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 cases ...associated with severe respiratory disease that 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 the detection of lung abnormalities in early-stage disease and quantitative assessment of severity and progression of COVID-19 pneumonia. Although chest X-ray (CXR) is considered not sensitive for the detection of pulmonary involvement in the early stage of the 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 short communication, we present our experimental CXR scoring system that we are applying to 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.
•Brixia score is a new chest X-ray scoring system designed for COVID-19 pneumonia.•Brixia score, patient age and immunosuppressive conditions predict fatal outcome.•High Brixiascore and at least one ...other predictor confer the highest risk of death.
This study aimed to assess the usefulness of a new chest X-ray scoring system — the Brixia score — to predict the risk of in-hospital mortality in hospitalized patients with coronavirus disease 2019 (COVID-19).
Between March 4, 2020 and March 24, 2020, all CXR reports including the Brixia score were retrieved. We enrolled only hospitalized Caucasian patients with COVID-19 for whom the final outcome was available. For each patient, age, sex, underlying comorbidities, immunosuppressive therapies, and the CXR report containing the highest score were considered for analysis. These independent variables were analyzed using a multivariable logistic regression model to extract the predictive factors for in-hospital mortality.
302 Caucasian patients who were hospitalized for COVID-19 were enrolled. In the multivariable logistic regression model, only Brixia score, patient age, and conditions that induced immunosuppression were the significant predictive factors for in-hospital mortality. According to receiver operating characteristic curve analyses, the optimal cutoff values for Brixia score and patient age were 8 points and 71 years, respectively. Three different models that included the Brixia score showed excellent predictive power.
Patients with a high Brixia score and at least one other predictive factor had the highest risk of in-hospital death.
Purpose
To improve the risk stratification of patients infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), an experimental chest X-ray (CXR) scoring system for quantifying ...lung abnormalities was introduced in our Diagnostic Imaging Department. The purpose of this study was to retrospectively evaluate correlations between the CXR score and the age or sex of Italian patients infected with SARS-CoV-2.
Materials and methods
Between March 4, 2020, and March 18, 2020, all CXR reports containing the new scoring system were retrieved. Only hospitalized patients with SARS-CoV-2 infection were enrolled. For each patient, age, sex, and the CXR report containing the highest score were considered for the analysis. Patients were also divided into seven groups according to age. Nonparametric statistical tests were used to examine the relationship between the severity of lung disease and the age or sex.
Results
783 Italian patients (532 males and 251 females) with SARS-CoV-2 infection were enrolled. The CXR score was significantly higher in males than in females only in groups aged 50 to 79 years. A significant correlation was observed between the CXR score and age in both males and females. Males aged 50 years or older and females aged 80 years or older with coronavirus disease 2019 showed the highest CXR score (median ≥ 8).
Conclusions
Males aged 50 years or older and females aged 80 years or older showed the highest risk of developing severe lung disease. Our results may help to identify the highest-risk patients and those who require specific treatment strategies.
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.
The latest (4th) edition of the World Health Organization (WHO) Classification of Head and Neck Tumours, published in January 2017, has reclassified keratocystic odontogenic tumour as odontogenic ...keratocyst. Therefore, odontogenic keratocysts (OKCs) are now considered benign cysts of odontogenic origin that account for about 10% of all odontogenic cysts. OKCs arise from the dental lamina and are characterised by a cystic space containing desquamated keratin with a uniform lining of parakeratinised squamous epithelium. The reported age distribution of OKCs is considerably wide, with a peak of incidence in the third decade of life and a slight male predominance. OKCs originate in tooth-bearing regions and the mandible is more often affected than the maxilla. In the mandible, the most common location is the posterior sextant, the angle or the ramus. Conversely, the anterior sextant and the third molar region are the most common sites of origin in the maxilla. OKCs are characterised by an aggressive behaviour with a relatively high recurrence rate, particularly when OKCs are associated with syndromes. Multiple OKCs are typically associated with the nevoid basal cell carcinoma syndrome (NBCCS), an autosomal dominant multisystemic disease. Radiological imaging, mainly computed tomography (CT) and, in selected cases, magnetic resonance imaging (MRI), plays an important role in the diagnosis and management of OKCs. Therefore, the main purpose of this pictorial review is to present the imaging appearance of OKCs underlining the specific findings of different imaging modalities and to provide key radiologic features helping the differential diagnoses from other cystic and neoplastic lesions of odontogenic origin.
Key Points
•
Panoramic radiography is helpful in the preliminary assessment of OKCs
.
•
CT is considered the tool of choice in the evaluation of OKCs
.
•
MRI with DWI or DKI can help differentiate OKCs from other odontogenic lesions
.
•
Ameloblastoma, dentigerous and radicular cysts should be considered in the differential diagnosis
.
•
The presence of multiple OKCs is one of the major criteria for the diagnosis of NBCCS
.
