Can chest CT improve sensitivity of COVID-19 diagnosis in comparison to PCR? A meta-analysis study

Investigation of the diagnostic importance and accuracy of CT in the chest compared to the RT-PCR test for suspected COVID-19 patients in Jordan

This article aims to synthesize the existing literature on the implementation of public policies to incentivize the development of treatments for rare diseases, (diseases with very low prevalence and therefore with low commercial interest) otherwise known as orphan drugs. The implementation of these incentives in the United States (US), Japan, and in the European Union (EU) seems to be related to a substantial increase in treatments for these diseases, and has influenced the way the pharmaceutical research & development (R&D) system operates beyond this policy area. Despite the success of the Orphan Drug model, the academic literature also highlights the negative implications that these public policies have on affordability and access to orphan drugs, as well as on the prioritization of certain disease rare areas over others. The synthesis focuses mostly on the United States' Orphan Drug Act (ODA) as a model for subsequent policies in other regions and countries. It starts with a historical overview of the creation of the term "rare diseases", continues with a summary of the evidence available on the US ODA's positive and negative impacts, and provides a summary of the different proposals to reform these incentives in light of the negative outcomes described. Finally, it describes some key aspects of the Japanese and European policies, as well as some of the challenges captured in the literature related to their impact in Low- and Middle-Income Countries (LMICs).

COVID-19 and spontaneous pneumothorax: a survival analysis

INTRODUCTION

COVID-19 Patients may be at risk for involving with spontaneous pneumothorax. However, clinical data are lacking in this regard. In this study, we aimed to investigate the demographic, clinical, and radiological characteristics and survival predictors in COVID-19 patients with pneumothorax.

METHODS

This is a retrospectivestudy conducted on COVID-19 patients with pneumothorax that had been hospitalized at hospital. l from December 2021 to March 2022. The chest computed tomography (CT) scan of all patients was reviewed by an experienced pulmonologist in search of pulmonary pneumothorax. Survival analysis was conducted to identify the predictors of survival in patients with COVID-19 and pneumothorax.

RESULTS

A total of 67 patients with COVID-19 and pneumothorax were identified. Of these, 40.7% were located in the left lung, 40.7% were in the right lung, and 18.6% were found bilaterally. The most common symptoms in the patient with pneumothorax were dyspnea (65.7%), increased cough severity (53.7%), chest pain (25.4%), and hemoptysis (16.4%). The frequency of pulmonary left and right bullae, pleural effusion, andfungus ball were 22.4%, 22.4%, 22.4%, and 7.5%, respectively. Pneumothorax was managed with chest drain (80.6%), chest drain and surgery (6%), and conservatively (13.4%). The 50-day mortality rate was 52.2% (35 patients). The average survival time for deceased patients was 10.06 (2.17) days.

CONCLUSIONS

Our results demonstrated that those with pleural effusion or pulmonary bullae have a lower survival rate. Further studies are required to investigate the incidence and causality relation between COVID-19 and pneumothorax.

Glioma diagnosis and comprehensive management during COVID-19 pandemic: A proposed algorithm

The coronavirus disease 2019 (COVID-19) has significantly changed the health-care system. COVID-19 patients with comorbidities are more likely to have severe disease, often leading to death. As one primary concern in this pandemic era, glioma patients have an incidence of 30%. It has a high mortality rate. Glioma has multiple comorbidities, at risk of contracting COVID-19, such as elderly, taking high-dose steroid therapy with adjuvant radiotherapy (RT) and chemotherapy. An algorithm for patient-doctor communication, inpatient-outpatient selection, and treatment goals in glioma patients should be carefully made according to local preparation for COVID-19. Surgery, RT, and chemotherapy should be tailored individually to increase survival rate, quality of life, and reduce the risk of COVID-19 exposure. All communication between the health-care provider and patient will be using telemedicine. The patient who requires to visit the inpatient ward will be carefully selected. Asymptomatic glioma or with no progressivity of the disease should have the treatment postponed. Symptomatic high-grade glioma patients with progressive neurological deficits and increased intracranial pressure will be treated with COVID-19 protocols. Surgery, RT, and chemotherapy, especially Temozolomide, will be given after evaluating the patient's age, Karnofsky Performance Scale (KPS) Score, and molecular finding of O6-methylguanine DNA methyltransferase (MGMT), isocitrate dehydrogenase, and gene 1p/9q. Therefore, it is necessary to have a modified algorithm for glioma patients during this pandemic.

