Clinical characteristics, prognostic factors, and outcomes of heat‐related illness (Heatstroke Study 2017–2018)

Shimazaki, Junya; Hifumi, Toru; Shimizu, Keiki; Oda, Yasutaka; Kanda, Jun; Kondo, Yutaka; Shiraishi, Shin‐ichiro; Takauji, Shuhei; Hayashida, Kei; Moriya, Takuya; Yagi, Masaharu; Yamaguchi, Junko; Yokota, Hiroyuki; Yokobori, Shoji; Wakasugi, Masahiro; Yaguchi, Arino; Miyake, Yoshihiro

doi:10.1002/ams2.516

Cited by 36 publications

(31 citation statements)

References 26 publications

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“…AT as well as RH plays an important role in the onset of heatstroke. For example, heatstroke is common even during the damp rainy days of early summer [ 21 ]. Therefore, we developed a mouse heatstroke model that mimicked temperate to subtropical regional weather conditions using WBGT as an indicator.…”

Section: Discussionmentioning

confidence: 99%

A novel mouse model of heatstroke accounting for ambient temperature and relative humidity

et al. 2021

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Background Heatstroke is associated with exposure to high ambient temperature (AT) and relative humidity (RH), and an increased risk of organ damage or death. Previously proposed animal models of heatstroke disregard the impact of RH. Therefore, we aimed to establish and validate an animal model of heatstroke considering RH. To validate our model, we also examined the effect of hydration and investigated gene expression of cotransporter proteins in the intestinal membranes after heat exposure. Methods Mildly dehydrated adult male C57/BL6J mice were subjected to three AT conditions (37 °C, 41 °C, or 43 °C) at RH > 99% and monitored with WetBulb globe temperature (WBGT) for 1 h. The survival rate, body weight, core body temperature, blood parameters, and histologically confirmed tissue damage were evaluated to establish a mouse heatstroke model. Then, the mice received no treatment, water, or oral rehydration solution (ORS) before and after heat exposure; subsequent organ damage was compared using our model. Thereafter, we investigated cotransporter protein gene expressions in the intestinal membranes of mice that received no treatment, water, or ORS. Results The survival rates of mice exposed to ATs of 37 °C, 41 °C, and 43 °C were 100%, 83.3%, and 0%, respectively. From this result, we excluded AT43. Mice in the AT 41 °C group appeared to be more dehydrated than those in the AT 37 °C group. WBGT in the AT 41 °C group was > 44 °C; core body temperature in this group reached 41.3 ± 0.08 °C during heat exposure and decreased to 34.0 ± 0.18 °C, returning to baseline after 8 h which showed a biphasic thermal dysregulation response. The AT 41 °C group presented with greater hepatic, renal, and musculoskeletal damage than did the other groups. The impact of ORS on recovery was greater than that of water or no treatment. The administration of ORS with heat exposure increased cotransporter gene expression in the intestines and reduced heatstroke-related damage. Conclusions We developed a novel mouse heatstroke model that considered AT and RH. We found that ORS administration improved inadequate circulation and reduced tissue injury by increasing cotransporter gene expression in the intestines.

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Section: Discussionmentioning

confidence: 99%

A novel mouse model of heatstroke accounting for ambient temperature and relative humidity

et al. 2021

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“…On the other hand, there are few studies on the prognosis of patients who actually develop heatstroke 15 , 16 . Owing to the lack of a specific mortality prediction tool for heat-related illness, general scoring systems for critically ill patients, such as sequential organ failure assessment (SOFA) and APACHE-II scores, have been commonly used to estimate the severity of this disease 12 , 17 . The development of specific and reliable prognostic models for heat-related illnesses is anticipated so that clinicians can make an informed decision for optimized treatment.…”

Section: Discussionmentioning

confidence: 99%

“…Thus, data from the heatstroke studies in 2014 and 2017–2020, from 109 to 142 participating hospitals, were extracted for our study. The heatstroke study has been described elsewhere 11 , 12 .…”

Section: Methodsmentioning

confidence: 99%

Machine learning-based mortality prediction model for heat-related illness

Hirano

Kondo

Hifumi

et al. 2021

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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.

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“…We examined all patients who were registered in three prospective, nationwide, observational multicenter registries for heat-related illness in Japan: the Heatstroke STUDY of 2017, 2018, 8 and 2019. These registries are maintained by the Japanese Association for Acute Medicine Committee.…”

Section: Heat-related Illness Populationmentioning

confidence: 99%

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

et al. 2021

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Background: Coronavirus disease 2019 and heat related-illness are systemic febrile diseases. In the summer during the COVID-19 pandemic, a differential diagnosis between the two conditions is important. However, no studies have compared and distinguished heat-related illness from COVID-19. We aimed to compare the data between patients with early-stage heat-related illness and those with COVID-19.Methods: This retrospective observational study included 90 patients with early-stage heat-related illness selected from Heatstroke STUDY 2017-2019 (nationwide registries of heat-related illness in Japan) and 86 patients with laboratory-confirmed COVID-19 with complaints of fever or fatigue and were admitted to one of two hospitals in Tokyo, Japan.Results: Among vital signs, systolic blood pressure (119 vs. 125 mmHg, p = 0.02), oxygen saturation (98% vs. 97%, p < 0.001), and body temperature (36.6 vs. 37.6 °C, p<0.001) showed significant between-group differences for the heatstroke and COVID-19 groups, respectively. Numerous intergroup differences in laboratory findings were present, including white blood cell counts (10.8 vs. 5.2 × 10 3 /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 4 4 using these three factors. A Random Forest machine learning model achieved 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 key in distinguishing or evaluating patients with fever in the summer during the COVID-19 pandemic.

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Clinical characteristics, prognostic factors, and outcomes of heat‐related illness (Heatstroke Study 2017–2018)

Cited by 36 publications

References 26 publications

A novel mouse model of heatstroke accounting for ambient temperature and relative humidity

A novel mouse model of heatstroke accounting for ambient temperature and relative humidity

Machine learning-based mortality prediction model for heat-related illness

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

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