The estimation of diagnostic accuracy of tests for COVID-19: A scoping review

Pitfalls and biases in neuroepidemiological studies of COVID-19 and the nervous system: a critical appraisal of the current evidence and future directions

BACKGROUND

Neurological manifestations frequently occur in individuals with COVID-19, manifesting during the acute phase, persisting beyond the resolution of acute symptoms, and appearing days or weeks after the initial onset of COVID-19 symptoms. However, predicting the incidence, course, and outcome of these neurological manifestations at the individual patient level remains challenging. Biases in study design and limitations in data collection may contribute to the inconsistency and limited validity of the reported findings. Herein, we focused on critically appraising pitfalls and biases of prior reports and provide guidance for improving the quality and standardization of future research. Patients with COVID-19 exhibit diverse demographic features, sociocultural backgrounds, lifestyle habits, and comorbidities, all of which can influence the severity and progression of the infection and its impact on other organ systems. Overlooked or undocumented comorbidities and related treatments may contribute to neurological sequelae, which may not solely be attributable to COVID-19. It is crucial to consider the potential side effects of vaccines in relation to neurological manifestations.

CONCLUSION

To investigate neurological manifestations of COVID-19, it is essential to employ valid and reliable diagnostic criteria and standard definitions of the factors of interest. Although population-based studies are lacking, well-defined inception cohorts, including hospitalized individuals, outpatients, and community residents, can serve as valuable compromises. These cohorts should be evaluated for the presence of common comorbidities, alongside documenting the primary non-neurological manifestations of the infectious disease. Lastly, patients with COVID-19 should be followed beyond the acute phase to assess the persistence, duration, and severity of neurological symptoms, signs, or diseases.

Droplet digital recombinase polymerase amplification for multiplexed detection of human coronavirus

Since the outbreak of coronavirus 2019 (COVID-19), detection technologies have been attracting a great deal of attention in molecular diagnosis applications. In particular, the droplet digital PCR (ddPCR) has become a promising tool as it offers absolute quantification of target nucleic acids with high specificity and sensitivity. In recent years, the combination of the isothermal amplification strategies has made ddPCR a popular method for on-site testing by enabling amplification at a constant temperature. However, the current isothermal ddPCR assays are still challenging due to inherent non-specific amplification. In this paper, we present a multiplexed droplet digital recombinase polymerase amplification (MddRPA) with precise initiation of the reaction. First, the reaction temperature and dynamic range of reverse transcription (RT) and RPA were characterized by real-time monitoring of fluorescence intensities. Using a droplet-based microfluidic chip, the master mix and the initiator were fractionated and rapidly mixed within well-confined droplets. Due to the high heat transfer and mass transfer of the droplets, the precise initiation of the amplification was enabled and the entire assay could be conducted within 30 min. The concentrations of target RNA in the range from 5 copies per μL to 2500 copies per μL could be detected with high linearity (R² > 0.999). Furthermore, the multiplexed detection of three types of human coronaviruses was successfully demonstrated with high specificity (>96%). Finally, we compared the performance of the assay with a commercial RT-qPCR system using COVID-19 clinical samples. The MddRPA assay showed a 100% concordance with the RT-qPCR results, indicating its reliability and accuracy in detecting SARS-CoV-2 nucleic acids in clinical samples. Therefore, our MddRPA assay with rapid detection, precise quantification, and multiplexing capability would be an interesting method for molecular diagnosis of viral infections.

Diagnostic accuracy of point-of-care lung ultrasound for COVID-19: a systematic review and meta-analysis

BACKGROUND

Point-of-care (POC) lung ultrasound (LUS) is widely used in the emergency setting and there is an established evidence base across a range of respiratory diseases, including previous viral epidemics. The necessity for rapid testing combined with the limitations of other diagnostic tests has led to the proposal of various potential roles for LUS during the COVID-19 pandemic. This systematic review and meta-analysis focused specifically on the diagnostic accuracy of LUS in adult patients presenting with suspected COVID-19 infection.

METHODS

Traditional and grey-literature searches were performed on 1 June 2021. Two authors independently carried out the searches, selected studies and completed the Quality Assessment Tool for Diagnostic Test Accuracy Studies (QUADAS-2). Meta-analysis was carried out using established open-source packages in R. We report overall sensitivity, specificity, positive and negative predictive values, and the hierarchical summary receiver operating characteristic curve for LUS. Heterogeneity was determined using the I² statistic.

RESULTS

Twenty studies were included, published between October 2020 and April 2021, providing data from a total of 4314 patients. The prevalence and admission rates were generally high across all studies. Overall, LUS was found to be 87.2% sensitive (95% CI 83.6 to 90.2) and 69.5% specific (95% CI 62.2 to 72.5) and demonstrated overall positive and negative likelihood ratios of 3.0 (95% CI 2.3 to 4.1) and 0.16 (95% CI 0.12 to 0.22), respectively. Separate analyses for each reference standard revealed similar sensitivities and specificities for LUS. Heterogeneity was found to be high across the studies. Overall, the quality of studies was low with a high risk of selection bias due to convenience sampling. There were also applicability concerns because all studies were undertaken during a period of high prevalence.

