Relationship Between Pulmonary Vascular Dilatation and Clinical Symptoms on Chest Computed Tomography in Patients with Confirmed COVID-19

BACKGROUND: Chest computed tomography (CT) is important in establishing a diagnosis, including detecting pulmonary vascular dilatation as a radiological feature of COVID-19, and consequently in providing comprehensive treatment. AIM: This study aimed to analyze the relationship between pulmonary vascular dilatation and clinical symptoms on chest CT in patients with confirmed COVID-19. PATIENTS AND METHODS: This retrospective and cross-sectional study was conducted at the Radiology Department of Dr. Wahidin Sudirohusodo Hospital and Hasanuddin University Hospital, Makassar, Indonesia, from July to September 2021 in a total of 231 patients with confirmed COVID-19. The Chi-squared correlation test was used to analyze the data, with p < 0.05 considered significant. RESULT(S): Pulmonary vascular dilatation was observed in 31 (37.8%) of the 82 patients with confirmed COVID-19 with mild-to-moderate clinical symptoms and in 51 (69.8%) of the 73 patients with confirmed COVID-19 with severe-to-critical clinical symptoms. The incidence of pulmonary vascular dilatation increased in the patients with confirmed COVID-19 with severe-to-critical clinical symptoms. The chief complaints of most patients were cough, shortness of breath, and fever. In the patients with mild-to-moderate clinical symptoms, the most common chief complaint was cough (n = 53;64.63%), while in those with severe-to-critical clinical symptoms, the most common chief complaint was shortness of breath (n = 60;82.19%). CONCLUSION(S): Based on chest CT findings, pulmonary vascular dilatation is related to clinical symptoms in patients with confirmed COVID-19.Copyright © 2023 Sri Asriyani, Nikmatia Latief, Andi Alfian Zainuddin, Muzakkir Amir, Bachtiar Murtala, Hendra Toreh.

Radiological Patterns and Lung Ultrasound

The early diagnosis of coronavirus disease 2019 (COVID-19) is one of the crucial points in order to reduce virus spread, also containing morbidity and mortality of the pandemic. Despite the utility of specific molecular tests (such as real time polymerase chain reaction, RT-PCR), imaging is considered one of the key strategies for an early diagnostic typing of the disease, and to individualize patient management [1-3]. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.

A Novel Combined Model to Predict the Prognosis of COVID-19: Radiological-Metabolic Scoring.

AIM: To investigate the performance of a novel radiological-metabolic scoring (RM-S) system to predict mortality and intensive care unit (ICU) requirements among COVID-19 patients and to compare performance with the chest computed-tomography severity-scoring (C-CT-SS). The RM-S was created from scoring systems such as visual coronary-artery-calcification scoring (V-CAC-S), hepatic-steatosis scoring (HS-S) and pancreatic-steatosis scoring (PS-S). METHODS: Between May 2021 and January 2022, 397 patients with COVID-19 were included in this retrospective cohort study. All demographic, clinical and laboratory data and chest CT images of patients were retrospectively reviewed. RM-S, V-CAC-S, HS-S, PS-S and C-CT-SS scores were calculated, and their performance in predicting mortality and ICU requirement were evaluated by univariate and multivariable analyses. RESULTS: A total of 32 (8.1%) patients died, and 77 (19.4%) patients required ICU admission. Mortality and ICU admission were both associated with older age (p < 0.001). Sex distribution was similar in the deceased vs. survivor and ICU vs. non-ICU comparisons (p = 0.974 and p = 0.626, respectively). Multiple logistic regression revealed that mortality was independently associated with having a C-CT-SS score of ≥14 (p < 0.001) and severe RM-S category (p = 0.010), while ICU requirement was independently associated with having a C-CT-SS score of ≥14 (p < 0.001) and severe V-CAC-S category (p = 0.010). CONCLUSION: RM-S, C-CT-SS, and V-CAC-S are useful tools that can be used to predict patients with poor prognoses for COVID-19. Long-term prospective follow-up of patients with high RM-S scores can be useful for predicting long COVID.

Severity prediction of pulmonary diseases using chest CT scans via cost-sensitive label multi-kernel distribution learning.

