A Pilot Study of Application of the Stroke Riskometer Mobile App for Assessment of the Course and Clinical Outcomes of COVID-19 among Hospitalized Patients

Introduction: Early determination of COVID-19 severity and health outcomes could facilitate better treatment of patients. Different methods and tools have been developed for predicting outcomes of COVID-19, but they are difficult to use in routine clinical practice. Methods: We conducted a prospective cohort study of inpatients aged 20-92 years, diagnosed with COVID-19 to determine whether their individual 5-year absolute risk of stroke at the time of hospital admission predicts the course of COVID-19 severity and mortality. The risk of stroke was determined by the Stroke Riskometer mobile application. Results: We examined 385 patients hospitalized with COVID-19 (median age 61 years). The participants were categorized based on COVID-19 severity: 271 (70.4%) to the "not severe"and 114 (29.6%) to the "severe"groups. The median risk of stroke the next day after hospitalization was significantly higher among patients in the severe group (2.83, 95% CI: 2.35-4.68) versus the not severe group (1.11, 95% CI: 1.00-1.29). The median risk of stroke and median systolic blood pressure (SBP) were significantly higher among non-survivors (12.04, 95% CI: 2.73-21.19) and (150, 95% CI: 140-170) versus survivors (1.31, 95% CI: 1.14-1.52) and (134, 95% CI: 130-135), respectively. Those who spent more than 2.5 h a week on physical activity were 3.1 times more likely to survive from COVID-19. Those who consumed more than one standard alcohol drink a day, or suffered with atrial fibrillation, or had poor memory were 2.5, 2.3, and 2.6 times more likely not to survive from COVID-19, respectively. Conclusions: High risk of stroke, physical inactivity, alcohol intake, high SBP, and atrial fibrillation are associated with severity and mortality of COVID-19. Our findings suggest that the Stroke Riskometer app could be used as a simple predictive tool of COVID-19 severity and mortality. © 2023 The Author(s). Published by S. Karger AG, Basel.

Авторы
Merkin A. , Akinfieva S. , Medvedev O.N. , Krishnamurthi R. , Gutsaluk A. , Reips U.-D. , Kuliev R. , Dinov E. , Nikiforov I. , Shamalov N. , Shafran P. , Popova L. , Burenchev D. , Feigin V.
Издательство
S. Karger AG
Номер выпуска
1
Язык
Английский
Страницы
47-55
Статус
Опубликовано
Том
13
Год
2023
Организации
  • 1 National Institute for Stroke and Applied Neurosciences, Faculty of Health & Environmental Sciences, AUT University, Auckland, New Zealand
  • 2 IScience Group, Department of Psychology, University of Konstanz, Konstanz, Germany
  • 3 National Centre for Development of Social Support and Rehabilitation, Moscow, Russian Federation
  • 4 School of Psychology, University of Waikato, Hamilton, New Zealand
  • 5 City Clinical Hospital Named after A.K. Eramishantsev, Moscow, Russian Federation
  • 6 Department of Psychology, Russian Peoples' Friendship University, Moscow, Russian Federation
  • 7 Academy for Postgraduate Education, Moscow, Russian Federation
  • 8 Pirogov Russian National Research Medical University, Moscow, Russian Federation
  • 9 I.M. Sechenov First Moscow State Medical University, Moscow, Russian Federation
Ключевые слова
Comorbidity; COVID-19; Prediction; Severity; Stroke; Stroke Riskometer mobile app
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