CONCLUSION

During a period of high prevalence, LUS had a sensitivity of 87% for the diagnosis of COVID-19 infection. However, more research is required to confirm these results in more generalisable populations, including those less likely to be admitted to hospital.

PROSPERO REGISTRATION NUMBER

CRD42021250464.

SARS-CoV-2 Diagnosis Using Transcriptome Data: A Machine Learning Approach

SARS-CoV-2 pandemic is the big issue of the whole world right now. The health community is struggling to rescue the public and countries from this spread, which revives time to time with different waves. Even the vaccination seems to be not prevents this spread. Accurate identification of infected people on time is essential these days to control the spread. So far, Polymerase chain reaction (PCR) and rapid antigen tests are widely used in this identification, accepting their own drawbacks. False negative cases are the menaces in this scenario. To avoid these problems, this study uses machine learning techniques to build a classification model with higher accuracy to filter the COVID-19 cases from the non-COVID individuals. Transcriptome data of the SARS-CoV-2 patients along with the control are used in this stratification using three different feature selection algorithms and seven classification models. Differently expressed genes also studied between these two groups of people and used in this classification. Results shows that mutual information (or DEGs) along with naïve Bayes (or SVM) gives the best accuracy (0.98 ± 0.04) among these methods.

Supplementary Information

The online version contains supplementary material available at 10.1007/s42979-023-01703-6.

Clinical features and outcomes of COVID-19 patients hospitalized for psychiatric disorders: a French multi-centered prospective observational study

BACKGROUND

Patients with psychiatric disorders are exposed to high risk of COVID-19 and increased mortality. In this study, we set out to assess the clinical features and outcomes of patients with current psychiatric disorders exposed to COVID-19.

METHODS

This multi-center prospective study was conducted in 22 psychiatric wards dedicated to COVID-19 inpatients between 28 February and 30 May 2020. The main outcomes were the number of patients transferred to somatic care units, the number of deaths, and the number of patients developing a confusional state. The risk factors of confusional state and transfer to somatic care units were assessed by a multivariate logistic model. The risk of death was analyzed by a univariate analysis.

RESULTS

In total, 350 patients were included in the study. Overall, 24 (7%) were transferred to medicine units, 7 (2%) died, and 51 (15%) patients presented a confusional state. Severe respiratory symptoms predicted the transfer to a medicine unit [odds ratio (OR) 17.1; confidence interval (CI) 4.9-59.3]. Older age, an organic mental disorder, a confusional state, and severe respiratory symptoms predicted mortality in univariate analysis. Age >55 (OR 4.9; CI 2.1-11.4), an affective disorder (OR 4.1; CI 1.6-10.9), and severe respiratory symptoms (OR 4.6; CI 2.2-9.7) predicted a higher risk, whereas smoking (OR 0.3; CI 0.1-0.9) predicted a lower risk of a confusional state.

CONCLUSION

COVID-19 patients with severe psychiatric disorders have multiple somatic comorbidities and have a risk of developing a confusional state. These data underline the need for extreme caution given the risks of COVID-19 in patients hospitalized for psychiatric disorders.

Sensitivity and Diagnostic Yield of the First SARS-CoV-2 Nucleic Acid Amplification Test Performed for Patients Presenting to the Hospital

IMPORTANCE

Early and accurate diagnostic testing for SARS-CoV-2 is essential to initiate appropriate treatment and infection control and prevention measures among patients presenting to the hospital.

OBJECTIVE

To evaluate the diagnostic sensitivity of the SARS-CoV-2 nucleic acid amplification test (NAAT) performed within 24 hours of arrival to the emergency department among a nationally representative sample of patients.

DESIGN, SETTING, AND PARTICIPANTS

This diagnostic study was conducted at 47 hospitals across 7 provinces in Canada participating in the Canadian COVID-19 Rapid Response Emergency Department Network among consecutive eligible patients presenting to a participating emergency department who were tested for SARS-CoV-2 from March 1, 2020, to December 31, 2021. Patients not tested within 24 hours of arrival and those presenting with a positive result from a test performed in the community were excluded.

MAIN OUTCOMES AND MEASURES

The primary outcome was a positive result from the SARS-CoV-2 NAAT. Outcome measures were the diagnostic sensitivity and yield of the SARS-CoV-2 NAAT.

RESULTS

Of 132 760 eligible patients (66 433 women [50.0%]; median age, 57 years [IQR, 37-74 years]), 17 174 (12.9%) tested positive for SARS-CoV-2 within 14 days of their first NAAT. The diagnostic sensitivity of the SARS-CoV-2 NAAT was 96.2% (17 070 of 17 740 [95% CI, 95.9%-96.4%]) among all of the tests performed. Estimates ranged from a high of 97.7% (1710 of 1751 [95% CI, 96.8%-98.3%]) on day 2 of symptoms to a low of 90.4% (170 of 188 [95% CI, 85.3%-94.2%]) on day 11 of symptoms among patients presenting with COVID-19 symptoms. Among patients reporting COVID-19 symptoms, the sensitivity of the SARS-CoV-2 NAAT was 97.1% (11 870 of 12 225 [95% CI, 96.7%-97.3%]) compared with 87.6% (812 of 927 [95% CI, 85.2%-89.6%]) among patients without COVID-19 symptoms. The diagnostic yield of the SARS-CoV-2 NAAT was 12.0% (18 985 of 158 004 [95% CI, 11.8%-12.2%]) and varied from a high of 20.0% (445 of 2229 [95% CI, 18.3%-21.6%]) among patients tested on day 10 after symptom onset to a low of 8.1% (1686 of 20 719 [95% CI, 7.7%-8.5%]) among patients presenting within the first 24 hours of symptom onset.