BACKGROUND AND OBJECTIVES: The progression of pulmonary diseases is a complex progress. Timely predicting whether the patients will progress to the severe stage or not in its early stage is critical to take appropriate hospital treatment. However, this task suffers from the "insufficient and incomplete" data issue since it is clinically impossible to have adequate training samples for one patient at each day. Besides, the training samples are extremely imbalanced since the patients who will progress to the severe stage is far less than those who will not progress to the non-severe stage. METHOD: We consider the severity prediction of pulmonary diseases as a time estimation problem based on CT scans. To handle the issue of "insufficient and incomplete" training samples, we introduced label distribution learning (LDL). Specifically, we generate a label distribution for each patient, making a CT image contribute to not only the learning of its chronological day, but also the learning of its neighboring days. In addition, a cost-sensitive mechanism is introduced to explore the imbalance data issue. To identify the importance of pulmonary segments in pulmonary disease severity prediction, multi-kernel learning in composite kernel space is further incorporated and particle swarm optimization (PSO) is used to find the optimal kernel weights. RESULTS: We compare the performance of the proposed CS-LD-MKSVR algorithm with several classical machine learning algorithms and deep learning (DL) algorithms. The proposed method has obtained the best classification results on the in-house data, fully indicating its effectiveness in pulmonary disease severity prediction. CONTRIBUTIONS: The severity prediction of pulmonary diseases is considered as a time estimation problem, and label distribution is introduced to describe the conversion time from non-severe stage to severe stage. The cost-sensitive mechanism is also introduced to handle the data imbalance issue to further improve the classification performance.

Total severity score and age predict long-term hospitalization in COVID-19 pneumonia.

Background: Severe COVID-19 pneumonia implies increased oxygen demands and length of hospitalization (LOS). We aimed to assess a possible correlation between LOS and COVID-19 patients' clinical laboratory data of admission, including the total severity score (TSS) from chest computed tomography (CT). Methods: Data were assessed retrospectively at the General Hospital "Agios Pavlos" in Greece. Clinical laboratory data, TSS, and LOS were recorded. Results: A total of 317 patients, 136 women and 181 men, with a mean age of 66.58 ± 16.02 years were studied. Significant comorbidities were hypertension (56.5%), dyslipidemia (33.8%), type 2 diabetes mellitus (22.7%), coronary heart disease (12.9%), underlying pulmonary disease (10.1%), and malignancy (4.4%). Inpatient time was related to age (p < 0.001), TSS (p < 0.001), time from symptom onset to hospitalization (p = 0.006), inhaled oxygen fraction (p < 0.001), fibrinogen (p = 0.024), d-dimers (p < 0.001), and C-reactive protein (p = 0.025), as well as a history of hypertension (p < 0.001) and type 2 diabetes mellitus (p < 0.008). The multivariate analysis showed a significant association of the LOS with age (p < 0.001) and TSS (p < 0.001) independent of the above-mentioned factors. Conclusion: Early identification of disease severity using the TSS and patients' age could be useful for inpatient resource allocation and for maintaining vigilance for those requiring long-term hospitalizations.

Application value of post-discharge chest low-dose CT for patients with COVID-19.

Objective: To explore the value of chest low-dose CT (LDCT) in post-discharge follow-up assessments of patients with coronavirus disease 2019 (COVID-19). Method(s): The chest CT findings of 58 patients with COVID-19 from March 17 to March 25, 2020 at Remin Hospital of Wuhan University were retrospectively analyzed. Two radiologists independently scored the subjective image quality on a 5-point Likert scale. The signal-to-noise ratio (SNR) and SDair of images and the CT radiation dose parameters were calculated, including the CT volume dose index (CTDIvol), dose length product (DLP), and effective radiation dose (E). Result(s): The subjective image quality scores on CT images obtained before and after discharge by readers 1 and 2, were 4.45+/-0.22, 3.88+/-0.33 (P>0.05) and 4.37+/-0.18, 3.91+/-0.35 (P>0.05), respectively. The SNR and SDair in LDCT after discharge were 4.39+/-0.95 and 7.19+/-2.41, which were significantly lower than those in routine chest CT before discharge (5.14+/-1.06, Z=-5.551, P<0.001;6.48+/-1.57, Z=-3.217, P<0.001). All of the obtained images were sufficient for diagnosis. The CTDIvol, DLP, and E in LDCT were significantly lower than those in routine CT [(2.41+/-0.09), (10.53+/-1.03)mGy, Z=-6.568, P<0.001;(88.03+/-5.33), (338.74+/-34.64)mGy*cm, Z=-6.624, P<0.001;and (1.23+/-0.17), (4.74+/-0.48)mSv, Z=-5.976, P<0.001]. Conclusion(s): Patients with COVID-19 can be followed up with low-dose chest CT after discharge.Copyright © 2020 by the Chinese Medical Association.

