Chest radiograph at admission predicts early intubation among inpatient COVID-19 patients

Lung Ultrasound Score in COVID-19 Patients Correlates with PO2/FiO2, Intubation Rates, and Mortality

Introduction

The point-of-care lung ultrasound (LUS) score has been used in coronavirus 2019 (COVID-19) patients for diagnosis and risk stratification, due to excellent sensitivity and infection control concerns. We studied the ratio of partial pressure of oxygen in arterial blood to the fraction of inspiratory oxygen concentration (PO2/FiO2), intubation rates, and mortality correlation to the LUS score.

Methods

We conducted a systematic review using PRISMA guidelines. Included were articles published from December 1, 2019-November 30, 2021 using LUS in adult COVID-19 patients in the intensive care unit or the emergency department. Excluded were studies on animals and on pediatric and pregnant patients. We assessed bias using QUADAS-2. Outcomes were LUS score and correlation to PO2/FiO2, intubation, and mortality rates. Random effects model pooled the meta-analysis results.

Results

We reviewed 27 of 5,267 studies identified. Of the 27 studies, seven were included in the intubation outcome, six in the correlation to PO2/FiO2 outcome, and six in the mortality outcome. Heterogeneity was found in ultrasound protocols and outcomes. In the pooled results of 267 patients, LUS score was found to have a strong negative correlation to PO2/FiO2 with a correlation coefficient of -0.69 (95% confidence interval [CI] -0.75, -0.62). In pooled results, 273 intubated patients had a mean LUS score that was 6.95 points higher (95% CI 4.58-9.31) than that of 379 non-intubated patients. In the mortality outcome, 385 survivors had a mean LUS score that was 4.61 points lower (95% CI 3.64-5.58) than that of 181 non-survivors. There was significant heterogeneity between the studies as measured by the I2 and Cochran Q test.

Conclusion

A higher LUS score was strongly correlated with a decreasing PO2/FiO2 in COVID-19 pneumonia patients. The LUS score was significantly higher in intubated vs non-intubated patients with COVID-19. The LUS score was significantly lower in critically ill patients with COVID-19 pneumonia that survive.

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.

The association of clinically relevant variables with chest radiograph lung disease burden quantified in real-time by radiologists upon initial presentation in individuals hospitalized with COVID-19

Objectives

We aimed to correlate lung disease burden on presentation chest radiographs (CXR), quantified at the time of study interpretation, with clinical presentation in patients hospitalized with coronavirus disease 2019 (COVID-19).

Material and methods

This retrospective cross-sectional study included 5833 consecutive adult patients, aged 18 and older, hospitalized with a diagnosis of COVID-19 with a CXR quantified in real-time while hospitalized in 1 of 12 acute care hospitals across a multihospital integrated healthcare network between March 24, 2020, and May 22, 2020. Lung disease burden was quantified in real-time by 118 radiologists on 5833 CXR at the time of exam interpretation with each lung annotated by the degree of lung opacity as clear (0%), mild (1–33%), moderate (34–66%), or severe (67–100%). CXR findings were classified as (1) clear versus disease, (2) unilateral versus bilateral, (3) symmetric versus asymmetric, or (4) not severe versus severe. Lung disease burden was characterized on initial presentation by patient demographics, co-morbidities, vital signs, and lab results with chi-square used for univariate analysis and logistic regression for multivariable analysis.

Results

Patients with severe lung disease were more likely to have oxygen impairment, an elevated respiratory rate, low albumin, high lactate dehydrogenase, and high ferritin compared to non-severe lung disease. A lack of opacities in COVID-19 was associated with a low estimated glomerular filtration rate, hypernatremia, and hypoglycemia.

Conclusions

COVID-19 lung disease burden quantified in real-time on presentation CXR was characterized by demographics, comorbidities, emergency severity index, Charlson Comorbidity Index, vital signs, and lab results on 5833 patients. This novel approach to real-time quantified chest radiograph lung disease burden by radiologists needs further research to understand how this information can be incorporated to improve clinical care for pulmonary-related diseases.. An absence of opacities in COVID-19 may be associated with poor oral intake and a prerenal state as evidenced by the association of clear CXRs with a low eGFR, hypernatremia, and hypoglycemia.

