Correlation of chest radiography findings with the severity and progression of COVID-19 pneumonia

The chest X-ray score baseline in predicting continuous oxygen therapy failure in low-risk aged patients after thoracic surgery

Background

Radiographic severity assessment can be instrumental in diagnosing postoperative pulmonary complications (PPCs) and guiding oxygen therapy. The radiographic assessment of lung edema (RALE) and Brixia scores correlate with disease severity, but research on low-risk elderly patients is lacking. This study aimed to assess the efficacy of two chest X-ray scores in predicting continuous oxygen therapy (COT) treatment failure in patients over 70 years of age after thoracic surgery.

Methods

From January 2019 to December 2021, we searched for patients aged 70 years and above who underwent thoracic surgery and received COT treatment, with a focus on those at low risk of respiratory complications. Bedside chest X-rays, RALE, Brixia scores, and patient data were collected. Univariate, multivariate analyses, and 1:2 matching identified risk factors. Receiver operating characteristic (ROC) curves determined score sensitivity, specificity, and predictive values.

Results

Among the 242 patients surviving to discharge, 19 (7.9%) patients experienced COT failure. COT failure correlated with esophageal cancer surgeries, thoracotomies (36.8% vs. 9%, P=0.003; 26.3% vs. 9.4%, P=0.004), and longer operation time (3.4 vs. 2.8 h, P=0.003). Surgical approach and RALE score were independent risk factors. The prediction model had an area under the curve (AUC) of 0.839 [95% confidence interval (CI), 0.740-0.938]. Brixia and RALE scores predicted COT failure with AUCs of 0.764 (95% CI, 0.650-0.878) with a cut-off value of 6.027 and 0.710 (95% CI, 0.588-0.832) with a cut-off value of 17.134, respectively, after 1:2 matching.

Conclusions

The RALE score predict the risk of COT failure in elderly, low-risk thoracic patients better than the Brixia score. This simple, cheap, and noninvasive method helps evaluate postoperative lung damage, monitor treatment response, and provide early warning for oxygen therapy escalation. Further studies are required to confirm the validity and applicability of this model in different settings and populations.

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.

Clinical and Laboratory Predictors of Mortality in Severe COVID-19 Pneumonia: A Retrospective Study from India

OBJECTIVE

Wide arrays of laboratory parameters have been proposed by many studies for prognosis in COVID-19 patients. In this study, we wanted to determine if the International Severe Acute Respiratory and Emerging Infections Consortium-Coronavirus Clinical Characterization Consortium score in addition to certain clinical and laboratory parameters would help in predicting mortality. We wanted to determine if a greater severity score on chest x-ray at presentation translated to poor patient outcomes using the COVID-19 chest radiography score.

MATERIAL AND METHODS

This retrospective study was conducted at SDS TRC and Rajiv Gandhi Institute of chest diseases, Bangalore from March 2021 to June 2021. This study included 202 real-time-polymerase chain reaction-positive COVID-19 patients aged above 18 years admitted to the intensive care unit of our hospital. Demographic characteristics and baseline hematological and inflammatory markers (serum C-reactive protein, lactate dehydrogenase, troponin-I, ferritin, and d-dimer) were collected. Radiological severity on a chest x-ray was assessed using the validated COVID-19 chest radiography score. The International Severe Acute Respiratory and Emerging Infections Consortium-Coronavirus Clinical Characterization Consortium score was assigned to each patient within 24 hours of intensive care unit admission. Outcome studied was in-hospital mortality.

RESULTS

The overall mortality was 54.9% (111 cases). Age more than 50 years, >4 days of symptoms, peripheral oxygen saturation/ fraction of inspired oxygen ratio less than 200, elevated serum lactate dehydrogenase >398.5 IU/L, and hypoalbuminemia (<2.95 g/dL) were detected as independent predictors of mortality. A significant correlation of risk stratification with mortality (P = .057) was seen with International Severe Acute Respiratory and Emerging Infections Consortium-Coronavirus Clinical Characterization Consortium score. There was no significant correlation between the COVID-19 chest radiography score and mortality.

