Are the thorax Computed Tomography findings of ICU patients diagnosed with COVID-19 pneumonia related to the duration of hospital stay and mortality?

INTRODUCTION: Quantitative thorax Computed Tomography (CT) is used to determine the severity of COVID-19 pneumonia. With a new approach, quantitative thoracic CT is to contribute to the triage of patients with severe COVID-19 pneumonia in the ICU and to evaluate its relation with mortality by taking into account the vaccination status. METHODS: Fifty-six patients who had a diagnosis of COVID-19 pneumonia confirmed in the adult ICU were evaluated retrospectively. To evaluate the degree of parenchymal involvement, the quantitative CT "craniocaudal diameter of the thorax/craniocaudal largest lesion diameter (CCDT/CCDL)" ratio and semi-quantitative total CT severity scores (TCTSS) (0:0%; 1:1-25%; 2:26-50%; 3:51-75% and 4:76-100%) were calculated. Both methods were analyzed with comparative ROC curves for predicting mortality. The effects of vaccines on thorax CT findings and laboratory parameters were also investigated. RESULTS: The sensitivities and specificities were found to be 72.5%, 75.61%, and 80%, 73.33% when CCDT/CCDL and TCTSS cutoff value was taken <1.4, and >9, respectively, to predict mortality in COVID-19 pneumonia (Area Under the Curve = AUC = 0.797 and 0.752). Both methods predicted mortality well and no statistical differences were detected between them (p = 0.3618). In vaccinated patients, CRP was higher (p = 0.045), and LDH and ferritin were lower (p = 0.049, p = 0.004). The number of lobes involved was lower in the vaccinated group (p = 0.001). CONCLUSIONS: The quantitative CT score (CCDT/CCDL) may play as important a role as TCTSS in diagnosing COVID-19 pneumonia, determining the severity of the disease, and predicting the related mortality. COVID-19 vaccines may affect laboratory parameters and cause less pneumonia on thoracic CT than in unvaccinated individuals.

Automated grading of chest x-ray images for viral pneumonia with convolutional neural networks ensemble and region of interest localization.

Following its initial identification on December 31, 2019, COVID-19 quickly spread around the world as a pandemic claiming more than six million lives. An early diagnosis with appropriate intervention can help prevent deaths and serious illness as the distinguishing symptoms that set COVID-19 apart from pneumonia and influenza frequently don't show up until after the patient has already suffered significant damage. A chest X-ray (CXR), one of many imaging modalities that are useful for detection and one of the most used, offers a non-invasive method of detection. The CXR image analysis can also reveal additional disorders, such as pneumonia, which show up as anomalies in the lungs. Thus these CXRs can be used for automated grading aiding the doctors in making a better diagnosis. In order to classify a CXR image into the Negative for Pneumonia, Typical, Indeterminate, and Atypical, we used the publicly available CXR image competition dataset SIIM-FISABIO-RSNA COVID-19 from Kaggle. The suggested architecture employed an ensemble of EfficientNetv2-L for classification, which was trained via transfer learning from the initialised weights of ImageNet21K on various subsets of data (Code for the proposed methodology is available at: https://github.com/asadkhan1221/siim-covid19.git). To identify and localise opacities, an ensemble of YOLO was combined using Weighted Boxes Fusion (WBF). Significant generalisability gains were made possible by the suggested technique's addition of classification auxiliary heads to the CNN backbone. The suggested method improved further by utilising test time augmentation for both classifiers and localizers. The results for Mean Average Precision score show that the proposed deep learning model achieves 0.617 and 0.609 on public and private sets respectively and these are comparable to other techniques for the Kaggle dataset.

DMFL_Net: A Federated Learning-Based Framework for the Classification of COVID-19 from Multiple Chest Diseases Using X-rays.