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.

[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.

COVID-19 Diagnosis on Chest Radiograph Using Artificial Intelligence

BACKGROUND

The coronavirus disease 2019 (COVID-19) pandemic has disrupted the world since 2019, causing significant morbidity and mortality in developed and developing countries alike. Although substantial resources have been diverted to developing diagnostic, preventative, and treatment measures, disparities in the availability and efficacy of these tools vary across countries. We seek to assess the ability of commercial artificial intelligence (AI) technology to diagnose COVID-19 by analyzing chest radiographs.

MATERIALS AND METHODS

Chest radiographs taken from symptomatic patients within two days of polymerase chain reaction (PCR) tests were assessed for COVID-19 infection by board-certified radiologists and commercially available AI software. Sixty patients with negative and 60 with positive COVID reverse transcription-polymerase chain reaction (RT-PCR) tests were chosen. Results were compared against results of the PCR test for accuracy and statistically analyzed by receiver operating characteristic (ROC) curves along with area under the curve (AUC) values.

RESULTS

A total of 120 chest radiographs (60 positive and 60 negative RT-PCR tests) radiographs were analyzed. The AI software performed significantly better than chance (p = 0.001) and did not differ significantly from the radiologist ROC curve (p = 0.78).

CONCLUSION

Commercially available AI software was not inferior compared with trained radiologists in accurately identifying COVID-19 cases by analyzing radiographs. While RT-PCR testing remains the standard, current advances in AI help correctly analyze chest radiographs to diagnose COVID-19 infection.

A comparison of Covid-19 early detection between convolutional neural networks and radiologists

Background

The role of chest radiography in COVID-19 disease has changed since the beginning of the pandemic from a diagnostic tool when microbiological resources were scarce to a different one focused on detecting and monitoring COVID-19 lung involvement. Using chest radiographs, early detection of the disease is still helpful in resource-poor environments. However, the sensitivity of a chest radiograph for diagnosing COVID-19 is modest, even for expert radiologists. In this paper, the performance of a deep learning algorithm on the first clinical encounter is evaluated and compared with a group of radiologists with different years of experience.

Methods

The algorithm uses an ensemble of four deep convolutional networks, Ensemble4Covid, trained to detect COVID-19 on frontal chest radiographs. The algorithm was tested using images from the first clinical encounter of positive and negative cases. Its performance was compared with five radiologists on a smaller test subset of patients. The algorithm's performance was also validated using the public dataset COVIDx.

Results

Compared to the consensus of five radiologists, the Ensemble4Covid model achieved an AUC of 0.85, whereas the radiologists achieved an AUC of 0.71. Compared with other state-of-the-art models, the performance of a single model of our ensemble achieved nonsignificant differences in the public dataset COVIDx.

Conclusion

The results show that the use of images from the first clinical encounter significantly drops the detection performance of COVID-19. The performance of our Ensemble4Covid under these challenging conditions is considerably higher compared to a consensus of five radiologists. Artificial intelligence can be used for the fast diagnosis of COVID-19.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13244-022-01250-3.

COVID-19 in Kidney Transplant Recipient and Waitlist Patients: Implications of Chest Radiographic Severity Score

PURPOSE

To evaluate the chest radiographic severity score (CXR-SS) for coronavirus disease 2019 (COVID-19) patients who are kidney transplant recipients compared with patients on the waitlist.

STUDY DESIGN AND METHODS

This retrospective cohort includes 78 kidney transplant recipients (50 men, mean age 59.9±11.9 y) and 59 kidney transplant waitlist patients (33 men, mean age 58.8±10.8 y) diagnosed with COVID-19 between March 15 and May 30, 2020 with reverse transcriptase-polymerase chain reaction. Patient chest radiographs were divided into 6 zones and examined for consolidation. Primary outcome was mortality. Secondary outcomes included hospital admission, intensive care unit (ICU) admission, and intubation. Predictors of our primary and secondary outcomes were identified by bivariate analysis and multivariate regression analysis.