CONCLUSION

Age >50 years, peripheral oxygen saturation/fraction of inspired oxygen ratio <200, mean symptom duration of >4 days, elevated serum lactate dehydrogenase, and hypoalbuminemia are independent predictors of mortality in severe COVID-19 pneumonia. International Severe Acute Respiratory and Emerging Infections Consortium-Coronavirus Clinical Characterization Consortium score was different in the survivors and deceased.

Prediction of oxygen requirement in patients with COVID-19 using a pre-trained chest radiograph xAI model: efficient development of auditable risk prediction models via a fine-tuning approach

Risk prediction requires comprehensive integration of clinical information and concurrent radiological findings. We present an upgraded chest radiograph (CXR) explainable artificial intelligence (xAI) model, which was trained on 241,723 well-annotated CXRs obtained prior to the onset of the COVID-19 pandemic. Mean area under the receiver operating characteristic curve (AUROC) for detection of 20 radiographic features was 0.955 (95% CI 0.938-0.955) on PA view and 0.909 (95% CI 0.890-0.925) on AP view. Coexistent and correlated radiographic findings are displayed in an interpretation table, and calibrated classifier confidence is displayed on an AI scoreboard. Retrieval of similar feature patches and comparable CXRs from a Model-Derived Atlas provides justification for model predictions. To demonstrate the feasibility of a fine-tuning approach for efficient and scalable development of xAI risk prediction models, we applied our CXR xAI model, in combination with clinical information, to predict oxygen requirement in COVID-19 patients. Prediction accuracy for high flow oxygen (HFO) and mechanical ventilation (MV) was 0.953 and 0.934 at 24 h and 0.932 and 0.836 at 72 h from the time of emergency department (ED) admission, respectively. Our CXR xAI model is auditable and captures key pathophysiological manifestations of cardiorespiratory diseases and cardiothoracic comorbidities. This model can be efficiently and broadly applied via a fine-tuning approach to provide fully automated risk and outcome predictions in various clinical scenarios in real-world practice.

Prognostic value of radiological index and clinical data in patients with COVID-19 infection

During the Coronavirus-19 pandemic, chest X-ray scoring system have been validated by Al-Smadi and Toussie in this group of patients and even RALE score, previously designed for ARDS, have been used to estimate correlation with mortality. The aim of this study was to evaluate the prognostic value of As-Smadi, Tuossie and RALE scores in predicting death in the same population of patients when associated to clinical data. In this retrospective clinical study, data of patients with COVID-19, admitted to our hospital from 1st October 2020 to 31st December 2020 were collected. CXR images of each patient were analyzed with the three different scores above mentioned. 144 patients (male 96 aged 68.5 years) were included in the study. 93 patients reported a least 1 comorbidity and 36 died. The association with increasing age, presence of comorbidities, and lower hemoglobin was significantly associated with risk of death for all the regression models. When considering the radiological score, a significant effect was found for the Al Smadi and RALE scores, while no evidence of association was found for the Toussie score. The fraction of new information is 16.7% for the Al Smadi score, 12.9% for the RALE and 5.1% for the Toussie score. The improvement in the prognostic usefulness with respect to the base model is particularly interesting for the Al Smadi score. The highest c-index was also obtained by the model with the Al Smadi score.

Low-Dose Whole Lung Irradiation for Treatment of COVID-19 Pneumonia: A Systematic Review and Meta-Analysis

PURPOSE

Studies dating back to a century ago have reported using low-dose radiation therapy for the treatment of viral and bacterial pneumonia. In the modern era, since the COVID-19 pandemic began, several groups worldwide have researched the applicability of whole lung irradiation (WLI) for the treatment of COVID-19. We aimed to bring together the results of these experimental studies.