Dynamic chest computed tomography change analysis and prediction of length of stay for delta variant COVID-19 patients

OBJECTIVE: As hospital admission rate is high during the COVID-19 pandemic, hospital length of stay (LOS) is a key indicator of medical resource allocation. This study aimed to elucidate specific dynamic longitudinal computed tomography (CT) imaging changes for patients with COVID-19 over in-hospital and predict individual LOS of COVID-19 patients with Delta variant of SARS-CoV-2 using the machine learning method. MATERIALS AND METHODS: This retrospective study recruited 448 COVID-19 patients with a total of 1761 CT scans from July 14, 2021 to August 20, 2021 with an averaged hospital LOS of 22.5 ± 7.0 days. Imaging features were extracted from each CT scan, including CT morphological characteristics and artificial intelligence (AI) extracted features. Clinical features were obtained from each patient's initial admission. The infection distribution in lung fields and progression pattern tendency was analyzed. Then, to construct a model to predict patient LOS, each CT scan was considered as an independent sample to predict the LOS from the current CT scan time point to hospital discharge combining with the patients' corresponding clinical features. The 1761 follow-up CT data were randomly split into training set and testing set with a ratio of 7:3 at patient-level. A total of 85 most related clinical and imaging features selected by Least Absolute Shrinkage and Selection Operator were used to construct LOS prediction model. RESULTS: Infection-related features were obtained, such as the percentage of the infected region of lung, ground-glass opacity (GGO), consolidation and crazy-paving pattern, and air bronchograms. Their longitudinal changes show that the progression changes significantly in the earlier stages (0–3 days to 4–6 days), and then, changes tend to be statistically subtle, except for the intensity range between (−470 and −70) HU which exhibits a significant increase followed by a continuous significant decrease. Furthermore, the bilateral lower lobes, especially the right lower lobe, present more severe. Compared with other models, combining the clinical, imaging reading, and AI features to build the LOS prediction model achieved the highest R2 of 0.854 and 0.463, Pearson correlation coefficient of 0.939 and 0.696, and lowest mean absolute error of 2.405 and 4.426, and mean squared error of 9.176 and 34.728 on the training and testing set. CONCLUSION: The most obvious progression changes were significantly in the earlier stages (0–3 days to 4–6 days) and the bilateral lower lobes, especially the right lower lobe. GGO, consolidation, and crazy-paving pattern and air bronchograms are the most main CT findings according to the longitudinal changes of infection-related features with LOS (day). The LOS prediction model of combining clinical, imaging reading, and AI features achieved optimum performance.

Correlation of olfactory dysfunction with lung involvement and severity of COVID-19.

BACKGROUND: Olfactory dysfunction (OD) is a significant symptom of COVID-19 and may be the earliest symptom, or it may sometimes be the only manifestation of the disease. AIMS: To investigate whether OD is correlated with chest computed tomography (CT) findings, blood test parameters, and disease severity in COVID-19 patients. METHODS: The files of COVID-19 patients were retrospectively reviewed, and the ones who had information about smelling status and CT were taken into consideration. A total of 180 patients were divided into two groups: the OD group consisted of 89 patients with self-reported OD, and the No-OD group consisted of 91 subjects who did not complain of OD. The two groups were compared for the amount of lung consolidation on CT, intensive care unit (ICU) admission, and blood test parameters (complete blood count, alanine aminotransferase (ALT), aspartate aminotransferase (AST), creatine kinase (CK), lactate dehydrogenase (LDH), C-reactive protein (CRP), erythrocyte sedimentation rate (ESR), ferritin, D-dimer, interleukin-6 (IL-6)). RESULTS: The amount of lung consolidation and ICU admission were significantly higher in the No-OD group (p < 0.001 for both). White blood cell (p = 0.06), monocyte (p = 0.26), and platelet (p = 0.13) counts and hemoglobin (p = 0.63), ALT (p = 0.89), and D-dimer (p = 0.45) levels of the two groups were similar. Lymphocyte count (p = 0.01), neutrophil count (p = 0.01), and AST (p = 0.03), CK (p = 0.01), LDH (p < 0.001), CRP (p < 0.001), ESR (p < 0.001), ferritin (p < 0.001), and IL-6 (p < 0.001) levels were significantly higher in the No-OD group. CONCLUSIONS: The patients presenting to the hospital with self-reported OD may have less lung involvement and a milder disease course compared to patients without OD on admission.

