Can a chest HRCT-based crash course on COVID-19 cases make inexperienced thoracic radiologists readily available to face the next pandemic?

OBJECTIVE: To test the inter-reader agreement in assessing lung disease extent, HRCT signs, and Radiological Society of North America (RSNA) categorization between a chest-devoted radiologist (CR) and two HRCT-naïve radiology residents (RR1 and RR2) after the latter attended a COVID-19-based chest high-resolution computed tomography (HRCT) "crash course". METHODS: The course was built by retrospective inclusion of 150 patients who underwent HRCT for COVID-19 pneumonia between November 2020 and January 2021. During a first 10-days-long "training phase", RR1 and RR2 read a pool of 100/150 HRCTs, receiving day-by-day access to CR reports as feedback. In the subsequent 2-days-long "test phase", they were asked to report 50/150 HRCTs with no feedback. Test phase reports of RR1/RR2 were then compared with CR using unweighted or linearly-weighted Cohen's kappa (k) statistic and intraclass correlation coefficient (ICC). RESULTS: We observed almost perfect agreement in assessing disease extent between RR1-CR (k = 0.83, p < 0.001) and RR2-CR (k = 0.88, p < 0.001). The agreement between RR1-CR and RR2-CR on consolidation, crazy paving pattern, organizing pneumonia (OP) pattern, and pulmonary artery (PA) diameter was substantial (k = 0.65 and k = 0.68), moderate (k = 0.42 and k = 0.51), slight (k = 0.10 and k = 0.20), and good-to-excellent (ICC = 0.87 and ICC = 0.91), respectively. The agreement in providing RSNA categorization was moderate for R1 versus CR (k = 0.56) and substantial for R2 versus CR (k = 0.67). CONCLUSION: HRCT-naïve readers showed an acceptable overall agreement with CR, supporting the hypothesis that a crash course can be a tool to readily make non-subspecialty radiologists available to cooperate in reading high burden of HRCT examinations during a pandemic/epidemic.

Fifty years of Shannon information theory in assessing the accuracy and agreement of diagnostic tests.

Since 1948, Shannon theoretic methods for modeling information have found a wide range of applications in several areas where information plays a key role, which goes well beyond the original scopes for which they have been conceived, namely data compression and error correction over a noisy channel. Among other uses, these methods have been applied in the broad field of medical diagnostics since the 1970s, to quantify diagnostic information, to evaluate diagnostic test performance, but also to be used as technical tools in image processing and registration. This review illustrates the main contributions in assessing the accuracy of diagnostic tests and the agreement between raters, focusing on diagnostic test performance measurements and paired agreement evaluation. This work also presents a recent unified, coherent, and hopefully, final information-theoretical approach to deal with the flows of information involved among the patient, the diagnostic test performed to appraise the state of disease, and the raters who are checking the test results. The approach is assessed by considering two case studies: the first one is related to evaluating extra-prostatic cancers; the second concerns the quality of rapid tests for COVID-19 detection.

Lung Ultrasound Signs and Their Correlation With Clinical Symptoms in COVID-19 Pregnant Women: The "PINK-CO" Observational Study.

OBJECTIVE: To analyze the application of lung ultrasound (LUS) diagnostic approach in obstetric patients with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and compare LUS score and symptoms of the patients. DESIGN: A single-center observational retrospective study from October 31, 2020 to March 31, 2021. SETTING: Department of Ob/Gyn at the University-Hospital of Udine, Italy. PARTICIPANTS: Pregnant women with SARS-CoV-2 diagnosed with reverse transcription-PCR (RT-PCR) swab test were subdivided as symptomatic and asymptomatic patients with COVID-19. EXPOSURE: Lung ultrasound evaluation both through initial evaluation upon admission and through serial evaluations. MAIN OUTCOME: Reporting LUS findings and LUS score characteristics. RESULTS: Symptomatic patients with COVID-19 showed a higher LUS (median 3.5 vs. 0, p < 0.001). LUS was significantly correlated with COVID-19 biomarkers as C-reactive protein (CPR; p = 0.011), interleukin-6 (p = 0.013), and pro-adrenomedullin (p = 0.02), and inversely related to arterial oxygen saturation (p = 0.004). The most frequent ultrasound findings were focal B lines (14 vs. 2) and the light beam (9 vs. 0). CONCLUSION: Lung ultrasound can help to manage pregnant women with SARS-CoV-2 infection during a pandemic surge. STUDY REGISTRATION: ClinicalTrials.gov, NCT04823234. Registered on March 29, 2021.

Post-COVID-19 Arthritis and Sacroiliitis: Natural History with Longitudinal Magnetic Resonance Imaging Study in Two Cases and Review of the Literature.