CONCLUSIONS AND RELEVANCE

This study suggests that the diagnostic sensitivity was high for the first SARS-CoV-2 NAAT performed in the hospital and did not vary significantly by symptom duration. Repeated testing of patients with negative test results should be avoided unless their pretest probability of disease is high.

Electrochemical biosensors for SARS-CoV-2 detection: Voltametric or impedimetric transduction?

During the COVID-19 pandemic, electrochemical biosensors have shown several advantages including accuracy, low cost, possibility of miniaturization and portability, which make them an interesting testing method for rapid point-of-care (POC) detection of SARS-CoV-2 infection, allowing the detection of both viral RNA and viral antigens. Herein, we reviewed advancements in electrochemical biosensing platforms towards the detection of SARS-CoV-2 based on voltametric and impedimetric transduction modes, highlighting the advantages and drawbacks of the two methods.

Evaluation of Diagnostic Strategies for Identifying SARS-CoV-2 Infection in Clinical Practice: a Systematic Review and Compliance with the Standards for Reporting Diagnostic Accuracy Studies Guideline (STARD)

We aimed to review strategies for identifying SARS-CoV-2 infection before the availability of molecular test results, and to assess the reporting quality of the studies identified through the application of the STARD guideline. We screened 3,821 articles published until 30 April 2021, of which 23 met the inclusion criteria: including at least two diagnostic variables, being designed for use in clinical practice or in a public health context and providing diagnostic accuracy rates. Data extraction and application of STARD criteria were performed independently by two researchers and discrepancies were discussed with a third author. Most of the studies (16, 69.6%) included symptomatic patients with suspected infection, six studies (26.1%) included patients already diagnosed and one study (4.3%) included individuals with close contact to a COVID-positive patient. The main variables considered in the studies, which included symptomatic patients, were imaging and demographic characteristics, symptoms, and lymphocyte count. The values for area under the receiver operating characteristic curve (AUC)ranged from 53-97.4. Seven studies (30.4%) validated the diagnostic model in an independent sample. The average number of STARD criteria fulfilled was 17.6 (maximum, 27 and minimum, 5). High diagnostic accuracy values are shown when more than one diagnostic variable is considered, mainly imaging and demographic characteristics, symptoms, and lymphocyte count. This could offer the potential to identify individuals with SARS-CoV-2 infection with high accuracy when molecular testing is not available. However, external validation for developed models and evaluations in populations as similar as possible to those in which they will be applied is urgently needed. IMPORTANCE According to this review, the inclusion of more than one diagnostic test in the diagnostic process for COVID-19 infection shows high diagnostic accuracy values. Imaging characteristics, patients' symptoms, demographic characteristics, and lymphocyte count were the variables most frequently included in the diagnostic models. However, developed models should be externally validated before reaching conclusions on their utility in practice. In addition, it is important to bear in mind that the test should be evaluated in populations as similar as possible to those in which it will be applied in practice.

Lab practices that improve coronavirus disease 2019 detection accuracy using real-time PCR

The number of coronavirus disease 2019 (COVID-19) cases significantly increased with the emergence of multiple variants of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). This has led to an ongoing effort focused on developing the diagnostic detection tests. Among the currently available tests, real-time reverse transcriptase PCR (RT-PCR) has been considered as the ‘golden method’ for the detection of SARS-COV-2. However, a significant number of inaccurate (false-negative/false-positive) results have been reported in spite of this method's reliability and effectiveness. These unreliable results may arise because of various issues encountered throughout the entire testing process starting with the sampling phase, going through the PCR process, and ending with the result analysis. This article aims to shed light on the errors that occur during the COVID-19 testing process and suggest ways to overcome them effectively. Accurate testing could be optimized by following the correct swabbing technique, using adequate RT-PCR kits and controls, setting clear lab guidelines, and properly interpreting the results.

SARS-CoV-2 surveillance strategy in essential workers of the Madrid City Council during the first epidemic wave in Spain, March-July 2020

OBJECTIVES

To study prevalence of infection in essential workers of Madrid City Council by occupation, related characteristics, use of protective devices, risk perception, and main concerns about COVID-19 during lockdown.

METHODS

A total of 30 231 workers were PCR tested for SARS-CoV-2 infection. Information was collected on COVID-19-related symptoms, risk factors, preventive equipment, and risk perception. The crude prevalence was calculated for infection, use of protective devices, perceived risk and main concerns. Additionally, adjusted prevalence and prevalence ratios (PR) were estimated for these variables using logistic regression models with age, gender, occupation, epidemiological week and laboratory as confounding factors.