Coronavirus Disease 2019 (COVID-19) is still a threat to global health and safety, and it is anticipated that deep learning (DL) will be the most effective way of detecting COVID-19 and other chest diseases such as lung cancer (LC), tuberculosis (TB), pneumothorax (PneuTh), and pneumonia (Pneu). However, data sharing across hospitals is hampered by patients' right to privacy, leading to unexpected results from deep neural network (DNN) models. Federated learning (FL) is a game-changing concept since it allows clients to train models together without sharing their source data with anybody else. Few studies, however, focus on improving the model's accuracy and stability, whereas most existing FL-based COVID-19 detection techniques aim to maximize secondary objectives such as latency, energy usage, and privacy. In this work, we design a novel model named decision-making-based federated learning network (DMFL_Net) for medical diagnostic image analysis to distinguish COVID-19 from four distinct chest disorders including LC, TB, PneuTh, and Pneu. The DMFL_Net model that has been suggested gathers data from a variety of hospitals, constructs the model using the DenseNet-169, and produces accurate predictions from information that is kept secure and only released to authorized individuals. Extensive experiments were carried out with chest X-rays (CXR), and the performance of the proposed model was compared with two transfer learning (TL) models, i.e., VGG-19 and VGG-16 in terms of accuracy (ACC), precision (PRE), recall (REC), specificity (SPF), and F1-measure. Additionally, the DMFL_Net model is also compared with the default FL configurations. The proposed DMFL_Net + DenseNet-169 model achieves an accuracy of 98.45% and outperforms other approaches in classifying COVID-19 from four chest diseases and successfully protects the privacy of the data among diverse clients.

Novel Comparative Study for the Detection of COVID-19 Using CT Scan and Chest X-ray Images.

The number of coronavirus disease (COVID-19) cases is constantly rising as the pandemic continues, with new variants constantly emerging. Therefore, to prevent the virus from spreading, coronavirus cases must be diagnosed as soon as possible. The COVID-19 pandemic has had a devastating impact on people's health and the economy worldwide. For COVID-19 detection, reverse transcription-polymerase chain reaction testing is the benchmark. However, this test takes a long time and necessitates a lot of laboratory resources. A new trend is emerging to address these limitations regarding the use of machine learning and deep learning techniques for automatic analysis, as these can attain high diagnosis results, especially by using medical imaging techniques. However, a key question arises whether a chest computed tomography scan or chest X-ray can be used for COVID-19 detection. A total of 17,599 images were examined in this work to develop the models used to classify the occurrence of COVID-19 infection, while four different classifiers were studied. These are the convolutional neural network (proposed architecture (named, SCovNet) and Resnet18), support vector machine, and logistic regression. Out of all four models, the proposed SCoVNet architecture reached the best performance with an accuracy of almost 99% and 98% on chest computed tomography scan images and chest X-ray images, respectively.

Yield of Adding chest CT to Abdominal CT to Detect COVID-19 in Patients Presenting With Acute Gastrointestinal Symptoms (SCOUT-3): Multicenter Study.

OBJECTIVE: To determine the incremental yield of standardized addition of chest CT to abdominal CT to detect COVID-19 in patients presenting with primarily acute gastrointestinal symptoms requiring abdominal imaging. Summary Background Data: Around 20% of patients with COVID-19 present with gastrointestinal symptoms. COVID-19 might be neglected in these patients, as the focus could be on finding abdominal pathology. During the COVID-19 pandemic, several centers have routinely added chest CT to abdominal CT to detect possible COVID-19 in patients presenting with gastrointestinal symptoms. However, the incremental yield of this strategy is unknown. METHODS: This multicenter study in 6 Dutch centers included consecutive adult patients presenting with acute nontraumatic gastrointestinal symptoms, who underwent standardized combined abdominal and chest CT between March 15, 2020 and April 30, 2020. All CT scans were read for signs of COVID-19 related pulmonary sequelae using the СО-RADS score. The primary outcome was the yield of high COVID-19 suspicion (СО-RADS 4-5) based on chest CT. RESULTS: A total of 392 patients were included. Radiologic suspicion for COVID-19 (СО-RADS 4-5) was present in 17 (4.3%) patients, eleven of which were diagnosed with COVID-19. Only 5 patients with СО-RADS 4-5 presented without any respiratory symptoms and were diagnosed with COVID-19. No relation with community prevalence could be detected. CONCLUSION: The yield of adding chest CT to abdominal CT to detect COVID-19 in patients presenting with acute gastrointestinal symptoms is extremely low with an additional detection rate of around 1%.

