AimTo clarify the features of stroke mimics.MethodsWe retrospectively investigated stroke mimic cases among the suspected stroke cases examined at our emergency department, over the past 9 years, during the tissue‐type plasminogen activator treatment time window.ResultsOf 1,557 suspected acute stroke cases examined at the emergency department, 137 (8.8%) were stroke mimics. The most common causes were symptomatic epilepsy (28 cases, 20.4%), neuropathy‐like symptoms (21 cases, 15.3%), and hypoglycemia (15 cases, 10.9%). Outcomes were survival to hospital discharge for 91.2% and death for 8.8% of the cases. Clinical results were significantly different between stroke mimics and the stroke group for low systolic blood pressure, low National Institutes of Health Stroke Scale score on initial treatment, history of diabetes, and no history of arrhythmia. On multivariate analysis, distinguishing factors for stroke mimics include systolic blood pressure ≤ 140 mmHg, National Institutes of Health Stroke Scale score ≤ 5 points, history of diabetes, and no history of arrhythmia.ConclusionsFrequency of stroke mimics in cases of acute stroke suspected cases is 8.8%, and the most common cause is epilepsy. In order to distinguish stroke mimics, it is useful to understand common diseases presenting as stroke mimics and evaluate clinical features different from stroke by medical interview or nerve examination.

This study describes the clinical characteristics and outcomes as well as the prognostic factors of patients with accidental hypothermia (AH) using Japan's nationwide registry data.

In this study, we aimed to develop and validate a machine learning-based mortality prediction model for hospitalized heat-related illness patients. After 2393 hospitalized patients were extracted from a multicentered heat-related illness registry in Japan, subjects were divided into the training set for development (n = 1516, data from 2014, 2017–2019) and the test set (n = 877, data from 2020) for validation. Twenty-four variables including characteristics of patients, vital signs, and laboratory test data at hospital arrival were trained as predictor features for machine learning. The outcome was death during hospital stay. In validation, the developed machine learning models (logistic regression, support vector machine, random forest, XGBoost) demonstrated favorable performance for outcome prediction with significantly increased values of the area under the precision-recall curve (AUPR) of 0.415 [95% confidence interval (CI) 0.336–0.494], 0.395 [CI 0.318–0.472], 0.426 [CI 0.346–0.506], and 0.528 [CI 0.442–0.614], respectively, compared to that of the conventional acute physiology and chronic health evaluation (APACHE)-II score of 0.287 [CI 0.222–0.351] as a reference standard. The area under the receiver operating characteristic curve (AUROC) values were also high over 0.92 in all models, although there were no statistical differences compared to APACHE-II. This is the first demonstration of the potential of machine learning-based mortality prediction models for heat-related illnesses.

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.

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.