METHODS AND MATERIALS

We performed a systematic review and meta-analysis searching PubMed and Scopus databases for clinical trials incorporating WLI for the treatment of patients with COVID-19. Required data were extracted from each study. Using the random-effects model, the overall pooled day 28 survival rate, survival hazard ratio, and intubation-free days within 15 days after WLI were calculated, and forest plots were produced.

RESULTS

Ten studies were identified, and eventually, 5 were included for meta-analysis. The overall survival hazard ratio was calculated to be 0.85 (0.46-1.57). The pooled mean difference of intubation-free days within 15 days after WLI was 1.87, favoring the WLI group (95% confidence interval, -0.02 to 3.76). The overall day 28 survival rate of patients receiving WLI for the 9 studies with adequate follow-up data was 74% (95% confidence interval, 61-87). Except for 2 studies, the other 8 studies were assessed to have moderate to high risk of bias, and there were many differences among the designs of the studies, included patients, primary endpoints, outcome measurement methods, and reporting of the results.

CONCLUSIONS

Despite a mild improvement in intubation-free days, WLI had no significant effect on patients' overall survival. Currently, we cannot recommend routine use of WLI for the treatment of patients with moderate-to-severe COVID-19.

Multi-branch fusion auxiliary learning for the detection of pneumonia from chest X-ray images

Lung infections caused by bacteria and viruses are infectious and require timely screening and isolation, and different types of pneumonia require different treatment plans. Therefore, finding a rapid and accurate screening method for lung infections is critical. To achieve this goal, we proposed a multi-branch fusion auxiliary learning (MBFAL) method for pneumonia detection from chest X-ray (CXR) images. The MBFAL method was used to perform two tasks through a double-branch network. The first task was to recognize the absence of pneumonia (normal), COVID-19, other viral pneumonia and bacterial pneumonia from CXR images, and the second task was to recognize the three types of pneumonia from CXR images. The latter task was used to assist the learning of the former task to achieve a better recognition effect. In the process of auxiliary parameter updating, the feature maps of different branches were fused after sample screening through label information to enhance the model’s ability to recognize case of pneumonia without impacting its ability to recognize normal cases. Experiments show that an average classification accuracy of 95.61% is achieved using MBFAL. The single class accuracy for normal, COVID-19, other viral pneumonia and bacterial pneumonia was 98.70%, 99.10%, 96.60% and 96.80%, respectively, and the recall was 97.20%, 98.60%, 96.10% and 89.20%, respectively, using the MBFAL method. Compared with the baseline model and the model constructed using the above methods separately, better results for the rapid screening of pneumonia were achieved using MBFAL.

Chest Radiograph Severity Scores, Comorbidity Prevalence, and Outcomes of Patients with Coronavirus Disease Treated at the King Abdullah University Hospital in Jordan: A Retrospective Study

Introduction

Hospitalized patients with coronavirus disease (COVID-19) often undergo chest x-ray (CXR). Utilizing CXR findings could reduce the cost of COVID-19 treatment and the resultant pressure on the Jordanian healthcare system.

Methods

We evaluated the association between the CXR severity score, based on the Radiographic Assessment of Lung Edema (RALE) scoring system, and outcomes of patients with COVID-19. The main objective of this work is to assess the role of the RALE scoring system in predicting in-hospital mortality and clinical outcomes of patients with COVID-19. Adults with a positive severe acute respiratory syndrome COVID-19 two reverse-transcription polymerase chain reaction test results and a baseline CXR image, obtained in November 2020, were included. The RALE severity scores were calculated by expert radiologists and categorized as normal, mild, moderate, and severe. Chi-square tests and multivariable logistic regression were used to assess the association between the severity category and admission location and clinical characteristics.

Results

Based on the multivariable regression analysis, it has been found that male sex, hypertension, and the RALE severity score were significantly associated with in-hospital mortality. The baseline RALE severity score was associated with the need for critical care (P<0.001), in-hospital mortality (P<0.001), and the admission location (P=0.002).

Discussion

The utilization of RALE severity scores helps to predict clinical outcomes and promote prudent use of resources during the COVID-19 pandemic.

