Combination of CT and RT-PCR in the screening or diagnosis of COVID-19

MiniSeg: An Extremely Minimum Network Based on Lightweight Multiscale Learning for Efficient COVID-19 Segmentation

The rapid spread of the new pandemic, i.e., coronavirus disease 2019 (COVID-19), has severely threatened global health. Deep-learning-based computer-aided screening, e.g., COVID-19 infected area segmentation from computed tomography (CT) image, has attracted much attention by serving as an adjunct to increase the accuracy of COVID-19 screening and clinical diagnosis. Although lesion segmentation is a hot topic, traditional deep learning methods are usually data-hungry with millions of parameters, easy to overfit under limited available COVID-19 training data. On the other hand, fast training/testing and low computational cost are also necessary for quick deployment and development of COVID-19 screening systems, but traditional methods are usually computationally intensive. To address the above two problems, we propose MiniSeg, a lightweight model for efficient COVID-19 segmentation from CT images. Our efforts start with the design of an attentive hierarchical spatial pyramid (AHSP) module for lightweight, efficient, effective multiscale learning that is essential for image segmentation. Then, we build a two-path (TP) encoder for deep feature extraction, where one path uses AHSP modules for learning multiscale contextual features and the other is a shallow convolutional path for capturing fine details. The two paths interact with each other for learning effective representations. Based on the extracted features, a simple decoder is added for COVID-19 segmentation. For comparing MiniSeg to previous methods, we build a comprehensive COVID-19 segmentation benchmark. Extensive experiments demonstrate that the proposed MiniSeg achieves better accuracy because its only 83k parameters make it less prone to overfitting. Its high efficiency also makes it easy to deploy and develop. The code has been released at https://github.com/yun-liu/MiniSeg.

Fully feature fusion based neural network for COVID-19 lesion segmentation in CT images

Coronavirus Disease 2019 (COVID-19) spreads around the world, seriously affecting people's health. Computed tomography (CT) images contain rich semantic information as an auxiliary diagnosis method. However, the automatic segmentation of COVID-19 lesions in CT images faces several challenges, including inconsistency in size and shape of the lesion, the high variability of the lesion, and the low contrast of pixel values between the lesion and normal tissue surrounding the lesion. Therefore, this paper proposes a Fully Feature Fusion Based Neural Network for COVID-19 Lesion Segmentation in CT Images (F3-Net). F3-Net uses an encoder-decoder architecture. In F3-Net, the Multiple Scale Module (MSM) can sense features of different scales, and Dense Path Module (DPM) is used to eliminate the semantic gap between features. The Attention Fusion Module (AFM) is the attention module, which can better fuse the multiple features. Furthermore, we proposed an improved loss function L o s s C o v i d - B C E that pays more attention to the lesions based on the prior knowledge of the distribution of COVID-19 lesions in the lungs. Finally, we verified the superior performance of F3-Net on a COVID-19 segmentation dataset, experiments demonstrate that the proposed model can segment COVID-19 lesions more accurately in CT images than benchmarks of state of the art.

Diagnostic accuracy of a set of clinical and radiological criteria for screening of COVID-19 using RT-PCR as the reference standard

BACKGROUND

The gold-standard method for establishing a microbiological diagnosis of COVID-19 is reverse-transcriptase polymerase chain reaction (RT-PCR). This study aimed to evaluate the accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of a set of clinical-radiological criteria for COVID-19 screening in patients with severe acute respiratory failure (SARF) admitted to intensive care units (ICUs), using reverse-transcriptase polymerase chain reaction (RT-PCR) as the reference standard.

METHODS

Diagnostic accuracy study including a historical cohort of 1009 patients consecutively admitted to ICUs across six hospitals in Curitiba (Brazil) from March to September, 2020. The sample was stratified into groups by the strength of suspicion for COVID-19 (strong versus weak) using parameters based on three clinical and radiological (chest computed tomography) criteria. The diagnosis of COVID-19 was confirmed by RT-PCR (referent).

RESULTS

With respect to RT-PCR, the proposed criteria had 98.5% (95% confidence interval [95% CI] 97.5-99.5%) sensitivity, 70% (95% CI 65.8-74.2%) specificity, 85.5% (95% CI 83.4-87.7%) accuracy, PPV of 79.7% (95% CI 76.6-82.7%) and NPV of 97.6% (95% CI 95.9-99.2%). Similar performance was observed when evaluated in the subgroups of patients admitted with mild/moderate respiratory disfunction, and severe respiratory disfunction.

CONCLUSION

The proposed set of clinical-radiological criteria were accurate in identifying patients with strong versus weak suspicion for COVID-19 and had high sensitivity and considerable specificity with respect to RT-PCR. These criteria may be useful for screening COVID-19 in patients presenting with SARF.

Robust framework for COVID-19 identication from a multicenter dataset of chest CT scans

The main objective of this study is to develop a robust deep learning-based framework to distinguish COVID-19, Community-Acquired Pneumonia (CAP), and Normal cases based on volumetric chest CT scans, which are acquired in different imaging centers using different scanners and technical settings. We demonstrated that while our proposed model is trained on a relatively small dataset acquired from only one imaging center using a specific scanning protocol, it performs well on heterogeneous test sets obtained by multiple scanners using different technical parameters. We also showed that the model can be updated via an unsupervised approach to cope with the data shift between the train and test sets and enhance the robustness of the model upon receiving a new external dataset from a different center. More specifically, we extracted the subset of the test images for which the model generated a confident prediction and used the extracted subset along with the training set to retrain and update the benchmark model (the model trained on the initial train set). Finally, we adopted an ensemble architecture to aggregate the predictions from multiple versions of the model. For initial training and development purposes, an in-house dataset of 171 COVID-19, 60 CAP, and 76 Normal cases was used, which contained volumetric CT scans acquired from one imaging center using a single scanning protocol and standard radiation dose. To evaluate the model, we collected four different test sets retrospectively to investigate the effects of the shifts in the data characteristics on the model's performance. Among the test cases, there were CT scans with similar characteristics as the train set as well as noisy low-dose and ultra-low-dose CT scans. In addition, some test CT scans were obtained from patients with a history of cardiovascular diseases or surgeries. This dataset is referred to as the "SPGC-COVID" dataset. The entire test dataset used in this study contains 51 COVID-19, 28 CAP, and 51 Normal cases. Experimental results indicate that our proposed framework performs well on all test sets achieving total accuracy of 96.15% (95%CI: [91.25-98.74]), COVID-19 sensitivity of 96.08% (95%CI: [86.54-99.5]), CAP sensitivity of 92.86% (95%CI: [76.50-99.19]), Normal sensitivity of 98.04% (95%CI: [89.55-99.95]) while the confidence intervals are obtained using the significance level of 0.05. The obtained AUC values (One class vs Others) are 0.993 (95%CI: [0.977-1]), 0.989 (95%CI: [0.962-1]), and 0.990 (95%CI: [0.971-1]) for COVID-19, CAP, and Normal classes, respectively. The experimental results also demonstrate the capability of the proposed unsupervised enhancement approach in improving the performance and robustness of the model when being evaluated on varied external test sets.

