Indicators of acute kidney injury as biomarkers to differentiate heatstroke from coronavirus disease 2019: A retrospective multicenter analysis

Hirofumi Obinata, Shoji Yokobori, Kei Ogawa, Yasuhiro Takayama, Shuichi Kawano, Toshimitsu Ito, Toru Takiguchi, Yutaka Igarashi, Ryuta Nakae, Tomohiko Masuno, Hayato Ohwada

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

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 se-lected 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 inter-group 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 us-ing these three factors. A Random Forest machine learning model achieved an accuracy, precision, re-call, 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.

Original languageEnglish
Pages (from-to)80-86
Number of pages7
JournalJournal of Nippon Medical School
Volume88
Issue number1
DOIs
Publication statusPublished - 15 Feb 2021

Keywords

  • COVID-19
  • Coronavirus disease
  • Heat-related illness
  • Heatstroke
  • Machine learning

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