Clinical and radiological findings of silent hypoxia among COVID-19 patients

Performance of chest X-ray scoring in predicting disease severity and outcomes of patients hospitalised with COVID-19 in Bangladesh

Introduction

Evaluation of potential outcomes of COVID-19-affected pneumonia patients using computed tomography scans may not be conceivable in low-resource settings. Thus, we aimed to evaluate the performance of chest X-ray scoring in predicting the disease severity and outcomes of adults hospitalised with COVID-19.

Methods

This was a retrospective chart analysis consuming data from COVID-19-positive adults who had chest X-ray availability and were admitted to a temporary COVID unit, in Bangladesh from 23rd April 2020 to 15th November 2021. At least one clinical intensivist and one radiologist combinedly reviewed each admission chest X-ray for the different lung findings. Chest X-ray scoring varied from 0 to 8, depending on the area of lung involvement with 0 indicating no involvement and 8 indicating ⩾75% involvement of both lungs. The receiver operating characteristic curve was used to determine the optimum chest X-ray cut-off score for predicting the fatal outcomes.

Result

A total of 218 (82.9%) out of 263 COVID-19-affected adults were included in the study. The receiver operating characteristic curve demonstrated the optimum cut-off as ⩾3 and ⩾5 for disease severity and death, respectively. In multivariate logistic regression analysis, a chest X-ray score of ⩾3 was found to be independently associated with disease severity (aOR: 8.70; 95% CI: 3.82, 19.58, p < 0.001) and a score of ⩾5 with death (aOR: 16.53; 95% CI: 4.74, 57.60, p < 0.001) after adjusting age, sex, antibiotic usage before admission, history of fever, cough, diabetes mellitus, hypertension, total leukocytes count and C-reactive protein.

Conclusion

Using chest X-ray scoring derived cut-off at admission might help to identify the COVID-19-affected adults who are at risk of severe disease and mortality. This may help to initiate early and aggressive management of such patients, thereby reducing their fatal outcomes.

Percutaneous Coronary Intervention Associated with a Higher Risk of Hypoxemia and COVID-19 Severity

OBJECTIVE

The primary goal of the present study was to measure the implications of hypoxemia in COVID-19 patients with a history of coronary artery disease (CAD).

METHODS

A systematic search of the literature published from November 1, 2019 to May 1, 2021, was conducted on PubMed/MEDLINE, Embase, and Web of Science databases. Afterwards, an observational study was designed based on the electronic health records of COVID-19 patients hospitalized in a tertiary referral hospital during the same period. A total of 179 COVID-19 cases were divided into two groups: cases with a history of CAD and percutaneous coronary intervention (CAD/PCI+, n = 89) and controls (n = 90). Clinical data were extracted from the electronic database of the hospital and statistically analyzed.

RESULTS

After the application of inclusion/exclusion criteria, only three studies were deemed eligible, one of which was concerned with the impact of CAD on the all-cause mortality of COVID-19. Results from our observational study indicated that the cases were older (median age: 74 vs. 45) and more likely to develop hypoxemia (25.8% vs. 8.8%) than the controls. CAD/PCI+ was correlated with a more severe COVID-19 (11% vs. 1%). Age was a moderately significant independent predictor of increased COVID-19 severity, while hypoxemia was not.

CONCLUSION

Considering the negative impact of hypoxemia on the prognosis of COVID-19 and its higher prevalence among COVID-19 patients with underlying CAD, further research is warranted to unravel the negative effects of COVID-19 on the mechanisms of gas exchange and delivery in patients with pre-existing CAD.

Respiratory psychophysiology and COVID-19: A research agenda

After multiple waves of the COVID-19 pandemic, it has become clear that the impact of SARS-CoV-2 will carry on for years to come. Acutely infected patients show a broad range of disease severity, depending on virus variant, vaccination status, age and the presence of underlying medical and physical conditions, including obesity. Additionally, a large number of patients who have been infected with the virus present with post-COVID syndrome. In September 2020, the International Society for the Advancement of Respiratory Psychophysiology organized a virtual interest meeting on 'Respiratory research in the age of COVID-19', which aimed to discuss how research in respiratory psychophysiology could contribute to a better understanding of psychophysiological interactions in COVID-19. In the resulting current paper, we propose an interdisciplinary research agenda discussing selected research questions on acute and long-term neurobiological, physiological and psychological outcomes and mechanisms related to respiration and the airways in COVID-19, as well as research questions on comorbidity and potential treatment options, such as physical rehabilitation.