Multi-Features Disease Analysis Based Smart Diagnosis for COVID-19

Coronavirus 2019 (COVID -19) is the current global buzzword, putting the world at risk. The pandemic's exponential expansion of infected COVID-19 patients has challenged the medical field's resources, which are already few. Even established nations would not be in a perfect position to manage this epidemic correctly, leaving emerging countries and countries that have not yet begun to grow to address the problem. These problems can be solved by using machine learning models in a realistic way, such as by using computer-aided images during medical examinations. These models help predict the effects of the disease outbreak and help detect the effects in the coming days. In this paper, Multi-Features Decease Analysis (MFDA) is used with different ensemble classifiers to diagnose the disease's impact with the help of Computed Tomography (CT) scan images. There are various features associated with chest CT images, which help know the possibility of an individual being affected and how COVID-19 will affect the persons suffering from pneumonia. The current study attempts to increase the precision of the diagnosis model by evaluating various feature sets and choosing the best combination for better results. The model's performance is assessed using Receiver Operating Characteristic (ROC) curve, the Root Mean Square Error (RMSE), and the Confusion Matrix. It is observed from the resultant outcome that the performance of the proposed model has exhibited better efficient. © 2023 CRL Publishing. All rights reserved.

Role of Sodium Bicarbonate as Adjuvant Treatment of Nonsevere Computed Tomography-identified COVID-19 Pneumonia: A Preliminary Report

Background: Coronaviruses are classified as pH dependent. Alkaline media induced by sodium bicarbonate (SB) could impede viral entry into cells. We aimed to study the possible role of SB as an adjuvant treatment of nonsevere COVID-19 pneumonia. Methods: The study included 182 adults with confirmed nonsevere COVID-19 and chest computed tomography (CT) pneumonia;127 assigned as study received conventional treatment plus adjuvant SB inhalation and nasal drops, as well as 55 assigned as control treated by conventional treatment only. Clinical and radiological assessments using chest CT score specific for COVID-19 were done at days 0 and 30. Results: Both the groups were comparable regarding demographic, clinical, and radiological characteristics. Clinical recovery was reported in 43/127 (33.9%) and 10/55 (18.2%) of the study and control groups, respectively (P = 0.03). The mean +/- standard deviation time to clinical improvement was 3.31 +/- 0.99 and 9.79 +/- 6.29 days for the study and control groups, respectively (P < 0.001). The median of the total chest CT score was reduced from 10 (4-15) to 3 (0-19) in the study group (P = 0.000) and from 13 (2-15) to 11 (2-19) in the control group (P = 0.53) on days 0 and 30, respectively. Conclusions: SB could be a possible adjuvant therapy for selected patients with nonsevere COVID-19 pneumonia.

Attention-based Multi-flow Network for COVID-19 Classification and Lesion Localization from Chest CT

COVID-19 has been rapidly spreading worldwide and infected more than 1 million people with over 690k deaths reported. It is urgent and crucial to identify COVID-19-infected patients by computed tomography (CT) accurately and rapidly. However, we found that two problems, weak supervision and lack of interpretability, hindered its development. To address these challenges, we propose an attention-based multi-flow network for COVID-19 classification and lesion localization from chest CT. In the proposed model, we built a Resnet-based multi-flow network to learn the local information and the longitudinal information from the full chest sequence slice. To assist doctors in decision-making, the attention mechanism integrated into the network, which can locate the key slices and key parts from a full chest CT sequence of patients. We have systematically evaluated our method on the CT images of 1031 cases, including 420 COVID-19 cases, 311CAP cases, and 300 non-pneumonia cases. Our method could obtain an average accuracy of 82.3%, with 85.7% sensitivity and 86.4 % specificity, which outperformed previous works. © 2022 IEEE.

Study of epidemiological, clinical and evolutionary characteristics of a population of patients hospitalized for COVID-19 in Morocco

The outbreak of COVID-19 still represents a real risk for the increased death rate for the whole of human kind. In this context, the present research work aims at describing evolutionary data in a population of hospitalized COVID-19 positive patients based on selected epidemiological, clinical and paraclinical data at admission. In this cross-sectional study, we examined the data obtained from 108 patients hospitalized with COVID-19 in the VINCI clinic, Casablanca (Morocco) between August and September 2020. General characteristic, clinical, radiological and biological data as well as therapeutic management were assessed. The patients' median age was 45-50 years old. Among our studied patients, 4.6% were transferred to the intensive care unit (ICU), 16.7% were cured after more than 15 days, while 78.7% were cured within 15 days. Those transferred to the ICU unit were either smokers, obese, or over 65 years of age. The majority of patients with normal radiological and cardiac parameters were cured within<15 days, while biological disorders were observed in approximately all cases that were transferred to ICU. Regarding therapeutic treatment, 98.1% of the subjects were treated with hydroxychloroquine + azithromycin in combination with vitamin C and zinc supplementation. The study shows that the minimal healing time is well conditioned by the general and clinical characteristics of patients. Furthermore, the administration of hydroxychloroquine + azithromycin showed a beneficial effect with no associated adverse effects in the study cases. © The Authors, published by EDP Sciences. This is an open access article distributed under the terms of the Creative Commons Attribution License 4.0 (http://creativecommons.org/licenses/by/4.0/)

