scholarly journals COVID-19 outbreak in Italy: Experimental chest x-ray scoring system for quantifying and monitoring disease progression

2020 ◽  
Author(s):  
Andrea Borghesi ◽  
Roberto Maroldi

Abstract Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a new virus recently isolated from humans. SARS-CoV-2 was discovered to be the pathogen responsible for a cluster of pneumonia associated with severe respiratory disease occurred in December 2019 in China. This novel pulmonary infection, formally called coronavirus disease 2019 (COVID-19), has spread rapidly in China and beyond. On 8 March 2020, the number of Italians with SARS-CoV-2 infection was 7375 with a 48% hospitalization rate. At present, chest computed tomography imaging is considered the most effective method for detection of lung abnormalities in early-stage disease and for quantitative assessment of severity and progression of COVID-19 infection. Although chest x-ray (CXR) is considered not sensitive for the detection of pulmonary involvement in the early stage of disease, we believe that, in the current emergency setting, CXR can be a useful diagnostic tool for monitoring the rapid progression of lung abnormalities in infected patients, particularly in intensive care units. In this article we present our experimental CXR scoring system that we are applying in hospitalized patients with COVID-19 pneumonia to quantify and monitor the severity and progression of this new infectious disease. We also present the results of our preliminary validation study on a sample of 100 hospitalized patients with SARS-CoV-2 infection for whom the final outcome (recovery or death) was available.

Author(s):  
Nishant Agrawal ◽  
Samruddhi Dhanaji Chougale ◽  
Prashant Jedge ◽  
Shivakumar Iyer ◽  
John Dsouza

Introduction: In early stage of disease of Coronavirus Disease 2019 (COVID-19) infection chest Computed Tomography (CT) imaging is considered as the most effective method for detecting lung abnormalities. A Brixia Chest X-ray (CXR) scoring system which uses an 18-point severity scale to grade lung abnormalities due to COVID-19 was developed to improve the risk stratification for infected patients. Aim: To ascertain the validity of Brixia scoring system and to measure the outcome in COVID-19 patients. Materials and Methods: A retrospective study was conducted from 1st April 2020 to 31st July 2020, at a tertiary care hospital in India. Baseline CXR of COVID-19 patients were scored based on Brixia scoring system. The lungs were divided into six equal zones. Subsequently, scores (from 0-3) were assigned to each zone, based on lung abnormalities. A group comparison was implemented using Chi-Square test for categorical variables. Whereas an independent t-test was applied for continuous variables that followed normal distribution. Results: The study included 130 patients. The mean age was 57.09±13.73 years, 70.8% patients included were males. Out of 130 patients, 79 patients died. Among patients who died the mean CXR score was calculated to be 12.13±2.50. The mean CXR score was calculated to be 11.18±2.30 in patients who recovered and got discharged. During the process of comparison of CXR scores with the outcomes, the t-value came out to be 2.20 and the resulting p-value was 0.03 (statistically significant). Conclusion: Brixia score more than 12 was associated with increased mortality due to COVID-19, with p-value of 0.03.


Author(s):  
Akın Çinkooğlu ◽  
Selen Bayraktaroğlu ◽  
Naim Ceylan ◽  
Recep Savaş

Abstract Background There is no consensus on the imaging modality to be used in the diagnosis and management of Coronavirus disease 2019 (COVID-19) pneumonia. The purpose of this study was to make a comparison between computed tomography (CT) and chest X-ray (CXR) through a scoring system that can be beneficial to the clinicians in making the triage of patients diagnosed with COVID-19 pneumonia at their initial presentation to the hospital. Results Patients with a negative CXR (30.1%) had significantly lower computed tomography score (CTS) (p < 0.001). Among the lung zones where the only infiltration pattern was ground glass opacity (GGO) on CT images, the ratio of abnormality seen on CXRs was 21.6%. The cut-off value of X-ray score (XRS) to distinguish the patients who needed intensive care at follow-up (n = 12) was 6 (AUC = 0.933, 95% CI = 0.886–0.979, 100% sensitivity, 81% specificity). Conclusions Computed tomography is more effective in the diagnosis of COVID-19 pneumonia at the initial presentation due to the ease detection of GGOs. However, a baseline CXR taken after admission to the hospital can be valuable in predicting patients to be monitored in the intensive care units.


Proceedings ◽  
2020 ◽  
Vol 54 (1) ◽  
pp. 31
Author(s):  
Joaquim de Moura ◽  
Lucía Ramos ◽  
Plácido L. Vidal ◽  
Jorge Novo ◽  
Marcos Ortega

The new coronavirus (COVID-19) is a disease that is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). On 11 March 2020, the coronavirus outbreak has been labelled a global pandemic by the World Health Organization. In this context, chest X-ray imaging has become a remarkably powerful tool for the identification of patients with COVID-19 infections at an early stage when clinical symptoms may be unspecific or sparse. In this work, we propose a complete analysis of separability of COVID-19 and pneumonia in chest X-ray images by means of Convolutional Neural Networks. Satisfactory results were obtained that demonstrated the suitability of the proposed system, improving the efficiency of the medical screening process in the healthcare systems.