Diagnostic accuracy of autoverification and guidance system for COVID-19 RT-PCR results

BACKGROUND

To date, most countries worldwide have declared that the pandemic of COVID-19 is over, while the WHO has not officially ended the COVID-19 pandemic, and China still insists on the personalized dynamic COVID-free policy. Large-scale nucleic acid testing in Chinese communities and the manual interpretation for SARS-CoV-2 nucleic acid detection results pose a huge challenge for labour, quality and turnaround time (TAT) requirements. To solve this specific issue while increase the efficiency and accuracy of interpretation, we created an autoverification and guidance system (AGS) that can automatically interpret and report the COVID-19 reverse transcriptase-polymerase chain reaction (RT-PCR) results relaying on computer-based autoverification procedure and then validated its performance in real-world environments. This would be conductive to transmission risk prediction, COVID-19 prevention and control and timely medical treatment for positive patients in the context of the predictive, preventive and personalized medicine (PPPM).

METHODS

A diagnostic accuracy test was conducted with 380,693 participants from two COVID-19 test sites in China, the Hong Kong Hybribio Medical Laboratory (n = 266,035) and the mobile medical shelter at a Shanghai airport (n = 114,658). These participants underwent SARS-CoV-2 RT-PCR from March 28 to April 10, 2022. All RT-PCR results were interpreted by laboratorians and by using AGS simultaneously. Considering the manual interpretation as gold standard, the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and accuracy were applied to evaluate the diagnostic value of the AGS on the interpretation of RT-PCR results.

RESULTS

Among the 266,035 samples in Hong Kong, there were 16,356 (6.15%) positive, 231,073 (86.86%) negative, 18,606 (6.99%) indefinite, 231,073 (86.86%, negative) no retest required and 34,962 (13.14%, positive and indefinite) retest required; the 114,658 samples in Shanghai consisted of 76 (0.07%) positive, 109,956 (95.90%) negative, 4626 (4.03%) indefinite, 109,956 (95.90%, negative) no retest required and 4702 (4.10%, positive and indefinite) retest required. Compared to the fashioned manual interpretation, the AGS is a procedure of high accuracy [99.96% (95%CI, 99.95-99.97%) in Hong Kong and 100% (95%CI, 100-100%) in Shanghai] with perfect sensitivity [99.98% (95%CI, 99.97-99.98%) in Hong Kong and 100% (95%CI, 100-100%) in Shanghai], specificity [99.87% (95%CI, 99.82-99.90%) in Hong Kong and 100% (95%CI, 99.92-100%) in Shanghai], PPV [99.98% (95%CI, 99.97-99.99%) in Hong Kong and 100% (95%CI, 99.99-100%) in Shanghai] and NPV [99.85% (95%CI, 99.80-99.88%) in Hong Kong and 100% (95%CI, 99.90-100%) in Shanghai]. The need for manual interpretation of total samples was dramatically reduced from 100% to 13.1% and the interpretation time fell from 53 h to 26 min in Hong Kong; while the manual interpretation of total samples was decreased from 100% to 4.1% and the interpretation time dropped from 20 h to 16 min at Shanghai.

CONCLUSIONS

The AGS is a procedure of high accuracy and significantly relieves both labour and time from the challenge of large-scale screening of SARS-CoV-2 using RT-PCR. It should be recommended as a powerful screening, diagnostic and predictive system for SARS-CoV-2 to contribute timely the ending of the COVID-19 pandemic following the concept of PPPM.

Multi-Attention Segmentation Networks Combined with the Sobel Operator for Medical Images

Medical images are used as an important basis for diagnosing diseases, among which CT images are seen as an important tool for diagnosing lung lesions. However, manual segmentation of infected areas in CT images is time-consuming and laborious. With its excellent feature extraction capabilities, a deep learning-based method has been widely used for automatic lesion segmentation of COVID-19 CT images. However, the segmentation accuracy of these methods is still limited. To effectively quantify the severity of lung infections, we propose a Sobel operator combined with multi-attention networks for COVID-19 lesion segmentation (SMA-Net). In our SMA-Net method, an edge feature fusion module uses the Sobel operator to add edge detail information to the input image. To guide the network to focus on key regions, SMA-Net introduces a self-attentive channel attention mechanism and a spatial linear attention mechanism. In addition, the Tversky loss function is adopted for the segmentation network for small lesions. Comparative experiments on COVID-19 public datasets show that the average Dice similarity coefficient (DSC) and joint intersection over union (IOU) of the proposed SMA-Net model are 86.1% and 77.8%, respectively, which are better than those in most existing segmentation networks.

Conventional and Novel Diagnostic Tools for the Diagnosis of Emerging SARS-CoV-2 Variants

Accurate identification at an early stage of infection is critical for effective care of any infectious disease. The "coronavirus disease 2019 (COVID-19)" outbreak, caused by the virus "Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2)", corresponds to the current and global pandemic, characterized by several developing variants, many of which are classified as variants of concern (VOCs) by the "World Health Organization (WHO, Geneva, Switzerland)". The primary diagnosis of infection is made using either the molecular technique of RT-PCR, which detects parts of the viral genome's RNA, or immunodiagnostic procedures, which identify viral proteins or antibodies generated by the host. As the demand for the RT-PCR test grew fast, several inexperienced producers joined the market with innovative kits, and an increasing number of laboratories joined the diagnostic field, rendering the test results increasingly prone to mistakes. It is difficult to determine how the outcomes of one unnoticed result could influence decisions about patient quarantine and social isolation, particularly when the patients themselves are health care providers. The development of point-of-care testing helps in the rapid in-field diagnosis of the disease, and such testing can also be used as a bedside monitor for mapping the progression of the disease in critical patients. In this review, we have provided the readers with available molecular diagnostic techniques and their pitfalls in detecting emerging VOCs of SARS-CoV-2, and lastly, we have discussed AI-ML- and nanotechnology-based smart diagnostic techniques for SARS-CoV-2 detection.

Plasminogen activator inhibitor-1 levels as an indicator of severity and mortality for COVID-19

OBJECTIVE

Coronavirus disease-19 (COVID-19) is a multisystemic disease that can cause severe illness and mortality by exacerbating symptoms such as thrombosis, fibrinolysis, and inflammation. Plasminogen activator inhibitor-1 (PAI-1) plays an important role in regulating fibrinolysis and may cause thrombotic events to develop. The goal of this study is to examine the relationship between PAI-1 levels and disease severity and mortality in relation to COVID-19.

METHODS

A total of 71 hospitalized patients were diagnosed with COVID-19 using real time-polymerase chain reaction tests. Each patient underwent chest computerized tomography (CT). Data from an additional 20 volunteers without COVID-19 were included in this single-center study. Each patient's PAI-1 data were collected at admission, and the CT severity score (CT-SS) was then calculated for each patient.