Severe acute respiratory coronavirus-2 syndrome (SARS-CoV-2) is a well-known pandemic infectious disease caused by an RNA virus belonging to the coronaviridae family. The most important involvement during the acute phase of infection concerns the respiratory tract and may be fatal. However, COVID-19 may become a systemic disease with a wide spectrum of manifestations. Herein, we report the natural history of sacroiliac inflammatory involvement in two females who developed COVID-19 infection with mild flu-like symptoms. After the infection they reported inflammatory back pain, with magnetic resonance imaging (MRI) studies showing typical aspects of sacroiliitis. Symptoms improved with NSAIDs therapy over the following months while MRI remained positive. A literature review was performed on this emerging topic. To our knowledge, this is the first MRI longitudinal study of post-COVID-19 sacroiliitis with almost one year of follow-up. Predisposing factors for the development of articular involvement are unclear but a long-lasting persistence of the virus, demonstrated by nasopharyngeal swab, may enhance the probability of altering the immune system in a favourable background.

Chest high-resolution computed tomography is associated to short-time progression to severe disease in patients with COVID-19 pneumonia.

OBJECTIVE: In patients with mild COVID-19 pneumonia, chest high-resolution computed tomography (HRCT) is advised when risk factors for severe disease (i.e., age > 65 years and/or comorbidities) are present, and can influence management strategy. The objective was to assess whether HRCT is associated to short-time development of severe disease in patients with COVID-19 pneumonia. METHODS: Seventy-seven consecutive patients (mean age, 64 ± 15 years) with mild COVID-19 pneumonia (no or mild respiratory failure) that underwent HRCT were retrospectively identified. Fifty-two on 77 patients had reported risk factors for severe disease. A chest-imaging devoted radiologist recorded, on a per-examination basis, the following HRCT features: ground-glass opacity, crazy-paving pattern, consolidation, organizing pneumonia (OP) pattern, mosaic attenuation, and nodules. The extent of each feature (total feature score, TFS) was semi-quantitatively assessed. Total lung involvement (TLI) was defined as the sum of all TFSs. The study outcome was defined as the occurrence of severe disease (moderate-to-severe respiratory failure) within 15 days from HRCT. Logistic regression analysis was performed to assess if age, comorbidities, and HRCT features were associated to severe disease. RESULTS: On univariable analysis, severe disease was significantly associated with age > 59 years (29/47 patients, 61.7%) (p = 0.013), and not significantly associated with having comorbidities (22/44 patients, 50.0%). On multivariable analysis, TLI >15 and OP pattern >5 were independently associated to severe disease, with odds ratio of 8.380 (p = 0.003), and of 4.685 (p = 0.035), respectively. CONCLUSION: Short-time onset of severe COVID-19 was associated to TLI >15 and OP pattern score > 5. Severe disease was not associated to comorbidities.

Inter-reader agreement of high-resolution computed tomography findings in patients with COVID-19 pneumonia: A multi-reader study.

PURPOSE: To investigate the inter-reader agreement in assessing high-resolution computed tomography (HRCT) features of coronavirus disease 2019 (COVID-19) pneumonia. METHOD: Seventy-seven consecutive patients (mean age, 64 ± 15 years) with mild COVID-19 pneumonia that underwent HRCT were retrospectively included. Three radiologists [two devoted to thoracic imaging (R1, R2), and one generalist (R3)] on a per-examination basis independently assessed ground-glass opacity (GGO), consolidation, and crazy-paving pattern. The extent of each feature (total feature score, TFS) was semi-quantitatively assessed, and each TFS summed up to obtain total lung score (TLS). Presence of organizing pneumonia (OP) pattern was also recorded. The inter-reader agreement was calculated with Cohen's Kappa (k) and Free-Marginal Multirater k. Multivariable analysis was run to determine whether imaging features were predictive of short-term evolution to severe disease (need for ventilation). RESULTS: Most features showed substantial inter-reader agreement, including TLS > 6 (k = 0.69), which was an independent predictor of short-term occurrence of severe disease, regardless of the reader (OR 9-53.19). Consolidation TFS > 2 and OP pattern showed substantial and moderate agreement, respectively, only when comparing R1 and R2. Consolidation TFS > 2 and OP pattern were independent predictors of severe disease for R2 (OR 4.87) and R1 (OR 6), respectively. CONCLUSIONS: The inter-reader agreement for most HRCT features of COVID-19 pneumonia ranges moderate-to-substantial, though it depends on readers' experience in the case of consolidation and OP pattern.

The "pandemic" increase in lung ultrasound use in response to Covid-19: can we complement computed tomography findings? A narrative review.

Coronavirus disease of 2019 (COVID-19) is a highly infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which has rapidly spread to a global pandemic in March 2020. This emergency condition has been putting a severe strain on healthcare systems worldwide, and a prompt, dynamic response is instrumental in its management. While a definite diagnosis is based on microbiological evidence, the relationship between lung ultrasound (LU) and high-resolution computed tomography (HRCT) in the diagnosis and management of COVID-19 is less clear. Lung ultrasound is a point-of-care imaging tool that proved to be useful in the identification and severity assessment of different pulmonary conditions, particularly in the setting of emergency and critical care patients in intensive care units; HRCT of the thorax is regarded as the mainstay of imaging evaluation of lung disorders, enabling characterization and quantification of pulmonary involvement. Aims of this review are to describe LU and chest HRCT main imaging features of COVID-19 pneumonia, and to provide state-of-the-art insights regarding the integrated role of these techniques in the clinical decision-making process of patients affected by this infectious disease.