Symptomatic Ventricular Bigeminy and Trigeminy Associated With COVID-19.

Cardiac manifestations of COVID-19 are well-described in the current literature, although electrocardiogram analyses of COVID-19 patients are limited. The most common arrhythmias experienced by patients with COVID-19 include sinus tachycardia and atrial fibrillation. Ventricular bigeminy associated with COVID-19 is exceedingly rare and requires further studies to determine its incidence and clinical significance. Here, we present the case of a 57-year-old male with no prior cardiac history who was found to have COVID-19 and new-onset, symptomatic premature ventricular contraction bigeminy. This case highlights a rare potential association between COVID-19 and ventricular bigeminy/trigeminy.

Generative adversarial network for automatic quantification of Coronavirus disease 2019 pneumonia on chest radiographs.

PURPOSE: To develop a generative adversarial network (GAN) to quantify COVID-19 pneumonia on chest radiographs automatically. MATERIALS AND METHODS: This retrospective study included 50,000 consecutive non-COVID-19 chest CT scans in 2015-2017 for training. Anteroposterior virtual chest, lung, and pneumonia radiographs were generated from whole, segmented lung, and pneumonia pixels from each CT scan. Two GANs were sequentially trained to generate lung images from radiographs and to generate pneumonia images from lung images. GAN-driven pneumonia extent (pneumonia area/lung area) was expressed from 0% to 100%. We examined the correlation of GAN-driven pneumonia extent with semi-quantitative Brixia X-ray severity score (one dataset, n = 4707) and quantitative CT-driven pneumonia extent (four datasets, n = 54-375), along with analyzing a measurement difference between the GAN and CT extents. Three datasets (n = 243-1481), where unfavorable outcomes (respiratory failure, intensive care unit admission, and death) occurred in 10%, 38%, and 78%, respectively, were used to examine the predictive power of GAN-driven pneumonia extent. RESULTS: GAN-driven radiographic pneumonia was correlated with the severity score (0.611) and CT-driven extent (0.640). 95% limits of agreements between GAN and CT-driven extents were -27.1% to 17.4%. GAN-driven pneumonia extent provided odds ratios of 1.05-1.18 per percent for unfavorable outcomes in the three datasets, with areas under the receiver operating characteristic curve (AUCs) of 0.614-0.842. When combined with demographic information only and with both demographic and laboratory information, the prediction models yielded AUCs of 0.643-0.841 and 0.688-0.877, respectively. CONCLUSION: The generative adversarial network automatically quantified COVID-19 pneumonia on chest radiographs and identified patients with unfavorable outcomes.

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.

Can nirmatrelvir/ritonavir treatment shorten the duration of COVID-19 isolation?

Background: The impact of nirmatrelvir/ritonavir treatment on shedding of viable virus in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is unclear. Methods: A prospective cohort study evaluating mildly ill COVID-19 patients was conducted. Virologic responses were compared between nirmatrelvir/ritonavir-treatment and supportive care groups. Risk factors and relevant clinical factors for shedding of viable virus were investigated. Results: A total of 80 COVID-19 patients were enrolled and 222 sputum specimens were collected. Ten patients were dropped during follow-up, and 33 patients in the nirmatrelvir/ritonavir and 37 in the supportive care groups were compared. The median age was 67 years, and 67% were male. Clinical characteristics were similar between groups. Viral loads decreased significantly faster in the nirmatrelvir/ritonavir group compared with the supportive care group (P < 0.001), and the slope was significantly steeper (-2.99 ± 1.54 vs. -1.44 ± 1.52; P < 0.001). The duration of viable virus shedding was not statistically different between groups. In the multivariable analyses evaluating all collected specimens, male gender (OR 2.51, 95% CI 1.25-5.03, P = 0.010), symptom score (OR 1.41, 95% CI 1.07-1.87, P = 0.015), days from symptom onset (OR 0.72, 95% CI 0.59-0.88, P = 0.002), complete vaccination (OR 0.09, 95% CI 0.01-0.87, P = 0.038), and BA.2 subtype (OR 0.49, 95% CI 0.26-0.91, P = 0.025) were independently associated with viable viral shedding, while nirmatrelvir/ritonavir treatment was not. Conclusion: Nirmatrelvir/ritonavir treatment effectively reduced viral loads of SARS-CoV-2 Omicron variants but did not decrease the duration of viable virus shedding.