Application of the advanced lung cancer inflammation index for patients with coronavirus disease 2019 pneumonia: Combined risk prediction model with lung cancer inflammation index, computed tomography and chest radiograph

The purpose of the present study was to evaluate the feasibility of applying the advanced lung cancer inflammation index (ALI) in patients with coronavirus disease 2019 (COVID-19) and to establish a combined ALI and radiologic risk prediction model for disease exacerbation. The present study included patients diagnosed with COVID-19 infection in our single institution from March to October 2020. Patients without clinical information and/or chest computed tomography (CT) upon admission were excluded. A radiologist assessed the CT severity score and abnormality on chest radiograph. The combined ALI and radiologic risk prediction model was developed via random forest classification. Among 79 patients (age, 43±19 years; male/female, 45:34), 72 experienced improvement and seven patients experienced exacerbation after admission. Significant differences were observed between the improved and exacerbated groups in the ALI (median, 47.6 vs. 13.2; P=0.011), frequency of chest radiograph abnormality (24.7 vs. 83.3%; P<0.001), and chest CT score (CCTS; median, 1 vs. 9; P<0.001). For the accuracy of predicting exacerbation, the receiver-operating characteristic curve analysis demonstrated an area under the curve of 0.79 and 0.92 for the ALI and CCTS, respectively. The combined ALI and radiologic risk prediction model had a sensitivity of 1.00 and a specificity of 0.81. Overall, ALI alone and CCTS alone modestly predicted the exacerbation of COVID-19, and the combined ALI and radiologic risk prediction model exhibited decent sensitivity and specificity.

Tracking and predicting COVID-19 radiological trajectory on chest X-rays using deep learning

Radiological findings on chest X-ray (CXR) have shown to be essential for the proper management of COVID-19 patients as the maximum severity over the course of the disease is closely linked to the outcome. As such, evaluation of future severity from current CXR would be highly desirable. We trained a repurposed deep learning algorithm on the CheXnet open dataset (224,316 chest X-ray images of 65,240 unique patients) to extract features that mapped to radiological labels. We collected CXRs of COVID-19-positive patients from an open-source dataset (COVID-19 image data collection) and from a multi-institutional local ICU dataset. The data was grouped into pairs of sequential CXRs and were categorized into three categories: ‘Worse’, ‘Stable’, or ‘Improved’ on the basis of radiological evolution ascertained from images and reports. Classical machine-learning algorithms were trained on the deep learning extracted features to perform immediate severity evaluation and prediction of future radiological trajectory. Receiver operating characteristic analyses and Mann-Whitney tests were performed. Deep learning predictions between “Worse” and “Improved” outcome categories and for severity stratification were significantly different for three radiological signs and one diagnostic (‘Consolidation’, ‘Lung Lesion’, ‘Pleural effusion’ and ‘Pneumonia’; all P < 0.05). Features from the first CXR of each pair could correctly predict the outcome category between ‘Worse’ and ‘Improved’ cases with a 0.81 (0.74–0.83 95% CI) AUC in the open-access dataset and with a 0.66 (0.67–0.64 95% CI) AUC in the ICU dataset. Features extracted from the CXR could predict disease severity with a 52.3% accuracy in a 4-way classification. Severity evaluation trained on the COVID-19 image data collection had good out-of-distribution generalization when testing on the local dataset, with 81.6% of intubated ICU patients being classified as critically ill, and the predicted severity was correlated with the clinical outcome with a 0.639 AUC. CXR deep learning features show promise for classifying disease severity and trajectory. Once validated in studies incorporating clinical data and with larger sample sizes, this information may be considered to inform triage decisions.

Intubation and mortality prediction in hospitalized COVID-19 patients using a combination of convolutional neural network-based scoring of chest radiographs and clinical data

Objective

To predict short-term outcomes in hospitalized COVID-19 patients using a model incorporating clinical variables with automated convolutional neural network (CNN) chest radiograph analysis.

