Chest X-ray in new Coronavirus Disease 2019 (COVID-19) infection: findings and correlation with clinical outcome

Prevalence of Ear-Related Problems in Individuals Recovered From COVID-19

Introduction

The aim was to assess prevalence of tinnitus, hyperacusis, hearing and balance problems among patients recovered from COVID-19 infection. Self-reported ear and hearing symptoms were compared in three groups comprising: confirmed COVID-19, possible COVID-19, and non-COVID-19.

Materials and Methods

1649 participants completed the survey in this cross-sectional study. The mean age was 34 years and 65% were female. Participants with confirmed and possible COVID-19 were asked if after their infection (compared to the past) they experienced hearing loss, ringing or whistling noises, fullness or blockage in their ears, loudness of the sounds that are normal to other people bother them more (an indication of hyperacusis), dizziness, giddiness, or imbalance.

Results

Among participants with confirmed COVID-19, 16% reported that compared to the past their hearing has decreased, 21.5% noticed tinnitus, 22.5% aural fullness, 26.1% hyperacusis and 17.3% balance problems. Regression models showed that compared to the non-COVID-19 group, participants with confirmed COVID-19 had odds ratios (ORs) of significantly greater than 1 in predicting presence of self-reported symptoms of hearing loss, tinnitus, aural fullness, hyperacusis and balance problems, OR=1.96 (p=0.001), OR=1.63 (p=0.003), OR=1.8 (p<0.001), OR=2.2 (p<0.001), and OR=2.99 (p<0.001), respectively.

Conclusions

There seem to be higher prevalence of self-report symptoms of ear-related problems among individuals with confirmed COVID-19 infection compared to a non-COVID-19 group during the pandemic.

Relationship between interleukin-6 (IL-6) levels and chest X-ray severity scoring in COVID-19 patients

The severity of coronavirus disease 2019 (COVID-19) may be measured by interleukin-6 (IL-6) and chest X-rays. Brixia score of the chest radiographs is usually used to monitor COVID-19 patients' lung problems. The aim of this study was to demonstrate the relationship between IL-6 levels and chest radiographs (Brixia score) that represent COVID-19 severity. A retrospective cohort study was conducted among COVID-19 patients who had a chest X-ray and examination of IL-6 levels at H. Adam Malik General Hospital, Medan, Indonesia. A multinomial logistic regression analysis was conducted to evaluate the association between IL-6 levels and the severity of the chest radiograph. A total of 76 COVID-19 patients were included in the study and 39.5% of them were 60-69 years old, with more than half were female (52.6%). A total of 17.1%, 48.7%, and 34.2% had IL-6 level of <7 pg/mL, 7-50 pg/mL and >50 pg/mL, respectively. There were 39.5%, 36.8% and 23.7% of the patients had mild, moderate and severe chest X-rays based on Brixia score, respectively. Statistics analysis revealed that moderate (OR: 1.77; 95% CI: 1.05- 3.32) and severe (OR: 1.33; 95% CI: 1.03-3.35) lung conditions in the chest X-rays were significantly associated with IL-6 levels of 7-50 pg/mL. IL-6 more than 50 pg/mL was associated with severe chest X-ray condition (OR: 1.97; 95% CI: 1.15-3.34). In conclusion, high IL-6 levels significantly reflected COVID-19 severity through chest X-rays in COVID-19 patients.

Association between chest X-ray score and clinical outcome in COVID-19 patients: A study on modified radiographic assessment of lung edema score (mRALE) in Indonesia

Radiological examinations such as chest X-rays (CXR) play a crucial role in the early diagnosis and determining disease severity in coronavirus disease 2019 (COVID-19). Various CXR scoring systems have been developed to quantitively assess lung abnormalities in COVID-19 patients, including CXR modified radiographic assessment of lung edema (mRALE). The aim of this study was to determine the relationship between mRALE scores and clinical outcome (mortality), as well as to identify the correlation between mRALE score and the severity of hypoxia (PaO2/FiO2 ratio). A retrospective cohort study was conducted among hospitalized COVID-19 patients at Dr. Soetomo General Academic Hospital Surabaya, Indonesia, from February to April 2022. All CXR data at initial admission were scored using the mRALE scoring system, and the clinical outcomes at the end of hospitalization were recorded. Of the total 178 COVID-19 patients, 62.9% survived after completing the treatment. Patients within non-survived had significantly higher quick sequential organ failure assessment (qSOFA) score (p<0.001), lower PaO2/FiO2 ratio (p=0.004), and higher blood urea nitrogen (p<0.001), serum creatinine (p<0.008) and serum glutamic oxaloacetic transaminase (p=0.001) levels. There was a significant relationship between mRALE score and clinical outcome (survived vs deceased) (p=0.024; contingency coefficient of 0.184); and mRALE score of ≥2.5 served as a risk factor for mortality among COVID-19 patients (relative risk of 1.624). There was a significant negative correlation between the mRALE score and PaO2/FiO2 ratio based on the Spearman correlation test (r=-0.346; p<0.001). The findings highlight that the initial mRALE score may serve as an independent predictor of mortality among hospitalized COVID-19 patients as well as proves its potential prognostic role in the management of COVID-19.

One-step immunoassay of SARS-CoV-2 using screened Fv-antibodies and switching peptides

The Fv-antibodies were correponded to VH region of immunoglobulin G, which were composed of three complementarity determining regions (CDRs) for the specific binding of antigens. In this work, the Fv-antibodies against SARS-CoV-2 spike protein (SP) were screened from an autodisplayed Fv-antibody library which was expressed on E. coli outer membrane, and the receptor binding domain (RBD) of SP was used as a screening probe. The screened target clones were analyzed to have quantitative binding properties to the RBD, and the Fv-antibodies from the screened target clones were expressed as soluble proteins. The binding affinity (KD) of expressed Fv-antibodies to the RBD was estimated to be 70-85 nM using SPR biosensor. The specific binding properties of Fv-antibodies were analyzed for pseudo-virus particles with SARS-CoV-2 SP on the Lenti-virus envelope, such as wild type (Wuhan-1) and variants (Delta, Omicron BA.2, Omicron BA.4/5) using a SPR biosensor. The detection of real SARS-CoV-2 (Wild type, Wuhan-1) based on a SPR biosensor was also presented using the Fv-antibodies with the binding constant (KD) of cycle threshold value (Ct) = 33.8-32.9 (2.19-4.08 copies/μL) and LOD of 0.67-0.83 copies/μL (Ct = 35.5-35.2). Finally, one-step immunoassay based on switching peptide was demonstrated for the detection of the real SARS-CoV-2 (Wuhan-1) without any washing step. The binding constant (KD) was estimated to be Ct = 35.2-33.9 (0.83-2.04 copies/μL), and LOD was estimated to be 0.14-0.47 copies/μL (Ct = 37.8-36.0). Considering the LOD of the conventional RT-PCR (Ct = 35), the LOD of the one-step immunoassay based on the switching peptide was determined to be feasible for the medical diagnosis of COVID-19.

Characteristics of patients with COVID-19 who have deteriorating chest X-ray findings within 48 h: a retrospective cohort study

The severity of chest X-ray (CXR) findings is a prognostic factor in patients with coronavirus disease 2019 (COVID-19). We investigated the clinical and genetic characteristics and prognosis of patients with worsening CXR findings during early hospitalization. We retrospectively included 1656 consecutive Japanese patients with COVID-19 recruited through the Japan COVID-19 Task Force. Rapid deterioration of CXR findings was defined as increased pulmonary infiltrates in ≥ 50% of the lung fields within 48 h of admission. Rapid deterioration of CXR findings was an independent risk factor for death, most severe illness, tracheal intubation, and intensive care unit admission. The presence of consolidation on CXR, comorbid cardiovascular and chronic obstructive pulmonary diseases, high body temperature, and increased serum aspartate aminotransferase, potassium, and C-reactive protein levels were independent risk factors for rapid deterioration of CXR findings. Risk variant at the ABO locus (rs529565-C) was associated with rapid deterioration of CXR findings in all patients. This study revealed the clinical features, genetic features, and risk factors associated with rapid deterioration of CXR findings, a poor prognostic factor in patients with COVID-19.

Performance in prognostic capacity and efficiency of the Thoracic Care Suite GE AI tool applied to chest radiography of patients with COVID-19 pneumonia

OBJECTIVE

Rapid progression of COVID-19 pneumonia may put patients at risk of requiring ventilatory support, such as non-invasive mechanical ventilation or endotracheal intubation. Implementing tools that detect COVID-19 pneumonia can improve the patient's healthcare. We aim to evaluate the efficacy and efficiency of the artificial intelligence (AI) tool GE Healthcare's Thoracic Care Suite (featuring Lunit INSIGHT CXR, TCS) to predict the ventilatory support need based on pneumonic progression of COVID-19 on consecutive chest X-rays.

METHODS

Outpatients with confirmed SARS-CoV-2 infection, with chest X-ray (CXR) findings probable or indeterminate for COVID-19 pneumonia, who required a second CXR due to unfavorableclinical course, were collected. The number of affected lung fields for the two CXRs was assessed using the AI tool.

RESULTS

One hundred fourteen patients (57.4±14.2 years, 65-57%-men) were retrospectively collected. Fifteen (13.2%) required ventilatory support. Progression of pneumonic extension ≥0.5 lung fields per day compared to pneumonia onset, detected using the TCS tool, increased the risk of requiring ventilatory support by 4-fold. Analyzing the AI output required 26s of radiological time.

