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Three unsuspected CT diagnoses of COVID-19.

ABSTRACT: PURPOSE:Coronavirus disease 2019 (COVID-19) is caused by a novel strain of coronavirus named severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that has quickly spread around the globe. Health care facilities in the USA currently do not have an adequate supply of COVID-19 tests to meet the growing demand. Imaging findings for COVID-19 are non-specific but include pulmonary parenchymal ground-glass opacities in a predominantly basal and peripheral distribution. METHODS:Three patients were imaged for non-respiratory-related symptoms with a portion of the lungs in the imaged field. RESULTS:Each patient had suspicious imaging findings for COVID-19, prompting the interpreting radiologist to suggest testing for COVID-19. All 3 patients turned out to be infected with COVID-19, and one patient is the first reported case of the coincident presentation of COVID-19 and an intraparenchymal hemorrhage. CONCLUSION:Using imaging characteristics of COVID-19 on abdominal or neck CT when a portion of the lungs is included, patients not initially suspected of COVID-19 infection can be quarantined earlier to limit exposure to others.

SUBMITTER: Vu D

PROVIDER: S-EPMC7152619 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature

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Three unsuspected CT diagnoses of COVID-19.

Vu David D Ruggiero Maryanne M Choi Woo Sung WS Masri Daniel D Flyer Mark M Shyknevsky Inna I Stein Evan G EG

Emergency radiology 20200413 3

<h4>Purpose</h4>Coronavirus disease 2019 (COVID-19) is caused by a novel strain of coronavirus named severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that has quickly spread around the globe. Health care facilities in the USA currently do not have an adequate supply of COVID-19 tests to meet the growing demand. Imaging findings for COVID-19 are non-specific but include pulmonary parenchymal ground-glass opacities in a predominantly basal and peripheral distribution.<h4>Methods</h4>Th ...[more]

PMID: 32285222

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Project description:BackgroundThe coronavirus disease 2019 (COVID-19) pandemic has been accompanied by significant reductions in patient volumes for non-COVID-19-related conditions ranging from acute coronary syndrome to ischemic strokes to acute trauma. However, the impact of the COVID-19 pandemic on patient volumes for a broad range of orthopedic conditions remains unknown. The purpose of this study was to investigate the association of the COVID-19 pandemic with changes in patient volumes of 35 emergent (e.g. dislocations, open fractures), urgent (e.g. fractures), and nonurgent orthopedic conditions (e.g. osteoarthritis, sprains).MethodsA retrospective interrupted time-series analysis of patient volumes was conducted for 35 orthopedic conditions based on ICD-10 diagnosis codes. Patient hospitalizations and new problem visits were aggregated across two institutions in New York state, including one urban tertiary care orthopedic hospital, one urban academic medical center, and all state outpatient facilities affiliated with the orthopedic institution. Patient volumes in the COVID-19 peak period (03/2020-05/2020) and COVID-19 recovery period (06/2020-10/2020) were compared against pre-COVID-19 vol (01/2018-02/2020).ResultsOverall, 169,047 cases were included in the analysis across 35 conditions with 3775 emergent cases, 6376 urgent cases, and 158,896 nonurgent cases. During the COVID-19 peak period, patient caseloads for 1 out of 7 emergent conditions (p = 0.02) and 26 out of 28 urgent and nonurgent conditions (p < 0.05) were significantly reduced compared to the pre-COVID-19 period. During the COVID-19 recovery period, patient volumes in 3 out of 13 emergent and urgent conditions (p < 0.03) and 11 out of 22 nonurgent conditions (p < 0.04) were decreased compared to pre-COVID-19 vol.ConclusionsThis study found that the pandemic was associated with considerable changes in patient patterns for non-COVID-19 orthopedic conditions. The long-term effects of patient volume reductions on both patient outcomes and orthopedic health systems remain to be seen.Level of evidenceCohort study; level of evidence IV.

Project description:INTRODUCTION:COVID-19 pneumonia is characterized by ground-glass opacities (GGOs) and consolidations on Chest CT, although these CT features cannot be considered specific, at least on a qualitative analysis. The aim is to evaluate if Quantitative Chest CT could provide reliable information in discriminating COVID-19 from non-COVID-19 patients. MATERIALS AND METHODS:From March 31, 2020 until April 18, 2020, patients with Chest CT suggestive for interstitial pneumonia were retrospectively enrolled and divided into two groups based on positive/negative COVID-19 RT-PCR results. Patients with pulmonary resection and/or CT motion artifacts were excluded. Quantitative Chest CT analysis was performed with a dedicated software that provides total lung volume, healthy parenchyma, GGOs, consolidations and fibrotic alterations, expressed both in liters and percentage. Two radiologists in consensus revised software analysis and adjusted areas of lung impairment in case of non-adequate segmentation. Data obtained were compared between COVID-19 and non-COVID-19 patients and p?<?0.05 were considered statistically significant. Performance of statistically significant parameters was tested by ROC curve analysis. RESULTS:Final population enrolled included 190 patients: 136 COVID-19 patients (87 male, 49 female, mean age 66?±?16) and 54 non-COVID-19 patients (25 male, 29 female, mean age 63?±?15). Lung quantification in liters showed significant differences between COVID-19 and non-COVID-19 patients for GGOs (0.55?±?0.26L vs 0.43?±?0.23L, p?=?0.0005) and fibrotic alterations (0.05?±?0.03 L vs 0.04?±?0.03 L, p?<?0.0001). ROC analysis of GGOs and fibrotic alterations showed an area under the curve of 0.661 (cutoff 0.39 L, 68% sensitivity and 59% specificity, p?<?0.001) and 0.698 (cutoff 0.02 L, 86% sensitivity and 44% specificity, p?<?0.001), respectively. CONCLUSIONS:Quantification of GGOs and fibrotic alterations on Chest CT could be able to identify patients with COVID-19.

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Three unsuspected CT diagnoses of COVID-19.

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Three unsuspected CT diagnoses of COVID-19.

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