RESULTS

The patients were categorized into the control group (n=20), the survivor group (n=47), and the non-survivor group (n=24). In the non-survivor group, the mean age was 75.3±13.8, which is higher than in the survivor group (61.7±16.9) and in the control group (59.5±11.2), (p=0.001). When the PAI-1 levels were compared between each group, the non-survivor group showed the highest levels, followed by the survivor group and then the control group (p<0.001). Logistic regression analysis revealed that age, PAI-1, and disease severity independently predicted COVID-19 mortality rates. In this study, it was observed that PAI-1 levels with >10.2 ng/mL had 83% sensitivity and an 83% specificity rate when used to predict mortality after COVID-19. Then, patients were divided into severe (n=33) and non-severe (n=38) groups according to disease severity levels. The PAI-1 levels found were higher in the severe group (p<0.001) than in the non-severe group. In the regression analysis that followed, high sensitive troponin I and PAI-1 were found to indicate disease severity levels. The CT-SS was estimated as significantly higher in the non-survivor group compared to the survivor group (p<0.001). When comparing CT-SS between the severe group and the non-severe group, this was significantly higher in the severe group (p<0.001). In addition, a strong statistically significant positive correlation was found between CT-SS and PAI-1 levels (r: 0.838, p<0.001).

CONCLUSION

Anticipating poor clinical outcomes in relation to COVID-19 is crucial. This study showed that PAI-1 levels could independently predict disease severity and mortality rates for patients with COVID-19.

Usefulness of High-Resolution Computed Tomography in Early Diagnosis of Patients with Suspected COVID-19

BACKGROUND

Diagnosis of coronavirus disease 2019 (COVID-19) is mainly based on molecular testing. General population studies have shown that chest Computed Tomography (CT) can also be useful.

OBJECTIVE

The study aims to examine the usefulness of high-resolution chest CT for early diagnosis of patients with suspected COVID-19.

DESIGN AND SETTING

This is a cross-sectional study from May 1, 2020, to August 31, 2021, at the COVID Hospital, Mexico City.

METHODS

This study examined the clinical, high-resolution chest CT imaging, and laboratory data of 160 patients who were suspected to have COVID-19. Patients with positive Reverse Transcription- Polymerase Chain Reaction (RT-PCR) testing and those with negative RT-PCR testing but clinical data compatible with COVID-19 and positive antibody testing were considered to have COVID-19 (positive). Sensitivity and specificity of CT for diagnosis of COVID-19 were calculated. p < 0.05 was considered significant.

RESULTS

Median age of 160 study patients was 58 years. The proportion of patients with groundglass pattern was significantly higher in patients with COVID-19 than in those without COVID (65.1% versus 0%; P = 0.005). COVID-19 was ruled out in sixteen (11.1%). Only four of the 132 patients diagnosed with COVID-19 (3.0%) did not show CT alterations (p < 0.001). Sensitivity and specificity of CT for COVID-19 diagnosis were 96.7% and 42.8%, respectively.

CONCLUSIONS

Chest CT can identify patients with COVID-19, as characteristic disease patterns are observed on CT in the early disease stage.

Chest multidetector computed tomography imaging of COVID-19 pneumonia patients with hematologic malignancies

Background

Data on the association between coronavirus disease 2019 (COVID-19) and the epidemiology and outcomes of hematological malignancies are limited. Hence, the present study aimed to assess the imaging findings using chest multidetector computed tomography (MDCT) in patients with hematologic malignancies who developed COVID-19 pneumonia.

Methods

This retrospective study included two groups, the first group consisted of COVID-19 infected patients with hematologic malignancies (100 patients), while the second group consisted of COVID-19 infected patients without hematologic malignancies or other comorbidities (100 patients). The hematological malignancies included in this study were non-Hodgkin’s lymphoma (40 patients), acute myeloid leukemia (25 patients), chronic lymphocytic leukemia (15 patients), multiple myeloma (10 patients), Hodgkin’s lymphoma (8 patients), and myelodysplastic syndrome (2 patients). Chest multidetector CT imaging was performed in all patients to assess for ground-glass opacity, consolidation, pleural effusion, and airway abnormalities.

Results

More than one CT finding was reported in each patient. No significant difference was observed in the ground-glass opacities (P=0.0594), nodule formation (P=0.2278), or airway thickening/dilatation (P=0.0566) between the two groups; meanwhile, a significant difference was observed in the degree of consolidation, the number of lobes affected, and pleural effusion (P=0.0001) as well as in the total lung severity (P=0.0001); minimal, mild, and severe affection rates; and (P=0.0047) moderate affection rates.

Conclusion

Early and reliable diagnosis of lung disease in COVID-19-infected patients may be achieved through multidetector CT imaging. Patients with hematological malignancies are more likely to have severe COVID-19 pneumonia, and radiologists should recognize the CT characteristics of this infection.

Is adenotonsillectomy safe in covid-19 era? Investigation of sars-cov2 in adenoid and tonsil tissues

OBJECTIVES

COVID-19 has seriously altered physicians' approach to patients and diseases, with a tendency to postpone elective procedures. Tonsillectomy, alone or with adenoidectomy, is one of the most common surgeries performed by otolaryngologists. Although they are generally accepted as elective surgeries, they significantly improve the quality of life, and postponing these surgeries for a long time can have deteriorative effects on the patients. We aimed to investigate the presence of SARS CoV-2 in the adenotonsillectomy materials to find out if performing adenotonsillectomy is safe during the COVID-19 pandemic.

METHODS

Forty-eight tissue samples from 32 patients that underwent tonsillectomy with or without adenoidectomy were investigated whose SARS-CoV-2 RT-PCR test in the samples obtained from nasopharyngeal (NP) and oropharyngeal (OP) swabs were negative within 24 h before the operation. While 16 patients underwent only tonsillectomy and one of their tonsils was investigated, 16 of the patients underwent adenotonsillectomy and their adenoid tissues were sent along with one of their tonsils. SARS-CoV-2 viral RNA was investigated with Real-Time PCR in tissue samples.

RESULTS

Two (4.2%) tissue samples had positive PCR tests for SARS-CoV-2, while 46 of them were negative. One of the positive patients had undergone tonsillectomy with the indication of chronic recurrent tonsillitis, and the other patient had undergone adenotonsillectomy for obstructive adenotonsillar hypertrophy. PCR test was positive in the adenoidectomy specimen and negative in the tonsillectomy specimen in this patient.

CONCLUSIONS

Adenotonsillectomy can be done safely in asymptomatic patients without a history of Covid-19, with a negative PCR test result obtained within the last 24 h.

Neutrophil-to-Lymphocyte Ratio and Absolute Lymphocyte Count as Early Diagnostic Tools for Corona Virus Disease 2019

Background and objectives

In comparison to real-time polymerase chain reaction (RT-PCR) testing, blood-related parameters including absolute lymphocyte count (ALC) and neutrophil-to-lymphocyte ratio (NLR) carry an indeterminate potential in the assessment of corona virus disease 2019 (COVID-19). Our main objective was to assess their efficacy in timely identification of COVID-19 patients and to determine whether these biomarkers can be employed as an early diagnostic tool in patients presenting with symptoms suggestive of COVID-19.