CONCLUSIONS

Applying the AI tool, Thoracic Care Suite, to CXR of patients with COVID-19 pneumonia allows us to anticipate ventilatory support requirements requiring less than half a minute.

Immunoaffinity biosensors for the detection of SARS-CoV-1 using screened Fv-antibodies from an autodisplayed Fv-antibody library

The detection of severe acute respiratory syndrome coronavirus (SARS-CoV-1) was demonstrated using screened Fv-antibodies for SPR biosensor and impedance spectrometry. The Fv-antibody library was first prepared on the outer membrane of E. coli using autodisplay technology and the Fv-variants (clones) with a specific affinity toward the SARS-CoV-1 spike protein (SP) were screened using magnetic beads immobilized with the SP. Upon screening the Fv-antibody library, two target Fv-variants (clones) with a specific binding affinity toward the SARS-CoV-1 SP were determined and the Fv-antibodies on two clones were named "Anti-SP1" (with CDR3 amino acid sequence: 1GRTTG5NDRPD11Y) and "Anti-SP2" (with CDR3 amino acid sequence: 1CLRQA5GTADD11V). The binding affinities of the two screened Fv-variants (clones) were analyzed using flow cytometry and the binding constants (KD) were estimated to be 80.5 ± 3.6 nM for Anti-SP1 and 45.6 ± 8.9 nM for Anti-SP2 (n = 3). In addition, the Fv-antibody including three CDR regions (CDR1, CDR2, and CDR3) and frame regions (FRs) between the CDR regions was expressed as a fusion protein (Mw. 40.6 kDa) with a green fluorescent protein (GFP) and the KD values of the expressed Fv-antibodies toward the SP estimated to be 15.3 ± 1.5 nM for Anti-SP1 (n = 3) and 16.3 ± 1.7 nM for Anti-SP2 (n = 3). Finally, the expressed Fv-antibodies screened against SARS-CoV-1 SP (Anti-SP1 and Anti-SP2) were applied for the detection of SARS-CoV-1. Consequently, the detection of SARS-CoV-1 was demonstrated to be feasible using the SPR biosensor and impedance spectrometry utilizing the immobilized Fv-antibodies against the SARS-CoV-1 SP.

Evaluation of Reliability and Validity of the RALE and BRIXIA Chest-X Ray Scores in Patients Hospitalized with COVID-19 Pneumonia

INTRODUCTION

Chest X-rays are commonly used to assess the severity in patients that present in the emergency department with suspected COVID-19 pneumonia, but in clinical practice quantitative scales are rarely employed.

AIMS

To evaluate the reliability and validity of two semi-quantitative radiological scales in patients hospitalized for COVID-19 pneumonia (BRIXIA score and RALE score).

METHODS

Patients hospitalized between October 2021 and March 2022 with confirmed COVID-19 pneumonia diagnosis were eligible for inclusion. All included patients had a chest X-ray taken in the ED before admission. Three raters that participated in the treatment and management of patients with COVID-19 during the pandemic independently assessed chest X-rays.

RESULTS

Intraclass coefficients for BRIXΙA and RALES was 0.781 (0.729-0.826) and 0.825 (0.781-0.862) respectively, showing good to excellent reliability overall. Pairwise analysis was performed using quadratic weighted kappa showing significant variability in the inter-rater agreement. The prognostic accuracy of the two scores for in-hospital mortality for all raters was between 0.753 and 0.763 for BRIXIA and 0.737 and 0.790 for RALES, demonstrating good to excellent prognostic value. Both radiological scores were significantly associated with inhospital mortality after adjustment for 4C Mortality score. We found a consistent upwards trend with significant differences between severity groups in both radiological scores.

CONCLUSION

Our findings suggest that BRIXIA and RALES are reliable and can be used to assess the prognosis of patients with COVID-19 requiring hospitalization. However, the inherent subjectivity of radiological scores might make it difficult to set a cut-off value suitable for all assessors.

Low CRB-65 Scores Effectively Rule out Adverse Clinical Outcomes in COVID-19 Irrespective of Chest Radiographic Abnormalities

Background: CRB-65 (Confusion; Respiratory rate ≥ 30/min; Blood pressure ≤ 90/60 mmHg; age ≥ 65 years) is a risk score for prognosticating patients with COVID-19 pneumonia. However, a significant proportion of COVID-19 patients have normal chest X-rays (CXRs). The influence of CXR abnormalities on the prognostic value of CRB-65 is unknown, limiting its wider applicability. Methods: We assessed the influence of CXR abnormalities on the prognostic value of CRB-65 in COVID-19. Results: In 589 study patients (71 years (IQR: 57-83); 57% males), 186 (32%) had normal CXRs. On ROC analysis, CRB-65 performed similarly in patients with normal vs. abnormal CXRs for predicting inpatient mortality (AUC 0.67 ± 0.05 vs. 0.69 ± 0.03). In patients with normal CXRs, a CRB-65 of 0 ruled out mortality, NIV requirement and critical illness (intubation and/or ICU admission) with negative predictive values (NPVs) of 94%, 98% and 99%, respectively. In patients with abnormal CXRs, a CRB-65 of 0 ruled out the same endpoints with NPVs of 91%, 83% and 86%, respectively. Patients with low CRB-65 scores had better inpatient survival than patients with high CRB-65 scores, irrespective of CXR abnormalities (all p < 0.05). Conclusions: CRB-65, CXR and CRP are independent predictors of mortality in COVID-19. Adding CXR findings (dichotomised to either normal or abnormal) to CRB-65 does not improve its prognostic accuracy. A low CRB-65 score of 0 may be a good rule-out test for adverse clinical outcomes in COVID-19 patients with normal or abnormal CXRs, which deserves prospective validation.

COVID-19 Screening Score for Patients without Acute Respiratory Symptoms Undergoing Emergency Medical Procedures in Indonesia

To rule out coronavirus disease-2019 (COVID-19) in patients scheduled to undergo emergency medical procedures, SARS-CoV-2 reverse transcription polymerase chain reaction (RT-PCR) must be performed. In developing countries, the use of SARS-CoV-2 RT-PCR has been limited by its unavailability and long processing time. Hence, a quick screening score to predict COVID-19 may help healthcare practitioners determine which patients without acute respiratory symptoms can safely undergo an emergency medical procedure. We conducted a cross-sectional study of adult patients without acute respiratory symptoms who were admitted to the emergency department and underwent an emergency medical procedure within 24 hours after admittance. We collected baseline demographic data, COVID-19 screening variables, and SARS-CoV-2 RT-PCR as the gold standard for COVID-19 diagnosis. Bivariate and multivariate analyses were performed, and a scoring system was developed using statistically significant variables from the multivariate analysis. With data from 357 patients, multivariate backward stepwise logistic regression analysis resulted in two significant COVID-19 predictors: the presence of SARS-CoV-2-IgM antibody (adjusted odds ratio [aOR]: 7.02 [95% CI: 1.49-32.96]) and typical chest x-ray (aOR: 23.21 [95% CI: 10.01-53.78]). A scoring system was developed using these predictors with an area under the receiver operating characteristic curve of 0.71 (95% CI: 0.64-0.78). For a cutoff point of ≥ 2, the scoring system showed 42.5% sensitivity and 97.1% specificity but had poor calibration (Hosmer-Lemeshow test P value < 0.001). We believe that the development of this COVID-19 quick screening score may be helpful in a resource-limited clinical setting, but its moderate discrimination and poor calibration hinder its use as a replacement for the SARS-CoV-2 RT-PCR test for COVID-19 screening.

A single-centred retrospective observational analysis on mortality trends during the COVID-19 pandemic

BACKGROUND

South Africa experienced high mortality during the COVID-19 pandemic. Resources were limited, particularly at the district hospital (DH) level. Overwhelmed healthcare facilities and a lack of research at a primary care level made the management of patients with COVID-19 challenging. The objective of this study was to describe the in-hospital mortality trends among individuals with COVID-19 at a DH in South Africa.

METHODS

Retrospective observational analysis of all adults who demised in hospital from COVID-19 between 01 March 2020 and 31 August 2021 at a DH in South Africa. Variables analysed included: background history, clinical presentation, investigations and management.

RESULTS

Of the 328 participants who demised in hospital, 60.1% were female, 66.5% were older than 60 and 59.6% were of black African descent. Hypertension and diabetes mellitus were the most common comorbidities (61.3% and 47.6%, respectively). The most common symptoms were dyspnoea (83.8%) and cough (70.1%). 'Ground-glass' features on admission chest X-rays were visible in 90.0% of participants, and 82.8% had arterial oxygen saturations less than 95% on admission. Renal impairment was the most common complication present on admission (63.7%). The median duration of admission before death was four days (interquartile range [IQR]: 1.5-8). The overall crude fatality rate was 15.3%, with the highest crude fatality rate found in wave two (33.0%).

CONCLUSION

Older participants with uncontrolled comorbidities were most likely to demise from COVID-19. Wave two (characterised by the 'Beta' variant) had the highest mortality rate.Contribution: This study provides insight into the risk factors associated with death in a resource-constrained environment.