The impact of COVID-19 on trends in alcohol use disorder treatment in Veterans Health Administration.

BACKGROUND AND AIMS: The COVID-19 pandemic disrupted health-care provision in the United States and prompted increases in telehealth-delivery of care. This study measured alcohol use disorder (AUD) treatment trends across visit modalities before and during COVID-19. DESIGN, SETTING, PARTICIPANTS AND MEASUREMENTS: We conducted a national, retrospective cohort study with interrupted time-series models to estimate the impact of COVID-19 on AUD treatment in the Veterans Health Administration (VHA) in the United States during pre-COVID-19 (March 2019 to February 2020) and COVID-19 (March 2020 to February 2021) periods. We analyzed monthly trends in telephone, video and in-person visits for AUD treatment and compared patient and treatment characteristics of patients receiving AUD treatment between the pre-COVID-19 and COVID-19 periods. AUD was defined using International Classification of Diseases, 10th revision (ICD-10) codes for alcohol abuse (F10.1) and alcohol dependence (F10.2), which have previously been used to study AUD in VHA. FINDINGS: The predicted percentage of VHA patients with an AUD diagnosis receiving any AUD treatment at the beginning of the pre-COVID period was 13.8% (n = 49 494). The predicted percentage decreased by 4.3% (P = 0.001) immediately at the start of the COVID-19 period due to a decline in AUD psychotherapy. Despite an increase of 0.3% per month (P = 0.026) following the start of COVID-19, the predicted percentage of VHA patients with an AUD diagnosis receiving any AUD treatment at the end of the study period remained below the pre-COVID-19 period. In February 2021, AUD psychotherapy visits were primarily delivered by video (50%, 58 748), followed by in-person (36.6%, 43 251) and telephone (13.8%, 16 299), while AUD pharmacotherapy visits were delivered by telephone (38.9%, 3623) followed by in-person (34.3%, 3193) and video (26.8%, 2498) modalities. Characteristics of VHA patients receiving AUD treatment were largely similar between pre-COVID-19 and COVID-19 periods. CONCLUSIONS: Despite increased telehealth use, the percentage of United States Veterans Health Administration patients with an alcohol use disorder (AUD) diagnosis receiving AUD treatment declined during COVID-19 (March 2020 to February 2021) mainly due to a decrease in psychotherapy.

Implementation of a Deep Learning-Based Computer-Aided Detection System for the Interpretation of Chest Radiographs in Patients Suspected for COVID-19.

OBJECTIVE: To describe the experience of implementing a deep learning-based computer-aided detection (CAD) system for the interpretation of chest X-ray radiographs (CXR) of suspected coronavirus disease (COVID-19) patients and investigate the diagnostic performance of CXR interpretation with CAD assistance. MATERIALS AND METHODS: In this single-center retrospective study, initial CXR of patients with suspected or confirmed COVID-19 were investigated. A commercialized deep learning-based CAD system that can identify various abnormalities on CXR was implemented for the interpretation of CXR in daily practice. The diagnostic performance of radiologists with CAD assistance were evaluated based on two different reference standards: 1) real-time reverse transcriptase-polymerase chain reaction (rRT-PCR) results for COVID-19 and 2) pulmonary abnormality suggesting pneumonia on chest CT. The turnaround times (TATs) of radiology reports for CXR and rRT-PCR results were also evaluated. RESULTS: Among 332 patients (male:female, 173:159; mean age, 57 years) with available rRT-PCR results, 16 patients (4.8%) were diagnosed with COVID-19. Using CXR, radiologists with CAD assistance identified rRT-PCR positive COVID-19 patients with sensitivity and specificity of 68.8% and 66.7%, respectively. Among 119 patients (male:female, 75:44; mean age, 69 years) with available chest CTs, radiologists assisted by CAD reported pneumonia on CXR with a sensitivity of 81.5% and a specificity of 72.3%. The TATs of CXR reports were significantly shorter than those of rRT-PCR results (median 51 vs. 507 minutes; p < 0.001). CONCLUSION: Radiologists with CAD assistance could identify patients with rRT-PCR-positive COVID-19 or pneumonia on CXR with a reasonably acceptable performance. In patients suspected with COVID-19, CXR had much faster TATs than rRT-PCRs.