Deep Learning-Based Automatic Assessment of Lung Impairment in COVID-19 Pneumonia: Predicting Markers of Hypoxia With Computer Vision

Background

Hypoxia is a potentially life-threatening condition that can be seen in pneumonia patients.

Objective

We aimed to develop and test an automatic assessment of lung impairment in COVID-19 associated pneumonia with machine learning regression models that predict markers of respiratory and cardiovascular functioning from radiograms and lung CT.

Materials and Methods

We enrolled a total of 605 COVID-19 cases admitted to Al Ain Hospital from 24 February to 1 July 2020 into the study. The inclusion criteria were as follows: age ≥ 18 years; inpatient admission; PCR positive for SARS-CoV-2; lung CT available at PACS. We designed a CNN-based regression model to predict systemic oxygenation markers from lung CT and 2D diagnostic images of the chest. The 2D images generated by averaging CT scans were analogous to the frontal and lateral view radiograms. The functional (heart and breath rate, blood pressure) and biochemical findings (SpO₂, HCO3-, K⁺, Na⁺, anion gap, C-reactive protein) served as ground truth.

Results

Radiologic findings in the lungs of COVID-19 patients provide reliable assessments of functional status with clinical utility. If fed to ML models, the sagittal view radiograms reflect dyspnea more accurately than the coronal view radiograms due to the smaller size and the lower model complexity. Mean absolute error of the models trained on single-projection radiograms was approximately 11÷12% and it dropped by 0.5÷1% if both projections were used (11.97 ± 9.23 vs. 11.43 ± 7.51%; p = 0.70). Thus, the ML regression models based on 2D images acquired in multiple planes had slightly better performance. The data blending approach was as efficient as the voting regression technique: 10.90 ± 6.72 vs. 11.96 ± 8.30%, p = 0.94. The models trained on 3D images were more accurate than those on 2D: 8.27 ± 4.13 and 11.75 ± 8.26%, p = 0.14 before lung extraction; 10.66 ± 5.83 and 7.94 ± 4.13%, p = 0.18 after the extraction. The lung extraction boosts 3D model performance unsubstantially (from 8.27 ± 4.13 to 7.94 ± 4.13%; p = 0.82). However, none of the differences between 3D and 2D were statistically significant.

Conclusion

The constructed ML algorithms can serve as models of structure-function association and pathophysiologic changes in COVID-19. The algorithms can improve risk evaluation and disease management especially after oxygen therapy that changes functional findings. Thus, the structural assessment of acute lung injury speaks of disease severity.

Predictors of silent hypoxia in hospitalized patients with COVID-19 in Japan

INTRODUCTION

Silent hypoxia (SH) is common in patients with coronavirus disease (COVID-19) in Japan and other countries. Early identification of SH is important as more treatment options for COVID-19 have become available. This study aimed to identify predictors of SH using a nationwide COVID-19 registry of hospitalized patients.

METHODS

Adult patients who were admitted to hospital with COVID-19 between January 2020 and June 2021 and who were hypoxic on admission (SpO₂: 70-93%), not transferred from another facility, and who did not have disturbance of consciousness, confusion, or dementia, were included. SH was defined as hypoxia in the absence of shortness of breath/dyspnea upon admission. Predictors of SH were identified using univariable and multivariable logistic regression.

RESULTS

The study included 1904 patients, of whom 990 (52%) satisfied the criteria for SH. Compared to patients without SH, patients with SH were older, more likely to be female, and had a slightly higher SpO₂ on admission. Compared to patients without SH, patients with SH had a lower prevalence of chronic lung disease (CLD) other than chronic obstructive pulmonary disease (COPD), asthma, and obesity. Multivariable analysis revealed that the independent predictors of SH were older age, a shorter interval from symptom onset to admission, higher SpO₂, and an absence of CLD or COPD.

