID-19 pandemic, prepared by the "Working group on heat stroke medical care during the COVID-19 epidemic," composed of members from four organizations in different medical and related fields. The group was established by the Japanese Association for Acute Medicine Heatstroke and Hypothermia Surveillance Committee. This second edition includes new knowledge, and conventional evidence gleaned from a primary selection of 60 articles from MEDLINE, one article from Cochrane, 13 articles from Ichushi, and a secondary/final selection of 56 articles. This work summarizes the contents that have been clarified in the prevention and treatment of infectious diseases and heat stroke to provide guidance for the prevention, diagnosis, and treatment of heat stroke during the COVID-19 pandemic.

Trends in the number of patients from traffic accidents and the state of emergency.

Aim: During the coronavirus disease 2019 pandemic, the number of traffic accidents and injured patients was reported to be lower than that before the pandemic. However, little is known regarding the relationship between periods of the state of emergency and the number of patients who met with traffic accidents. Methods: The numbers of trauma patients and deaths due to traffic accidents in Tokyo and Osaka were collected monthly from the statistics published by the police department. A state of emergency was declared four times in both cities. The number of trauma patients and deaths was compared between the emergency and other periods. Results: The number of monthly patients per 100,000 due to traffic accidents during the state of emergency was significantly lower than that during other periods in Tokyo (16.56 versus 18.20; P = 0.008) and Osaka (24.12 versus 28.79; P = 0.002). However, the monthly number of deaths during the state of emergency was not significantly different compared with those during the other periods in Tokyo (0.08 versus 0.08; P = 0.65) and Osaka (0.10 versus 0.14; P = 0.082). A decrease in the number of trauma patients was observed before the emergency period; however, the reduction rate dropped as the period passed. Conclusion: There were significantly fewer trauma patients due to traffic accidents during the state of emergency than during the other periods, with no significant difference in the number of deaths.

Influence of the coronavirus disease 2019 (COVID-19) pandemic on the incidence of heat stroke and heat exhaustion in Japan: a nationwide observational study based on the Heatstroke STUDY 2019 (without COVID-19) and 2020 (with COVID-19).

AIM: To assess heat stroke and heat exhaustion occurrence and response during the coronavirus disease 2019 pandemic in Japan. METHODS: This retrospective, multicenter, registry-based study describes and compares the characteristics of patients between the months of July and September in 2019 and 2020. Factors affecting heat stroke and heat exhaustion were statistically analyzed. Cramér's V was calculated to determine the effect size for group comparisons. We also investigated the prevalence of mask wearing and details of different cooling methods. RESULTS: No significant differences were observed between 2019 and 2020. In both years, in-hospital mortality rates just exceeded 8%. Individuals >65 years old comprised 50% of cases and non-exertional onset (office work and everyday life) comprised 60%-70%, respectively. The recommendations from the Working Group on Heat Stroke Medicine given during the coronavirus disease pandemic in 2019 had a significant impact on the choice of cooling methods. The percentage of cases, for which intravascular temperature management was performed and cooling blankets were used increased, whereas the percentage of cases in which evaporative plus convective cooling was performed decreased. A total of 49 cases of heat stroke in mask wearing were reported. CONCLUSION: Epidemiological assessments of heat stroke and heat exhaustion did not reveal significant changes between 2019 and 2020. The findings suggest that awareness campaigns regarding heat stroke prevention among the elderly in daily life should be continued in the coronavirus disease 2019 pandemic. In the future, it is also necessary to validate the recommendations of the Working Group on Heatstroke Medicine.

Wearing a face mask during controlled‐intensity exercise is not a risk factor for exertional heatstroke: A pilot study

Aim This study aimed to measure the influence of wearing face masks on individuals’ physical status in a hot and humid environment. Methods Each participant experienced different physical situations: (i) not wearing a mask (control), (ii) wearing a surgical mask, (iii) wearing a sport mask. An ingestible capsule thermometer was used to measure internal core body temperature during different exercises (standing, walking, and running, each for 20 min) in an artificial weather room with the internal wet‐bulb globe temperature set at 28°C. The change in the participants’ physical status and urinary liver fatty acid‐binding protein (L‐FABP) were measured. Results Six healthy male volunteers were enrolled in the study. In each participant, significant changes were observed in the heart rate and internal core temperatures after increased exercise intensity;however, no significant differences were observed between these parameters and urinary L‐FABP among the three intervention groups. Conclusion Mask wearing is not a risk factor for heatstroke during increased exercise intensity. This study aimed to measure the influence of wearing face masks in hot and humid environments in different physical situations: (i) not wearing a mask (control), (ii) wearing a surgical mask, (iii) wearing a sport mask. In all six participants, significant changes were observed in heart rate and internal core temperature with increased exercise intensity;however, no significant differences were observed between these parameters and urinary liver fatty acid‐binding protein among the three intervention groups. Mask wearing is not a risk factor for heatstroke during increased‐intensity exercise.

Wearing a face mask during controlled-intensity exercise is not a risk factor for exertional heatstroke: A pilot study.