Impact of COVID-19 Telehealth Policy Changes on Buprenorphine Treatment for Opioid Use Disorder.

OBJECTIVE: The authors examined the impact of COVID-19-related policies reducing barriers to telehealth delivery of buprenorphine treatment for opioid use disorder (OUD) on buprenorphine treatment across different modalities (telephone, video, and in-person visits). METHODS: This was a national retrospective cohort study with interrupted time-series analyses to examine the impact of policy changes in March 2020 on buprenorphine treatment for OUD in the Veterans Health Administration, during the year before the start of the COVID-19 pandemic (March 2019 to February 2020) and during the first year of the pandemic (March 2020 to February 2021). The authors also examined trends in the use of telephone, video, and in-person visits for buprenorphine treatment and compared patient demographic characteristics and retention in buprenorphine treatment across the two periods. RESULTS: The number of patients receiving buprenorphine increased from 13,415 in March 2019 to 15,339 in February 2021. By February 2021, telephone visits were used by the most patients (50.2%; 4,456 visits), followed by video visits (32.4%; 2,870 visits) and in-person visits (17.4%; 1,544 visits). During the pre-pandemic period, the number of patients receiving buprenorphine increased significantly by 103 patients per month. After the COVID-19 policy changes, there was an immediate increase of 265 patients in the first month, and the number continued to increase significantly, at a rate of 47 patients per month. The demographic characteristics of patients receiving buprenorphine during the pandemic period were similar to those during the pre-pandemic period, but the proportion of patients reaching 90-day retention on buprenorphine treatment decreased significantly from 49.6% to 47.7%, while days on buprenorphine increased significantly from 203.8 to 208.7. CONCLUSIONS: The number of patients receiving buprenorphine continued to increase after the COVID-19 policy changes, but the delivery of care shifted to telehealth visits, suggesting that any reversal of COVID-19 policies must be carefully considered.

Neuroretinitis after the second injection of a SARS-CoV-2-vaccine: A case report.

Purpose: We report the first case of neuroretinitis after administration of a second dose of a messenger RNA vaccine for coronavirus disease-2019 (COVID-19). Observations: An 83-year-old healthy woman presented with subacute, painless, and progressive visual loss in the right eye that started 2 days after the second injection of the COVID-19 vaccine (Comirnaty®) from Pfizer (New York, NY, USA) and BioNTech (Mainz, Germany). Visual acuities were hand motion perception in the right eye and 20/30 in the left eye. There was optic nerve head swelling in the right eye and subretinal fluid and disruption of the photoreceptor layers in both eyes. Magnetic resonance imaging revealed an enhancement of the right optic nerve, consistent with optic neuritis. She was treated with intravenous corticosteroids, and the optic nerve swelling in the right eye resolved promptly. However, the amount of subretinal fluid worsened for 1 month and did not improve until 6 months from onset. Her visual acuity was slightly improved to finger count perception in the right eye and 20/20 in the left eye during an examination 6 months from onset. Conclusions and Importance: Considering the temporal relation between the second dose of vaccination and the symptom onset in our patient, the ophthalmic symptoms here reported might be considered a rare adverse effect of the Comirnaty® COVID-19 vaccine. Although a causal relationship is not established, to our knowledge, this is the first report of neuroretinitis after vaccination with Comirnaty®, and any further similar cases should be examined in detail.

Deep Learning-Based Automatic CT Quantification of Coronavirus Disease 2019 Pneumonia: An International Collaborative Study.