Experimental analysis of machine learning methods to detect Covid-19 from x-rays

To automate the detection of covid-19 patients most have proposed deep learning neural networks to classify patients using large databases of chest x-rays. Very few used classical machine learning methods. Machine learning methods may require less computational power and perform well if the data set is small. We experiment with classical machine learning methods on three different data sources varying in size from 55 to almost 4000 samples. We experiment with four feature extraction methods of Gabor, SURF, LBP, and HOG. Backpropagation neural networks and k-nearest neighbor classifiers are combined using one of the four combining methods of bagging, RSM, ARCx4 boosting and Ada-boosting. Results show that using the proper feature extraction and feature selection methods very high performance can be reached using simple backpropagation neural network classifiers. Regardless of combiner method used, the best classification rate achieved was 99.06% for the largest data set, and 100% for the smallest data set.

Portable Chest Radiograph: A Boon for Critically Ill Patients With COVID-19 Pneumonia

OBJECTIVE

In the present study, we evaluated the role of portable chest radiographs in critically ill patients with COVID-19 pneumonia in whom computed tomography (CT) of the chest was not feasible.

METHODS

A retrospective chest X-ray study of patients under investigation for COVID-19 was performed in our dedicated COVID hospital (DCH) during the exponential growth phase of the COVID-19 outbreak (August-October, 2020). A total of 562 on-bed chest radiographs were examined comprising 289 patients (critically ill who couldn't be mobilized for CT) along with positive reverse transcription-polymerase chain reaction (RT-PCR) tests. We categorized each chest radiograph as progressive, with changes, or improvement in appearance for COVID-19, utilizing well-documented COVID-19 imaging patterns.

RESULTS

 In our study, portable radiographs provided optimum image quality for diagnosing pneumonia, in critically ill patients. Although less informative than CT, nevertheless radiographs detected serious complications like pneumothorax or lung cavitation and estimated the evolution of pneumonia.

CONCLUSION

A portable chest X-ray is a simple but reliable alternative for critically ill SARS-CoV-2 patients who could not undergo chest CT. With the help of portable chest radiographs, we could monitor the severity of the disease as well as different complications with minimal radiation exposure which would help in identifying the prognosis of the patient and thus help in medical management.

Longitudinal Chest X-ray Scores and their Relations with Clinical Variables and Outcomes in COVID-19 Patients

Background: This study evaluated the temporal characteristics of lung chest X-ray (CXR) scores in COVID-19 patients during hospitalization and how they relate to other clinical variables and outcomes (alive or dead). Methods: This is a retrospective study of COVID-19 patients. CXR scores of disease severity were analyzed for: (i) survivors (N = 224) versus non-survivors (N = 28) in the general floor group, and (ii) survivors (N = 92) versus non-survivors (N = 56) in the invasive mechanical ventilation (IMV) group. Unpaired t-tests were used to compare survivors and non-survivors and between time points. Comparison across multiple time points used repeated measures ANOVA and corrected for multiple comparisons. Results: For general-floor patients, non-survivor CXR scores were significantly worse at admission compared to those of survivors (p < 0.05), and non-survivor CXR scores deteriorated at outcome (p < 0.05) whereas survivor CXR scores did not (p > 0.05). For IMV patients, survivor and non-survivor CXR scores were similar at intubation (p > 0.05), and both improved at outcome (p < 0.05), with survivor scores showing greater improvement (p < 0.05). Hospitalization and IMV duration were not different between groups (p > 0.05). CXR scores were significantly correlated with lactate dehydrogenase, respiratory rate, D-dimer, C-reactive protein, procalcitonin, ferritin, SpO2, and lymphocyte count (p < 0.05). Conclusions: Longitudinal CXR scores have the potential to provide prognosis, guide treatment, and monitor disease progression.

Relationship between hyponatremia at hospital admission and cardiopulmonary profile at follow-up in patients with SARS-CoV-2 (COVID-19) infection

PURPOSE

Hyponatremia occurs in about 30% of patients with pneumonia, including those with SARS-CoV-2 (COVID-19) infection. Hyponatremia predicts a worse outcome in several pathologic conditions and in COVID-19 has been associated with a higher risk of non-invasive ventilation, ICU transfer and death. The main objective of this study was to determine whether early hyponatremia is also a predictor of long-term sequelae at follow-up.

METHODS

In this observational study, we collected 6-month follow-up data from 189 laboratory-confirmed COVID-19 patients previously admitted to a University Hospital. About 25% of the patients (n = 47) had hyponatremia at the time of hospital admission.

RESULTS

Serum [Na⁺] was significantly increased in the whole group of 189 patients at 6 months, compared to the value at hospital admission (141.4 ± 2.2 vs 137 ± 3.5 mEq/L, p < 0.001). In addition, IL-6 levels decreased and the PaO₂/FiO₂ increased. Accordingly, pulmonary involvement, evaluated at the chest X-ray by the RALE score, decreased. However, in patients with hyponatremia at hospital admission, higher levels of LDH, fibrinogen, troponin T and NT-ProBNP were detected at follow-up, compared to patients with normonatremia at admission. In addition, hyponatremia at admission was associated with worse echocardiography parameters related to right ventricular function, together with a higher RALE score.

CONCLUSION

These results suggest that early hyponatremia in COVID-19 patients is associated with the presence of laboratory and imaging parameters indicating a greater pulmonary and right-sided heart involvement at follow-up.

Utility of chest radiograph severity scoring in emergency department for predicting outcomes in COVID-19: A study of 1275 patients

OBJECTIVE

To measure the reliability and reproducibility of a chest radiograph severity score (CSS) in prognosticating patient's severity of disease and outcomes at the time of disease presentation in the emergency department (ED) with coronavirus disease 2019 (COVID-19).

MATERIALS AND METHODS

We retrospectively studied 1275 consecutive RT-PCR confirmed COVID-19 adult patients presenting to ED from March 2020 through June 2020. Chest radiograph severity score was assessed for each patient by two blinded radiologists. Clinical and laboratory parameters were collected. The rate of admission to intensive care unit, mechanical ventilation or death up to 60 days after the baseline chest radiograph were collected. Primary outcome was defined as occurrence of ICU admission or death. Multivariate logistic regression was performed to evaluate the relationship between clinical parameters, chest radiograph severity score, and primary outcome.

RESULTS

CSS of 3 or more was associated with ICU admission (78 % sensitivity; 73.1 % specificity; area under curve 0.81). CSS and pre-existing diabetes were independent predictors of primary outcome (odds ratio, 7; 95 % CI: 3.87, 11.73; p < 0.001 & odds ratio, 2; 95 % CI: 1-3.4, p 0.02 respectively). No significant difference in primary outcome was observed for those with history of hypertension, asthma, chronic kidney disease or coronary artery disease.

CONCLUSION

Semi-quantitative assessment of CSS at the time of disease presentation in the ED predicted outcomes in adults of all age with COVID-19.

Conv-CapsNet: capsule based network for COVID-19 detection through X-Ray scans

Coronavirus, a virus that spread worldwide rapidly and was eventually declared a pandemic. The rapid spread made it essential to detect Coronavirus infected people to control the further spread. Recent studies show that radiological images such as X-Rays and CT scans provide essential information in detecting infection using deep learning models. This paper proposes a shallow architecture based on Capsule Networks with convolutional layers to detect COVID-19 infected persons. The proposed method combines the ability of the capsule network to understand spatial information with convolutional layers for efficient feature extraction. Due to the model's shallow architecture, it has 23M parameters to train and requires fewer training samples. The proposed system is fast and robust and correctly classifies the X-Ray images into three classes, i.e. COVID-19, No Findings, and Viral Pneumonia. Experimental results on the X-Ray dataset show that our model performs well despite having fewer samples for the training and achieved an average accuracy of 96.47% for multi-class and 97.69% for binary classification on 5-fold cross-validation. The proposed model would be useful to researchers and medical professionals for assistance and prognosis for COVID-19 infected patients.

A dataset of COVID-19 x-ray chest images

The distinction between normal chest x-ray (CXR) images and abnormal ones containing features of disease (e.g., opacities, consolidation, etc.) is important for accurate medical diagnosis. CXR images contain valuable information concerning the physiological and pathological state of the lungs and airways. In addition, they provide information about the heart, chest bones, and some arteries (e.g., Aorta and pulmonary arteries). Deep learning artificial intelligence has taken great strides in the development of sophisticated medical models in a wide range of applications. More specifically, it has been shown to provide highly accurate diagnosis and detection tools. The dataset presented in this article contains the chest x-ray images from the examination of confirmed COVID-19 subjects, who were admitted for a multiday stay at a local hospital in northern Jordan. To provide a diverse dataset, only one CXR image per subject was included in the data. The dataset can be used for the development of automated methods that detect COVID-19 from CXR images (COVID-19 vs. normal) and distinguish pneumonia caused by COVID-19 from other pulmonary diseases. ©202x The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Deep Learning With Chest Radiographs for Making Prognoses in Patients With COVID-19: Retrospective Cohort Study

BACKGROUND

An artificial intelligence (AI) model using chest radiography (CXR) may provide good performance in making prognoses for COVID-19.

OBJECTIVE

We aimed to develop and validate a prediction model using CXR based on an AI model and clinical variables to predict clinical outcomes in patients with COVID-19.