Methodology

This cross-sectional study was conducted at the Emergency Department of a Tertiary Care Hospital in Rawalpindi, Pakistan from November 2020 to March 2021. Patients suspected to have COVID-19 on a clinical basis (fever, cough or shortness of breath) were selected by using convenience non-probability sampling. RT-PCR was used to diagnose COVID-19 after evaluating NLR and ALC of the sample population. An NLR = 3.5 and ALC < 1 x 10³ cells/mm³ was considered as the cut-off value. Statistical analysis was conducted via SPSS 23.0 (IBM Corp., Armonk, NY). Chi-square and independent t-tests were used to correlate various data variables, while p-value <0.05 was considered significant.

Results

Out of the 172 subjects included in the study, the mean age was 40.6 ± 10.0 years, while 51% of individuals were males. Fever was found to be the most prevalent complaint (94%). Double RT-PCR testing showed that 51.2% of the population was RT-PCR positive, having a mean ALC of 1.4 ± 0.9 x 10³/mm³, significantly lower than RT-PCR negative cases (p < 0.001). In addition, NLR was drastically elevated for RT-PCR-positive individuals (p < 0.001) while it also had a distinctly high specificity of 91.7% among COVID-19 patients. Additionally, NLR did not correlate with any of the baseline patient-related parameters (presenting complaint, age, and gender).

Conclusion

NLR and ALC are potentially efficacious measures for an early diagnosis of COVID-19, and can be possibly utilized for an early diagnosis of COVID-19 suspects.

The broad spectrum of COVID-like patients initially negative at RT-PCR testing: a cohort study

BACKGROUND

Patients that arrive in the emergency department (ED) with COVID-19-like syndromes testing negative at the first RT-PCR represent a clinical challenge because of the lack of evidence about their management available in the literature. Our first aim was to quantify the proportion of patients testing negative at the first RT-PCR performed in our Emergency Department (ED) that were confirmed as having COVID-19 at the end of hospitalization by clinical judgment or by any subsequent microbiological testing. Secondly, we wanted to identify which variables that were available in the first assessment (ED variables) would have been useful in predicting patients, who at the end of the hospital stay were confirmed as having COVID-19 (false-negative at the first RT-PCR).

METHODS

We retrospectively collected data of 115 negative patients from2020, March 1st to 2020, May 15th. Three experts revised patients' charts collecting information on the whole hospital stay and defining patients as COVID-19 or NOT-COVID-19. We compared ED variables in the two groups by univariate analysis and logistic regression.

RESULTS

We classified 66 patients as COVID-19 and identified the other 49 as having a differential diagnosis (NOT-COVID), with a concordance between the three experts of 0.77 (95% confidence interval (95%CI) 0.66- 0.73). Only 15% of patients tested positive to a subsequent RT-PCR test, accounting for 25% of the clinically suspected. Having fever (odds ratio (OR) 3.32, (95%CI 0.97-12.31), p = 0.06), showing a typical pattern at the first lung ultrasound (OR 6.09, (95%CI 0.87-54.65), p = 0.08) or computed tomography scan (OR 4.18, (95%CI 1.11-17.86), p = 0.04) were associated with a higher probability of having COVID-19.

CONCLUSIONS

In patients admitted to ED with COVID-19 symptoms and negative RT-PCR a comprehensive clinical evaluation integrated with lung ultrasound and computed tomography could help to detect COVID-19 patients with a false negative RT-PCR result.

All around suboptimal health - a joint position paper of the Suboptimal Health Study Consortium and European Association for Predictive, Preventive and Personalised Medicine

First two decades of the twenty-first century are characterised by epidemics of non-communicable diseases such as many hundreds of millions of patients diagnosed with cardiovascular diseases and the type 2 diabetes mellitus, breast, lung, liver and prostate malignancies, neurological, sleep, mood and eye disorders, amongst others. Consequent socio-economic burden is tremendous. Unprecedented decrease in age of maladaptive individuals has been reported. The absolute majority of expanding non-communicable disorders carry a chronic character, over a couple of years progressing from reversible suboptimal health conditions to irreversible severe pathologies and cascading collateral complications. The time-frame between onset of SHS and clinical manifestation of associated disorders is the operational area for an application of reliable risk assessment tools and predictive diagnostics followed by the cost-effective targeted prevention and treatments tailored to the person. This article demonstrates advanced strategies in bio/medical sciences and healthcare focused on suboptimal health conditions in the frame-work of Predictive, Preventive and Personalised Medicine (3PM/PPPM). Potential benefits in healthcare systems and for society at large include but are not restricted to an improved life-quality of major populations and socio-economical groups, advanced professionalism of healthcare-givers and sustainable healthcare economy. Amongst others, following medical areas are proposed to strongly benefit from PPPM strategies applied to the identification and treatment of suboptimal health conditions:Stress overload associated pathologiesMale and female healthPlanned pregnanciesPeriodontal healthEye disordersInflammatory disorders, wound healing and pain management with associated complicationsMetabolic disorders and suboptimal body weightCardiovascular pathologiesCancersStroke, particularly of unknown aetiology and in young individualsSleep medicineSports medicineImproved individual outcomes under pandemic conditions such as COVID-19.

External validation of Machine Learning models for COVID-19 detection based on Complete Blood Count

PURPOSE

The rRT-PCR for COVID-19 diagnosis is affected by long turnaround time, potential shortage of reagents, high false-negative rates and high costs. Routine hematochemical tests are a faster and less expensive alternative for diagnosis. Thus, Machine Learning (ML) has been applied to hematological parameters to develop diagnostic tools and help clinicians in promptly managing positive patients. However, few ML models have been externally validated, making their real-world applicability unclear.

METHODS

We externally validate 6 state-of-the-art diagnostic ML models, based on Complete Blood Count (CBC) and trained on a dataset encompassing 816 COVID-19 positive cases. The external validation was performed based on two datasets, collected at two different hospitals in northern Italy and encompassing 163 and 104 COVID-19 positive cases, in terms of both error rate and calibration.

RESULTS AND CONCLUSION

We report an average AUC of 95% and average Brier score of 0.11, out-performing existing ML methods, and showing good cross-site transportability. The best performing model (SVM) reported an average AUC of 97.5% (Sensitivity: 87.5%, Specificity: 94%), comparable with the performance of RT-PCR, and was also the best calibrated. The validated models can be useful in the early identification of potential COVID-19 patients, due to the rapid availability of CBC exams, and in multiple test settings.

False-negative real-time polymerase chain reaction tests in COVID-19 patients: an epidemiological analysis of 302 patients

OBJECTIVES

Patients who arrive at the emergency department (ED) with COVID-19, who test negative at the first real-time polymerase chain reaction (RT-PCR), represent a clinical challenge. This study aimed to evaluate if the clinical manifestation at presentation, the laboratory and imaging results, and the prognosis of COVID-19 differ in patients who tested negative at the first RT-PCR compared with those who tested positive and also to evaluate if comorbid conditions patient-related or the period of arrival are associated with negative testing.

STUDY DESIGN

We retrospectively collected clinical data of patients who accessed the ED from March 1 to May 15, 2020.

