ABSTRACT
Background Rehabilitation therapy for patients with severe coronavirus disease 2019 (COVID-19) is crucial; however, studies regarding rehabilitation strategies for intensive care unit (ICU) patients with COVID-19 are limited. We report a case of severe COVID-19 in an ICU patient whose physical function and basic movement ability rapidly improved after the initiation of active aerobic exercise in the supine position.Case A 70-year-old man with hypertension and obesity was admitted to the ICU and managed with a ventilator because of severe COVID-19. Physical therapy started on postadmission day 34. Problems encountered during physical therapy included low saturation of percutaneous oxygen (SpO2; <90%), dyspnea with a light exercise load, advancing muscle weakness, and endurance decline. The rehabilitation program included getting out of bed and resistance training of the upper and lower limbs twice daily while maintaining SpO2 at ≥90%. After ventilator weaning, we initiated aerobic training using a supine ergometer with varying load volume. On discharge from the ICU on postadmission day 45, the patient' s physical function (handgrip strength, Medical Research Council score, and Borg scale) and basic movement ability (Functional Status Score for ICU) rapidly improved.Conclusion Rehabilitation therapy involving aerobic cycling training based on a quantitative load setting may be effective in treating COVID-19.
ABSTRACT
BACKGROUND: The coronavirus disease (COVID-19) poses an urgent threat to global public health and is characterized by rapid disease progression even in mild cases. In this study, we investigated whether machine learning can be used to predict which patients will have a deteriorated condition and require oxygenation in asymptomatic or mild cases of COVID-19. METHODS: This single-center, retrospective, observational study included COVID-19 patients admitted to the hospital from February 1, 2020, to May 31, 2020, and who were either asymptomatic or presented with mild symptoms and did not require oxygen support on admission. Data on patient characteristics and vital signs were collected upon admission. We used seven machine learning algorithms, assessed their capability to predict exacerbation, and analyzed important influencing features using the best algorithm. RESULTS: In total, 210 patients were included in the study. Among them, 43 (19%) required oxygen therapy. Of all the models, the logistic regression model had the highest accuracy and precision. Logistic regression analysis showed that the model had an accuracy of 0.900, precision of 0.893, and recall of 0.605. The most important parameter for predictive capability was SpO2, followed by age, respiratory rate, and systolic blood pressure. CONCLUSION: In this study, we developed a machine learning model that can be used as a triage tool by clinicians to detect high-risk patients and disease progression earlier. Prospective validation studies are needed to verify the application of the tool in clinical practice.