METHODS

This retrospective longitudinal study included patients hospitalized for COVID-19 at multiple COVID-19 medical centers between February 2020 and October 2020. Patients at Boramae Medical Center were randomly classified into training, validation, and internal testing sets (at a ratio of 8:1:1, respectively). An AI model using initial CXR images as input, a logistic regression model using clinical information, and a combined model using the output of the AI model (as CXR score) and clinical information were developed and trained to predict hospital length of stay (LOS) ≤2 weeks, need for oxygen supplementation, and acute respiratory distress syndrome (ARDS). The models were externally validated in the Korean Imaging Cohort of COVID-19 data set for discrimination and calibration.

RESULTS

The AI model using CXR and the logistic regression model using clinical variables were suboptimal to predict hospital LOS ≤2 weeks or the need for oxygen supplementation but performed acceptably in the prediction of ARDS (AI model area under the curve [AUC] 0.782, 95% CI 0.720-0.845; logistic regression model AUC 0.878, 95% CI 0.838-0.919). The combined model performed better in predicting the need for oxygen supplementation (AUC 0.704, 95% CI 0.646-0.762) and ARDS (AUC 0.890, 95% CI 0.853-0.928) compared to the CXR score alone. Both the AI and combined models showed good calibration for predicting ARDS (P=.079 and P=.859).

CONCLUSIONS

The combined prediction model, comprising the CXR score and clinical information, was externally validated as having acceptable performance in predicting severe illness and excellent performance in predicting ARDS in patients with COVID-19.

Risk stratification of hospital admissions for COVID-19 pneumonia by chest radiographic scoring in a Johannesburg tertiary hospital

BACKGROUND

Chest radiographic scoring systems for COVID-19 pneumonia have been developed. However, little is published on the utilityof these scoring systems in low- and middle-income countries.

OBJECTIVES

To perform risk stratification of COVID-19 pneumonia in Johannesburg, South Africa (SA), by comparing the Brixia score withclinical parameters, disease course and clinical outcomes. To assess inter-rater reliability and developing predictive models of the clinicaloutcome using the Brixia score and clinical parameters.

METHODS

Retrospective investigation was conducted of adult participants with established COVID-19 pneumonia admitted at a tertiaryinstitution from 1 May to 30 June 2020. Two radiologists, blinded to clinical data, assigned Brixia scores. Brixia scores were compared withclinical parameters, length of stay and clinical outcomes (discharge/death). Inter-rater agreement was determined. Multivariable logisticregression extracted variables predictive of in-hospital demise.

RESULTS

The cohort consisted of 263 patients, 51% male, with a median age of 47 years (interquartile range (IQR) = 20; 95% confidenceinterval (CI) 46.5 - 49.9). Hypertension (38.4%), diabetes (25.1%), obesity (19.4%) and HIV (15.6%) were the most common comorbidities.The median length of stay for 258 patients was 7.5 days (IQR = 7; 95% CI 8.2 - 9.7) and 6.5 days (IQR = 8; 95% CI 6.5 - 12.5) for intensivecare unit stay. Fifty (19%) patients died, with a median age of 55 years (IQR = 23; 95% CI 50.5 - 58.7) compared with survivors, of medianage 46 years (IQR = 20; 95% CI 45 - 48.6) (p=0.01). The presence of one or more comorbidities resulted in a higher death rate (23% v. 9.2%;p=0.01) than without comorbidities. The median Brixia score for the deceased was higher (14.5) than for the discharged patients (9.0)(p<0.001). Inter-rater agreement for Brixia scores was good (intraclass correlation coefficient 0.77; 95% CI 0.6 - 0.85; p<0.001). A modelcombining Brixia score, age, male gender and obesity (sensitivity 84%; specificity 63%) as well as a model with Brixia score and C-reactiveprotein (CRP) count (sensitivity 81%; specificity 63%) conferred the highest risk for in-hospital mortality.

CONCLUSION

We have demonstrated the utility of the Brixia scoring system in a middle-income country setting and developed the first SArisk stratification models incorporating comorbidities and a serological marker. When used in conjunction with age, male gender, obesityand CRP, the Brixia scoring system is a promising and reliable risk stratification tool. This may help inform the clinical decision pathway inresource-limited settings like ours during future waves of COVID-19.

Deep Learning-Based Time-to-Death Prediction Model for COVID-19 Patients Using Clinical Data and Chest Radiographs

Accurate estimation of mortality and time to death at admission for COVID-19 patients is important and several deep learning models have been created for this task. However, there are currently no prognostic models which use end-to-end deep learning to predict time to event for admitted COVID-19 patients using chest radiographs and clinical data. We retrospectively implemented a new artificial intelligence model combining DeepSurv (a multiple-perceptron implementation of the Cox proportional hazards model) and a convolutional neural network (CNN) using 1356 COVID-19 inpatients. For comparison, we also prepared DeepSurv only with clinical data, DeepSurv only with images (CNNSurv), and Cox proportional hazards models. Clinical data and chest radiographs at admission were used to estimate patient outcome (death or discharge) and duration to the outcome. The Harrel's concordance index (c-index) of the DeepSurv with CNN model was 0.82 (0.75-0.88) and this was significantly higher than the DeepSurv only with clinical data model (c-index = 0.77 (0.69-0.84), p = 0.011), CNNSurv (c-index = 0.70 (0.63-0.79), p = 0.001), and the Cox proportional hazards model (c-index = 0.71 (0.63-0.79), p = 0.001). These results suggest that the time-to-event prognosis model became more accurate when chest radiographs and clinical data were used together.

Regional ventilation in spontaneously breathing COVID-19 patients during postural maneuvers assessed by electrical impedance tomography

BACKGROUND

Gravity-dependent positioning therapy is an established concept in the treatment of severe acute respiratory distress syndrome and improves oxygenation in spontaneously breathing patients with hypoxemic acute respiratory failure. In patients with coronavirus disease 2019, this therapy seems to be less effective. Electrical impedance tomography as a point-of-care functional imaging modality for visualizing regional ventilation can possibly help identify patients who might benefit from positioning therapy and guide those maneuvers in real-time. Therefore, in this prospective observational study, we aimed to discover typical patterns in response to positioning maneuvers.

METHODS

Distribution of ventilation in 10 healthy volunteers and in 12 patients with hypoxemic respiratory failure due to coronavirus disease 2019 was measured in supine, left, and right lateral positions using electrical impedance tomography.

RESULTS

In this study, patients with coronavirus disease 2019 showed a variety of ventilation patterns, which were not predictable, whereas all but one healthy volunteer showed a typical and expected gravity-dependent distribution of ventilation with the body positions.

CONCLUSION

Distribution of ventilation and response to lateral positioning is variable and thus unpredictable in spontaneously breathing patients with coronavirus disease 2019. Electrical impedance tomography might add useful information on the immediate reaction to postural maneuvers and should be elucidated further in clinical studies. Therefore, we suggest a customized individualized positioning therapy guided by electrical impedance tomography.

[Performance in prognostic capacity and efficiency of the Thoracic Care Suite GE AI tool applied to chest radiography of patients with COVID-19 pneumonia]

OBJECTIVE

Rapid progression of COVID-19 pneumonia may put patients at risk of requiring ventilatory support, such as non-invasive mechanical ventilation or endotracheal intubation. Implementing tools that detect COVID-19 pneumonia can improve the patient's healthcare. We aim to evaluate the efficacy and efficiency of the artificial intelligence (AI) tool GE Healthcare's Thoracic Care Suite (featuring Lunit INSIGHT CXR, TCS) to predict the ventilatory support need based on pneumonic progression of COVID-19 on consecutive chest X-rays.

METHODS

Outpatients with confirmed SARS-CoV-2 infection, with chest X-ray (CXR) findings probable or indeterminate for COVID-19 pneumonia, who required a second CXR due to unfavorable clinical course, were collected. The number of affected lung fields for the two CXRs was assessed using the AI tool.

RESULTS

One hundred fourteen patients (57.4 ± 14.2 years, 65 -57%- men) were retrospectively collected. Fifteen (13.2%) required ventilatory support. Progression of pneumonic extension ≥ 0.5 lung fields per day compared to pneumonia onset, detected using the TCS tool, increased the risk of requiring ventilatory support by 4-fold. Analyzing the AI output required 26 seconds of radiological time.

CONCLUSIONS

Applying the AI tool, Thoracic Care Suite, to CXR of patients with COVID-19 pneumonia allows us to anticipate ventilatory support requirements requiring less than half a minute.

Inter-rater reliability and prognostic value of baseline Radiographic Assessment of Lung Edema (RALE) scores in observational cohort studies of inpatients with COVID-19

OBJECTIVES

To reliably quantify the radiographic severity of COVID-19 pneumonia with the Radiographic Assessment of Lung Edema (RALE) score on clinical chest X-rays among inpatients and examine the prognostic value of baseline RALE scores on COVID-19 clinical outcomes.

SETTING

Hospitalised patients with COVID-19 in dedicated wards and intensive care units from two different hospital systems.

PARTICIPANTS

425 patients with COVID-19 in a discovery data set and 415 patients in a validation data set.

PRIMARY AND SECONDARY OUTCOMES

We measured inter-rater reliability for RALE score annotations by different reviewers and examined for associations of consensus RALE scores with the level of respiratory support, demographics, physiologic variables, applied therapies, plasma host-response biomarkers, SARS-CoV-2 RNA load and clinical outcomes.