RESULTS

Overall prevalence of infection was 3.2% (95% CI 3.0% to 3.4%), being higher among policemen (4.4%) and bus drivers (4.2%), but lower among emergency healthcare personnel, firefighters, food market workers and burial services (<2%). Lower excess risk was observed in workers reporting occupational contact with COVID-19 cases only (PR=1.42; 95% CI 1.18 to 1.71) compared with household exposure only (PR=2.75; 95% CI 2.32 to 3.25). Infection was more frequent in symptomatic workers (PR=1.28; 95% CI 1.11 to 1.48), although 42% of detected infections were asymptomatic. Use of facial masks (78.7%) and disinfectants (86.3%) was common and associated with lower infection prevalence (PRmasks=0.68; 95% CI 0.58 to 0.79; PRdisinfectants=0.75; 95% CI 0.61 to 0.91). Over 50% of workers felt being at high risk of infection and worried about infecting others, yet only 2% considered quitting their work.

CONCLUSIONS

This surveillance system allowed for detecting and isolating SARS-CoV-2 cases among essential workers, identifying characteristics related to infection and use of protective devices, and revealing specific needs for work-safety information and psychological support.

Dogs Detecting COVID-19 From Sweat and Saliva of Positive People: A Field Experience in Mexico

Context

Molecular tests are useful in detecting COVID-19, but they are expensive in developing countries. COVID-19-sniffing dogs are an alternative due to their reported sensitivity (&gt;80%) and specificity (&gt;90%). However, most of the published evidence is experimental, and there is a need to determine the performance of the dogs in field conditions. Hence, we aimed to test the sensitivity and specificity of COVID-19-sniffing dogs in the field.

Methods

We trained four dogs with sweat and three dogs with saliva of COVID-19-positive patients, respectively, for 4.5 months. The samples were obtained from a health center in Hermosillo, Sonora, with the restriction to spend 5 min per patient. We calculated sensitivity, specificity, and their 95% confidence intervals (CI).

Results

Two sweat-sniffing dogs reached 76 and 80% sensitivity, with the 95% CI not overlapping the random value of 50%, and 75 and 88% specificity, with the 95% CI not overlapping the 50% value. The 95% CI of the sensitivity and specificity of the other two sweat dogs overlapped the 50% value. Two saliva-sniffing dogs had 70 and 78% sensitivity, and the 95% CI of their sensitivity and specificity did not overlap the 50% value. The 95% CI of the third dog's sensitivity and specificity overlapped the 50% value.

Conclusion

Four of the six dogs were able to detect positive samples of patients with COVID-19, with sensitivity and specificity values significantly different from random in the field. We considered the performance of the dogs promising because it is reasonable to expect that with gauze exposed for a longer time to sweat and saliva of people with COVID-19, their detection capacity would improve. The target is to reach the sensitivity range requested by the World Health Organization for the performance of an antigen test (≥80% sensitivity, ≥97% specificity). If so, dogs could become important allies for the control of the COVID-19 pandemic, especially in developing countries.

Classification Schemes of COVID-19 High Risk Areas and Resulting Policies: A Rapid Review

The COVID-19 pandemic has posed a significant global health threat since January 2020. Policies to reduce human mobility have been recognized to effectively control the spread of COVID-19; although the relationship between mobility, policy implementation, and virus spread remains contentious, with no clear pattern for how countries classify each other, and determine the destinations to- and from which to restrict travel. In this rapid review, we identified country classification schemes for high-risk COVID-19 areas and associated policies which mirrored the dynamic situation in 2020, with the aim of identifying any patterns that could indicate the effectiveness of such policies. We searched academic databases, including PubMed, Scopus, medRxiv, Google Scholar, and EMBASE. We also consulted web pages of the relevant government institutions in all countries. This rapid review's searches were conducted between October 2020 and December 2021. Web scraping of policy documents yielded additional 43 country reports on high-risk area classification schemes. In 43 countries from which relevant reports were identified, six issued domestic classification schemes. International classification schemes were issued by the remaining 38 countries, and these mainly used case incidence per 100,000 inhabitants as key indicator. The case incidence cut-off also varied across the countries, ranging from 20 cases per 100,000 inhabitants in the past 7 days to more than 100 cases per 100,000 inhabitants in the past 28 days. The criteria used for defining high-risk areas varied across countries, including case count, positivity rate, composite risk scores, community transmission and satisfactory laboratory testing. Countries either used case incidence in the past 7, 14 or 28 days. The resulting policies included restrictions on internal movement and international travel. The quarantine policies can be summarized into three categories: (1) 14 days self-isolation, (2) 10 days self-isolation and (3) 14 days compulsory isolation.

Identifying SARS-COV-2 infected patients through canine olfactive detection on axillary sweat samples; study of observed sensitivities and specificities within a group of trained dogs

There is an increasing need for rapid, reliable, non-invasive, and inexpensive mass testing methods as the global COVID-19 pandemic continues. Detection dogs could be a possible solution to identify individuals infected with SARS-CoV-2. Previous studies have shown that dogs can detect SARS-CoV-2 on sweat samples. This study aims to establish the dogs’ sensitivity (true positive rate) which measures the proportion of people with COVID-19 that are correctly identified, and specificity (true negative rate) which measures the proportion of people without COVID-19 that are correctly identified. Seven search and rescue dogs were tested using a total of 218 axillary sweat samples (62 positive and 156 negative) in olfaction cones following a randomised and double-blind protocol. Sensitivity ranged from 87% to 94%, and specificity ranged from 78% to 92%, with four dogs over 90%. These results were used to calculate the positive predictive value and negative predictive value for each dog for different infection probabilities (how likely it is for an individual to be SARS-CoV-2 positive), ranging from 10–50%. These results were compared with a reference diagnostic tool which has 95% specificity and sensitivity. Negative predictive values for six dogs ranged from ≥98% at 10% infection probability to ≥88% at 50% infection probability compared with the reference tool which ranged from 99% to 95%. Positive predictive values ranged from ≥40% at 10% infection probability to ≥80% at 50% infection probability compared with the reference tool which ranged from 68% to 95%. This study confirms previous results, suggesting that dogs could play an important role in mass-testing situations. Future challenges include optimal training methods and standardisation for large numbers of detection dogs and infrastructure supporting their deployment.