RESULTS

No significant difference was found in CXR-SS between 2 groups (P=0.087). Transplant recipients had significantly higher rates of hospitalization (odds ratio, 6.8; 95% confidence interval: 1.7, 39.3; P<0.001), ICU admission (odds ratio, 6.5; 95% confidence interval [CI]: 1.8-35.9; P=0.002), intubation (odds ratio, 11; 95% CI: 2.4-96.9; P=0.001), and mortality (odds ratio, 17; 95% CI: 3.9-153.1; P<0.001). A higher CXR-SS was not predictive of mortality, intubation, or ICU admission. CXR-SS was associated with hospital admission overall (odds ratio, 1.613; 95% CI: 1.04-2.49; P=0.0314).

CONCLUSION

The CXR-SS was not predictive of mortality, ICU admission or intubation in our population. Kidney transplant patients with COVID-19 had near universal hospital admission, more than one-third mortality and about a quarter were intubated and admitted to the ICU-all significantly worse outcomes than for patients on the transplant waitlist.

Chest X-ray Does Not Predict the Risk of Endotracheal Intubation and Escalation of Treatment in COVID-19 Patients Requiring Noninvasive Respiratory Support

Forms of noninvasive respiratory support (NIRS) have been widely used to avoid endotracheal intubation in patients with coronavirus disease-19 (COVID-19). However, inappropriate prolongation of NIRS may delay endotracheal intubation and worsen patient outcomes. The aim of this retrospective study was to assess whether the CARE score, a chest X-ray score previously validated in COVID-19 patients, may predict the need for endotracheal intubation and escalation of respiratory support in COVID-19 patients requiring NIRS. From December 2020 to May 2021, we included 142 patients receiving NIRS who had a first chest X-ray available at NIRS initiation and a second one after 48–72 h. In 94 (66%) patients, the level of respiratory support was increased, while endotracheal intubation was required in 83 (58%) patients. The CARE score at NIRS initiation was not predictive of the need for endotracheal intubation (odds ratio (OR) 1.01, 95% confidence interval (CI) 0.96–1.06) or escalation of treatment (OR 1.01, 95% CI 0.96–1.07). In conclusion, chest X-ray severity, as assessed by the CARE score, did not allow predicting endotracheal intubation or escalation of respiratory support in COVID-19 patients undergoing NIRS.

Brief communication: Chest radiography score in young COVID-19 patients: Does one size fit all?

During the SARS-CoV-2 pandemic, chest X-Ray (CXR) scores are essential to rapidly assess patients’ prognoses. This study evaluates a published CXR score in a different national healthcare system. In our study, this CXR score maintains a prognostic role in predicting length of hospital stay, but not disease severity. However, our results show that the predictive role of CXR score could be influenced by socioeconomic status and healthcare system.

Early radiological worsening of SARS-CoV-2 pneumonia predicts the need for ventilatory support

Objectives

Identifying early markers of poor prognosis of coronavirus disease 2019 (COVID-19) is mandatory. Our purpose is to analyze by chest radiography if rapid worsening of COVID-19 pneumonia in the initial days has predictive value for ventilatory support (VS) need.

Methods

Ambispective observational ethically approved study in COVID-19 pneumonia inpatients, validated in a second outpatient sample. Brixia score (BS) was applied to the first and second chest radiography required for suspected COVID-19 pneumonia to determine the predictive capacity of BS worsening for VS need. Intraclass correlation coefficient (ICC) was previously analyzed among three radiologists. Sensitivity, specificity, likelihood ratios, AUC, and odds ratio were calculated using ROC curves and binary logistic regression analysis. A value of p < .05 was considered statistically significant.

Results

A total of 120 inpatients (55 ± 14 years, 68 men) and 112 outpatients (56 ± 13 years, 61 men) were recruited. The average ICC of the BS was between 0.812 (95% confidence interval 0.745–0.878) and 0.906 (95% confidence interval 0.844–0.940). According to the multivariate analysis, a BS worsening per day > 1.3 points within 10 days of the onset of symptoms doubles the risk for requiring VS in inpatients and 5 times in outpatients (p < .001). The findings from the second chest radiography were always better predictors of VS requirement than those from the first one.