An early-warning model to predict in-hospital mortality on admission of COVID-19 patients at an emergency department (ED) was developed and validated using a machine-learning model. In total, 2782 ...patients were enrolled between March 2020 and December 2020, including 2106 patients (first wave) and 676 patients (second wave) in the COVID-19 outbreak in Italy. The first-wave patients were divided into two groups with 1474 patients used to train the model, and 632 to validate it. The 676 patients in the second wave were used to test the model. Age, 17 blood analytes, and Brescia chest X-ray score were the variables processed using a random forests classification algorithm to build and validate the model. Receiver operating characteristic (ROC) analysis was used to assess the model performances. A web-based death-risk calculator was implemented and integrated within the Laboratory Information System of the hospital. The final score was constructed by age (the most powerful predictor), blood analytes (the strongest predictors were lactate dehydrogenase, D-dimer, neutrophil/lymphocyte ratio, C-reactive protein, lymphocyte %, ferritin std, and monocyte %), and Brescia chest X-ray score (https://bdbiomed.shinyapps.io/covid19score/). The areas under the ROC curve obtained for the three groups (training, validating, and testing) were 0.98, 0.83, and 0.78, respectively. The model predicts in-hospital mortality on the basis of data that can be obtained in a short time, directly at the ED on admission. It functions as a web-based calculator, providing a risk score which is easy to interpret. It can be used in the triage process to support the decision on patient allocation.
Long-term pulmonary sequelae following hospitalization for SARS-CoV-2 pneumonia is largely unclear. The aim of this study was to identify and characterise pulmonary sequelae caused by SARS-CoV-2 ...pneumonia at 12-month from discharge.
In this multicentre, prospective, observational study, patients hospitalised for SARS-CoV-2 pneumonia and without prior diagnosis of structural lung diseases were stratified by maximum ventilatory support ("oxygen only", "continuous positive airway pressure (CPAP)" and "invasive mechanical ventilation (IMV)") and followed up at 12 months from discharge. Pulmonary function tests and diffusion capacity for carbon monoxide (DLCO), 6 min walking test, high resolution CT (HRCT) scan, and modified Medical Research Council (mMRC) dyspnea scale were collected.
Out of 287 patients hospitalized with SARS-CoV-2 pneumonia and followed up at 1 year, DLCO impairment, mainly of mild entity and improved with respect to the 6-month follow-up, was observed more frequently in the "oxygen only" and "IMV" group (53% and 49% of patients, respectively), compared to 29% in the "CPAP" group. Abnormalities at chest HRCT were found in 46%, 65% and 80% of cases in the "oxygen only", "CPAP" and "IMV" group, respectively. Non-fibrotic interstitial lung abnormalities, in particular reticulations and ground-glass attenuation, were the main finding, while honeycombing was found only in 1% of cases. Older patients and those requiring IMV were at higher risk of developing radiological pulmonary sequelae. Dyspnea evaluated through mMRC scale was reported by 35% of patients with no differences between groups, compared to 29% at 6-month follow-up.
DLCO alteration and non-fibrotic interstitial lung abnormalities are common after 1 year from hospitalization due to SARS-CoV-2 pneumonia, particularly in older patients requiring higher ventilatory support. Studies with longer follow-ups are needed.
Designing and evaluating energy policies is a difficult challenge because the energy sector is a complex system that cannot be adequately understood without using models merging economic, social and ...individual perspectives. Appropriate models allow policy makers to assess the impact of policy measures, satisfy strategic objectives and develop sustainable policies. Often the implementation of a policy cannot be directly enforced by governments, but falls back to many stakeholders, such as private citizens and enterprises. We propose to integrate two basic cornerstones to devise realistic models: the self-reported behaviour, derived from surveys, and the observed behaviour, from historical data. The self-reported behaviour enables the identification of drivers and barriers pushing or limiting people in their decision making process, while the observed behaviour is used to tune these drivers/barriers in a model. We test our methodology on a case-study: the adoption of photovoltaic panels among private citizens in the Emilia–Romagna region, Italy. We propose an agent-based model devised using self-reported data and then empirically tuned using historical data. The results reveal that our model can predict with great accuracy the photovoltaic (PV) adoption rate and thus support the energy policy-making process.
Purpose: The gap between software development requirements and the available resources of software developers continues to widen. This requires changes in the development and organization of software ...development. Objectives: Presented is a model introducing a quantitative software development management methodology that estimates the relative importance and risk of functionality retention or abundance, which determines the final value of the software product. Method: The final value of the software product is interpreted as a function of the requirements and functionalities, represented as a computational graph (called a software product graph). The software product graph allows the relative importance of functionalities to be estimated by calculating the corresponding partial derivatives of the value function. The risk of not implementing the functionality is estimated by reducing the final value of a product. Validation: This model has been applied to two EU projects: CareHD and vINCI. In vINCI, the functionalities with the most significant added value to the application were developed based on the implemented model and those that brought the least value were abandoned. Optimization was not implemented in the CareHD project and proceeded as initially designed. Consequently, only 71% of the CareHD's potential value has been realized. Conclusions: Presented model enables rational management and organization of software product development with real-time quantitative evaluation of functionalities impacts, assessment of the risks of omitting them without a significant impact. A quantitative evaluation of the impacts and risks of retention or abundance is possible based on the proposed algorithm, which is the core of the model. This model is a tool for rational organization and development of software products.
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