ESC CardioMed ◽  
2018 ◽  
pp. 411-412
Author(s):  
Nicola Sverzellati ◽  
Gianluca Milanese ◽  
Mario Silva

Both the detection and interpretation of focal abnormalities on chest X-ray (CXR) are challenging tasks. CXR accuracy depends on the view (e.g. the supine view has limited sensitivity) and technological equipment. The detection of small focal abnormalities (e.g. lung nodules) varies between anatomical regions according to the presence of dense anatomic structures, such as the bones and the hila. The interpretation of focal abnormalities on CXR is paramount within the whole clinical assessment, because CXR findings can guide the patient’s management, or warrant further investigations, such as computed tomography. Focal lung abnormalities on CXR are still a cornerstone of diagnostic algorithms; however, the radiological approach has progressively changed in the last decade because of the progressive development of both hardware and software applications that enable sensitive detection and accurate characterization.


Author(s):  
Roberto Maroldi ◽  
Paolo Rondi ◽  
Giorgio Maria Agazzi ◽  
Marco Ravanelli ◽  
Andrea Borghesi ◽  
...  

Abstract Objective We aim to demonstrate that a chest X-ray (CXR) scoring system for COVID-19 patients correlates with patient outcome and has a prognostic value. Methods This retrospective study included CXRs of COVID-19 patients that reported the Brixia score, a semi-quantitative scoring system rating lung involvement from 0 to 18. The highest (H) and lowest (L) values were registered along with scores on admission (A) and end of hospitalization (E). The Brixia score was correlated with the outcome (death or discharge). Results A total of 953 patients met inclusion criteria. In total, 677/953 were discharged and 276/953 died during hospitalization. A total of 524/953 had one CXR and 429/953 had more than one CXR. H-score was significantly higher in deceased (median, 12; IQR 9–14) compared to that in discharged patients (median, 8; IQR 5–11) (p < 0.0001). In 429/953 patients with multiple CXR, A-score, L-score, and E-score were higher in deceased than in discharged patients (A-score 9 vs 8; p = 0.039; L-score 7 vs 5; p < 0.0003; E-score 12 vs 7; p < 0.0001). In the entire cohort, logistic regression showed a significant predictive value for age (p < 0.0001, OR 1.13), H-score (p < 0.0001, OR 1.25), and gender (p = 0.01, male OR 1.67). AUC was 0.863. In patients with ≥ 2 CXR, A-, L-, and E-scores correlated significantly with the outcome. Cox proportional hazards regression indicated age (p < 0.0001, HR 4.17), H-score (< 9, HR 0.36, p = 0.0012), and worsening of H-score vs A score > 3 (HR 1.57, p = 0.0227) as associated with worse outcome. Conclusions The Brixia score correlates strongly with disease severity and outcome; it may support the clinical decision-making, particularly in patients with moderate-to-severe signs and symptoms. The Brixia score should be incorporated in a prognostic model, which would be desirable, particularly in resource-constraint scenarios. Key Points • To demonstrate the importance of the Brixia score in assessing and monitoring COVID-19 lung involvement. • The Brixia score strongly correlates with patient outcome and can be easily implemented in the routine reporting of CXR.


2015 ◽  
Vol 19 (2) ◽  
pp. 159-162 ◽  
Author(s):  
Rachel Asiniwasis ◽  
Maha T. Dutil ◽  
Scott Walsh

Background/Objectives The clinical and histopathologic findings of a rare simultaneous occurrence of papulonecrotic tuberculid and nodular tuberclid in a patient with active but asymptomatic pulmonary tuberculosis are presented. Papulonecrotic tuberculid was observed at a very early stage, presenting as molluscum-like lesions. This has been described once in the literature. This was observed in conjunction with lesions compatible with the rare clinicopathologic variant of nodular tuberculid. Critical to the diagnosis of active pulmonary tuberculosis was the use of induced sputum testing, which confirmed the diagnosis despite the lack of a cough and a chest x-ray negative for active tuberculosis. Methods/Results A 40-year-old male presented with a 2-week history of fever and a skin eruption consisting of molluscum-like papules on the ears, arms, and abdomen and nodules on his legs. Biopsies from both lesions were consistent with papulonecrotic and nodular tuberculid, respectively. Despite the lack of any respiratory symptoms, induced sputum grew Mycobacterium tuberculosis, and the lesions resolved on antituberculous therapy. Conclusions and Relevance Tuberculids are rare in Western countries but must be considered in the differential diagnosis of eruptions in patients from endemic countries. An active tuberculous focus must be sought out.