Key Messages

A strategy to minimize hospital contact for glioma patients in a pandemic crisis while not delaying their diagnostics and treatments.

LHSPred: A web based application for predicting lung health severity

Background and objectives

The computed tomography (CT) scan facilities are crucial for diagnosis of pulmonary diseases and are overburdened during the current pandemic of novel coronavirus disease 2019 (COVID-19). LHSPred (Lung Health Severity Prediction) is a web based tool that enables users to determine a score that evaluates CT scans, without radiologist intervention, and predict risk of pneumonia with features of blood examination and age of patient. It can help in early assessment of lung health severity of patients without CT-scan results and also enable monitoring of post-COVID lung health for recovered patients.

Methods

This tool uses Support Vector Regression (SVR) and Multi-Layer Perceptron Regression (MLPR), trained on COVID-19 patient data reported in the literature. It allows to compute a score (CT severity score) that evaluates the involvement of lesions in lung lobes and to predict risk of pneumonia. A web application was implemented that uses the trained regression models.

Results

The application has proven to be effective and user friendly in a clinical setting for pulmonary disease treatment. The SVR model achieved Pearson correlation coefficient (PCC) of 0.77 and mean absolute error (MAE) of 2.239 while determining the computed tomography (CT) severity score. The MLPR model achieved PCC of 0.77 and MAE of 2.309. Thus, it can be applied as a useful tool in predicting pneumonia in the post COVID-19 era.

Conclusion

LHSPred can be used as a decision support system by the clinicians and as a tool for self-assessment by the patients with only six blood test input features.

Diagnostic techniques for COVID-19: A mini-review

COVID-19, a new respiratory infectious disease, was first reported at the end of 2019, in Wuhan, China. Now, COVID-19 is still causing major loss of human life and economic productivity in almost all countries around the world. Early detection, early isolation, and early diagnosis of COVID-19 patients and asymptomatic carriers are essential to blocking the spread of the pandemic. This paper briefly reviewed COVID-19 diagnostic assays for clinical application, including nucleic acid tests, immunological methods, and Computed Tomography (CT) imaging. Nucleic acid tests (NAT) target the virus genome and indicates the existence of the SARS-CoV-2 virus. Currently, real-time quantitative PCR (qPCR) is the most widely used NAT and, basically, is the most used diagnostic assay for COVID-19. Besides qPCR, many novel rapid and sensitive NAT assays were also developed. Serological testing (detection of serum antibodies specific to SARS-CoV-2), which belongs to the immunological methods, is also used in the diagnosis of COVID-19. The positive results of serological testing indicate the presence of antibodies specific to SARS-CoV-2 resulting from being infected with the virus. Viral antigen detection assays are also important immunological methods used mainly for rapid virus detection. However, only a few of these assays had been reported. CT imaging is still an important auxiliary diagnosis tool for COVID-19 patients, especially for symptomatic patients in the early stage, whose viral load is low and different to be identified by NAT. These diagnostic techniques are all good in some way and applying a combination of them will greatly improve the accuracy of COVID-19 diagnostics.

Diagnostic Test Accuracy of Deep Learning Detection of COVID-19: A Systematic Review and Meta-Analysis

RATIONALE AND OBJECTIVE

To perform a meta-analysis to compare the diagnostic test accuracy (DTA) of deep learning (DL) in detecting coronavirus disease 2019 (COVID-19), and to investigate how network architecture and type of datasets affect DL performance.

MATERIALS AND METHODS

We searched PubMed, Web of Science and Inspec from January 1, 2020, to December 3, 2020, for retrospective and prospective studies on deep learning detection with at least reported sensitivity and specificity. Pooled DTA was obtained using random-effect models. Sub-group analysis between studies was also carried out for data source and network architectures.

RESULTS

The pooled sensitivity and specificity were 91% (95% confidence interval [CI]: 88%, 93%; I2 = 69%) and 92% (95% CI: 88%, 94%; I2 = 88%), respectively for 19 studies. The pooled AUC and diagnostic odds ratio (DOR) were 0.95 (95% CI: 0.88, 0.92) and 112.5 (95% CI: 57.7, 219.3; I2 = 90%) respectively. The overall accuracy, recall, F1-score, LR+ and LR- are 89.5%, 89.5%, 89.7%, 23.13 and 0.13. Sub-group analysis shows that the sensitivity and DOR significantly vary with the type of network architectures and sources of data with low heterogeneity are (I2 = 0%) and (I2 = 18%) for ResNet architecture and single-source datasets, respectively.