OBJECTIVE: We aimed to develop and validate the automatic quantification of coronavirus disease 2019 (COVID-19) pneumonia on computed tomography (CT) images. METHODS: This retrospective study included 176 chest CT scans of 131 COVID-19 patients from 14 Korean and Chinese institutions from January 23 to March 15, 2020. Two experienced radiologists semiautomatically drew pneumonia masks on CT images to develop the 2D U-Net for segmenting pneumonia. External validation was performed using Japanese (n = 101), Italian (n = 99), Radiopaedia (n = 9), and Chinese data sets (n = 10). The primary measures for the system's performance were correlation coefficients for extent (%) and weight (g) of pneumonia in comparison with visual CT scores or human-derived segmentation. Multivariable logistic regression analyses were performed to evaluate the association of the extent and weight with symptoms in the Japanese data set and composite outcome (respiratory failure and death) in the Spanish data set (n = 115). RESULTS: In the internal test data set, the intraclass correlation coefficients between U-Net outputs and references for the extent and weight were 0.990 and 0.993. In the Japanese data set, the Pearson correlation coefficients between U-Net outputs and visual CT scores were 0.908 and 0.899. In the other external data sets, intraclass correlation coefficients were between 0.949-0.965 (extent) and between 0.978-0.993 (weight). Extent and weight in the top quartile were independently associated with symptoms (odds ratio, 5.523 and 10.561; P = 0.041 and 0.016) and the composite outcome (odds ratio, 9.365 and 7.085; P = 0.021 and P = 0.035). CONCLUSIONS: Automatically quantified CT extent and weight of COVID-19 pneumonia were well correlated with human-derived references and independently associated with symptoms and prognosis in multinational external data sets.

CT Examinations for COVID-19: A Systematic Review of Protocols, Radiation Dose, and Numbers Needed to Diagnose and Predict.

Purpose: Although chest CT has been discussed as a first-line test for coronavirus disease 2019 (COVID-19), little research has explored the implications of CT exposure in the population. To review chest CT protocols and radiation doses in COVID-19 publications and explore the number needed to diagnose (NND) and the number needed to predict (NNP) if CT is used as a first-line test. Materials and Methods: We searched nine highly cited radiology journals to identify studies discussing the CT-based diagnosis of COVID-19 pneumonia. Study-level information on the CT protocol and radiation dose was collected, and the doses were compared with each national diagnostic reference level (DRL). The NND and NNP, which depends on the test positive rate (TPR), were calculated, given a CT sensitivity of 94% (95% confidence interval [CI]: 91%-96%) and specificity of 37% (95% CI: 26%-50%), and applied to the early outbreak in Wuhan, New York, and Italy. Results: From 86 studies, the CT protocol and radiation dose were reported in 81 (94.2%) and 17 studies (19.8%), respectively. Low-dose chest CT was used more than twice as often as standard-dose chest CT (39.5% vs.18.6%), while the remaining studies (44.2%) did not provide relevant information. The radiation doses were lower than the national DRLs in 15 of the 17 studies (88.2%) that reported doses. The NND was 3.2 scans (95% CI: 2.2-6.0). The NNPs at TPRs of 50%, 25%, 10%, and 5% were 2.2, 3.6, 8.0, 15.5 scans, respectively. In Wuhan, 35418 (TPR, 58%; 95% CI: 27710-56755) to 44840 (TPR, 38%; 95% CI: 35161-68164) individuals were estimated to have undergone CT examinations to diagnose 17365 patients. During the early surge in New York and Italy, daily NNDs changed up to 5.4 and 10.9 times, respectively, within 10 weeks. Conclusion: Low-dose CT protocols were described in less than half of COVID-19 publications, and radiation doses were frequently lacking. The number of populations involved in a first-line diagnostic CT test could vary dynamically according to daily TPR; therefore, caution is required in future planning.

Erratum: Extension of Coronavirus Disease 2019 (COVID-19) on Chest CT and Implications for Chest Radiograph Interpretation.

[This corrects the article DOI: 10.1148/ryct.2020200107.].

COVID-19 pneumonia on chest X-rays: Performance of a deep learning-based computer-aided detection system.