Methods

A retrospective single center study was performed on patients consecutively admitted with COVID-19 between March 14 and April 21 2020. Demographic, clinical and laboratory data were collected, and automated CNN scoring of the admission chest radiograph was performed. The two outcomes of disease progression were intubation or death within 7 days and death within 14 days following admission. Multiple imputation was performed for missing predictor variables and, for each imputed data set, a penalized logistic regression model was constructed to identify predictors and their functional relationship to each outcome. Cross-validated area under the characteristic (AUC) curves were estimated to quantify the discriminative ability of each model.

Results

801 patients (median age 59; interquartile range 46-73 years, 469 men) were evaluated. 36 patients were deceased and 207 were intubated at 7 days and 65 were deceased at 14 days. Cross-validated AUC values for predictive models were 0.82 (95% CI, 0.79-0.86) for death or intubation within 7 days and 0.82 (0.78-0.87) for death within 14 days. Automated CNN chest radiograph score was an important variable in predicting both outcomes.

Conclusion

Automated CNN chest radiograph analysis, in combination with clinical variables, predicts short-term intubation and death in patients hospitalized for COVID-19 infection. Chest radiograph scoring of more severe disease was associated with a greater probability of adverse short-term outcome.

Advances in knowledge

Model-based predictions of intubation and death in COVID-19 can be performed with high discriminative performance using admission clinical data and convolutional neural network-based scoring of chest radiograph severity.

Does the initial chest radiograph severity in COVID-19 impact the short- and long-term outcome? - a perspective from India

PURPOSE

The chest radiograph (CXR) is among the most widely used investigations in coronavirus disease 2019 (COVID-19) patients. Little is known about its predictive role on the long-term outcome. The purpose of this study was to explore its association with the short and long-term outcome in COVID-19 patients.

METHODS

A total of 1530 patients were assessed for the presence, radiographic pattern and distribution of lung lesions observed on baseline chest radiographs obtained at admission. The Brixia scoring system was applied for semiquantitative assessment of lesion severity. Short-term outcome was determined by clinical severity, duration of hospitalization and mortality. The 1415 survivors in this group were assessed after 5-6 months for the presence of residual symptoms.

RESULTS

About 67% patients had an abnormal baseline CXR. Bilateral involvement with a basal preponderance was observed and ground-glass opacification was the most frequent finding. The Brixia score ranged from 0 to 16, median 2, interquartile range (IQR) [0-6]. About 36% patients were symptomatic on 5-6-month follow-up, with fatigability being the commonest symptom. A good correlation was observed between the CXR score and disease severity as well as duration of hospitalization. On multivariate analysis, the CXR score was found to be a significant independent predictor of in-patient mortality as well as presence of long-term residual symptoms in survivors.

CONCLUSIONS

Disease severity as seen on the chest radiograph appears to play an important role in driving the short and long-term consequences of COVID-19 and could serve as a prognostic indicator, which influences short-term management and long-term follow-up.

Usefulness of chest X-rays for evaluating prognosis in patients with COVID-19

Background and aims

The pandemia caused by SARS-CoV-2 (COVID-19) has been a diagnostic challenge in which chest X-rays have had a key role. This study aimed to determine whether the Radiological Scale for Evaluating Hospital Admission (RSEHA) applied to chest X-rays of patients with COVID-19 when they present at the emergency department is related with the severity of COVID-19 in terms of the need for admission to the hospital, the need for admission to the intensive care unit (ICU), and/or mortality.

Material and methods

This retrospective study included 292 patients with COVID-19 who presented at the emergency department between March 16, 2020 and April 30, 2020. To standardize the radiologic patterns, we used the RSEHA, categorizing the radiologic pattern as mild, moderate, or severe. We analyzed the relationship between radiologic severity according to the RSEHA with the need for admission to the hospital, admission to the ICU, and mortality.