Self-Supervised Contrastive Learning for Covid-19 Classification from Computed Tomography Images

Computer-aided diagnosis (CAD) emerges as an exhaustive diagnostic tool in the Covid-19 pandemic outbreak and is enormously investigated for automatic and more accurate detections. Artificial intelligence (AI) based radiographic images (Computed Tomography, X-Ray, Lung Ultrasound) interpretation improves the overall diagnosis efficiency of Covid-19 infections. In this paper, CAD based deep meta learning approach has been discussed for automatically quick analysis of chest computed tomography (CT) images regarding the early detection of corona virus (Covid-19) presence inside a subject. We incorporated a self-supervised contrastive-learning neural network for unbiased feature representation and classifications using fine-tuned pre-trained Inception module on 28203 chest CT images. This trainable multi-shot end-to-end deep learning architecture is validated on public dataset of normal and covid-19 CT images obtaining normalized accuracy of 0.9708. Results verify our model to be able enough to assist radiologists and specialists in screening and correct diagnosis of Covid-19 patients in less span of time. © 2022 IEEE.

Dual Windows Are Significant: Learning from Mediastinal Window and Focusing on Lung Window

Since the pandemic of COVID-19, several deep learning methods were proposed to analyze the chest Computed Tomography (CT) for diagnosis. In the current situation, the disease course classification is significant for medical personnel to decide the treatment. Most previous deep-learning-based methods extract features observed from the lung window. However, it has been proved that some appearances related to diagnosis can be observed better from the mediastinal window rather than the lung window, e.g., the pulmonary consolidation happens more in severe symptoms. In this paper, we propose a novel Dual Window RCNN Network (DWRNet), which mainly learns the distinctive features from the successive mediastinal window. Regarding the features extracted from the lung window, we introduce the Lung Window Attention Block (LWA Block) to pay additional attention to them for enhancing the mediastinal-window features. Moreover, instead of picking up specific slices from the whole CT slices, we use a Recurrent CNN and analyze successive slices as videos. Experimental results show that the fused and representative features improve the predictions of disease course by reaching the accuracy of 90.57%, against the baseline with an accuracy of 84.86%. Ablation studies demonstrate that combined dual window features are more efficient than lung-window features alone, while paying attention to lung-window features can improve the model's stability. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

How accurate are radiography and computed tomography in the diagnosis of COVID-19?-A Bayesian approach.

Background: The role of radiology in patients with clinical suspicion of COVID-19 is evolving with scientific evidence, but there are differences in opinion on when and how the technique should be used for clinical diagnosis. Purpose: To estimate the pre-test and post-test probability that a patient has COVID-19 in the event of a positive and/or negative result from chest X-ray and chest computed tomography (CT) radiological studies, comparing with those of real time polymerase chain reaction (RT-PCR) tests. Methods: The literature on the sensitivity and specificity of the chest X-ray, chest CT, and RT-PCR was reviewed. Based on these reported data, the likelihood ratios (LR) were estimated and the pre-test probabilities were related to the post-test probabilities after positive or negative results. Results: The chest X-ray has only a confirmatory value in cases of high suspicion. Chest CT analyses showed that when it is used as a general study, it has almost confirmatory value under high clinical suspicion. A chest CT classified with CO-RADS ≥ 4 has almost a diagnostic certainty of COVID-19 even with moderate or low clinical presumptions, and the CO-RADS 5 classification is almost pathognomonic before any clinical presumption. To rule out COVID-19 completely is only possible in very low clinical assumptions with negative RT-PCR and/or CT. Conclusions: Chest X-ray and especially CT are fast studies that have the capacity to report high probability of COVID-19, being a real contribution to the concept of "probable case" and allowing support to be installed in an early and timely manner.

Development and implementation of optimized chest CT protocol in COVID-19: A clinical audit.