METHODS

We compared clinical variables, comorbid conditions, and clinical outcomes in the two groups by univariate analysis and logistic regression.

RESULTS

Patients who tested negative at the first RT-PCR showed a higher prevalence of cardiopathy, immunosuppression, and diabetes, as well as a higher leukocyte and lower lymphocyte counts compared with patients who tested positive. A bilateral interstitial syndrome and a typical pattern at computed tomography scan were prevalent in the test-negative group. Test-negative patients were more likely to be admitted to the hospital but less likely to need admission in a high level of care ward. The false-negative rate increased from March to May.

CONCLUSION

False-negative RT-PCR COVID-19 patients present a similar spectrum of symptoms compared with positive cohort, but more comorbidities. Imaging helps to identify them. True positives had a higher risk of serious complications.

Contribution of neutrophil/lymphocyte ratio to the diagnostic efficiency of computed tomography and polymerase chain reaction in COVID-19 patients

Background:

6.5% of the country’s population was diagnosed with COVID-19 disease. Computed tomography scanning and polymerase chain reaction tests are considered reliable methods for the detection of COVID-19. However, the specificity and reliability of polymerase chain reaction tests and ground-glass opacity (GGO) on thorax computed tomography images in diagnosing COVID-19 are still being disputed. Our aim was to compare the neutrophil/lymphocyte ratio, whose efficiency in differentiating between viral and bacterial infections has previously been studied, with computed tomography and polymerase chain reaction for COVID-19 diagnosis.

Materials and methods:

This was a retrospective study that included patients treated in a tertiary care hospital emergency service pandemic polyclinic between 14 March and 1 June 2020. The neutrophil/lymphocyte ratios of patients with polymerase chain reaction tests and ground-glass opacities on computed tomography were calculated. The neutrophil/lymphocyte ratios of polymerase chain reaction-negative patients with computed tomography images were compared with the neutrophil/lymphocyte ratios of polymerase chain reaction-positive patients with computed tomography images.

Results:

A total of 631 patients were included in this study. Thorax computed tomography scans were obtained from all patients. The mean neutrophil/lymphocyte ratio of patients with ground-glass opacities was 3.50 ± 2.12, whereas that of patients without ground-glass opacities was 2.90 ± 2.01. This difference was also statistically significant. Polymerase chain reaction swab samples were obtained from 282 patients (44.7%). The mean neutrophil/lymphocyte ratio of polymerase chain reaction-positive patients was 2.38 ± 1.02, whereas that of polymerase chain reaction-negative patients was 3.97 ± 2.25. The difference was statistically significant.

Conclusion:

Many studies are undoubtedly required to determine the efficiency of the neutrophil/lymphocyte ratio in COVID-19 diagnosis. However, we postulate that evaluating the neutrophil/lymphocyte ratio along with computed tomography and polymerase chain reaction can assist in the diagnosis of patients.

Review of Recent Technologies for Tackling COVID-19

The current pandemic caused by the COVID-19 virus requires more effort, experience, and science-sharing to overcome the damage caused by the pathogen. The fast and wide human-to-human transmission of the COVID-19 virus demands a significant role of the newest technologies in the form of local and global computing and information sharing, data privacy, and accurate tests. The advancements of deep neural networks, cloud computing solutions, blockchain technology, and beyond 5G (B5G) communication have contributed to the better management of the COVID-19 impacts on society. This paper reviews recent attempts to tackle the COVID-19 situation using these technological advancements.

A Bayesian method for synthesizing multiple diagnostic outcomes of COVID-19 tests

The novel coronavirus disease 2019 (COVID-19) has spread worldwide and threatened human life. Diagnosis is crucial to contain the spread of SARS-CoV-2 infections and save lives. Diagnostic tests for COVID-19 have varying sensitivity and specificity, and the false-negative results would have substantial consequences to patient treatment and pandemic control. To detect all suspected infections, multiple testing is widely used. However, it may be challenging to build an assertion when the testing results are inconsistent. Considering the situation where there is more than one diagnostic outcome for each subject, we proposed a Bayesian probabilistic framework based on the sensitivity and specificity of each diagnostic method to synthesize a posterior probability of being infected by SARS-CoV-2. We demonstrated that the synthesized posterior outcome outperformed each individual testing outcome. A user-friendly web application was developed to implement our analytic framework with free access via http://www2.ccrb.cuhk.edu.hk/statgene/COVID_19/. The web application enables the real-time display of the integrated outcome incorporating two or more tests and calculated based on Bayesian posterior probability. A simulation-based assessment demonstrated higher accuracy and precision of the Bayesian probabilistic model compared with a single-test outcome. The online tool developed in this study can assist physicians in making clinical evaluations by effectively integrating multiple COVID-19 tests.

Chest CT findings in patients with coronavirus disease 2019 (COVID-19): a comprehensive review

The objective of this review was to summarize the most pertinent CT imaging findings in patients with coronavirus disease 2019 (COVID-19). A literature search retrieved eligible studies in PubMed, EMBASE, Cochrane Library and Web of Science up to June 1, 2020. A comprehensive review of publications of the Chinese Medical Association about COVID-19 was also performed. A total of 84 articles with more than 5340 participants were included and reviewed. Chest CT comprised 92.61% of abnormal CT findings overall. Compared with real-time polymerase chain reaction result, CT findings has a sensitivity of 96.14% but a low specificity of 40.48% in diagnosing COVID-19. Ground glass opacity (GGO), pure (57.31%) or mixed with consolidation (41.51%) were the most common CT features with a majority of bilateral (80.32%) and peripheral (66.21%) lung involvement. The opacity might associate with other imaging features, including air bronchogram (41.07%), vascular enlargement (54.33%), bronchial wall thickening (19.12%), crazy-paving pattern (27.55%), interlobular septal thickening (42.48%), halo sign (25.48%), reverse halo sign (12.29%), bronchiectasis (32.44%), and pulmonary fibrosis (26.22%). Other accompanying signs including pleural effusion, lymphadenopathy and pericardial effusion were rare, but pleural thickening was common. The younger or early stage patients tended to have more GGOs, while extensive/multilobar involvement with consolidation was prevalent in the older or severe population. Children with COVID-19 showed significantly lower incidences of some ancillary findings than those of adults and showed a better performance on CT during follow up. Follow-up CT showed GGO lesions gradually decreased, and the consolidation lesions first increased and then remained relatively stable at 6-13 days, and then absorbed and fibrosis increased after 14 days. Chest CT imaging is an important component in the diagnosis, staging, disease progression and follow-up of patients with COVID-19.

Preoperative chest computed tomography in emergency surgery during COVID-19 pandemic

BACKGROUND

The COVID-19 pandemic challenges the recommendations for patients' preoperative assessment for preventing severe acute respiratory syndrome coronavirus type 2 transmission and COVID-19-associated postoperative complications and morbidities.

PURPOSE

To evaluate the contribution of chest computed tomography for preoperatively assessing patients who are not suspected of being infected with COVID-19 at the time of referral.