Results

Hospital admission was necessary in 91.4% of the patients. The RSEHA was significantly associated with the need for hospital admission (p = 0.03) and with the need for ICU admission (p < 0.001). A total of 51 (17.5%) patients died; of these, 57% had the severe pattern on the RSEHA. When we analyzed mortality by grouping patients according to their results on the RSEHA and their age range, the percentage of patients who died increased after age 70 years in patients classified as moderate or severe on the RSEHA.

Conclusions

Chest X-rays in patients with COVID-19 obtained in the emergency department are useful for determining the prognosis in terms of admission to the hospital, admission to the ICU, and mortality; radiologic patterns categorized as severe on the RSEHA are associated with greater mortality and admission to the ICU.

Chest Radiograph Scoring Alone or Combined with Other Risk Scores for Predicting Outcomes in COVID-19

Background

Radiographic severity may help predict patient deterioration and outcomes from COVID-19 pneumonia.

Purpose

To assess the reliability and reproducibility of three chest radiograph reporting systems (radiographic assessment of lung edema [RALE], Brixia, and percentage opacification) in patients with proven SARS-CoV-2 infection and examine the ability of these scores to predict adverse outcomes both alone and in conjunction with two clinical scoring systems, National Early Warning Score 2 (NEWS2) and International Severe Acute Respiratory and Emerging Infection Consortium: Coronavirus Clinical Characterization Consortium (ISARIC-4C) mortality.

Materials and Methods

This retrospective cohort study used routinely collected clinical data of patients with polymerase chain reaction–positive SARS-CoV-2 infection admitted to a single center from February 2020 through July 2020. Initial chest radiographs were scored for RALE, Brixia, and percentage opacification by one of three radiologists. Intra- and interreader agreement were assessed with intraclass correlation coefficients. The rate of admission to the intensive care unit (ICU) or death up to 60 days after scored chest radiograph was estimated. NEWS2 and ISARIC-4C mortality at hospital admission were calculated. Daily risk for admission to ICU or death was modeled with Cox proportional hazards models that incorporated the chest radiograph scores adjusted for NEWS2 or ISARIC-4C mortality.

Results

Admission chest radiographs of 50 patients (mean age, 74 years ± 16 [standard deviation]; 28 men) were scored by all three radiologists, with good interreader reliability for all scores, as follows: intraclass correlation coefficients were 0.87 for RALE (95% CI: 0.80, 0.92), 0.86 for Brixia (95% CI: 0.76, 0.92), and 0.72 for percentage opacification (95% CI: 0.48, 0.85). Of 751 patients with a chest radiograph, those with greater than 75% opacification had a median time to ICU admission or death of just 1–2 days. Among 628 patients for whom data were available (median age, 76 years [interquartile range, 61–84 years]; 344 men), opacification of 51%–75% increased risk for ICU admission or death by twofold (hazard ratio, 2.2; 95% CI: 1.6, 2.8), and opacification greater than 75% increased ICU risk by fourfold (hazard ratio, 4.0; 95% CI: 3.4, 4.7) compared with opacification of 0%–25%, when adjusted for NEWS2 score.

Conclusion

Brixia, radiographic assessment of lung edema, and percentage opacification scores all reliably helped predict adverse outcomes in SARS-CoV-2 infection.

© RSNA, 2021

Online supplemental material is available for this article.

See also the editorial by Little in this issue.

Medical imaging and computational image analysis in COVID-19 diagnosis: A review

Coronavirus disease (COVID-19) is an infectious disease caused by a newly discovered coronavirus. The disease presents with symptoms such as shortness of breath, fever, dry cough, and chronic fatigue, amongst others. The disease may be asymptomatic in some patients in the early stages, which can lead to increased transmission of the disease to others. This study attempts to review papers on the role of imaging and medical image computing in COVID-19 diagnosis. For this purpose, PubMed, Scopus and Google Scholar were searched to find related studies until the middle of 2021. The contribution of this study is four-fold: 1) to use as a tutorial of the field for both clinicians and technologists, 2) to comprehensively review the characteristics of COVID-19 as presented in medical images, 3) to examine automated artificial intelligence-based approaches for COVID-19 diagnosis, 4) to express the research limitations in this field and the methods used to overcome them. Using machine learning-based methods can diagnose the disease with high accuracy from medical images and reduce time, cost and error of diagnostic procedure. It is recommended to collect bulk imaging data from patients in the shortest possible time to improve the performance of COVID-19 automated diagnostic methods.