RESULTS

Inter-rater agreement for RALE scores improved from fair to excellent following reviewer training and feedback (intraclass correlation coefficient of 0.85 vs 0.93, respectively). In the discovery cohort, the required level of respiratory support at the time of CXR acquisition (supplemental oxygen or non-invasive ventilation (n=178); invasive-mechanical ventilation (n=234), extracorporeal membrane oxygenation (n=13)) was significantly associated with RALE scores (median (IQR): 20.0 (14.1-26.7), 26.0 (20.5-34.0) and 44.5 (34.5-48.0), respectively, p<0.0001). Among invasively ventilated patients, RALE scores were significantly associated with worse respiratory mechanics (plateau and driving pressure) and gas exchange metrics (PaO2/FiO2 and ventilatory ratio), as well as higher plasma levels of IL-6, soluble receptor of advanced glycation end-products and soluble tumour necrosis factor receptor 1 (p<0.05). RALE scores were independently associated with 90-day survival in a multivariate Cox proportional hazards model (adjusted HR 1.04 (1.02-1.07), p=0.002). We replicated the significant associations of RALE scores with baseline disease severity and mortality in the independent validation data set.

CONCLUSIONS

With a reproducible method to measure radiographic severity in COVID-19, we found significant associations with clinical and physiologic severity, host inflammation and clinical outcomes. The incorporation of radiographic severity assessments in clinical decision-making may provide important guidance for prognostication and treatment allocation in COVID-19.

Densely attention mechanism based network for COVID-19 detection in chest X-rays

Automatic COVID-19 detection using chest X-ray (CXR) can play a vital part in large-scale screening and epidemic control. However, the radiographic features of CXR have different composite appearances, for instance, diffuse reticular-nodular opacities and widespread ground-glass opacities. This makes the automatic recognition of COVID-19 using CXR imaging a challenging task. To overcome this issue, we propose a densely attention mechanism-based network (DAM-Net) for COVID-19 detection in CXR. DAM-Net adaptively extracts spatial features of COVID-19 from the infected regions with various appearances and scales. Our proposed DAM-Net is composed of dense layers, channel attention layers, adaptive downsampling layer, and label smoothing regularization loss function. Dense layers extract the spatial features and the channel attention approach adaptively builds up the weights of major feature channels and suppresses the redundant feature representations. We use the cross-entropy loss function based on label smoothing to limit the effect of interclass similarity upon feature representations. The network is trained and tested on the largest publicly available dataset, i.e., COVIDx, consisting of 17,342 CXRs. Experimental results demonstrate that the proposed approach obtains state-of-the-art results for COVID-19 classification with an accuracy of 97.22%, a sensitivity of 96.87%, a specificity of 99.12%, and a precision of 95.54%.

Fast bilateral weighted least square for the detail enhancement of COVID-19 chest X-rays

Background

X-ray is an effective measure in the diagnosis of coronavirus disease 2019. However, it suffers from low visibility and poor details. A plausible solution is to decompose the captured images and enhance the details. The bilateral weighted least square model can be an effective tool for this task. However, it is highly computationally expensive.

Method

In this article, we propose an efficient algorithm for the bilateral weighted least square model. We approximate the bilateral weight with the bilateral grid and then incorporate it into the optimization model. This significantly reduces the number of variables in the linear system. Therefore, the model can be efficiently solved. We employ the proposed algorithm to decompose the input X-rays into base and detail layers. The detail layers are then boosted and added back to the input to derive the detail-enhanced results.

Results

The subjective results indicate that our method achieves higher contrast than the best-performing method (442.30 > 410.09 , 426.40 > 403.34 , 564.51 > 531.38 ). Furthermore, our method is highly efficient. It takes 0.92  s to process a 720P color image on an Intel i7-6700 CPU. The objective results derive from the chi-square test indicate that subjects hold more positive attitudes toward our detail-enhanced images than the original X-ray images (3.53 > 2.72 , 3.42 > 2.61 , 3.5 > 2.56 ).

Conclusion

We have conducted extensive experiments to evaluate the proposed image detail enhancement method. It can be concluded that (1) our method could significantly improve the visibility of the X-ray images. (2) our method is fast and effective, thus facilitating real applications.

More severe lung lesions in smoker patients with active pulmonary tuberculosis were associated with peripheral NK cell subsets

BACKGROUND

Both pulmonary tuberculosis (PTB) and cigarette smoke (CS) exposure may lead to lung damage. The potential impact of CS exposure on tuberculosis-associated lung damage and the disturbance of immune cells and mediators involved, need to be further elucidated.

METHODS

We firstly evaluated the chest X-ray (CXR) scores of a retrospective cohort of male patients with active PTB, followed for 6 months, and compared the scores between smoker (≥10 pack-years) and non-smoker patients. In a cross-sectional study, we measured the peripheral blood NK cell subsets and plasma inflammatory cytokines in male smoker and non-smoker patients with active PTB before anti-tuberculosis therapy, and the proportions of NK cell subsets and the levels of cytokines were analyzed for correlation with the CXR scores.

RESULTS

In the retrospective cohort, male smoker patients with active PTB showed a higher CXR score, characterized by more cavitary lesions, enlarged lymph nodes and emphysema, as compared to non-smokers. The cross-sectional study revealed that the CXR score in smoker patients was correlated inversely with the percentages of blood CD107a⁺, NKP46⁺, and TIGIT⁺ NK cells.

CONCLUSION

In patients with active PTB, CS exposure was associated with more severe lung lesions, which were correlated with peripheral NK cell subsets.

Chest x-ray imaging score is associated with severity of COVID-19 pneumonia: the MBrixia score

Spatial resolution in existing chest x-ray (CXR)-based scoring systems for coronavirus disease 2019 (COVID-19) pneumonia is low, and should be increased for better representation of anatomy, and severity of lung involvement. An existing CXR-based system, the Brixia score, was modified to increase the spatial resolution, creating the MBrixia score. The MBrixia score is the sum, of a rule-based quantification of CXR severity on a scale of 0 to 3 in 12 anatomical zones in the lungs. The MBrixia score was applied to CXR images from COVID-19 patients at a single tertiary hospital in the period May 4th-June 5th, 2020. The relationship between MBrixia score, and level of respiratory support at the time of performed CXR imaging was investigated. 37 hospitalized COVID-19 patients with 290 CXRs were identified, 22 (59.5%) were admitted to the intensive care unit and 10 (27%) died during follow-up. In a Poisson regression using all 290 MBrixia scored CXRs, a higher MBrixia score was associated with a higher level of respiratory support at the time of performed CXR. The MBrixia score could potentially be valuable as a quantitative surrogate measurement of COVID-19 pneumonia severity, and future studies should investigate the score's validity and capabilities of predicting clinical outcomes.

How accurate are radiography and computed tomography in the diagnosis of COVID-19?—A Bayesian approach

Background

The role of radiology in patients with clinical suspicion of COVID-19 is evolving with scientific evidence, but there are differences in opinion on when and how the technique should be used for clinical diagnosis.

Purpose

To estimate the pre-test and post-test probability that a patient has COVID-19 in the event of a positive and/or negative result from chest X-ray and chest computed tomography (CT) radiological studies, comparing with those of real time polymerase chain reaction (RT-PCR) tests.

Methods

The literature on the sensitivity and specificity of the chest X-ray, chest CT, and RT-PCR was reviewed. Based on these reported data, the likelihood ratios (LR) were estimated and the pre-test probabilities were related to the post-test probabilities after positive or negative results.

Results

The chest X-ray has only a confirmatory value in cases of high suspicion. Chest CT analyses showed that when it is used as a general study, it has almost confirmatory value under high clinical suspicion. A chest CT classified with CO-RADS ≥ 4 has almost a diagnostic certainty of COVID-19 even with moderate or low clinical presumptions, and the CO-RADS 5 classification is almost pathognomonic before any clinical presumption. To rule out COVID-19 completely is only possible in very low clinical assumptions with negative RT-PCR and/or CT.

Conclusions

Chest X-ray and especially CT are fast studies that have the capacity to report high probability of COVID-19, being a real contribution to the concept of “probable case” and allowing support to be installed in an early and timely manner.

Fast COVID-19 Detection from Chest X-Ray Images Using DCT Compression

Novel coronavirus (COVID-19) is a new strain of coronavirus, first identified in a cluster with pneumonia symptoms caused by SARS-CoV-2 virus. It is fast spreading all over the world. Most infected people will develop mild to moderate illness and recover without hospitalization. Currently, real-time quantitative reverse transcription-PCR (rqRT-PCR) is popular for coronavirus detection due to its high specificity, simple quantitative analysis, and higher sensitivity than conventional RT-PCR. Antigen tests are also commonly used. It is very essential for the automatic detection of COVID-19 from publicly available resources. Chest X-ray (CXR) images are used for the classification of COVID-19, normal, and viral pneumonia cases. The CXR images are divided into sub-blocks for finding out the discrete cosine transform (DCT) for every sub-block in this proposed method. In order to produce a compressed version for each CXR image, the DCT energy compaction capability is used. For each image, hardly few spectral DCT components are included as features. The dimension of the final feature vectors is reduced by scanning the compressed images using average pooling windows. In the 3-set classification, a multilayer artificial neural network is used. It is essential to triage non-COVID-19 patients with pneumonia to give out hospital resources efficiently. Higher size feature vectors are used for designing binary classification for COVID-19 and pneumonia. The proposed method achieved an average accuracy of 95% and 94% for the 3-set classification and binary classification, respectively. The proposed method achieves better accuracy than that of the recent state-of-the-art techniques. Also, the time required for the implementation is less.

