International Journal of Emergency Medicine (Apr 2023)

Evaluating the one-time chair stand test for predicting the coronavirus disease severity in patients during hospital admission: a cohort study in Japan

  • Atsushi Ishihara,
  • Takashi Yoshizane,
  • Teruki Mori,
  • Yui Sasaki,
  • Takahiro Hosokawa,
  • Jun Suzuki,
  • Akifumi Tsuzuku,
  • Fumihiro Asano,
  • Toshiyuki Noda

DOI
https://doi.org/10.1186/s12245-023-00497-x
Journal volume & issue
Vol. 16, no. 1
pp. 1 – 7

Abstract

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Abstract Background This study aimed to understand whether the one-time chair stand test (CS-1) is useful for predicting the severity of coronavirus disease (COVID-19) in 101 patients admitted to the hospital with acute respiratory failure. Methods This single-centered, prospective observational cohort study enrolled 101 critically ill adult patients hospitalized with COVID-19 who underwent the CS-1 as a dynamic evaluation tool in clinical practice between late April 2020 and October 2021. Data on demographic characteristics, symptoms, laboratory values, computed tomography findings, and clinical course after admission were collected. Furthermore, the data was compared, and the association between the intubation and non-intubation groups was determined. We also calculated the cutoff point, area under the curve (AUC), and 95% confidence interval (CI) of the change in oxygen saturation (ΔSpO2) during the CS-1. Results Thirty-three out of 101 patients (33%) were intubated during hospitalization. There was no significant difference in the resting SpO2 (93.3% versus 95.2%, P = 0.22), but there was a significant difference in ΔSpO2 during the CS-1 between the intubation and non-intubation groups (10.8% versus 5.5%, P < 0.01). In addition, there was a significant correlation between hospitalization and ΔSpO2 during the CS-1 (ρ = 0.60, P < 0.01). The generated cutoff point was calculated as 9.5% (AUC = 0.94, 95% CI = 0.88–1.00). Conclusion For COVID-19 patients with acute respiratory failure, the CS-1 performed on admission was useful for predicting the severity of COVID-19. Furthermore, the CS-1 can be utilized as a remote and simple evaluation parameter. Thus, it could have potential clinical applications in the future.

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