2008 ◽  
Vol 122 (9) ◽  
pp. 961-966 ◽  
Author(s):  
S C L Leong ◽  
F Javed ◽  
S Elliot ◽  
S Mortimore

AbstractObjectives:To evaluate the benefits of chest computed tomography and X-ray as screening tools in patients with newly diagnosed head and neck squamous cell carcinoma, to determine the incidence of lung metastases or synchronous pulmonary lesions, and to evaluate factors associated with positive radiological findings.Design:Five-year, retrospective survey of all newly diagnosed cases of head and neck squamous cell carcinoma.Results:We included 102 patients (63 men and 39 women), with a mean age of 67 years (range 33–91 years). The incidence of pulmonary involvement was 17 per cent. The sensitivity and specificity of computed tomography were 100 and 89.8 per cent, respectively. For chest X-ray, the sensitivity was 35.7 per cent and the specificity 92.7 per cent. The accuracy of computed tomography was 91.5 per cent and that of chest X-ray 83.1 per cent. There was a clear correlation between higher nodal stage and larger tumour with the development of distant metastases. In patients with a positive chest computed tomography scan, 86 per cent had T3or T4tumours, in contrast to 38 per cent of those with a negative chest scan (p < 0.05). In addition, 71 per cent of patients with positive findings had N2or N3nodal disease, compared with 29 per cent of those with negative findings (p < 0.05).Conclusion:There is currently no consensus on the use of chest X-ray and computer tomography for screening newly diagnosed cases of head and neck squamous cell carcinoma. We recommend routine scanning of high-staged head and neck squamous cell carcinoma. The National Institute of Health and Clinical Excellence guidelines should be reappraised.


Blood ◽  
2018 ◽  
Vol 132 (Supplement 1) ◽  
pp. 4399-4399
Author(s):  
Jared A Cohen ◽  
Francesca Maria Rossi ◽  
Riccardo Bomben ◽  
Lodovico Terzi-di-Bergamo ◽  
Pietro Bulian ◽  
...  