METHODS

Candidates for emergency surgery screened via chest computed tomography from 8 to 27 April 2020 were retrospectively evaluated. Computed tomography images were analysed for the presence of COVID-19-associated intrapulmonary changes. When applicable, laboratory and recorded clinical symptoms were extracted.

RESULTS

Eighty-eight patients underwent preoperative chest computed tomography; 24% were rated as moderately suspicious and 11% as highly suspicious on computed tomography. Subsequent reverse transcription polymerase chain reaction (RT-PCR) was performed for seven patients, all of whom tested negative for COVID-19. Seven patients showed COVID-19-associated clinical symptoms, and most were classified as being mildly to moderately severe as per the clinical classification grading system. Only one case was severe. Four cases underwent RT-PCR with negative results.

CONCLUSION

In a cohort without clinical suspicion of COVID-19 infection upon referral, preoperative computed tomography during the COVID-19 pandemic can yield a high suspicion of infection, even if the patient lacks clinical symptoms and is RT-PCR-negative. No recommendations can be made based on our results but contribute to the debate.

Asymptomatic COVID-19 infection: diagnosis, transmission, population characteristics

A novel coronavirus first discovered in late December 2019 has spread to many countries around the world. An increasing number of asymptomatic patients have been reported and their ability to spread the virus has been proven. This brings major challenges to the control of the transmission. The discovery and control of asymptomatic patients with COVID-19 are the key issues in future epidemic prevention and recovery. In this narrative review, we summarise the existing knowledge about asymptomatic patients and put forward detection methods that are suitable for finding such patients. Besides, we compared the characteristics and transmissibility of asymptomatic patients in different populations in order to find the best screening, diagnosis and control measures for different populations. Comprehensive preventive advice is also provided to prevent the spread of infection from asymptomatic patients.

Role of radiology in RT-PCR negative COVID-19 pneumonia: Review and recommendations

Currently, RT-PCR is the gold standard for diagnosing SARS-CoV-2 infection. However, due to the time-consuming laboratory tests and the low positivity rate of RT-PCR, it cannot be an ideal screening tool for infected population. In this review article, we have reviewed studies related to RT-PCR and CT chest and we would like to give our recommendations. Depending upon the patient's clinical symptoms and radiology imaging typical of viral pneumonia compatible with COVID-19 infection, clinicians need to consider isolation of these patients early even if the RT-PCR test is negative.

COVID-19 and Preparing Planetary Health for Future Ecological Crises: Hopes from Glycomics for Vaccine Innovation

A key lesson emerging from COVID-19 is that pandemic proofing planetary health against future ecological crises calls for systems science and preventive medicine innovations. With greater proximity of the human and animal natural habitats in the 21st century, it is also noteworthy that zoonotic infections such as COVID-19 that jump from animals to humans are increasingly plausible in the coming decades. In this context, glycomics technologies and the third alphabet of life, the sugar code, offer veritable prospects to move omics systems science from discovery to diverse applications of relevance to global public health and preventive medicine. In this expert review, we discuss the science of glycomics, its importance in vaccine development, and the recent progress toward discoveries on the sugar code that can help prevent future infectious outbreaks that are looming on the horizon in the 21st century. Glycomics offers veritable prospects to boost planetary health, not to mention the global scientific capacity for vaccine innovation against novel and existing infectious agents.

A deep learning integrated radiomics model for identification of coronavirus disease 2019 using computed tomography

Since its first outbreak, Coronavirus Disease 2019 (COVID-19) has been rapidly spreading worldwide and caused a global pandemic. Rapid and early detection is essential to contain COVID-19. Here, we first developed a deep learning (DL) integrated radiomics model for end-to-end identification of COVID-19 using CT scans and then validated its clinical feasibility. We retrospectively collected CT images of 386 patients (129 with COVID-19 and 257 with other community-acquired pneumonia) from three medical centers to train and externally validate the developed models. A pre-trained DL algorithm was utilized to automatically segment infected lesions (ROIs) on CT images which were used for feature extraction. Five feature selection methods and four machine learning algorithms were utilized to develop radiomics models. Trained with features selected by L1 regularized logistic regression, classifier multi-layer perceptron (MLP) demonstrated the optimal performance with AUC of 0.922 (95% CI 0.856-0.988) and 0.959 (95% CI 0.910-1.000), the same sensitivity of 0.879, and specificity of 0.900 and 0.887 on internal and external testing datasets, which was equivalent to the senior radiologist in a reader study. Additionally, diagnostic time of DL-MLP was more efficient than radiologists (38 s vs 5.15 min). With an adequate performance for identifying COVID-19, DL-MLP may help in screening of suspected cases.

Passive Microwave Radiometry for the Diagnosis of Coronavirus Disease 2019 Lung Complications in Kyrgyzstan

The global spread of severe acute respiratory syndrome coronavirus 2, which causes coronavirus disease 2019 (COVID-19), could be due to limited access to diagnostic tests and equipment. Currently, most diagnoses use the reverse transcription polymerase chain reaction (RT-PCR) and chest computed tomography (CT). However, challenges exist with CT use due to infection control, lack of CT availability in low- and middle-income countries, and low RT-PCR sensitivity. Passive microwave radiometry (MWR), a cheap, non-radioactive, and portable technology, has been used for cancer and other diseases’ diagnoses. Here, we tested MWR use first time for the early diagnosis of pulmonary COVID-19 complications in a cross-sectional controlled trial in order to evaluate MWR use in hospitalized patients with COVID-19 pneumonia and healthy individuals. We measured the skin and internal temperature using 30 points identified on the body, for both lungs. Pneumonia and lung damage were diagnosed by both CT scan and doctors’ diagnoses (pneumonia+/pneumonia−). COVID-19 was determined by RT-PCR (covid+/covid−). The best MWR results were obtained for the pneumonia−/covid− and pneumonia+/covid+ groups. The study suggests that MWR could be used for diagnosing pneumonia in COVID-19 patients. Since MWR is inexpensive, its use will ease the financial burden for both patients and countries. Clinical Trial Number: NCT04568525.

The relationship between positivity for COVID-19 RT-PCR and symptoms, clinical findings, and mortality in Turkey

Introduction: This study aimed to assess the correlation between nucleic acid amplification test (real-time reverse transcription-polymerase chain reaction, RT-PCR) positivity of patients presenting with suspected COVID-19 and pneumonic infiltration consistent with COVID-19-specific pneumonia diagnosis on thoracic computed tomography (CT), with symptoms, laboratory findings, and clinical progression.Methods: The study included 286 patients (female:male 131:155; mean age, 53.3 ± 17.9 years) who were divided into two groups according to their RT-PCR test results. The symptoms, laboratory examinations, clinical findings, and thoracic CT imaging of the patients were evaluated.Results: While the physical examination, comorbidities, and total CT scores were similar between the groups, taste/smell abnormalities were observed more frequently in the PCR-positive group. The use of moxifloxacin, lopinavir/ritonavir, and tocilizumab was higher in the PCR-positive group (p = 0.016, p < 0.001, and p = 0.002, respectively). The duration of hospitalization, intensive care requirement, and mortality rate of the studied groups did not differ between the groups.Conclusions: Among patients presenting with suspected COVID-19 and pneumonic infiltration consistent with COVID-19 on thoracic CT, the symptoms, physical examination, total CT scores, duration of hospitalization, intensive care requirement, and mortality rate were similar between RT-PCR-positive and RT-PCR-negative patients. However, PCR-positive patients appeared to require more specific treatments.

