Measurement of breathing in patients with post-COVID-19 using structured light plethysmography (SLP)

利用结构光体积描记法(SLP)测量新冠肺炎后遗症患者的呼吸情况

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Abstract

INTRODUCTION: COVID-19 pandemic has had a huge impact on global health to date, with 5.6 million cases in the UK since its emergence. The respiratory symptoms largely mimic those of pneumonia' with symptoms ranging from mild to severe. The effects on respiratory physiology are not yet fully understood, but evidence is emerging that there is much dysfunctional breathing reported but little information on tidal ventilation from the acute phase of the infection. Structured light plethysmography (SLP) is a contactless technique of respiratory function testing that measures tidal breathing parameters by assessing thoracoabdominal displacement. METHODS: In a postdischarge clinic, SLP was performed routinely on 110 hospitalised patients recovering from COVID-19 who had been screened for respiratory symptoms to confirm any respiratory changes occurring after the disease. Patients were categorised based on their hospital treatment in (1) the intensive therapy unit (ITU) (requiring intubation) (n=65) or (2) respiratory wards only (n=45). Data from these two patient cohorts were compared with preacquired data from healthy controls (n=30). RESULTS: We have found a significantly increased respiratory rate (p=0.006) in ITU patients compared with the healthy cohort and also a significant decrease in the inspiratory time (p=0.01), expiratory time (p=0.005) and the total breathing cycle (p=0.008). There were no significant differences between ITU and ward patients and no significant differences in healthy compared with ward patients. We examined the variability of breathing ('entropy') both in terms of the breath-to-breath interval and the volume-to-volume change. The breath-to-breath interval alone was significantly lower in ITU patients compared with healthy cohorts (p=0.02). CONCLUSION: Our findings suggest that abnormalities in tidal breathing can be detected in COVID-19 recovery patients, and SLP may be a promising tool in assessing the aftermath of diseases such as COVID-19, particularly if more intensive management strategies such as mechanical ventilation are required.

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