COVID-19 diagnosis by SARS-CoV-2 Spike protein detection in saliva using an ultrasensitive magneto-assay based on disposable electrochemical sensor

The outbreak of the COVID-19 pandemic, caused by Severe Acute Respiratory Syndrome of Coronavirus 2 (SARS-CoV-2), has fueled the search for diagnostic tests aiming at the control and reduction of the viral transmission. The main technique used for diagnosing the Coronavirus disease (COVID-19) is the reverse transcription-polymerase chain reaction (RT-PCR) technique. However, considering the high number of cases and the underlying limitations of the RT-PCR technique, especially with regard to accessibility and cost of the test, one does not need to overemphasize the need to develop new and less expensive testing techniques that can aid the early diagnosis of the disease. With that in mind, we developed an ultrasensitive magneto-assay using magnetic beads and gold nanoparticles conjugated to human angiotensin-converting enzyme 2 (ACE2) peptide (Gln²⁴-Gln⁴²) for the capturing and detection of SARS-CoV-2 Spike protein in human saliva. The technique applied involved the use of a disposable electrochemical device containing eight screen-printed carbon electrodes which allow the simultaneous analysis of eight samples. The magneto-assay exhibited an ultralow limit of detection of 0.35 ag mL^-1 for the detection of SARS-CoV-2 Spike protein in saliva. The magneto-assay was tested in saliva samples from healthy and SARS-CoV-2-infected individuals. In terms of efficiency, the proposed technique - which presented a sensitivity of 100.0% and specificity of 93.7% for SARS-CoV-2 Spike protein-exhibited great similarity with the RT-PCR technique. The results obtained point to the application potential of this simple, low-cost magneto-assay for saliva-based point-of-care COVID-19 diagnosis.

COVID-19 diagnosis by SARS-CoV-2 Spike protein detection in saliva using an ultrasensitive magneto-assay based on disposable electrochemical sensor

The outbreak of the COVID-19 pandemic, caused by Severe Acute Respiratory Syndrome of Coronavirus 2 (SARS-CoV-2), has fueled the search for diagnostic tests aiming at the control and reduction of the viral transmission. The main technique used for diagnosing the Coronavirus disease (COVID-19) is the reverse transcription-polymerase chain reaction (RT-PCR) technique. However, considering the high number of cases and the underlying limitations of the RT-PCR technique, especially with regard to accessibility and cost of the test, one does not need to overemphasize the need to develop new and less expensive testing techniques that can aid the early diagnosis of the disease. With that in mind, we developed an ultrasensitive magneto-assay using magnetic beads and gold nanoparticles conjugated to human angiotensin-converting enzyme 2 (ACE2) peptide (Gln²⁴-Gln⁴²) for the capturing and detection of SARS-CoV-2 Spike protein in human saliva. The technique applied involved the use of a disposable electrochemical device containing eight screen-printed carbon electrodes which allow the simultaneous analysis of eight samples. The magneto-assay exhibited an ultralow limit of detection of 0.35 ag mL^-1 for the detection of SARS-CoV-2 Spike protein in saliva. The magneto-assay was tested in saliva samples from healthy and SARS-CoV-2-infected individuals. In terms of efficiency, the proposed technique - which presented a sensitivity of 100.0% and specificity of 93.7% for SARS-CoV-2 Spike protein-exhibited great similarity with the RT-PCR technique. The results obtained point to the application potential of this simple, low-cost magneto-assay for saliva-based point-of-care COVID-19 diagnosis.

Saliva-based detection of COVID-19 infection in a real-world setting using reagent-free Raman spectroscopy and machine learning

SIGNIFICANCE

The primary method of COVID-19 detection is reverse transcription polymerase chain reaction (RT-PCR) testing. PCR test sensitivity may decrease as more variants of concern arise and reagents may become less specific to the virus.

AIM

We aimed to develop a reagent-free way to detect COVID-19 in a real-world setting with minimal constraints on sample acquisition. The machine learning (ML) models involved could be frequently updated to include spectral information about variants without needing to develop new reagents.

APPROACH

We present a workflow for collecting, preparing, and imaging dried saliva supernatant droplets using a non-invasive, label-free technique-Raman spectroscopy-to detect changes in the molecular profile of saliva associated with COVID-19 infection.

RESULTS

We used an innovative multiple instance learning-based ML approach and droplet segmentation to analyze droplets. Amongst all confounding factors, we discriminated between COVID-positive and COVID-negative individuals yielding receiver operating coefficient curves with an area under curve (AUC) of 0.8 in both males (79% sensitivity and 75% specificity) and females (84% sensitivity and 64% specificity). Taking the sex of the saliva donor into account increased the AUC by 5%.