Deep learning of longitudinal chest X-ray and clinical variables predicts duration on ventilator and mortality in COVID-19 patients

Objectives

To use deep learning of serial portable chest X-ray (pCXR) and clinical variables to predict mortality and duration on invasive mechanical ventilation (IMV) for Coronavirus disease 2019 (COVID-19) patients.

Methods

This is a retrospective study. Serial pCXR and serial clinical variables were analyzed for data from day 1, day 5, day 1–3, day 3–5, or day 1–5 on IMV (110 IMV survivors and 76 IMV non-survivors). The outcome variables were duration on IMV and mortality. With fivefold cross-validation, the performance of the proposed deep learning system was evaluated by receiver operating characteristic (ROC) analysis and correlation analysis.

Results

Predictive models using 5-consecutive-day data outperformed those using 3-consecutive-day and 1-day data. Prediction using data closer to the outcome was generally better (i.e., day 5 data performed better than day 1 data, and day 3–5 data performed better than day 1–3 data). Prediction performance was generally better for the combined pCXR and non-imaging clinical data than either alone. The combined pCXR and non-imaging data of 5 consecutive days predicted mortality with an accuracy of 85 ± 3.5% (95% confidence interval (CI)) and an area under the curve (AUC) of 0.87 ± 0.05 (95% CI) and predicted the duration needed to be on IMV to within 2.56 ± 0.21 (95% CI) days on the validation dataset.

Conclusions

Deep learning of longitudinal pCXR and clinical data have the potential to accurately predict mortality and duration on IMV in COVID-19 patients. Longitudinal pCXR could have prognostic value if these findings can be validated in a large, multi-institutional cohort.

Machine Learning Model Based on Radiomic Features for Differentiation between COVID-19 and Pneumonia on Chest X-ray

Machine learning approaches are employed to analyze differences in real-time reverse transcription polymerase chain reaction scans to differentiate between COVID-19 and pneumonia. However, these methods suffer from large training data requirements, unreliable images, and uncertain clinical diagnosis. Thus, in this paper, we used a machine learning model to differentiate between COVID-19 and pneumonia via radiomic features using a bias-minimized dataset of chest X-ray scans. We used logistic regression (LR), naive Bayes (NB), support vector machine (SVM), k-nearest neighbor (KNN), bagging, random forest (RF), extreme gradient boosting (XGB), and light gradient boosting machine (LGBM) to differentiate between COVID-19 and pneumonia based on training data. Further, we used a grid search to determine optimal hyperparameters for each machine learning model and 5-fold cross-validation to prevent overfitting. The identification performances of COVID-19 and pneumonia were compared with separately constructed test data for four machine learning models trained using the maximum probability, contrast, and difference variance of the gray level co-occurrence matrix (GLCM), and the skewness as input variables. The LGBM and bagging model showed the highest and lowest performances; the GLCM difference variance showed a high overall effect in all models. Thus, we confirmed that the radiomic features in chest X-rays can be used as indicators to differentiate between COVID-19 and pneumonia using machine learning.

The role of radiologic technologists during the COVID-19 pandemic

During the pandemic, stress of coronavirus disease 2019 (COVID-19) on a radiology department has caused major change in the workflow and protocol, which can inflame unnecessary anxiety among the staff. We have adapted and responded quickly however, to the volatile clinical situations owing to a close consultant in infection control. Our repeatedly revised procedures since the 2014 Ebola outbreak possess the expertise and were very useful. In-house training sessions have been held and updated accordingly. In-house networking service has now become more common in our department instead of the emergency contact network relaying the message to the person on the phone tree. Up until January 2022, we examined 10,861 chest X-rays with no in-hospital infection. We sincerely hope our chest X-ray strategies comply with infection prevention and control standards and minimize use of personal protective equipment will be embraced as a positive initiative by frontline radiologic technologists and relieve their anxiety.

The RALE Score Versus the CT Severity Score in Invasively Ventilated COVID-19 Patients—A Retrospective Study Comparing Their Prognostic Capacities

Background: Quantitative radiological scores for the extent and severity of pulmonary infiltrates based on chest radiography (CXR) and computed tomography (CT) scan are increasingly used in critically ill invasively ventilated patients. This study aimed to determine and compare the prognostic capacity of the Radiographic Assessment of Lung Edema (RALE) score and the chest CT Severity Score (CTSS) in a cohort of invasively ventilated patients with acute respiratory distress syndrome (ARDS) due to COVID-19. Methods: Two-center retrospective observational study, including consecutive invasively ventilated COVID-19 patients. Trained scorers calculated the RALE score of first available CXR and the CTSS of the first available CT scan. The primary outcome was ICU mortality; secondary outcomes were duration of ventilation in survivors, length of stay in ICU, and hospital-, 28-, and 90-day mortality. Prognostic accuracy for ICU death was expressed using odds ratios and Area Under the Receiver Operating Characteristic curves (AUROC). Results: A total of 82 patients were enrolled. The median RALE score (22 [15–37] vs. 26 [20–39]; p = 0.34) and the median CTSS (18 [16–21] vs. 21 [18–23]; p = 0.022) were both lower in ICU survivors compared to ICU non-survivors, although only the difference in CTSS reached statistical significance. While no association was observed between ICU mortality and RALE score (OR 1.35 [95%CI 0.64–2.84]; p = 0.417; AUC 0.50 [0.44–0.56], this was noticed with the CTSS (OR, 2.31 [1.22–4.38]; p = 0.010) although with poor prognostic capacity (AUC 0.64 [0.57–0.69]). The correlation between the RALE score and CTSS was weak (r² = 0.075; p = 0.012). Conclusions: Despite poor prognostic capacity, only CTSS was associated with ICU mortality in our cohort of COVID-19 patients.

Factors Affecting the Self-Isolation Monitoring Program for COVID-19 Patients at the Universitas Indonesia Hospital

When the outbreak of the COVID-19 delta variant occurred in June 2021, there was a marked increase in Indonesia's number of self-isolated patients. The Universitas Indonesia Hospital provided a One-Stop Service (OSS) to monitor COVID-19 patients on self-isolation. This study was conducted to determine the effectiveness of the self-isolation monitoring performed by hospitals and the factors that determined the outcomes of patients on self-isolation. This study was conducted using a cross-sectional method based on secondary data from electronic medical records. Data analysis was performed by determining the relationship of patient risk factors and characteristics with COVID-19 outcomes. The study found that poorer symptoms, administration of antibiotics, absence of shortness of breath, and normal ALT levels significantly improved the outcome of OSS patients. The study also suggested that during monitoring of patients on COVID-19 self-isolation, chest/thorax radiography is necessary. The self-isolation monitoring program is essential to observe the patient's condition and evaluate the possibility of deteriorating conditions that could lead to admission decisions in the early or middle stages of the program. This will be beneficial during pandemic emergencies.

Multi-population generalizability of a deep learning-based chest radiograph severity score for COVID-19

To tune and test the generalizability of a deep learning-based model for assessment of COVID-19 lung disease severity on chest radiographs (CXRs) from different patient populations. A published convolutional Siamese neural network-based model previously trained on hospitalized patients with COVID-19 was tuned using 250 outpatient CXRs. This model produces a quantitative measure of COVID-19 lung disease severity (pulmonary x-ray severity (PXS) score). The model was evaluated on CXRs from 4 test sets, including 3 from the United States (patients hospitalized at an academic medical center (N = 154), patients hospitalized at a community hospital (N = 113), and outpatients (N = 108)) and 1 from Brazil (patients at an academic medical center emergency department (N = 303)). Radiologists from both countries independently assigned reference standard CXR severity scores, which were correlated with the PXS scores as a measure of model performance (Pearson R). The Uniform Manifold Approximation and Projection (UMAP) technique was used to visualize the neural network results. Tuning the deep learning model with outpatient data showed high model performance in 2 United States hospitalized patient datasets (R = 0.88 and R = 0.90, compared to baseline R = 0.86). Model performance was similar, though slightly lower, when tested on the United States outpatient and Brazil emergency department datasets (R = 0.86 and R = 0.85, respectively). UMAP showed that the model learned disease severity information that generalized across test sets. A deep learning model that extracts a COVID-19 severity score on CXRs showed generalizable performance across multiple populations from 2 continents, including outpatients and hospitalized patients.

Automated diagnosis and prognosis of COVID-19 pneumonia from initial ER chest X-rays using deep learning

Background

Airspace disease as seen on chest X-rays is an important point in triage for patients initially presenting to the emergency department with suspected COVID-19 infection. The purpose of this study is to evaluate a previously trained interpretable deep learning algorithm for the diagnosis and prognosis of COVID-19 pneumonia from chest X-rays obtained in the ED.

Methods

This retrospective study included 2456 (50% RT-PCR positive for COVID-19) adult patients who received both a chest X-ray and SARS-CoV-2 RT-PCR test from January 2020 to March of 2021 in the emergency department at a single U.S. institution. A total of 2000 patients were included as an additional training cohort and 456 patients in the randomized internal holdout testing cohort for a previously trained Siemens AI-Radiology Companion deep learning convolutional neural network algorithm. Three cardiothoracic fellowship-trained radiologists systematically evaluated each chest X-ray and generated an airspace disease area-based severity score which was compared against the same score produced by artificial intelligence. The interobserver agreement, diagnostic accuracy, and predictive capability for inpatient outcomes were assessed. Principal statistical tests used in this study include both univariate and multivariate logistic regression.