JCS: An Explainable COVID-19 Diagnosis System by Joint Classification and Segmentation

Recently, the coronavirus disease 2019 (COVID-19) has caused a pandemic disease in over 200 countries, influencing billions of humans. To control the infection, identifying and separating the infected people is the most crucial step. The main diagnostic tool is the Reverse Transcription Polymerase Chain Reaction (RT-PCR) test. Still, the sensitivity of the RT-PCR test is not high enough to effectively prevent the pandemic. The chest CT scan test provides a valuable complementary tool to the RT-PCR test, and it can identify the patients in the early-stage with high sensitivity. However, the chest CT scan test is usually time-consuming, requiring about 21.5 minutes per case. This paper develops a novel Joint Classification and Segmentation (JCS) system to perform real-time and explainable COVID- 19 chest CT diagnosis. To train our JCS system, we construct a large scale COVID- 19 Classification and Segmentation (COVID-CS) dataset, with 144,167 chest CT images of 400 COVID- 19 patients and 350 uninfected cases. 3,855 chest CT images of 200 patients are annotated with fine-grained pixel-level labels of opacifications, which are increased attenuation of the lung parenchyma. We also have annotated lesion counts, opacification areas, and locations and thus benefit various diagnosis aspects. Extensive experiments demonstrate that the proposed JCS diagnosis system is very efficient for COVID-19 classification and segmentation. It obtains an average sensitivity of 95.0% and a specificity of 93.0% on the classification test set, and 78.5% Dice score on the segmentation test set of our COVID-CS dataset. The COVID-CS dataset and code are available at https://github.com/yuhuan-wu/JCS.

Thoracic imaging tests for the diagnosis of COVID-19

BACKGROUND

The respiratory illness caused by SARS-CoV-2 infection continues to present diagnostic challenges. Early research showed thoracic (chest) imaging to be sensitive but not specific in the diagnosis of coronavirus disease 2019 (COVID-19). However, this is a rapidly developing field and these findings need to be re-evaluated in the light of new research. This is the first update of this 'living systematic review'. This update focuses on people suspected of having COVID-19 and excludes studies with only confirmed COVID-19 participants.

OBJECTIVES

To evaluate the diagnostic accuracy of thoracic imaging (computed tomography (CT), X-ray and ultrasound) in people with suspected COVID-19.

SEARCH METHODS

We searched the COVID-19 Living Evidence Database from the University of Bern, the Cochrane COVID-19 Study Register, The Stephen B. Thacker CDC Library, and repositories of COVID-19 publications through to 22 June 2020. We did not apply any language restrictions.

SELECTION CRITERIA

We included studies of all designs that recruited participants of any age group suspected to have COVID-19, and which reported estimates of test accuracy, or provided data from which estimates could be computed. When studies used a variety of reference standards, we retained the classification of participants as COVID-19 positive or negative as used in the study.

DATA COLLECTION AND ANALYSIS

We screened studies, extracted data, and assessed the risk of bias and applicability concerns using the QUADAS-2 domain-list independently, in duplicate. We categorised included studies into three groups based on classification of index test results: studies that reported specific criteria for index test positivity (group 1); studies that did not report specific criteria, but had the test reader(s) explicitly classify the imaging test result as either COVID-19 positive or negative (group 2); and studies that reported an overview of index test findings, without explicitly classifying the imaging test as either COVID-19 positive or negative (group 3). We presented the results of estimated sensitivity and specificity using paired forest plots, and summarised in tables. We used a bivariate meta-analysis model where appropriate. We presented uncertainty of the accuracy estimates using 95% confidence intervals (CIs).

MAIN RESULTS

We included 34 studies: 30 were cross-sectional studies with 8491 participants suspected of COVID-19, of which 4575 (54%) had a final diagnosis of COVID-19; four were case-control studies with 848 cases and controls in total, of which 464 (55%) had a final diagnosis of COVID-19. Chest CT was evaluated in 31 studies (8014 participants, 4224 (53%) cases), chest X-ray in three studies (1243 participants, 784 (63%) cases), and ultrasound of the lungs in one study (100 participants, 31 (31%) cases). Twenty-six per cent (9/34) of all studies were available only as preprints. Nineteen studies were conducted in Asia, 10 in Europe, four in North America and one in Australia. Sixteen studies included only adults, 15 studies included both adults and children and one included only children. Two studies did not report the ages of participants. Twenty-four studies included inpatients, four studies included outpatients, while the remaining six studies were conducted in unclear settings. The majority of included studies had a high or unclear risk of bias with respect to participant selection, index test, reference standard, and participant flow. For chest CT in suspected COVID-19 participants (31 studies, 8014 participants, 4224 (53%) cases) the sensitivity ranged from 57.4% to 100%, and specificity ranged from 0% to 96.0%. The pooled sensitivity of chest CT in suspected COVID-19 participants was 89.9% (95% CI 85.7 to 92.9) and the pooled specificity was 61.1% (95% CI 42.3 to 77.1). Sensitivity analyses showed that when the studies from China were excluded, the studies from other countries demonstrated higher specificity compared to the overall included studies. When studies that did not classify index tests as positive or negative for COVID-19 (group 3) were excluded, the remaining studies (groups 1 and 2) demonstrated higher specificity compared to the overall included studies. Sensitivity analyses limited to cross-sectional studies, or studies where at least two reverse transcriptase polymerase chain reaction (RT-PCR) tests were conducted if the first was negative, did not substantively alter the accuracy estimates. We did not identify publication status as a source of heterogeneity. For chest X-ray in suspected COVID-19 participants (3 studies, 1243 participants, 784 (63%) cases) the sensitivity ranged from 56.9% to 89.0% and specificity from 11.1% to 88.9%. The sensitivity and specificity of ultrasound of the lungs in suspected COVID-19 participants (1 study, 100 participants, 31 (31%) cases) were 96.8% and 62.3%, respectively. We could not perform a meta-analysis for chest X-ray or ultrasound due to the limited number of included studies.

AUTHORS' CONCLUSIONS

Our findings indicate that chest CT is sensitive and moderately specific for the diagnosis of COVID-19 in suspected patients, meaning that CT may have limited capability in differentiating SARS-CoV-2 infection from other causes of respiratory illness. However, we are limited in our confidence in these results due to the poor study quality and the heterogeneity of included studies. Because of limited data, accuracy estimates of chest X-ray and ultrasound of the lungs for the diagnosis of suspected COVID-19 cases should be carefully interpreted. Future diagnostic accuracy studies should pre-define positive imaging findings, include direct comparisons of the various modalities of interest on the same participant population, and implement improved reporting practices. Planned updates of this review will aim to: increase precision around the accuracy estimates for chest CT (ideally with low risk of bias studies); obtain further data to inform accuracy of chest X-rays and ultrasound; and obtain data to further fulfil secondary objectives (e.g. 'threshold' effects, comparing accuracy estimates across different imaging modalities) to inform the utility of imaging along different diagnostic pathways.