CONCLUSIONS

The absence of underlying lung disease and older age were important predictors of SH. The results of this study, which is the largest such study reported to date in Japan, may help clarify the mechanism of SH.

Silent Hypoxia in Coronavirus disease-2019: Is it more dangerous? -A retrospective cohort study

BACKGROUND

Hypoxia in patients with COVID-19 is one of the strongest predictors of mortality. Silent hypoxia is characterised by the presence of hypoxia without dyspnoea. Silent hypoxia has been shown to affect the outcome in previous studies.

METHODS

This was a retrospective study of a cohort of patients with SARS-CoV-2 infection who were hypoxic at presentation. Clinical, laboratory and treatment parameters in patients with silent hypoxia and dyspnoeic hypoxia were compared. Multivariate logistic regression models were fitted to identify the factors predicting mortality.

RESULTS

Among 2080 patients with COVID-19 admitted to our hospital, 811 patients were hypoxic with SpO₂ <94% at the time of presentation. Among them, 174 (21.45%) did not have dyspnoea since the onset of COVID-19 symptoms. Further, 5.2% of patients were completely asymptomatic for COVID-19 and were found to be hypoxic only on pulse oximetry. The case fatality rate in patients with silent hypoxia was 45.4% as compared to 40.03% in dyspnoeic hypoxic patients (P = 0.202). The odds ratio of death was 1.1 (95% CI: 0.41-2.97) in the patients with silent hypoxia after adjusting for baseline characteristics, laboratory parameters, treatment and in-hospital complications, which did not reach statistical significance (P = 0.851).

CONCLUSION

Silent hypoxia may be the only presenting feature of COVID-19. As the case fatality rate is comparable between silent and dyspnoeic hypoxia, it should be recognised early and treated as aggressively. Because home isolation is recommended in patients with COVID-19, it is essential to use pulse oximetry in the home setting to identify these patients.

Prevalence, Presentation and Outcomes of Silent Hypoxemia in COVID-19

Dyspnea is reported in a minority of patients affected by coronavirus disease 2019 (COVID-19). Even patients with pneumonia can present hypoxemia without any respiratory distress, a phenomenon known as “silent” or “happy hypoxemia”. During the current pandemic there were only a few studies conducted on this subject and these were quite heterogeneous. Therefore, the prevalence of “silent hypoxemia” varied substantially. While studies did not show a clear tendency of “silent hypoxemia” to poorer outcomes compared to hypoxemia presenting with dyspnea, several showed that patients with “silent hypoxemia” are not protected from poor outcomes either. There is a need for a uniform definition of “silent hypoxemia”, in order to better guide clinicians and investigators. More studies are needed to shed light on the mechanisms of “silent hypoxemia”, as well as its presentation and influence in the disease's progression and outcomes, so as to better assist physicians in the care of COVID-19 patients.

Post-COVID-19 interstitial lung disease presenting with profound hypoxemia: Report of three cases demonstrating a good response to high-dose corticosteroid therapy

Coronavirus disease 2019 (COVID-19) is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which leads to critical pneumonia, although the clinical courses vary. In some cases, COVID-19 pneumonia causes secondary pulmonary fibrosis, which can retain radiological changes and prolong respiratory symptoms. Interstitial lung disease (ILD) secondary to COVID-19 is thought to be caused by multiple pathologies, such as excessive cytokines and abnormal repair processes elaborated by lung cells (epithelium, mesenchyme, and alveolar macrophages) after lung injury rather than viral invasion itself. Immunosuppression therapy may improve chronic respiratory symptoms and radiological changes in post-COVID-19 ILD, although the treatment is not yet established. Herein, we report three patients with post-COVID-19 ILD who presented with profound hypoxemia that had a good response to high-dose corticosteroid therapy. Further and larger studies are needed to establish post-COVID-19 ILD.