Lung ultrasound versus chest computed tomography for pregnant inpatients with COVID-19.

PURPOSE: To compare lung ultrasound (US) and computed tomography (CT) in the assessment of pregnant women with COVID-19. METHODS: Prospective study comprising 39 pregnant inpatients with COVID-19 who underwent pulmonary assessment with CT and US with a maximum span of 48 h between the exams. The thorax was divided into 12 regions and assessed in terms of the following: the presence of B-lines (>2), coalescent B-lines, consolidation on US; presence of interlobular thickening, ground glass, consolidation on CT. The two methods were scored by adding up the scores from each thoracic region. RESULTS: A significant correlation was found between the scores obtained by the two methods (rICC = 0.946; p < 0.001). They were moderately in agreement concerning the frequency of altered pulmonary regions (weighted kappa = 0.551). In US, a score over 15, coalescent B-lines, and consolidation were predictors of the need for oxygen, whereas the predictors in CT were a lung score over 16 and consolidation. The two methods, US (p < 0.001; AUC = 0.915) and CT (p < 0.001; AUC = 0.938), were fairly accurate in predicting the need for oxygen. CONCLUSION: In pregnant women, lung US and chest CT are of similar accuracy in assessing lungs affected by COVID-19 and can predict the need for oxygen.

Brief communication: Chest radiography score in young COVID-19 patients: Does one size fit all?

During the SARS-CoV-2 pandemic, chest X-Ray (CXR) scores are essential to rapidly assess patients' prognoses. This study evaluates a published CXR score in a different national healthcare system. In our study, this CXR score maintains a prognostic role in predicting length of hospital stay, but not disease severity. However, our results show that the predictive role of CXR score could be influenced by socioeconomic status and healthcare system.

Effective deep learning approaches for predicting COVID-19 outcomes from chest computed tomography volumes.

The rapid evolution of the novel coronavirus disease (COVID-19) pandemic has resulted in an urgent need for effective clinical tools to reduce transmission and manage severe illness. Numerous teams are quickly developing artificial intelligence approaches to these problems, including using deep learning to predict COVID-19 diagnosis and prognosis from chest computed tomography (CT) imaging data. In this work, we assess the value of aggregated chest CT data for COVID-19 prognosis compared to clinical metadata alone. We develop a novel patient-level algorithm to aggregate the chest CT volume into a 2D representation that can be easily integrated with clinical metadata to distinguish COVID-19 pneumonia from chest CT volumes from healthy participants and participants with other viral pneumonia. Furthermore, we present a multitask model for joint segmentation of different classes of pulmonary lesions present in COVID-19 infected lungs that can outperform individual segmentation models for each task. We directly compare this multitask segmentation approach to combining feature-agnostic volumetric CT classification feature maps with clinical metadata for predicting mortality. We show that the combination of features derived from the chest CT volumes improve the AUC performance to 0.80 from the 0.52 obtained by using patients' clinical data alone. These approaches enable the automated extraction of clinically relevant features from chest CT volumes for risk stratification of COVID-19 patients.

Multiclass Classification of Chest X-Ray Images for the Prediction of COVID-19 Using Capsule Network.

It is critical to establish a reliable method for detecting people infected with COVID-19 since the pandemic has numerous harmful consequences worldwide. If the patient is infected with COVID-19, a chest X-ray can be used to determine this. In this work, an X-ray showing a COVID-19 infection is classified by the capsule neural network model we trained to recognise. 6310 chest X-ray pictures were used to train the models, separated into three categories: normal, pneumonia, and COVID-19. This work is considered an improved deep learning model for the classification of COVID-19 disease through X-ray images. Viewpoint invariance, fewer parameters, and better generalisation are some of the advantages of CapsNet compared with the classic convolutional neural network (CNN) models. The proposed model has achieved an accuracy greater than 95% during the model's training, which is better than the other state-of-the-art algorithms. Furthermore, to aid in detecting COVID-19 in a chest X-ray, the model could provide extra information.

Evaluation of thorax computed tomographic findings in COVID-19 variant cases.