AIM: This study aimed to measure the influence of wearing face masks on individuals' physical status in a hot and humid environment. METHODS: Each participant experienced different physical situations: (i) not wearing a mask (control), (ii) wearing a surgical mask, (iii) wearing a sport mask. An ingestible capsule thermometer was used to measure internal core body temperature during different exercises (standing, walking, and running, each for 20 min) in an artificial weather room with the internal wet-bulb globe temperature set at 28°C. The change in the participants' physical status and urinary liver fatty acid-binding protein (L-FABP) were measured. RESULTS: Six healthy male volunteers were enrolled in the study. In each participant, significant changes were observed in the heart rate and internal core temperatures after increased exercise intensity; however, no significant differences were observed between these parameters and urinary L-FABP among the three intervention groups. CONCLUSION: Mask wearing is not a risk factor for heatstroke during increased exercise intensity.

Quantitative analysis of the impact of COVID-19 on the emergency medical services system in Tokyo.

AIM: The coronavirus disease 2019 (COVID-19) pandemic has imposed a heavy burden on emergency medical services (EMS) systems. Therefore, we quantitatively analyzed impacts of COVID-19 on the EMS system in Tokyo. METHODS: In this retrospective observational study, we used publicly available data, including the daily number of newly diagnosed patients with COVID-19, the weekly number of transport difficulties, and the field activity time, from March 2, 2020, to January 25, 2021. Data for the same period in the previous year were used as controls. RESULTS: The total number of EMS calls decreased by 12.7% in 2020 compared with that in 2019. The number of daily transport difficulties significantly increased in 2020 compared with that in 2019 (89 [72-120] vs 57 [49-63]; P < 0.001). Additionally, the field activity time significantly increased in 2020 compared with that in 2019 (22.7 [22.3-23.8] min vs 20.7 [20.6-21.2] min; P < 0.001). Furthermore, the daily number of new patients with COVID-19 was positively correlated with the number of transport difficulties (R = 0.76) and the field activity time on the scene (R = 0.74). With an increase in the number of people infected with COVID-19 by 1,000, the number of daily transport difficulties increased by 86.4. Per 1,000 infected patients per day, the field activity time increased by 3.48 min. CONCLUSION: This study revealed that the increase in the number of patients with COVID-19 indirectly affected the EMS system in Tokyo.

Machine Learning Prediction for Supplemental Oxygen Requirement in Patients with COVID-19.

BACKGROUND: The coronavirus disease (COVID-19) poses an urgent threat to global public health and is characterized by rapid disease progression even in mild cases. In this study, we investigated whether machine learning can be used to predict which patients will have a deteriorated condition and require oxygenation in asymptomatic or mild cases of COVID-19. METHODS: This single-center, retrospective, observational study included COVID-19 patients admitted to the hospital from February 1, 2020, to May 31, 2020, and who were either asymptomatic or presented with mild symptoms and did not require oxygen support on admission. Data on patient characteristics and vital signs were collected upon admission. We used seven machine learning algorithms, assessed their capability to predict exacerbation, and analyzed important influencing features using the best algorithm. RESULTS: In total, 210 patients were included in the study. Among them, 43 (19%) required oxygen therapy. Of all the models, the logistic regression model had the highest accuracy and precision. Logistic regression analysis showed that the model had an accuracy of 0.900, precision of 0.893, and recall of 0.605. The most important parameter for predictive capability was SpO2, followed by age, respiratory rate, and systolic blood pressure. CONCLUSION: In this study, we developed a machine learning model that can be used as a triage tool by clinicians to detect high-risk patients and disease progression earlier. Prospective validation studies are needed to verify the application of the tool in clinical practice.

Indicators of Acute Kidney Injury as Biomarkers to Differentiate Heatstroke from Coronavirus Disease 2019: A Retrospective Multicenter Analysis.

BACKGROUND: Coronavirus disease 2019 (COVID-19) and heat-related illness are systemic febrile diseases. These illnesses must be differentiated during a COVID-19 pandemic in summer. However, no studies have compared and distinguished heat-related illness and COVID-19. We compared data from patients with early heat-related illness and those with COVID-19. METHODS: This retrospective observational study included 90 patients with early heat-related illness selected from the Heatstroke STUDY 2017-2019 (nationwide registries of heat-related illness in Japan) and 86 patients with laboratory-confirmed COVID-19 who had fever or fatigue and were admitted to one of two hospitals in Tokyo, Japan. RESULTS: Among vital signs, systolic blood pressure (119 vs. 125 mm Hg, p = 0.02), oxygen saturation (98% vs. 97%, p < 0.001), and body temperature (36.6°C vs. 37.6°C, p<0.001) showed significant between-group differences in the heatstroke and COVID-19 groups, respectively. The numerous intergroup differences in laboratory findings included disparities in white blood cell count (10.8 × 103/µL vs. 5.2 × 103/µL, p<0.001), creatinine (2.2 vs. 0.85 mg/dL, p<0.001), and C-reactive protein (0.2 vs. 2.8 mg/dL, p<0.001), although a logistic regression model achieved an area under the curve (AUC) of 0.966 using these three factors. A Random Forest machine learning model achieved an accuracy, precision, recall, and AUC of 0.908, 0.976, 0.842, and 0.978, respectively. Creatinine was the most important feature of this model. CONCLUSIONS: Acute kidney injury was associated with heat-related illness, which could be essential in distinguishing or evaluating patients with fever in the summer during a COVID-19 pandemic.