Background: Several studies have justified use of chest computed tomography (CT) in diagnosis, evaluation of severity, treatment response, and complications of coronavirus disease 2019 (COVID-19) pneumonia. Increased utilization of CT in patients with known or suspected COVID-19 pneumonia has resulted in concerns of overuse, lack of protocol optimization, and radiation exposure. Aims: The study was conducted to develop and implement optimized protocol for chest CT for reducing radiation dose in adult patients suspected or diagnosed to have COVID-19 infection. Setting and Design: The study was conducted in the department of radiology of a rural tertiary care teaching hospital in western India. Clinical audit was used as a tool to impart and assess the impact of optimized chest CT protocol. Methods and Material: The pre-intervention audit included radiation dosimetry data, number of phases and length of scan of 50 adult patients, undergoing non-contrast chest CT scans in March 2021. A brief educational intervention outlining the parameters of optimized protocol was conducted on April 1, 2021.The post-intervention audit consisted of two cycles for 109 and 67 chest CT scans in the months April and May 2021. Results: The optimized protocol was found clinically adequate with a good inter-rater reliability. The compliance to the optimized protocol was weak in audit cycle 2, which improved significantly in audit cycle 3 after reinforcement. The mean (SD) per scan Computed Tomography Dose Index-Volume (CTDI-vol) reduced significantly across audit cycles [22.06 (12. 31) Vs. 10.58 (7.58) Vs. 4.51 (2.90) milli Gray, respectively, P < 0.001]. Similar findings were noted for Dose Length Product (DLP). Conclusion: Clinical audit of chest CT protocol and resultant radiation doses provided adequate feedback for dose optimization. A simple educational intervention helped achieve dose optimization.

Diverse COVID-19 CT Image-to-Image Translation with Stacked Residual Dropout.

Machine learning models are renowned for their high dependency on a large corpus of data in solving real-world problems, including the recent COVID-19 pandemic. In practice, data acquisition is an onerous process, especially in medical applications, due to lack of data availability for newly emerged diseases and privacy concerns. This study introduces a data synthesization framework (sRD-GAN) that generates synthetic COVID-19 CT images using a novel stacked-residual dropout mechanism (sRD). sRD-GAN aims to alleviate the problem of data paucity by generating synthetic lung medical images that contain precise radiographic annotations. The sRD mechanism is designed using a regularization-based strategy to facilitate perceptually significant instance-level diversity without content-style attribute disentanglement. Extensive experiments show that sRD-GAN can generate exceptional perceptual realism on COVID-19 CT images examined by an experiment radiologist, with an outstanding Fréchet Inception Distance (FID) of 58.68 and Learned Perceptual Image Patch Similarity (LPIPS) of 0.1370 on the test set. In a benchmarking experiment, sRD-GAN shows superior performance compared to GAN, CycleGAN, and one-to-one CycleGAN. The encouraging results achieved by sRD-GAN in different clinical cases, such as community-acquired pneumonia CT images and COVID-19 in X-ray images, suggest that the proposed method can be easily extended to other similar image synthetization problems.

The diagnostic performance of chest computed tomography scanning in the diagnosis of coronavirus disease 2019 compared to polymerase chain reaction: A retrospective study of 1240 cases from Tripoli University Hospital, Libya

OBJECTIVE: The increasing prevalence of suspected cases of coronavirus disease 2019 (COVID-19) presenting to emergency departments (EDs) requires a rapid and reliable triaging tool. The diagnostic performance of chest computed tomography (CT) has yet to be validated for triaging cases in the ED. We aimed to assess the diagnostic performance of chest CT compared to GeneXpert Xpress Xpert severe acute respiratory syndrome coronavirus 2 test in rapidly diagnosing COVID-19 among patients with respiratory symptoms presenting to the ED. MATERIALS AND METHODS: This was a retrospective, single-center study at Tripoli University Hospital including cases with respiratory symptoms who underwent chest CT as well as polymerase chain reaction (PCR) testing for suspected COVID-19 between May 18 and August 18, 2020. RESULTS: A total of 1240 cases were included, among whom 570 had radiologically evident COVID-19 on chest CT (46%). Five hundred and sixty-five cases had positive PCR results (45.6%), of whom 557 had radiologically evident COVID-19 on chest CT (97.7%). The calculated accuracy, sensitivity, specificity, positive predictive value, and negative predictive value were 98%, 98.5%, 98%, 97.7%, and 98.8%, respectively, in relation to the PCR results. CONCLUSION: During the current pandemic, chest CT is a quick and reliable diagnostic tool for COVID-19 in the ED.