Usefulness of chest X-rays for evaluating prognosis in patients with COVID-19

BACKGROUND AND AIMS

The pandemia caused by SARS-CoV-2 (COVID-19) has been a diagnostic challenge in which chest X-rays have had a key role. This study aimed to determine whether the Radiological Scale for Evaluating Hospital Admission (RSEHA) applied to chest X-rays of patients with COVID-19 when they present at the emergency department is related with the severity of COVID-19 in terms of the need for admission to the hospital, the need for admission to the intensive care unit (ICU), and/or mortality.

MATERIAL AND METHODS

This retrospective study included 292 patients with COVID-19 who presented at the emergency department between March 16, 2020 and April 30, 2020. To standardize the radiologic patterns, we used the RSEHA, categorizing the radiologic pattern as mild, moderate, or severe. We analyzed the relationship between radiologic severity according to the RSEHA with the need for admission to the hospital, admission to the ICU, and mortality.

RESULTS

Hospital admission was necessary in 91.4% of the patients. The RSEHA was significantly associated with the need for hospital admission (p=0.03) and with the need for ICU admission (p<0.001). A total of 51 (17.5%) patients died; of these, 57% had the severe pattern on the RSEHA. When we analyzed mortality by grouping patients according to their results on the RSEHA and their age range, the percentage of patients who died increased after age 70 years in patients classified as moderate or severe on the RSEHA.

CONCLUSIONS

Chest X-rays in patients with COVID-19 obtained in the emergency department are useful for determining the prognosis in terms of admission to the hospital, admission to the ICU, and mortality; radiologic patterns categorized as severe on the RSEHA are associated with greater mortality and admission to the ICU.

Highlighting COVID-19: What the imaging exams show about the disease

Coronavirus disease 2019 (COVID-19), a global emergency, is caused by severe acute respiratory syndrome coronavirus 2. The gold standard for its diagnosis is the reverse transcription polymerase chain reaction, but considering the high number of infected people, the low availability of this diagnostic tool in some contexts, and the limitations of the test, other tools that aid in the identification of the disease are necessary. In this scenario, imaging exams such as chest X-ray (CXR) and computed tomography (CT) have played important roles. CXR is useful for assessing disease progression because it allows the detection of extensive consolidations, besides being a fast and cheap method. On the other hand, CT is more sensitive for detecting lung changes in the early stages of the disease and is also useful for assessing disease progression. Of note, ground-glass opacities are the main COVID-19-related CT findings. Positron emission tomography combined with CT can be used to evaluate chronic and substantial damage to the lungs and other organs; however, it is an expensive test. Lung ultrasound (LUS) has been shown to be a promising technique in that context as well, being useful in the screening and monitoring of patients, disease classification, and management related to mechanical ventilation. Moreover, LUS is an inexpensive alternative available at the bedside. Finally, magnetic resonance imaging, although not usually requested, allows the detection of pulmonary, cardiovascular, and neurological abnormalities associated with COVID-19. Furthermore, it is important to consider the challenges faced in the radiology field in the adoption of control measures to prevent infection and in the follow-up of post-COVID-19 patients.

Clinical and Laboratory Findings of COVID-19 in High-Altitude Inhabitants of Saudi Arabia

Background: SARS-CoV-2, the causative agent of COVID-19, continues to cause a worldwide pandemic, with more than 147 million being affected globally as of this writing. People's responses to COVID-19 range from asymptomatic to severe, and the disease is sometimes fatal. Its severity is affected by different factors and comorbidities of the infected patients. Living at a high altitude could be another factor that affects the severity of the disease in infected patients.