Contribution of CT Features in the Diagnosis of COVID-19

The outbreak of novel coronavirus disease 2019 (COVID-19) first occurred in Wuhan, Hubei Province, China, and spread across the country and worldwide quickly. It has been defined as a major global health emergency by the World Health Organization (WHO). As this is a novel virus, its diagnosis is crucial to clinical treatment and management. To date, real-time reverse transcription-polymerase chain reaction (RT-PCR) has been recognized as the diagnostic criterion for COVID-19. However, the results of RT-PCR can be complemented by the features obtained in chest computed tomography (CT). In this review, we aim to discuss the diagnosis and main CT features of patients with COVID-19 based on the results of the published literature, in order to enhance the understanding of COVID-19 and provide more detailed information regarding treatment.

Preoperative COVID-19 testing for elective vitreoretinal surgeries: Experience from a major tertiary care institute in South India

PURPOSE

To study the prevalence of asymptomatic SARS-CoV-2 virus infection (COVID-19) among patients undergoing elective vitreoretinal surgeries at a tertiary care eye hospital.

METHODS

This cross-sectional, observational study was performed between July 16, 2020 and August 31, 2020, in the retina clinic of a tertiary care eye hospital in south India. All patients undergoing elective retinal surgical procedures underwent RT-PCR testing for SARS-CoV-2 before being posted for surgery and after obtaining informed consent. Patients planned for surgery under general anesthesia underwent additional computed tomography of the chest. Testing strategies and outcomes were documented.

RESULTS

Out of a total of 413 patients who were given appointments for surgery during this period, nine patients (2.2%) were found to have positive RT-PCR for SARS-CoV-2, and their surgeries were postponed. The test positivity (prevalence) rate of asymptomatic COVID-19 infection among all elective vitreoretinal surgical patients in our hospital was 2.2%. None of the patients were symptomatic for COVID-19.

CONCLUSION

Our results showed that among patients visiting high volume ophthalmic centers in the near future, approximately 1 in 45 patients may be asymptomatic, SARS-CoV-2 RT-PCR positive. Asymptomatic COVID-19 patients may lead to chances of transmission of the virus inside healthcare facilities among other visiting patients and healthcare workers.

A higher D-dimer threshold for predicting pulmonary embolism in patients with COVID-19: a retrospective study

Purpose

COVID-19 raises D-dimer (DD) levels even in the absence of pulmonary embolism (PE), resulting in an increase in computed tomography pulmonary angiogram (CTPA) requests. Our purpose is to determine whether there are differences between DD values in PE-positive and PE-negative COVID-19 patients and, if so, to establish a new cutoff value which accurately determines when a CTPA is needed.

Methods

This study retrospectively analyzed all COVID-19 patients who underwent a CTPA due to suspected PE between March 1 and April 30, 2020, at Ramón y Cajal University Hospital, Madrid (Spain). DD level comparisons between PE-positive and PE-negative groups were made using Student’s t test. The optimal DD cutoff value to predict PE risk in COVID-19 patients was calculated in the ROC curve.

Results

Two hundred forty-two patients were included in the study. One hundred fifty-one (62%) were men and the median age was 68 years (IQR 55–78). An increase of DD (median 3260; IQR 1203–9625 ng/mL) was detected in 205/242 (96%) patients. 73/242 (30%) of the patients were diagnosed with PE on CTPA. The DD median value was significantly higher (p < .001) in the PE-positive group (7872, IQR 3150–22,494 ng/mL) compared with the PE-negative group (2009, IQR 5675–15,705 ng/mL). The optimal cutoff value for DD to predict PE was 2903 ng/mL (AUC was 0.76 [CI 95% 0.69–0.83], sensitivity 81%). The overall mortality rate was 16% (39/242).

Conclusion

A higher threshold (2903 ng/mL) for D-dimer could predict the risk of PE in COVID-19 patients with a sensitivity of 81%.

Reverse-transcriptase polymerase chain reaction versus chest computed tomography for detecting early symptoms of COVID-19. A diagnostic accuracy systematic review and meta-analysis

BACKGROUND

A positive real-time reverse-transcriptase polymerase chain reaction (RT-PCR) for SARS CoV-2, from nasopharyngeal swabs, is the current gold standard diagnostic test for this virus and has sensitivity of 60-70%. Some studies have demonstrated a significant number of false-negative RT-PCR tests while displaying significant tomographic findings, in the early days of symptoms of COVID-19.

OBJECTIVE

To compare accuracy between RT-PCR and computed tomography (CT) for detecting COVID-19 in the first week of its symptoms during the pandemic.

DESIGN AND SETTING

Systematic review of comparative studies of diagnostic accuracy within the Evidence-based Health Program of a federal university in São Paulo (SP), Brazil.

METHODS

A systematic search of the relevant literature was conducted in the PubMed, EMBASE, Cochrane Library, CINAHL and LILACS databases, for articles published up to June 6, 2020, relating to studies evaluating the diagnostic accuracy of RT-PCR and chest CT for COVID-19 diagnoses. The QUADAS 2 tool was used for methodological quality evaluation.

RESULTS

In total, 1204 patients with COVID-19 were evaluated; 1045 had tomographic findings while 755 showed positive RT-PCR for COVID-19. RT-PCR demonstrated 81.4% sensitivity, 100% specificity and 92.3% accuracy. Chest CT demonstrated 95.3% sensitivity, 43.8% specificity and 63.3% accuracy.

CONCLUSION

The high sensitivity and detection rates shown by CT demonstrate that this technique has a high degree of importance in the early stages of the disease. During an outbreak, the higher prevalence of the condition increases the positive predictive value of CT.

REGISTRATION NUMBER

DOI: 10.17605/OSF.IO/UNGHA in the Open Science Framework.

Are two consecutive negative RT-PCR results enough to rule out COVID-19?

The coronavirus disease 2019 (COVID-19) pandemic is redefining the world we live in, and scientists are struggling to find the best severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) diagnostic tool. Routine testing is currently performed using real-time reverse transcription PCR (RT-PCR) of upper or lower respiratory tract secretions. We sought to demonstrate the importance of conducting RT-PCR using deep sampling when initial upper respiratory testing is negative in cases of high index of suspicion for COVID-19. We present the case of a 47-year-old man admitted for fever and bilateral pneumonia diagnosed via chest computed tomographic scan amidst the early peak of the COVID-19 pandemic, suggesting a SARS-CoV-2 infection. Two RT-PCR results from nasopharyngeal swab samples were negative. A bronchoscopy was then performed, and RT-PCR testing on bronchoalveolar lavage samples yielded positive results, confirming the diagnosis of COVID-19 pneumonia. RT-PCR samples of the lower respiratory tract likely contain a higher virus load and thus retain a higher sensitivity for SARS-CoV-2 detection.