Conclusion

The early radiological worsening of SARS-CoV-2 pneumonia after symptoms onset is a determining factor of the final prognosis. In elderly patients with some comorbidity and pneumonia, a 48–72-h follow-up radiograph is recommended.

Key Points

• An early worsening on chest X-ray in patients with SARS-CoV-2 pneumonia is highly predictive of the need for ventilatory support.

• This radiological worsening rate can be easily assessed by comparing the first and the second chest X-ray.

• In elderly patients with some comorbidity and SARS-CoV-2 pneumonia, close early radiological follow-up is recommended.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00330-021-08418-3.

Still a role for the chest radiograph - humble but helpful!

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[Development of severity and mortality prediction models for covid-19 patients at emergency department including the chest x-ray]

Objectives

To develop prognosis prediction models for COVID-19 patients attending an emergency department (ED) based on initial chest X-ray (CXR), demographics, clinical and laboratory parameters.

Methods

All symptomatic confirmed COVID-19 patients admitted to our hospital ED between February 24th and April 24th 2020 were recruited. CXR features, clinical and laboratory variables and CXR abnormality indices extracted by a convolutional neural network (CNN) diagnostic tool were considered potential predictors on this first visit. The most serious individual outcome defined the three severity level: 0) home discharge or hospitalization ≤ 3 days, 1) hospital stay >3 days and 2) intensive care requirement or death. Severity and in-hospital mortality multivariable prediction models were developed and internally validated. The Youden index was used for the optimal threshold selection of the classification model.

Results

A total of 440 patients were enrolled (median 64 years; 55.9% male); 13.6% patients were discharged, 64% hospitalized, 6.6% required intensive care and 15.7% died. The severity prediction model included oxygen saturation/inspired oxygen fraction (SatO2/FiO2), age, C-reactive protein (CRP), lymphocyte count, extent score of lung involvement on CXR (ExtScoreCXR), lactate dehydrogenase (LDH), D-dimer level and platelets count, with AUC-ROC = 0.94 and AUC-PRC = 0.88. The mortality prediction model included age, SatO2/FiO2, CRP, LDH, CXR extent score, lymphocyte count and D-dimer level, with AUC-ROC = 0.97 and AUC-PRC = 0.78. The addition of CXR CNN-based indices did not improve significantly the predictive metrics.

Conclusion

The developed and internally validated severity and mortality prediction models could be useful as triage tools in ED for patients with COVID-19 or other virus infections with similar behaviour.

COVID-19 pneumonia: Relationship between initial chest X-rays and laboratory findings

OBJECTIVE

To analyze the initial findings in chest X-rays of patients with RT-PCR positive for SARS-CoV-2, and to determine whether there is a relationship between the severity of these findings and the clinical and laboratory findings.

MATERIALS AND METHODS

This retrospective study analyzed the relationship between initial chest X-rays and initial laboratory tests in symptomatic adults with nasopharyngeal RT-PCR results positive for SARS-CoV-2 seen at our center between February 29 and March 23, 2020. Among other radiologic findings, we analyzed ground-glass opacities, consolidations, linear opacities, and pleural effusion. We also used a scale of radiologic severity to assess the distribution and extent of these findings. Among initial laboratory findings, we analyzed leukocytes, lymphocytes, platelets, neutrophil-to-lymphocyte ratio, and C-reactive protein.

RESULTS

Of 761 symptomatic patients, 639 (84%) required hospitalization and 122 were discharged to their homes. The need for admission increased with increasing scores on the scale of radiologic severity. The extent of initial lung involvement was significantly associated with the laboratory parameters analyzed (P<.05 for platelets, P<.01 for lymphocytes, and P<.001 for the remaining parameters), as well as with the time from the onset of symptoms (P<.001).

CONCLUSION

It can be useful to use a scale of radiologic severity to classify chest X-ray findings in diagnosing patients with COVID-19, because the greater the radiologic severity, the greater the need for hospitalization and the greater the alteration in laboratory parameters.

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.

Characteristics and outcomes of 974 COVID-19 patients in intensive care units in Turkey

BACKGROUND

In our previous report on Turkish COVID-19 patients requiring intensive care, the 24 patients in a single ICU were elderly and mortality was high. We extended our analysis to include patients admitted to ten ICUs.