Methods: In the present study, we have analyzed the clinical, laboratory, and radiological findings of COVID-19-infected patients in Taif, a high-altitude region of Saudi Arabia. In addition, we compared matched diseased subjects to those living at sea level. We hypothesized that people living in high-altitude locations are prone to develop a more severe form of COVID-19 than those living at sea level.

Results: Age and a high Charlson comorbidity score were associated with increased numbers of intensive care unit (ICU) admissions and mortality among COVID-19 patients. These ICU admissions and fatalities were found mainly in patients with comorbidities. Rates of leukocytosis, neutrophilia, higher D-dimer, ferritin, and highly sensitive C-reactive protein (CRP) were significantly higher in ICU patients. CRP was the most independent of the laboratory biomarkers found to be potential predictors of death. COVID-19 patients who live at higher altitude developed a less severe form of the disease and had a lower mortality rate, in comparison to matched subjects living at sea level.

Conclusion: CRP and Charlson comorbidity scores can be considered predictive of disease severity. People living at higher altitudes developed less severe forms of COVID-19 disease than those living at sea level, due to a not-yet-known mechanism.

SARS-CoV-2 reinfection: "New baseline" imaging concept in the era of COVID-19

Recent reports have suggested COVID-19 relapse or reinfection may lead to readmission, which may cause a diagnostic challenge between recently infected patients and reinfections. Compounding this problem is the post-viral lung sequela that may be expected after COVID-19 pneumonia, similar to both SARS and MERS. Although chest imaging may play a role in the diagnosis of primary SARS-CoV-2 infection, reinfection or relapse of COVID-19 will have similar imaging findings. A “new-baseline” imaging can be obtained from COVID-19 patients at the time of hospital discharge or clinical recovery. This new reference can not only determine if readmissions are from relapse or reinfection of COVID-19, resolving COVID-19 or potentially a different viral infection (influenza), but also for long term sequela of COVID-19 lung infection. Strategic use of imaging before discharge may be helpful in the subset of the population at the highest risk of a secondary viral infection such as influenza. Determining the residual abnormalities in post-discharge imaging can guide us in the long-term management of patients for many years to come.

Portable Chest Radiography as an Exclusionary Test for Adverse Clinical Outcomes During the COVID-19 Pandemic

Background

Chest radiography (CXR) often is performed in the acute setting to help understand the extent of respiratory disease in patients with COVID-19, but a clearly defined role for negative chest radiograph results in assessing patients has not been described.

Research Question

Is portable CXR an effective exclusionary test for future adverse clinical outcomes in patients suspected of having COVID-19?

Study Design and Methods

Charts of consecutive patients suspected of having COVID-19 at five EDs in New York City between March 19, 2020, and April 23, 2020, were reviewed. Patients were categorized based on absence of findings on initial CXR. The primary outcomes were hospital admission, mechanical ventilation, ARDS, and mortality.

Results

Three thousand two hundred forty-five adult patients, 474 (14.6%) with negative initial CXR results, were reviewed. Among all patients, negative initial CXR results were associated with a low probability of future adverse clinical outcomes, with negative likelihood ratios of 0.27 (95% CI, 0.23-0.31) for hospital admission, 0.24 (95% CI, 0.16-0.37) for mechanical ventilation, 0.19 (95% CI, 0.09-0.40) for ARDS, and 0.38 (95% CI, 0.29-0.51) for mortality. Among the subset of 955 patients younger than 65 years and with a duration of symptoms of at least 5 days, no patients with negative CXR results died, and the negative likelihood ratios were 0.17 (95% CI, 0.12-0.25) for hospital admission, 0.09 (95% CI, 0.02-0.36) for mechanical ventilation, and 0.09 (95% CI, 0.01-0.64) for ARDS.

Interpretation

Initial CXR in adult patients suspected of having COVID-19 is a strong exclusionary test for hospital admission, mechanical ventilation, ARDS, and mortality. The value of CXR as an exclusionary test for adverse clinical outcomes is highest among young adults, patients with few comorbidities, and those with a prolonged duration of symptoms.