CONCLUSION

These findings may pave the way for new rapid Raman spectroscopic screening tools for COVID-19 and other infectious diseases.

Rapid diagnostic tests in the management of pneumonia

INTRODUCTION

Pneumonia is one of the main causes of mortality associated with infectious diseases worldwide. Several challenges have been identified in the management of patients with pneumonia, ranging from accurate and cost-effective microbiological investigations, prompt and adequate therapeutic management, and optimal treatment duration.

AREAS COVERED

In this review, an updated summary on the current management of pneumonia patients is provided and the epidemiological issues of infectious respiratory diseases, which in the current pandemic situation are of particular concern, are addressed. The clinical and microbiological approaches to pneumonia diagnosis are reviewed, including discussion about the new molecular assays pointing out both their strengths and limitations. Finally, the current recommendations about antibiotic treatment are examined and discussed depending on the epidemiological contexts, including those with high prevalence of multidrug-resistant bacteria.

EXPERT OPINION

We claim that rapid diagnostic tests, if well-positioned in the diagnostic workflow and reserved for the subset of patients who could most benefit from these technologies, may represent an interesting and feasible tool to optimize timing of targeted treatments especially in terms of early de-escalation or discontinuation of antibiotic therapy.

Chasing the storm: Recruiting non-hospitalized patients for a multi-site randomized controlled trial in the United States during the COVID-19 pandemic

Randomized controlled trials (RCTs) remain the gold standard to evaluate clinical interventions, producing the highest level of evidence while minimizing potential bias. Inadequate recruitment is a commonly encountered problem that undermines the completion and generalizability of RCTs—and is even more challenging when enrolling amidst a pandemic. Here, we reflect on our experiences with virtual recruitment of non‐hospitalized patients in the United States for ColCorona, an international, multicenter, randomized, placebo‐controlled coronavirus disease 2019 (COVID‐19) drug trial. Recruitment challenges during a pandemic include constraints created by shelter‐in‐place policies and targeting enrollment according to national and local fluctuations in infection rate. Presenting a study to potential participants who are sick with COVID‐19 and may be frightened, overwhelmed, or mistrusting of clinical research remains a challenge. Strategies previously reported to improve recruitment include transparency, patient and site education, financial incentives, and person‐to‐person outreach. Active measures taken during ColCorona to optimize United States recruitment involved rapid expansion of sites, adjustment of recruitment scripts, assessing telephone calls versus text messages for initial contact with participants, institutional review board‐approved financial compensation, creating an infrastructure to systematically identify potentially eligible patients, partnering with testing sites, appealing to both self‐interest and altruism, and large‐scale media efforts with varying degrees of success.

A practical approach to SARS-CoV-2 testing in a pre and post-vaccination era

As countries globally are in the process of planning, introducing or implementing mass vaccination strategies while continuing to deal with the ongoing SARS-CoV-2 pandemic, an evolution in testing strategies may be required to minimize spread in mixed vaccinated and non-vaccinated populations. This mini-review explores the key public health questions associated with the widely varying efficacy of commercially available vaccines and their persistence of protection in the context of a growing number of variant virus strains. A new strategy for SARS-CoV-2 testing that accommodates the current and evolving pandemic paradigm is proposed.

Potential diagnosis of COVID-19 from chest X-ray and CT findings using semi-supervised learning

COVID-19 is an infectious disease, which has adversely affected public health and the economy across the world. On account of the highly infectious nature of the disease, rapid automated diagnosis of COVID-19 is urgently needed. A few recent findings suggest that chest X-rays and CT scans can be used by machine learning for the diagnosis of COVID-19. Herein, we employed semi-supervised learning (SSL) approaches to detect COVID-19 cases accurately by analyzing digital chest X-rays and CT scans. On a relatively small COVID-19 radiography dataset, which contains only 219 COVID-19 positive images, 1341 normal and 1345 viral pneumonia images, our algorithm, COVIDCon, which takes advantage of data augmentation, consistency regularization, and multicontrastive learning, attains 97.07% average class prediction accuracy, with 1000 labeled images, which is 7.65% better than the next best SSL method, virtual adversarial training. COVIDCon performs even better on a larger COVID-19 CT Scan dataset that contains 82,767 images. It achieved an excellent accuracy of 99.13%, at 20,000 labels, which is 6.45% better than the next best pseudo-labeling approach. COVIDCon outperforms other state-of-the-art algorithms at every label that we have investigated. These results demonstrate COVIDCon as the benchmark SSL algorithm for potential diagnosis of COVID-19 from chest X-rays and CT-Scans. Furthermore, COVIDCon performs exceptionally well in identifying COVID-19 positive cases from a completely unseen repository with a confirmed COVID-19 case history. COVIDCon, may provide a fast, accurate, and reliable method for screening COVID-19 patients.