Results

Overall ICC was 0.820 (95% CI 0.790–0.840). The diagnostic AUC for SARS-CoV-2 RT-PCR positivity was 0.890 (95% CI 0.861–0.920) for the neural network and 0.936 (95% CI 0.918–0.960) for radiologists. Airspace opacities score by AI alone predicted ICU admission (AUC = 0.870) and mortality (0.829) in all patients. Addition of age and BMI into a multivariate log model improved mortality prediction (AUC = 0.906).

Conclusion

The deep learning algorithm provides an accurate and interpretable assessment of the disease burden in COVID-19 pneumonia on chest radiographs. The reported severity scores correlate with expert assessment and accurately predicts important clinical outcomes. The algorithm contributes additional prognostic information not currently incorporated into patient management.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12879-022-07617-7.

Usefulness of chest X-rays for detecting COVID 19 pneumonia during the SARS-CoV-2 pandemic

Objective

To review the prognostic usefulness of chest X-rays in selecting patients with suspected SARS-CoV-2 infection.

Material and methods

This cross-sectional descriptive observational study analyzed 978 patients with suspected SARS-CoV-2 infections who underwent chest X-ray examinations in the emergency department of a tertiary hospital in March 2020. We separately analyzed demographic, clinical, and prognostic variables in two groups of patients: those in whom reverse-transcriptase polymerase chain reaction (RT-PCR) was done (n = 535) and those in whom RT-PCR was not done because of low clinical suspicion (n = 443).

Results

In the group of patients with RT-PCR, the prevalence of SARS-CoV-2 was 70.4%, and the sensitivity of chest X-rays was 62.8%. In the group of patients without RT-PCR, chest X-rays were negative in 97.5%, corroborating the low clinical suspicion; these patients were discharged, and 5.6% of them reconsulted with mild forms of the disease. In the group of patients with RT-PCR, we observed no statistically significant differences in the percentage of pathologic chest X-rays between patients hospitalized in the ICU (72.9%) and in those hospitalized in other wards (68.3%) (p = 0.22).

Conclusion

In the context of the pandemic, patients with low clinical suspicion and negative chest X-rays can be discharged with a low probability of reconsultation or of developing severe COVID19. In patients with RT-PCR positive for SARS-CoV-2, chest X-rays have no prognostic usefulness.

Black Fungus and beyond: COVID-19 associated infections

Globally, many hospitalized COVID-19 patients can experience an unexpected acute change in status, prompting rapid and expert clinical assessment. Superimposed infections can be a significant cause of clinical and radiologic deviations in this patient population, further worsening clinical outcome and muddling the differential diagnosis. As thrombotic, inflammatory, and medication-induced complications can also trigger an acute change in COVID-19 patient status, imaging early and often plays a vital role in distinguishing the cause of patient decline and monitoring patient outcome. While the common radiologic findings of COVID-19 infection are now widely reported, little is known about the clinical manifestations and imaging findings of superimposed infection. By discussing case studies of patients who developed bacterial, fungal, parasitic, and viral co-infections and identifying the most frequently reported imaging findings of superimposed infections, physicians will be more familiar with common infectious presentations and initiate a directed workup sooner. Ultimately, any abrupt changes in the expected COVID-19 imaging presentation, such as the presence of new consolidations or cavitation, should prompt further workup to exclude superimposed opportunistic infection.

Chest Radiograph Severity and Its Association With Outcomes in Subjects With COVID-19 Presenting to the Emergency Department

BACKGROUND

Severity of radiographic abnormalities on chest radiograph in subjects with COVID-19 has been shown to be associated with worse outcomes, but studies are limited by different scoring systems, sample size, subject age, and study duration. Data regarding the longitudinal evolution of radiographic abnormalities and its association with outcomes are scarce. We sought to evaluate these questions using a well-validated scoring system (the Radiographic Assessment of Lung Edema [RALE] score) using data over 6 months from a large, multihospital health care system.

METHODS

We collected clinical and demographic data and quantified radiographic edema on chest radiograph obtained in the emergency department (ED) as well as on days 1-2 and 3-5 (in those admitted) in subjects with a nasopharyngeal swab positive for SARS-CoV-2 by polymerase chain reaction (PCR) visiting the ED for coronavirus disease 2019 (COVID)-19-related complaints between March-September 2020. We examined the association of baseline and longitudinal evolution of radiographic edema with severity of hypoxemia and clinical outcomes.

RESULTS

Eight hundred and seventy subjects were included (median age 53.6; 50.8% female). Inter-rate agreement for RALE scores was excellent (interclass correlation coefficient 0.84 [95% CI 0.82-0.87], P < .001). RALE scores correlated with hypoxemia as quantified by S_pO2 /F_IO2 (r = -0.42, P < .001). Admitted subjects had higher RALE scores than those discharged (6 [2-11] vs 0 [0-3], P < .001). An increase of RALE score ≥ 4 was associated with worse 30-d survival (P = .006). Larger increases in the RALE score were associated with worse survival.

CONCLUSIONS

The RALE score was reproducible and easily implementable in adult subjects presenting to the ED with COVID-19. Its association with physiologic parameters and outcomes at baseline and longitudinally makes it a readily available tool for prognostication and early ICU triage, particularly in patients with worsening radiographic edema.

Utilidad de la radiografía de tórax para la detección de neumonía COVID 19 durante la pandemia por SARS-CoV-2

Objetivo

Material y métodos

Resultados

Conclusión

COVIDetection: deep convolutional neural networks-based automatic detection of COVID-19 with chest x-ray images

Purpose

Until recently, COVID-19 was considered a highly contagious air borne infection that leads to fatal pneumonia and other health hazardous infections. The new coronavirus, or type SARS-COV-2, is responsible for COVID-19 and has demonstrated the deadly nature of the respiratory disease that threatens many people worldwide. A clinical study found that a person infected with COVID-19 can experience dry cough, muscle pain, headache, fever, sore throat, and mild to moderate respiratory illness. At the same time, it has a negative effect on the lungs if there is a viral infection. Thus, the lungs can become visible internal organs for diagnosing the severity of COVID-19 infection using chest X-rays and CT scans. Despite the long testing time, RT-PCR is a proven testing method for detecting coronavirus infection. Sometimes there are more false positives and false negatives than the desired percentage. The concept of artificial neural network (ANN) is inspired by the biological neural networks which consists of inter-connected units called artificial neurons. Convolutional neural network (CNN) which is a variant of multilayer perceptron that belongs to a class of feedforward ANN is widely used for various applications due to its enhanced accuracy.

Method

Traditional RT-PCR methodology supports for accurate clinical diagnosis, screening for COVID-19 using an X-ray or CT scan of the human lung that can be considered. In this work, a new multi-image augmentation system is proposed based on CNN to detect COVID-19 in the chest using chest X-rays or CT images of people suspected of having the coronavirus.

Results

The optimal selection of slices/features has led to obtain best results for accuracy and loss. In addition to that the parameter selection reflected optimal true positive rate and false positive rates. The results look promising even with the small publicly available data set in a short period of time.

Conclusion

This work presented a model that found to detect positive cases of COVID-19 from chest X-rays using an in-depth training model. The system demonstrates a significant performance improvement over the publicly maintained COVID-19 positive X-ray classification kit, the same dataset of pneumonia chest X-rays. The results look promising even with the small publicly available data set in terms of accuracy and loss as well as with enhanced true positive results.

Usefulness of Hospital Admission Chest X-ray Score for Predicting Mortality and ICU Admission in COVID-19 Patients

We aimed to investigate the performance of a chest X-ray (CXR) scoring scale of lung injury in prediction of death and ICU admission among patients with COVID-19 during the 2021 peak pandemic in HCM City, Vietnam. CXR and clinical data were collected from Vinmec Central Park-hospitalized patients from July to September 2021. Three radiologists independently assessed the day-one CXR score consisting of both severity and extent of lung lesions (maximum score = 24). Among 219 included patients, 28 died and 34 were admitted to the ICU. There was a high consensus for CXR scoring among radiologists (κ = 0.90; CI95%: 0.89-0.92). CXR score was the strongest predictor of mortality (tdAUC 0.85 CI95% 0.69-1) within the first 3 weeks after admission. A multivariate model confirmed a significant effect of an increased CXR score on mortality risk (HR = 1.33, CI95%: 1.10 to 1.62). At a threshold of 16 points, the CXR score allowed for predicting in-hospital mortality and ICU admission with good sensitivity (0.82 (CI95%: 0.78 to 0.87) and 0.86 (CI95%: 0.81 to 0.90)) and specificity (0.89 (CI95%: 0.88 to 0.90) and 0.87 (CI95%: 0.86 to 0.89)), respectively, and can be used to identify high-risk patients in needy countries such as Vietnam.

Radiographic Assessment of Lung Edema (RALE) Scores are Highly Reproducible and Prognostic of Clinical Outcomes for Inpatients with COVID-19

INTRODUCTION

Chest imaging is necessary for diagnosis of COVID-19 pneumonia, but current risk stratification tools do not consider radiographic severity. We quantified radiographic heterogeneity among inpatients with COVID-19 with the Radiographic Assessment of Lung Edema (RALE) score on Chest X-rays (CXRs).