Abstract Introduction: Observation is the standard of care for asymptomatic early stage chronic lymphocytic leukemia (CLL) however these cases follow a heterogenous course. Recent studies show novel biomarkers can delineate indolent from aggressive early stage disease and current clinical trials are exploring the role of early intervention in high risk cases. Although several scoring systems have been established in CLL, most are designed for overall survival, do not circumscribe early stage disease, and require cumbersome calculations relying on extensive laboratory and clinical information. Aim: We propose a novel laboratory-based prognostic calculator to risk stratify time to first treatment (TTFT) in early stage CLL and guide candidate selection for early intervention. Methods: We included 1574 cases of early stage CLL in an international cohort from Italy, the United Kingdom and the United States using a training-validation model. Patient information was obtained from participating centers in accordance with the Declaration of Helsinki. The training cohort included 478 Rai 0 cases from a multicenter Italian cohort, all referred to a single center (Clinical and Experimental Onco-Hematology Unit of the Centro Riferimento Oncologico in Aviano, IT) for immunocytogenetic lab analyses. Considering TTFT as an endpoint, we evaluated 8 variables (age>65, WBC>32K, 17p-, 11q-, +12, IGHV status, CD49d+, gender) with univariate and multivariate Cox regression internally validated using bootstrapping procedures. FISH thresholds were 5% for 11q-, and +12 and 10% for 17p-. Cases were categorized according to the hierarchical model proposed by Dohner. IGHV status was considered unmutated at ≥98%. CD49d+ was set at >30%. WBC cutoff of >32K was established by maximally selected log rank analysis. Variables were weighted based on the proportion of their normalized hazard ratios rounded to the nearest whole integer. We used recursive partitioning for risk-category determination and Kaplan-Meier analysis to generate survival curves. We compared the concordance index (C-index) of our model with the CLL international prognostic index (CLL-IPI) for 381/478 cases in the training cohort with available beta-2-microglobulin data and for all validation cohorts. We used 3 independent single-center cohorts for external validation. Results: The training cohort had 478 cases of Rai 0 CLL with a median (95% CI) TTFT of 124 months (m) (104-183m). Five prognostic variables emerged with respect to TTFT, and each assigned a point value of 1 or 2 according to their respective normalized HR values as follows: 17p-, and UM IGHV (2 pts); 11q-, +12, and WBC>32K (1 pt). We identified three risk groups, based on point cut-offs of 0, 1-2, and 3-5 established by recursive partitioning analysis with a median (95% CI) TTFT of 216m (216-216m), 104m (93-140m) and 58m (44-68m) (p<0.0001, C-index 0.75) for the low, intermediate, and high-risk groups, respectively (figure 1). A comparison with the CLL-IPI was possible in 381 cases with available beta-2-microglobulin data. In this subset, the C-index was 0.75 compared to 0.68 when patient risk groups were split according to the CLL-IPI. The scoring system was then validated in 3 independent cohorts of early stage CLL: i) Gemelli Hospital in Rome, IT provided 144 Rai 0 cases. Median (95% CI) TTFT was 86m (80-94m, 95% CI). Median (95% CI) TTFT for the low, intermediate and high-risk groups was 239m (239-239m), 98m (92-132m) and 85m (60-109m) respectively (p=0.002 between low and intermediate groups, p=0.09 between intermediate and high groups; C-index 0.64 v 0.60 for CLL-IPI). ii) Cardiff University Hospital in Wales, UK provided 395 Binet A cases. Median (95% CI) TTFT was 74 m (67-81m) overall and NR, 111m (97-146m) and 70m (29-114m) for the low, intermediate and high-risk groups respectively (p<0.001 between low and intermediate groups, p=0.009 between intermediate and high groups; C-index 0.63 v 0.63 for CLL-IPI). iii) Mayo Clinic in Rochester, MN provided 557 Rai 0 cases. Median (95% CI) TTFT was 127m (96m-NR) overall and NR, 76m (64m-NR) and 36m (31-59m) for the low, intermediate and high-risk groups respectively (p<0.0001; C-index 0.72 v 0.68 for CLL-IPI). Conclusion: We present a novel laboratory-based scoring system for Rai 0/Binet A CLL to aid case selection in risk-adapted treatment for early disease. Further comparison to existing indices is needed to verify its utility in the clinical setting. Disclosures Zaja: Novartis: Honoraria, Research Funding; Takeda: Honoraria; Abbvie: Honoraria; Celgene: Honoraria, Research Funding; Amgen: Honoraria; Janssen: Honoraria; Sandoz: Honoraria. Fegan:Roche: Honoraria; Napp: Honoraria; Janssen: Honoraria; Gilead Sciences, Inc.: Honoraria; Abbvie: Honoraria. Pepper:Cardiff University: Patents & Royalties: Telomere measurement patents. Parikh:AstraZeneca: Honoraria, Research Funding; Janssen: Research Funding; MorphoSys: Research Funding; Abbvie: Honoraria, Research Funding; Gilead: Honoraria; Pharmacyclics: Honoraria, Research Funding. Kay:Janssen: Membership on an entity's Board of Directors or advisory committees; Acerta: Research Funding; Celgene: Membership on an entity's Board of Directors or advisory committees; Pharmacyclics: Membership on an entity's Board of Directors or advisory committees, Research Funding; Infinity Pharm: Membership on an entity's Board of Directors or advisory committees; Cytomx Therapeutics: Membership on an entity's Board of Directors or advisory committees; Tolero Pharmaceuticals: Membership on an entity's Board of Directors or advisory committees, Research Funding; Morpho-sys: Membership on an entity's Board of Directors or advisory committees; Gilead: Membership on an entity's Board of Directors or advisory committees; Agios Pharm: Membership on an entity's Board of Directors or advisory committees.


2021 ◽  
Vol 11 (21) ◽  
pp. 10301
Author(s):  
Muhammad Shoaib Farooq ◽  
Attique Ur Rehman ◽  
Muhammad Idrees ◽  
Muhammad Ahsan Raza ◽  
Jehad Ali ◽  
...  

COVID-19 has been difficult to diagnose and treat at an early stage all over the world. The numbers of patients showing symptoms for COVID-19 have caused medical facilities at hospitals to become unavailable or overcrowded, which is a major challenge. Studies have recently allowed us to determine that COVID-19 can be diagnosed with the aid of chest X-ray images. To combat the COVID-19 outbreak, developing a deep learning (DL) based model for automated COVID-19 diagnosis on chest X-ray is beneficial. In this research, we have proposed a customized convolutional neural network (CNN) model to detect COVID-19 from chest X-ray images. The model is based on nine layers which uses a binary classification method to differentiate between COVID-19 and normal chest X-rays. It provides COVID-19 detection early so the patients can be admitted in a timely fashion. The proposed model was trained and tested on two publicly available datasets. Cross-dataset studies are used to assess the robustness in a real-world context. Six hundred X-ray images were used for training and two hundred X-rays were used for validation of the model. The X-ray images of the dataset were preprocessed to improve the results and visualized for better analysis. The developed algorithm reached 98% precision, recall and f1-score. The cross-dataset studies also demonstrate the resilience of deep learning algorithms in a real-world context with 98.5 percent accuracy. Furthermore, a comparison table was created which shows that our proposed model outperforms other relative models in terms of accuracy. The quick and high-performance of our proposed DL-based customized model identifies COVID-19 patients quickly, which is helpful in controlling the COVID-19 outbreak.


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