BACKGROUND: Because of genetic mutations occurring during viral replication, new SARS-CoV-2 variants will continue to emerge. Throughout the COVID-19 pandemic, thorax computed tomographic (CT) findings have played a crucial role in the diagnosis and follow-up of patients with COVID-19. In this study, we compared the thorax CT findings of patients infected with SARS-CoV-2 variants (variant group) with those of patients infected with the non-variant strain (non-variant group) to assess if thorax CT findings may be utilized to discriminate between the groups. Furthermore, we compared demographic and laboratory data between the groups. METHODS: The study comprised a total of 77 patients who presented to our hospital with a preliminary diagnosis of COVID-19 based on clinical symptoms, a positive oropharyngeal/nasopharyngeal swab RT-PCR testing, and thorax CT examinations. Patients' laboratory and demographic features as well as thorax CT findings were retrospectively evaluated, and the results were grouped according to RT-PCR results. RESULTS: There were 42 patients in the non-variant group and 35 patients in the variant group. The average age of patients infected with the non-variant strain, alpha variant, and gamma variant was 63.52 ± 14.87 years, 54.86 ± 14.31 years, and 59.4 ± 17.79 years, respectively. The average age of the variant group was significantly lower than that of the non-variant group. There was no significant difference in thorax CT findings between the groups, and consolidation, ground glass densities, and cobblestone pattern in the bilateral lower lobes and peripheral areas were the most common thorax CT findings in both the groups. CONCLUSION: There is no significant difference in thorax CT findings between the variant and non-variant groups. Therefore, clinical and laboratory characteristics should take precedence over thorax CT findings for distinguishing between patients infected with SARS-CoV-2 variants and the non-variant strain.

Clinical findings, viral load, and outcomes of COVID-19: Comparison of patients with negative and positive initial chest computed tomography.

Reports detailing the clinical characteristics, viral load, and outcomes of patients with normal initial chest CT findings are lacking. We sought to compare the differences in clinical findings, viral loads, and outcomes between patients with confirmed COVID-19 who initially tested negative on chest CT (CT negative) with patients who tested initially positive on chest CT (CT positive). The clinical data, viral loads, and outcomes of initial CT-positive and CT-negative patients examined between January 2020 and April 2020 were retrospectively compared. The efficacy of viral load (cyclic threshold value [Ct value]) in predicting pneumonia was evaluated using receiver operating characteristic (ROC) curve and area under the curve (AUC). In total, 128 patients underwent initial chest CT (mean age, 54.3 ± 19.0 years, 50% male). Of those, 36 were initially CT negative, and 92 were CT positive. The CT-positive patients were significantly older (P < .001) than the CT-negative patients. Only age was significantly associated with the initial presence of pneumonia (odds ratio, 1.060; confidence interval (CI), 1.020-1-102; P = .003). In addition, age (OR, 1.062; CI, 1.014-1.112; P = .011), fever at diagnosis (OR, 6.689; CI, 1.715-26.096; P = .006), and CRP level (OR, 1.393; CI, 1.150-1.687; P = .001) were significantly associated with the need for O2 therapy. Viral load was significantly higher in the CT-positive group than in the CT-negative group (P = .017). The cutoff Ct value for predicting the presence of pneumonia was 27.71. Outcomes including the mean hospital stay, intensive care unit admission, and O2 therapy were significantly worse in the CT-positive group than in the CT-negative group (all P < .05). In conclusion, initially CT-negative patients showed better outcomes than initially CT-positive patients. Age was significantly associated with the initial presence of pneumonia, and viral load may help in predicting the initial presence of pneumonia.

H-SegNet: hybrid segmentation network for lung segmentation in chest radiographs using mask region-based convolutional neural network and adaptive closed polyline searching method.

Chest x-ray (CXR) is one of the most commonly used imaging techniques for the detection and diagnosis of pulmonary diseases. One critical component in many computer-aided systems, for either detection or diagnosis in digital CXR, is the accurate segmentation of the lung. Due to low-intensity contrast around lung boundary and large inter-subject variance, it has been challenging to segment lung from structural CXR images accurately. In this work, we propose an automatic Hybrid Segmentation Network (H-SegNet) for lung segmentation on CXR. The proposed H-SegNet consists of two key steps: (1) an image preprocessing step based on a deep learning model to automatically extract coarse lung contours; (2) a refinement step to fine-tune the coarse segmentation results based on an improved principal curve-based method coupled with an improved machine learning method. Experimental results on several public datasets show that the proposed method achieves superior segmentation results in lung CXRs, compared with several state-of-the-art methods.