Chest X-rays (CXRs) can help triage for Coronavirus disease (COVID-19) patients in resource-constrained environments, and a computer-aided detection system (CAD) that can identify pneumonia on CXR may help the triage of patients in those environment where expert radiologists are not available. However, the performance of existing CAD for identifying COVID-19 and associated pneumonia on CXRs has been scarcely investigated. In this study, CXRs of patients with and without COVID-19 confirmed by reverse transcriptase polymerase chain reaction (RT-PCR) were retrospectively collected from four and one institution, respectively, and a commercialized, regulatory-approved CAD that can identify various abnormalities including pneumonia was used to analyze each CXR. Performance of the CAD was evaluated using area under the receiver operating characteristic curves (AUCs), with reference standards of the RT-PCR results and the presence of findings of pneumonia on chest CTs obtained within 24 hours from the CXR. For comparison, 5 thoracic radiologists and 5 non-radiologist physicians independently interpreted the CXRs. Afterward, they re-interpreted the CXRs with corresponding CAD results. The performance of CAD (AUCs, 0.714 and 0.790 against RT-PCR and chest CT, respectively hereinafter) were similar with those of thoracic radiologists (AUCs, 0.701 and 0.784), and higher than those of non-radiologist physicians (AUCs, 0.584 and 0.650). Non-radiologist physicians showed significantly improved performance when assisted with the CAD (AUCs, 0.584 to 0.664 and 0.650 to 0.738). In addition, inter-reader agreement among physicians was also improved in the CAD-assisted interpretation (Fleiss' kappa coefficient, 0.209 to 0.322). In conclusion, radiologist-level performance of the CAD in identifying COVID-19 and associated pneumonia on CXR and enhanced performance of non-radiologist physicians with the CAD assistance suggest that the CAD can support physicians in interpreting CXRs and helping image-based triage of COVID-19 patients in resource-constrained environment.

Erratum: Extension of Coronavirus Disease 2019 on Chest CT and Implications for Chest Radiographic Interpretation.

[This corrects the article DOI: 10.1148/ryct.2020200107.].

Extension of Coronavirus Disease 2019 on Chest CT and Implications for Chest Radiographic Interpretation.

PURPOSE: To study the extent of pulmonary involvement in coronavirus 19 (COVID-19) with quantitative CT and to assess the impact of disease burden on opacity visibility on chest radiographs. MATERIALS AND METHODS: This retrospective study included 20 pairs of CT scans and same-day chest radiographs from 17 patients with COVID-19, along with 20 chest radiographs of controls. All pulmonary opacities were semiautomatically segmented on CT images, producing an anteroposterior projection image to match the corresponding frontal chest radiograph. The quantitative CT lung opacification mass (QCTmass) was defined as (opacity attenuation value + 1000 HU)/1000 × 1.065 (g/mL) × combined volume (cm3) of the individual opacities. Eight thoracic radiologists reviewed the 40 radiographs, and a receiver operating characteristic curve analysis was performed for the detection of lung opacities. Logistic regression analysis was performed to identify factors affecting opacity visibility on chest radiographs. RESULTS: The mean QCTmass per patient was 72.4 g ± 120.8 (range, 0.7-420.7 g), and opacities occupied 3.2% ± 5.8 (range, 0.1%-19.8%) and 13.9% ± 18.0 (range, 0.5%-57.8%) of the lung area on the CT images and projected images, respectively. The radiographs had a median sensitivity of 25% and specificity of 90% among radiologists. Nineteen of 186 opacities were visible on chest radiographs, and a median area of 55.8% of the projected images was identifiable on radiographs. Logistic regression analysis showed that QCTmass (P < .001) and combined opacity volume (P < .001) significantly affected opacity visibility on radiographs. CONCLUSION: QCTmass varied among patients with COVID-19. Chest radiographs had high specificity for detecting lung opacities in COVID-19 but a low sensitivity. QCTmass and combined opacity volume were significant determinants of opacity visibility on radiographs.Earlier incorrect version appeared online. This article was corrected on April 6, 2020 and December 14, 2020.Supplemental material is available for this article.© RSNA, 2020.