Ensemble Deep Learning for the Detection of COVID-19 in Unbalanced Chest X-ray Dataset

The ongoing COVID-19 pandemic has caused devastating effects on humanity worldwide. With practical advantages and wide accessibility, chest X-rays (CXRs) play vital roles in the diagnosis of COVID-19 and the evaluation of the extent of lung damages incurred by the virus. This study aimed to leverage deep-learning-based methods toward the automated classification of COVID-19 from normal and viral pneumonia on CXRs, and the identification of indicative regions of COVID-19 biomarkers. Initially, we preprocessed and segmented the lung regions usingDeepLabV3+ method, and subsequently cropped the lung regions. The cropped lung regions were used as inputs to several deep convolutional neural networks (CNNs) for the prediction of COVID-19. The dataset was highly unbalanced; the vast majority were normal images, with a small number of COVID-19 and pneumonia images. To remedy the unbalanced distribution and to avoid biased classification results, we applied five different approaches: (i) balancing the class using weighted loss; (ii) image augmentation to add more images to minority cases; (iii) the undersampling of majority classes; (iv) the oversampling of minority classes; and (v) a hybrid resampling approach of oversampling and undersampling. The best-performing methods from each approach were combined as the ensemble classifier using two voting strategies. Finally, we used the saliency map of CNNs to identify the indicative regions of COVID-19 biomarkers which are deemed useful for interpretability. The algorithms were evaluated using the largest publicly available COVID-19 dataset. An ensemble of the top five CNNs with image augmentation achieved the highest accuracy of 99.23% and area under curve (AUC) of 99.97%, surpassing the results of previous studies.

Neurosurgical Outcomes, Protocols, and Resource Management During Lockdown: Early Institutional Experience from One of the World's Largest COVID 19 Hotspots

BACKGROUND

As the COVID-19 pandemic surpasses 1 year, it is prudent to reflect on the challenges faced and the management strategies employed to tackle this overwhelming health care crisis. We undertook this study to validate our institutional protocols, which were formulated to cater to the change in volume and pattern of neurosurgical cases during the raging pandemic.

METHODS

All admitted patients scheduled to undergo major neurosurgical intervention during the lockdown period (15 March 2020 to 15 September 2020) were included in the study. The data involving surgery outcomes, disease pattern, anesthesia techniques, patient demographics, as well as COVID-19 status, were analyzed and compared with similar retrospective data of neurosurgical patients operated during the same time period in the previous year (15 March 2019 to 15 September 2019).

RESULTS

Barring significant increase in surgery for stroke (P = 0.008) and hydrocephalus (P <0.001), the overall case load of neurosurgery during the study period in 2020 was 42.75% of that in 2019 (P < 0.001), attributable to a significant reduction in elective spine surgeries (P < 0.001). However, no significant difference was observed in the overall incidence of emergency and essential surgeries undertaken during the 2 time periods (P = 0.482). There was an increased incidence in the use of monitored anesthesia care techniques during emergency and essential neurosurgical procedures by the anesthesia team in 2020 (P < 0.001). COVID-19 patients had overall poor outcomes (P = 0.003), with significant increase in mortality among those subjected to general anesthesia vis-a-vis monitored anesthesia care (P = 0.014).

CONCLUSIONS

Despite a significant decrease in neurosurgical workload during the COVID-19 lockdown period in 2020, the volume of emergency and essential surgeries did not change much compared with the previous year. Surgery in COVID-19 patients is best avoided, unless critical, as the outcome in these patients is not favorable. The employment of monitored anesthesia care techniques like awake craniotomy and regional anesthesia facilitate a better outcome in the ongoing COVID-19 era.

International comparisons of COVID-19 case and mortality data and the effectiveness of non-pharmaceutical interventions: a plea for reconsideration

International comparisons of the effectiveness of coronavirus disease 2019 (COVID-19) non-pharmaceutical interventions (NPIs) based on national case and mortality data are fraught with underestimated complexity. This article calls for stronger attention to just how extensive is the multifactorial nature of national case and mortality data, and argues that, unless a globally consistent benchmark of measurement can be devised, such comparisons are facile, if not misleading. This can lead to policy decisions and public support for the adoption of potentially harmful NPIs that are ineffective in combating the COVID-19 pandemic and damaging to mental health, social cohesion, human rights and economic development. The unscientific use of international comparisons of case and mortality data in public discourse, media reporting and policymaking on NPI effectiveness should be subject to greater scrutiny.

High Seroprevalence of SARS-CoV-2 (COVID-19)-Specific Antibodies among Healthcare Workers: A Cross-Sectional Study in Guilan, Iran

Background

This study was conducted to evaluate the anti‐SARS‐CoV‐2 IgM and IgG antibodies among healthcare workers in Guilan.

Methods

This cross-sectional study was conducted on 503 healthcare workers. Between April and May 2020, blood samples were collected from the healthcare workers of Razi Hospital in Rasht, Guilan, Iran. Enzyme-linked immunosorbent assay (ELISA) was used for the detection and quantitation of anti‐SARS‐CoV‐2 IgM/IgG antibodies by using kits made by Pishtaz Teb Company, Tehran, Iran.