OBJECTIVES

Report the demographics, clinical features, imaging findings, comorbidities, and outcomes in COVID-19 patients.

DESIGN

Retrospective.

SETTING

Intensive care unit.

PATIENTS AND METHODS

The study includes patients with clinical and radiological confirmed or laboratory-confirmed COVID-19 infection who were admitted to ten ICUs between 15 March and 30 June 2020.

MAIN OUTCOME MEASURES

Clinical outcomes, therapies, and death during hospitalization SAMPLE SIZE: 974, including 571 males (58%).

RESULTS

The median age (range) was 72 (21-101) years for patients who died (n=632, 64.9%) and 70 (16-99) years for patients who lived (n=432, 35.2%) (P<.001). APACHE scores, and SOFA scores were higher in patients who died than in those who survived (P<.001, both comparisons). Respiratory failure was the most common cause of hospitalization (82.5%), and respiratory failure on admission was associated with death (P=.013). Most (n=719, 73.8%) underwent invasive mechanical ventilation therapy.

CONCLUSIONS

The majority of patients admitted to the ICU with a diagnosis of COVID-19 require respiratory support.

LIMITATIONS

Although the Turkish Ministry of Health made recommendations for the treatment of COVID-19 patients, patient management may not have been identical in all ten units.

CONFLICT OF INTEREST

None.

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.

Noninvasive respiratory support and patient self-inflicted lung injury in COVID-19: a narrative review

COVID-19 pneumonia is associated with hypoxaemic respiratory failure, ranging from mild to severe. Because of the worldwide shortage of ICU beds, a relatively high number of patients with respiratory failure are receiving prolonged noninvasive respiratory support, even when their clinical status would have required invasive mechanical ventilation. There are few experimental and clinical data reporting that vigorous breathing effort during spontaneous ventilation can worsen lung injury and cause a phenomenon that has been termed patient self-inflicted lung injury (P-SILI). The aim of this narrative review is to provide an overview of P-SILI pathophysiology and the role of noninvasive respiratory support in COVID-19 pneumonia. Respiratory mechanics, vascular compromise, viscoelastic properties, lung inhomogeneity, work of breathing, and oesophageal pressure swings are discussed. The concept of P-SILI has been widely investigated in recent years, but controversies persist regarding its mechanisms. To minimise the risk of P-SILI, intensivists should better understand its underlying pathophysiology to optimise the type of noninvasive respiratory support provided to patients with COVID-19 pneumonia, and decide on the optimal timing of intubation for these patients.

COVID-19 pneumonia: relationship between initial chest X-rays and laboratory findings

OBJECTIVE

To analyze the initial findings in chest X-rays of patients with RT-PCR positive for SARS-CoV-2, and to determine whether there is a relationship between the severity of these findings and the clinical and laboratory findings.

MATERIALS AND METHODS

This retrospective study analyzed the relationship between initial chest X-rays and initial laboratory tests in symptomatic adults with nasopharyngeal RT-PCR results positive for SARS-CoV-2 seen at our center between February 29 and March 23, 2020. Among other radiologic findings, we analyzed ground-glass opacities, consolidations, linear opacities, and pleural effusion. We also used a scale of radiologic severity to assess the distribution and extent of these findings. Among initial laboratory findings, we analyzed leukocytes, lymphocytes, platelets, neutrophil-to-lymphocyte ratio, and C-reactive protein.

RESULTS

Of 761 symptomatic patients, 639 (84%) required hospitalization and 122 were discharged to their homes. The need for admission increased with increasing scores on the scale of radiologic severity. The extent of initial lung involvement was significantly associated with the laboratory parameters analyzed (p<0.05 for platelets, p<0.01 for lymphocytes, and p<0.001 for the remaining parameters), as well as with the time from the onset of symptoms (p<0.001).

CONCLUSION

It can be useful to use a scale of radiologic severity to classify chest X-ray findings in diagnosing patients with COVID-19, because the greater the radiologic severity, the greater the need for hospitalization and the greater the alteration in laboratory parameters.

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.