Recommendations for the Management of Patients With Systemic Rheumatic Diseases During the Coronavirus Disease Pandemic

Patients with systemic rheumatic diseases (SRD) are vulnerable for coronavirus disease (COVID-19). The Korean College of Rheumatology recognized the urgent need to develop recommendations for rheumatologists and other physicians to manage patients with SRD during the COVID-19 pandemic. The working group was organized and was responsible for selecting key health questions, searching and reviewing the available literature, and formulating statements. The appropriateness of the statements was evaluated by voting panels using the modified Delphi method. Four general principles and thirteen individual recommendations were finalized through expert consensus based on the available evidence. The recommendations included preventive measures against COVID-19, medicinal treatment for stable or active SRD patients without COVID-19, medicinal treatment for SRD patients with COVID-19, and patient evaluation and monitoring. Medicinal treatments were categorized according to the status with respect to both COVID-19 and SRD. These recommendations should serve as a reference for individualized treatment for patients with SRD. As new evidence is emerging, an immediate update will be required.

Recommendations for the management of patients with systemic rheumatic diseases during the coronavirus disease pandemic.

Patients with systemic rheumatic diseases (SRD) are vulnerable for coronavirus disease (COVID-19). The Korean College of Rheumatology recognized the urgent need to develop recommendations for rheumatologists and other physicians to manage patients with SRD during the COVID-19 pandemic. The working group was organized and was responsible for selecting key health questions, searching and reviewing the available literature, and formulating statements. The appropriateness of the statements was evaluated by voting panels using the modified Delphi method. Four general principles and thirteen individual recommendations were finalized through expert consensus based on the available evidence. The recommendations included preventive measures against COVID-19, medicinal treatment for stable or active SRD patients without COVID-19, medicinal treatment for SRD patients with COVID-19, and patient evaluation and monitoring. Medicinal treatments were categorized according to the status with respect to both COVID-19 and SRD. These recommendations should serve as a reference for individualized treatment for patients with SRD. As new evidence is emerging, an immediate update will be required.

Diagnostic Performance of CT and Reverse Transcriptase Polymerase Chain Reaction for Coronavirus Disease 2019: A Meta-Analysis.

Background Recent studies have suggested that chest CT scans could be used as a primary screening or diagnostic tool for coronavirus disease 2019 (COVID-19) in epidemic areas. Purpose To perform a meta-analysis to evaluate diagnostic performance measures, including predictive values of chest CT and initial reverse transcriptase polymerase chain reaction (RT-PCR). Materials and Methods Medline and Embase were searched from January 1, 2020, to April 3, 2020, for studies on COVID-19 that reported the sensitivity, specificity, or both of CT scans, RT-PCR assays, or both. The pooled sensitivity and specificity were estimated by using random-effects models. The actual prevalence (ie, the proportion of confirmed patients among those tested) in eight countries was obtained from web sources, and the predictive values were calculated. Meta-regression was performed to reveal the effect of potential explanatory factors on the diagnostic performance measures. Results The pooled sensitivity was 94% (95% confidence interval [CI]: 91%, 96%; I2 = 95%) for chest CT and 89% (95% CI: 81%, 94%; I2 = 90%) for RT-PCR. The pooled specificity was 37% (95% CI: 26%, 50%; I2 = 83%) for chest CT. The prevalence of COVID-19 outside China ranged from 1.0% to 22.9%. For chest CT scans, the positive predictive value (PPV) ranged from 1.5% to 30.7%, and the negative predictive value (NPV) ranged from 95.4% to 99.8%. For RT-PCR, the PPV ranged from 47.3% to 96.4%, whereas the NPV ranged from 96.8% to 99.9%. The sensitivity of CT was affected by the distribution of disease severity, the proportion of patients with comorbidities, and the proportion of asymptomatic patients (all P < .05). The sensitivity of RT-PCR was negatively associated with the proportion of elderly patients (P = .01). Conclusion Outside of China where there is a low prevalence of coronavirus disease 2019 (range, 1%-22.9%), chest CT screening of patients with suspected disease had low positive predictive value (range, 1.5%-30.7%). © RSNA, 2020 Online supplemental material is available for this article.

Outbreak of novel coronavirus (COVID-19): What is the role of radiologists?

KEY POINTS: • Novel coronavirus (COVID-19)-infected pneumonia usually manifests as bilateral ground-glass opacities in the lung periphery on chest CT scans. • Role of radiologists includes not only early detection of lung abnormality, but also suggestion of disease severity, potential progression to acute respiratory distress syndrome, and possible bacterial co-infection in hospitalized patients.