Biomarker evaluation for prognostic stratification of patients with COVID-19: the added value of quantitative chest CT.

Aim: Pulmonary disease burden and biomarkers are possible predictors of outcomes in patients with COVID-19 and provide complementary information. In this study, the prognostic value of adding quantitative chest computed tomography (CT) to a multiple biomarker approach was evaluated among 148 hospitalized patients with confirmed COVID-19. Materials & methods: Patients admitted between March and July 2020 who were submitted to chest CT and biomarker measurement (troponin I, D-dimer and C-reactive protein) were retrospectively analyzed. Biomarker and tomographic data were compared and associated with death and intensive care unit admission. Results: The number of elevated biomarkers was significantly associated with greater opacification percentages, lower lung volumes and higher death and intensive care unit admission rates. Total lung volume <3.0 l provided further stratification for mortality when combined with biomarker evaluation. Conclusion: Adding automated CT data to a multiple biomarker approach may provide a simple strategy for enhancing risk stratification of patients with COVID-19.

Augmenting existing deterioration indices with chest radiographs to predict clinical deterioration.

IMPORTANCE: When hospitals are at capacity, accurate deterioration indices could help identify low-risk patients as potential candidates for home care programs and alleviate hospital strain. To date, many existing deterioration indices are based entirely on structured data from the electronic health record (EHR) and ignore potentially useful information from other sources. OBJECTIVE: To improve the accuracy of existing deterioration indices by incorporating unstructured imaging data from chest radiographs. DESIGN, SETTING, AND PARTICIPANTS: Machine learning models were trained to predict deterioration of patients hospitalized with acute dyspnea using existing deterioration index scores and chest radiographs. Models were trained on hospitalized patients without coronavirus disease 2019 (COVID-19) and then subsequently tested on patients with COVID-19 between January 2020 and December 2020 at a single tertiary care center who had at least one radiograph taken within 48 hours of hospital admission. MAIN OUTCOMES AND MEASURES: Patient deterioration was defined as the need for invasive or non-invasive mechanical ventilation, heated high flow nasal cannula, IV vasopressor administration or in-hospital mortality at any time following admission. The EPIC deterioration index was augmented with unstructured data from chest radiographs to predict risk of deterioration. We compared discriminative performance of the models with and without incorporating chest radiographs using area under the receiver operating curve (AUROC), focusing on comparing the fraction and total patients identified as low risk at different negative predictive values (NPV). RESULTS: Data from 6278 hospitalizations were analyzed, including 5562 hospitalizations without COVID-19 (training cohort) and 716 with COVID-19 (216 in validation, 500 in held-out test cohort). At a NPV of 0.95, the best-performing image-augmented deterioration index identified 49 more (9.8%) individuals as low-risk compared to the deterioration index based on clinical data alone in the first 48 hours of admission. At a NPV of 0.9, the EPIC image-augmented deterioration index identified 26 more individuals (5.2%) as low-risk compared to the deterioration index based on clinical data alone in the first 48 hours of admission. CONCLUSION AND RELEVANCE: Augmenting existing deterioration indices with chest radiographs results in better identification of low-risk patients. The model augmentation strategy could be used in the future to incorporate other forms of unstructured data into existing disease models.

Can evaluating adrenal glands in computed tomography contribute to predicting the prognosis of hospitalized COVID-19 patients?