METHODS

We performed independent RALE scoring by ≥2 reviewers on baseline CXRs from 425 inpatients with COVID-19 (discovery dataset), we recorded clinical variables and outcomes, and measured plasma host-response biomarkers and SARS-CoV-2 RNA load from subjects with available biospecimens.

RESULTS

We found excellent inter-rater agreement for RALE scores (intraclass correlation co-efficient=0.93). The required level of respiratory support at the time of baseline CXRs (supplemental oxygen or non-invasive ventilation [n=178]; invasive-mechanical ventilation [n=234], extracorporeal membrane oxygenation [n=13]) was significantly associated with RALE scores (median [interquartile range]: 20.0[14.1-26.7], 26.0[20.5-34.0] and 44.5[34.5-48.0], respectively, p<0.0001). Among invasively-ventilated patients, RALE scores were significantly associated with worse respiratory mechanics (plateau and driving pressure) and gas exchange metrics (PaO2/FiO2 and ventilatory ratio), as well as higher plasma levels of IL-6, sRAGE and TNFR1 levels (p<0.05). RALE scores were independently associated with 90-day survival in a multivariate Cox proportional hazards model (adjusted hazard ratio 1.04[1.02-1.07], p=0.002). We validated significant associations of RALE scores with baseline severity and mortality in an independent dataset of 415 COVID-19 inpatients.

CONCLUSION

Reproducible assessment of radiographic severity revealed significant associations with clinical and physiologic severity, host-response biomarkers and clinical outcome in COVID-19 pneumonia. Incorporation of radiographic severity assessments may provide prognostic and treatment allocation guidance in patients hospitalized with COVID-19.

Imaging Severity COVID-19 Assessment in Vaccinated and Unvaccinated Patients: Comparison of the Different Variants in a High Volume Italian Reference Center

Purpose: To analyze the vaccine effect by comparing five groups: unvaccinated patients with Alpha variant, unvaccinated patients with Delta variant, vaccinated patients with Delta variant, unvaccinated patients with Omicron variant, and vaccinated patients with Omicron variant, assessing the “gravity” of COVID-19 pulmonary involvement, based on CT findings in critically ill patients admitted to Intensive Care Unit (ICU). Methods: Patients were selected by ICU database considering the period from December 2021 to 23 March 2022, according to the following inclusion criteria: patients with proven Omicron variant COVID-19 infection with known COVID-19 vaccination with at least two doses and with chest Computed Tomography (CT) study during ICU hospitalization. Wee also evaluated the ICU database considering the period from March 2020 to December 2021, to select unvaccinated consecutive patients with Alpha variant, subjected to CT study, consecutive unvaccinated and vaccinated patients with Delta variant, subjected to CT study, and, consecutive unvaccinated patients with Omicron variant, subjected to CT study. CT images were evaluated qualitatively using a severity score scale of 5 levels (none involvement, mild: ≤25% of involvement, moderate: 26−50% of involvement, severe: 51−75% of involvement, and critical involvement: 76−100%) and quantitatively, using the Philips IntelliSpace Portal clinical application CT COPD computer tool. For each patient the lung volumetry was performed identifying the percentage value of aerated residual lung volume. Non-parametric tests for continuous and categorical variables were performed to assess statistically significant differences among groups. Results: The patient study group was composed of 13 vaccinated patients affected by the Omicron variant (Omicron V). As control groups we identified: 20 unvaccinated patients with Alpha variant (Alpha NV); 20 unvaccinated patients with Delta variant (Delta NV); 18 vaccinated patients with Delta variant (Delta V); and 20 unvaccinated patients affected by the Omicron variant (Omicron NV). No differences between the groups under examination were found (p value > 0.05 at Chi square test) in terms of risk factors (age, cardiovascular diseases, diabetes, immunosuppression, chronic kidney, cardiac, pulmonary, neurologic, and liver disease, etc.). A different median value of aerated residual lung volume was observed in the Delta variant groups: median value of aerated residual lung volume was 46.70% in unvaccinated patients compared to 67.10% in vaccinated patients. In addition, in patients with Delta variant every other extracted volume by automatic tool showed a statistically significant difference between vaccinated and unvaccinated group. Statistically significant differences were observed for each extracted volume by automatic tool between unvaccinated patients affected by Alpha variant and vaccinated patients affected by Delta variant of COVID-19. Good statistically significant correlations among volumes extracted by automatic tool for each lung lobe and overall radiological severity score were obtained (ICC range 0.71−0.86). GGO was the main sign of COVID-19 lesions on CT images found in 87 of the 91 (95.6%) patients. No statistically significant differences were observed in CT findings (ground glass opacities (GGO), consolidation or crazy paving sign) among patient groups. Conclusion: In our study, we showed that in critically ill patients no difference were observed in terms of severity of disease or exitus, between unvaccinated and vaccinated patients. The only statistically significant differences were observed, with regard to the severity of COVID-19 pulmonary parenchymal involvement, between unvaccinated patients affected by Alpha variant and vaccinated patients affected by Delta variant, and between unvaccinated patients with Delta variant and vaccinated patients with Delta variant.

Efficacy of short-course colchicine treatment in hospitalized patients with moderate to severe COVID-19 pneumonia and hyperinflammation: a randomized clinical trial

Some patients with COVID-19 pneumonia develop an associated cytokine storm syndrome that aggravates the pulmonary disease. These patients may benefit of anti-inflammatory treatment. The role of colchicine in hospitalized patients with COVID-19 pneumonia and established hyperinflammation remains unexplored. In a prospective, randomized controlled, observer-blinded endpoint, investigator-initiated trial, 240 hospitalized patients with COVID-19 pneumonia and established hyperinflammation were randomly allocated to receive oral colchicine or not. The primary efficacy outcome measure was a composite of non-invasive mechanical ventilation (CPAP or BiPAP), admission to the intensive care unit, invasive mechanical ventilation requirement or death. The composite primary outcome occurred in 19.3% of the total study population. The composite primary outcome was similar in the two arms (17% in colchicine group vs. 20.8% in the control group; p = 0.533) and the same applied to each of its individual components. Most patients received steroids (98%) and heparin (99%), with similar doses in both groups. In this trial, including adult patients with COVID-19 pneumonia and associated hyperinflammation, no clinical benefit was observed with short-course colchicine treatment beyond standard care regarding the combined outcome measurement of CPAP/BiPAP use, ICU admission, invasive mechanical ventilation or death (Funded by the Community of Madrid, EudraCT Number: 2020-001841-38; 26/04/2020).

Brixia Chest X-ray Severity Scoring System is in Relation with C-reactive Protein and D-dimer Values in Patients with COVID-19

Background

The Brixia scoring system interpreted chest X-ray changes, serves as an indicator of the extent of changes in the lung parenchyma.

Objective

To indicate the effect of D-dimer and C-reactive protein (CRP) on Brixia score in patients with positive polymerase chain reaction (PCR) test for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2).

Methods

The research had prospective, descriptive and analytical character, and included patients (n=104) with Coronavirus disease 2019 (COVID-19) diagnosis. Chest X-ray, as well as calculation of Brixia score was done on admission, in the first week of hospitalization, on discharge, and 10 days after discharge (the patient was considered a post-COVID patient. Maximum CRP and D-dimer values were taken into account, along with data about dependence of mechanical ventilation and oxygen therapy.

Results

Initial Brixia score was significantly associated with the values of CRP (r = .23, p <.05). Higher level of CRP affected the higher result on the Brixia score after the initial X-ray. High CRP and D-dimer were significantly associated with oxygen use in patients, while high D-dimer was also statistically significantly associated with comorbidity. The mean value of Brixia score (during four time points) was significantly related to the values of CRP, D-dimer, the use of mechanical ventilation and oxygen therapy, but also with the existence of comorbidities. The largest statistically significant positive correlation of Brixia scora is with the values of D-dimer (r = .45, p <.000), but also with the values of CRP (r = .36, p <.000).

Conclusion

Values of CRP have an impact on Brixia score. Investigation of clinical characteristics and outcomes of severe clinical presentation of COVID-19 along with CXR scoring system will contribute to early prediction, accurate diagnosis and treatment as well as to improve the prognosis of patients with severe illness.

Towards robust diagnosis of COVID-19 using vision self-attention transformer

The outbreak of COVID-19, since its appearance, has affected about 200 countries and endangered millions of lives. COVID-19 is extremely contagious disease, and it can quickly incapacitate the healthcare systems if infected cases are not handled timely. Several Conventional Neural Networks (CNN) based techniques have been developed to diagnose the COVID-19. These techniques require a large, labelled dataset to train the algorithm fully, but there are not too many labelled datasets. To mitigate this problem and facilitate the diagnosis of COVID-19, we developed a self-attention transformer-based approach having self-attention mechanism using CT slices. The architecture of transformer can exploit the ample unlabelled datasets using pre-training. The paper aims to compare the performances of self-attention transformer-based approach with CNN and Ensemble classifiers for diagnosis of COVID-19 using binary Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection and multi-class Hybrid-learning for UnbiaSed predicTion of COVID-19 (HUST-19) CT scan dataset. To perform this comparison, we have tested Deep learning-based classifiers and ensemble classifiers with proposed approach using CT scan images. Proposed approach is more effective in detection of COVID-19 with an accuracy of 99.7% on multi-class HUST-19, whereas 98% on binary class SARS-CoV-2 dataset. Cross corpus evaluation achieves accuracy of 93% by training the model with Hust19 dataset and testing using Brazilian COVID dataset.