Initial findings in chest X-rays as predictors of worsening lung infection in patients with COVID-19: correlation in 265 patients

Background and aims

We aimed to analyze the relationship between the initial chest X-ray findings in patients with severe acute respiratory syndrome due to infection with SARS-CoV-2 and eventual clinical worsening and to compare three systems of quantifying these findings.

Material and methods

This retrospective study reviewed the clinical and radiological evolution of 265 adult patients with COVID-19 attended at our center between March 2020 and April 2020. We recorded data related to patients’ comorbidities, hospital stay, and clinical worsening (admission to the ICU, intubation, and death). We used three scoring systems taking into consideration 6 or 8 lung fields (designated 6A, 6B, and 8) to quantify lung involvement in each patient’s initial pathological chest X-ray and to classify its severity as mild, moderate, or severe, and we compared these three systems. We also recorded the presence of alveolar opacities and linear opacities (fundamentally linear atelectasis) in the first chest X-ray with pathologic findings.

Results

In the χ2 analysis, moderate or severe involvement in the three classification systems correlated with hospital admission (P = .009 in 6A, P = .001 in 6B, and P = .001 in 8) and with death (P = .02 in 6A, P = .01 in 6B, and P = .006 in 8). In the regression analysis, the most significant associations were 6B with alveolar involvement (OR 2.3; 95%CI 1.1.–4.7; P = .025;) and 8 with alveolar involvement (OR 2.07; 95% CI 1.01.–4.25; P = .046). No differences were observed in the ability of the three systems to predict clinical worsening by classifications of involvement in chest X-rays as moderate or severe.

Conclusion

Moderate/severe extension in the three chest X-ray scoring systems evaluating the extent of involvement over 6 or 8 lung fields and the finding of alveolar opacities in the first pathologic X-ray correlated with mortality and the rate of hospitalization in the patients studied. No significant difference was found in the predictive ability of the three classification systems proposed.

Discrepancies in the clinical and radiological profiles of COVID-19: A case-based discussion and review of literature

The current gold standard for the diagnosis of coronavirus disease-19 (COVID-19) is a positive reverse transcriptase polymerase chain reaction (RT-PCR) test, on the background of clinical suspicion. However, RT-PCR has its limitations; this includes issues of low sensitivity, sampling errors and appropriate timing of specimen collection. As pulmonary involvement is the most common manifestation of severe COVID-19, early and appropriate lung imaging is important to aid diagnosis. However, gross discrepancies can occur between the clinical and imaging findings in patients with COVID-19, which can mislead clinicians in their decision making. Although chest X-ray (CXR) has a low sensitivity for the diagnosis of COVID-19 associated lung disease, especially in the earlier stages, a positive CXR increases the pre-test probability of COVID-19. CXR scoring systems have shown to be useful, such as the COVID-19 opacification rating score which helps to predict the need of tracheal intubation. Furthermore, artificial intelligence-based algorithms have also shown promise in differentiating COVID-19 pneumonia on CXR from other lung diseases. Although costlier than CXR, unenhanced computed tomographic (CT) chest scans have a higher sensitivity, but lesser specificity compared to RT-PCR for the diagnosis of COVID-19 pneumonia. A semi-quantitative CT scoring system has been shown to predict short-term mortality. The routine use of CT pulmonary angiography as a first-line imaging modality in patients with suspected COVID-19 is not justifiable due to the risk of contrast nephropathy. Scoring systems similar to those pioneered in CXR and CT can be used to effectively plan and manage hospital resources such as ventilators. Lung ultrasound is useful in the assessment of critically ill COVID-19 patients in the hands of an experienced operator. Moreover, it is a convenient tool to monitor disease progression, as it is cheap, non-invasive, easily accessible and easy to sterilise. Newer lung imaging modalities such as magnetic resonance imaging (MRI) for safe imaging among children, adolescents and pregnant women are rapidly evolving. Imaging modalities are also essential for evaluating the extra-pulmonary manifestations of COVID-19: these include cranial imaging with CT or MRI; cardiac imaging with ultrasonography (US), CT and MRI; and abdominal imaging with US or CT. This review critically analyses the utility of each imaging modality to empower clinicians to use them appropriately in the management of patients with COVID-19 infection.

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.