OBJECTIVE: The novel coronavirus disease 2019 (COVID-19) may affect the adrenal glands. Therefore, it is important to evaluate the morphologic appearance of the adrenal glands by thorax computed tomography (CT). On CT scans, stranding in peripheral fatty tissue with enlarged adrenal glands may indicate signs of adrenal infarction (SAI). The present study aimed to evaluate the incidence of SAI and determine whether this finding may contribute to predictions of the prognosis of COVID-19. PATIENTS AND METHODS: A total of 343 patients who had been hospitalized at Malatya Training and Research Hospital between September 1 and 30, 2020, with a diagnosis of COVID-19 were enrolled in this study. All patients underwent thorax CT scans that included their adrenal glands. RESULTS: Of the enrolled patients, 16.0% had SAI. Moreover, 41.8% of patients with SAI and 15.3% of patients without SAI were treated in the Intensive Care Unit (ICU). Patients with SAI had a significantly higher rate of ICU admission (p < 0.001). Mortality rates were also significantly higher among patients with SAI than those without p < 0.001). CONCLUSIONS: In this study, it was found that COVID-19 patients with SAI may have a poorer prognosis. More comprehensive studies are needed on this subject, but the present study may provide helpful preliminary information in terms of prognosis.

Objective evaluation of deep uncertainty predictions for COVID-19 detection.

Deep neural networks (DNNs) have been widely applied for detecting COVID-19 in medical images. Existing studies mainly apply transfer learning and other data representation strategies to generate accurate point estimates. The generalization power of these networks is always questionable due to being developed using small datasets and failing to report their predictive confidence. Quantifying uncertainties associated with DNN predictions is a prerequisite for their trusted deployment in medical settings. Here we apply and evaluate three uncertainty quantification techniques for COVID-19 detection using chest X-Ray (CXR) images. The novel concept of uncertainty confusion matrix is proposed and new performance metrics for the objective evaluation of uncertainty estimates are introduced. Through comprehensive experiments, it is shown that networks pertained on CXR images outperform networks pretrained on natural image datasets such as ImageNet. Qualitatively and quantitatively evaluations also reveal that the predictive uncertainty estimates are statistically higher for erroneous predictions than correct predictions. Accordingly, uncertainty quantification methods are capable of flagging risky predictions with high uncertainty estimates. We also observe that ensemble methods more reliably capture uncertainties during the inference. DNN-based solutions for COVID-19 detection have been mainly proposed without any principled mechanism for risk mitigation. Previous studies have mainly focused on on generating single-valued predictions using pretrained DNNs. In this paper, we comprehensively apply and comparatively evaluate three uncertainty quantification techniques for COVID-19 detection using chest X-Ray images. The novel concept of uncertainty confusion matrix is proposed and new performance metrics for the objective evaluation of uncertainty estimates are introduced for the first time. Using these new uncertainty performance metrics, we quantitatively demonstrate when we could trust DNN predictions for COVID-19 detection from chest X-rays. It is important to note the proposed novel uncertainty evaluation metrics are generic and could be applied for evaluation of probabilistic forecasts in all classification problems.

Novel loss functions for ensemble-based medical image classification.

Medical images commonly exhibit multiple abnormalities. Predicting them requires multi-class classifiers whose training and desired reliable performance can be affected by a combination of factors, such as, dataset size, data source, distribution, and the loss function used to train deep neural networks. Currently, the cross-entropy loss remains the de-facto loss function for training deep learning classifiers. This loss function, however, asserts equal learning from all classes, leading to a bias toward the majority class. Although the choice of the loss function impacts model performance, to the best of our knowledge, we observed that no literature exists that performs a comprehensive analysis and selection of an appropriate loss function toward the classification task under study. In this work, we benchmark various state-of-the-art loss functions, critically analyze model performance, and propose improved loss functions for a multi-class classification task. We select a pediatric chest X-ray (CXR) dataset that includes images with no abnormality (normal), and those exhibiting manifestations consistent with bacterial and viral pneumonia. We construct prediction-level and model-level ensembles to improve classification performance. Our results show that compared to the individual models and the state-of-the-art literature, the weighted averaging of the predictions for top-3 and top-5 model-level ensembles delivered significantly superior classification performance (p < 0.05) in terms of MCC (0.9068, 95% confidence interval (0.8839, 0.9297)) metric. Finally, we performed localization studies to interpret model behavior and confirm that the individual models and ensembles learned task-specific features and highlighted disease-specific regions of interest. The code is available at https://github.com/sivaramakrishnan-rajaraman/multiloss_ensemble_models.

Time series analysis of the demand for COVID-19 related chest imaging during the first wave of the SARS-CoV-2 pandemic: An explorative study.