CONCLUSION

The diagnosis of COVID-19 via deep learning has achieved incredible performance, and the source of datasets, as well as network architectures, strongly affect DL performance.

COVID-19 and pregnant women - An overview on diagnosis, treatment approach with limitation, and clinical management

Coronavirus disease or more popularly called COVID-19 is known to be caused by a novel coronavirus 2. The COVID-19 has been identified to be originated from Wuhan, Hubei, China. This pandemic started in December 2019, and since then it has spread across the world within a short period. The health and family welfare ministry of the Government of India reported 227,546 active, 9,997,272 discharged cases, and 150,114 deaths due to COVID-19 as of 06 January 2021. Indian Council of Medical Research (ICMR) reports that the cumulative testing status of SARS-CoV-2 (COVID-19) was 931,408 up to November 03, 2020. Currently, no specific anti-viral drug for COVID-19 management is recommended in the current scenario. Vulnerable populations such as pregnant women affected by COVID-19 infection need to be recognized and followed up for effective handling concerning morbidity and mortality. At present, very few case reports on COVID-19 infected pregnant women have been published in India and there is no proven exclusive treatment protocol. This article summarizes a review of signs and symptoms, etiopathogenesis, risk factors, diagnosis, and possible management of COVID-19 infection in pregnant women. This overview may be useful for health care providers for practical approach and limitation of drugs used in the current management and considers the choice of drugs with their special attention given to adverse effects to improvise maternal health, pregnancy, and birth outcomes.

A deep learning algorithm to detect coronavirus (COVID-19) disease using CT images

BACKGROUND

COVID-19 pandemic imposed a lockdown situation to the world these past months. Researchers and scientists around the globe faced serious efforts from its detection to its treatment.

METHODS

Pathogenic laboratory testing is the gold standard but it is time-consuming. Lung CT-scans and X-rays are other common methods applied by researchers to detect COVID-19 positive cases. In this paper, we propose a deep learning neural network-based model as an alternative fast screening method that can be used for detecting the COVID-19 cases by analyzing CT-scans.

RESULTS

Applying the proposed method on a publicly available dataset collected of positive and negative cases showed its ability on distinguishing them by analyzing each individual CT image. The effect of different parameters on the performance of the proposed model was studied and tabulated. By selecting random train and test images, the overall accuracy and ROC-AUC of the proposed model can easily exceed 95% and 90%, respectively, without any image pre-selecting or preprocessing.

Coronavirus Disease 2019 (COVID-19) in Italy: Double Reading of Chest CT Examination

To assess the performance of the second reading of chest compute tomography (CT) examinations by expert radiologists in patients with discordance between the reverse transcription real-time fluorescence polymerase chain reaction (RT-PCR) test for COVID-19 viral pneumonia and the CT report. Three hundred and seventy-eight patients were included in this retrospective study (121 women and 257 men; 71 years median age, with a range of 29-93 years) and subjected to RT-PCR tests for suspicious COVID-19 infection. All patients were subjected to CT examination in order to evaluate the pulmonary disease involvement by COVID-19. CT images were reviewed first by two radiologists who identified COVID-19 typical CT patterns and then reanalyzed by another two radiologists using a CT structured report for COVID-19 diagnosis. Weighted к values were used to evaluate the inter-reader agreement. The median temporal window between RT-PCRs execution and CT scan was zero days with a range of (-9,11) days. The RT-PCR test was positive in 328/378 (86.8%). Discordance between RT-PCR and CT findings for viral pneumonia was revealed in 60 cases. The second reading changed the CT diagnosis in 16/60 (26.7%) cases contributing to an increase the concordance with the RT-PCR. Among these 60 cases, eight were false negative with positive RT-PCR, and 36 were false positive with negative RT-PCR. Sensitivity, specificity, positive predictive value and negative predictive value of CT were respectively of 97.3%, 53.8%, 89.0%, and 88.4%. Double reading of CT scans and expert second readers could increase the diagnostic confidence of radiological interpretation in COVID-19 patients.