OBJECTIVES: The aim of this study was to investigate possible patterns of demand for chest imaging during the first wave of the SARS-CoV-2 pandemic and derive a decision aid for the allocation of resources in future pandemic challenges. MATERIALS AND METHODS: Time data of requests for patients with suspected or confirmed coronavirus disease 2019 (COVID-19) lung disease were analyzed between February 27th and May 27th 2020. A multinomial logistic regression model was used to evaluate differences in the number of requests between 3 time intervals (I1: 6am - 2pm, I2: 2pm - 10pm, I3: 10pm - 6am). A cosinor model was applied to investigate the demand per hour. Requests per day were compared to the number of regional COVID-19 cases. RESULTS: 551 COVID-19 related chest imagings (32.8% outpatients, 67.2% in-patients) of 243 patients were conducted (33.3% female, 66.7% male, mean age 60 ± 17 years). Most exams for outpatients were required during I2 (I1 vs. I2: odds ratio (OR) = 0.73, 95% confidence interval (CI) 0.62-0.86, p = 0.01; I2 vs. I3: OR = 1.24, 95% CI 1.04-1.48, p = 0.03) with an acrophase at 7:29 pm. Requests for in-patients decreased from I1 to I3 (I1 vs. I2: OR = 1.24, 95% CI 1.09-1.41, p = 0.01; I2 vs. I3: OR = 1.16, 95% CI 1.05-1.28, p = 0.01) with an acrophase at 12:51 pm. The number of requests per day for outpatients developed similarly to regional cases while demand for in-patients increased later and persisted longer. CONCLUSIONS: The demand for COVID-19 related chest imaging displayed distinct distribution patterns depending on the sector of patient care and point of time during the SARS-CoV-2 pandemic. These patterns should be considered in the allocation of resources in future pandemic challenges with similar disease characteristics.

Systemic Inflammation and Complement Activation Parameters Predict Clinical Outcome of Severe SARS-CoV-2 Infections.

Overactivation of the complement system has been characterized in severe COVID-19 cases. Complement components are known to trigger NETosis via the coagulation cascade and have also been reported in human tracheobronchial epithelial cells. In this longitudinal study, we investigated systemic and local complement activation and NETosis in COVID-19 patients that underwent mechanical ventilation. Results confirmed significantly higher baseline levels of serum C5a (24.5 ± 39.0 ng/mL) and TCC (11.03 ± 8.52 µg/mL) in patients compared to healthy controls (p < 0.01 and p < 0.0001, respectively). Furthermore, systemic NETosis was significantly augmented in patients (5.87 (±3.71) × 106 neutrophils/mL) compared to healthy controls (0.82 (±0.74) × 106 neutrophils/mL) (p < 0.0001). In tracheal fluid, baseline TCC levels but not C5a and NETosis, were significantly higher in patients. Kinetic studies of systemic complement activation revealed markedly higher levels of TCC and CRP in nonsurvivors compared to survivors. In contrast, kinetic studies showed decreased local NETosis in tracheal fluid but comparable local complement activation in nonsurvivors compared to survivors. Systemic TCC and NETosis were significantly correlated with inflammation and coagulation markers. We propose that a ratio comprising systemic inflammation, complement activation, and chest X-ray score could be rendered as a predictive parameter of patient outcome in severe SARS-CoV-2 infections.

Comparison of clinical, laboratory and radiological features in confirmed and unconfirmed COVID-19 patients.

Background: We aimed to compare the clinical, laboratory and radiological findings of confirmed COVID-19 and unconfirmed patients. Methods: This was a single-center, retrospective study. Results: Overall, 620 patients (338 confirmed COVID-19 and 282 unconfirmed) were included. Confirmed COVID-19 patients had higher percentages of close contact with a confirmed or probable case. In univariate analysis, the presence of myalgia and dyspnea, decreased leukocyte, neutrophil and platelet counts were best predictors for SARS-CoV-2 RT-PCR positivity. Multivariate analyses revealed that only platelet count was an independent predictor for SARS-CoV-2 RT-PCR positivity. Conclusion: Routine complete blood count may be helpful for distinguishing COVID-19 from other respiratory illnesses at an early stage, while PCR testing is unique for the diagnosis of COVID-19.