Results

From a total of 503 participants, the result of the anti‐SARS‐CoV‐2 IgM antibody test was positive in 28 subjects (5.6%) and the anti‐SARS‐CoV‐2 IgG antibody test was positive in171 subjects (34%). Participants in the age group of 35–54 years were significantly more likely to have a positive anti‐SARS‐CoV‐2 antibody test than the age group of 20–34 years (odds ratio = 1.53, 95% CI: 1.04–2.25, P=0.029). Also, physicians were significantly more likely to have a positive antibody test than office workers (odds ratio = 1.92, 95% CI: 1.04–3.54, P=0.037). The wide range of symptoms was significantly associated with the positive anti‐SARS‐CoV‐2 antibody test. The most significant association was observed between fever and a positive anti‐SARS‐CoV‐2 antibody test (odds ratio = 3.03, 95% CI: 2.06–4.44, P < 0.001).

Conclusion

The results of the current study indicated that the seroprevalence of COVID-19 was high among healthcare workers of Guilan Province. It seems that this finding was due to the earlier exposure to COVID-19 and the lack of awareness and preparedness to deal with the pandemic in Iran, compared to other countries.

Comparison of diagnostic performance of five molecular assays for detection of SARS-CoV-2

We compared the performance of the Abbott Real Time SARS-CoV-2 assay (Abbott assay), Aptima™ SARS-CoV-2 assay (Aptima assay), BGI Real-Time SARS-CoV-2 assay (BGI assay), Lyra® SARS-CoV-2 assay (Lyra assay), and DiaSorin Simplexa™ COVID assay for SARS-CoV-2 detection. Residual nasopharyngeal samples (n = 201) submitted for routine SARS-CoV-2 testing by Simplexa assay during June-July 2020 and January 2021 were salvaged. Aliquots were tested on other assays and compared against the CDC 2019-nCoV Real-Time RT-PCR assay. Viral load in positive samples was determined by droplet digital PCR. Among 201 samples, 99 were positive and 102 were negative by the CDC assay. The Aptima and Abbott assays exhibited the highest positive percent agreement (PPA) at 98.9% while the BGI assay demonstrated the lowest PPA of 89.9% with 10 missed detections. Negative percent agreement for all 5 platforms was comparable, ranging from 96.1% to 100%. The performance of all five assays was comparable.

Detection of COVID-19 from Chest X-ray Images Using Deep Convolutional Neural Networks

The COVID-19 global pandemic has wreaked havoc on every aspect of our lives. More specifically, healthcare systems were greatly stretched to their limits and beyond. Advances in artificial intelligence have enabled the implementation of sophisticated applications that can meet clinical accuracy requirements. In this study, customized and pre-trained deep learning models based on convolutional neural networks were used to detect pneumonia caused by COVID-19 respiratory complications. Chest X-ray images from 368 confirmed COVID-19 patients were collected locally. In addition, data from three publicly available datasets were used. The performance was evaluated in four ways. First, the public dataset was used for training and testing. Second, data from the local and public sources were combined and used to train and test the models. Third, the public dataset was used to train the model and the local data were used for testing only. This approach adds greater credibility to the detection models and tests their ability to generalize to new data without overfitting the model to specific samples. Fourth, the combined data were used for training and the local dataset was used for testing. The results show a high detection accuracy of 98.7% with the combined dataset, and most models handled new data with an insignificant drop in accuracy.

Canadian Society of Clinical Chemists (CSCC) consensus guidance for testing, selection and quality management of SARS-CoV-2 point-of-care tests

OBJECTIVES

A consensus guidance is provided for testing, utility and verification of SARS-CoV-2 point-of-care test (POCT) performance and implementation of a quality management program, focusing on nucleic acid and antigen targeted technologies.

DESIGN AND METHODS

The recommendations are based on current literature and expert opinion from the members of Canadian Society of Clinical Chemists (CSCC), and are intended for use inside or outside of healthcare settings that have varied levels of expertise and experience with POCT.

RESULTS AND CONCLUSIONS

Here we discuss sampling requirements, biosafety, SARS-CoV-2 point-of-care testing methodologies (with focus on Health Canada approved tests), test performance and limitations, test selection, testing utility, development and implementation of quality management systems, quality improvement, and medical and scientific oversight.

Perioperative management for gynecologic minimally invasive surgery during the COVID-19 pandemic

PURPOSE OF REVIEW

This article will review current guidelines regarding surgical protocols for elective and nonelective surgeries during the severe acute respiratory syndrome coronavirus 2 pandemic.

RECENT FINDINGS

Perioperative management for surgical patients should be modified to promote the safety and wellbeing of patients and caregivers amidst the COVID-19 pandemic. COVID-19 testing should be performed preoperatively with subsequent preprocedure quarantine. Nonemergent or nonlife-threatening surgery should be postponed for COVID-19 positive patients. The consensus of surgical societies is to use a laparoscopic surgical approach for COVID-19 positive patients when appropriate and to avoid port venting at the end of procedures. For COVID-19 positive patients requiring an emergent procedure, the use of personal protective equipment is strongly recommended.

SUMMARY

After over a year of the COVID-19 pandemic, effective protocols and precautions have been established to decrease the morbidity and mortality of patients undergoing surgery and to promote the safety of healthcare personnel. Continued investigations are necessary as cases of new, possibly more virulent, strains of the virus arise.

Nanobiosensor-based diagnostic tools in viral infections: Special emphasis on Covid-19

The rapid propagation of novel human coronavirus 2019 and its emergence as a pandemic raising morbidity calls for taking more appropriate measures for rapid improvement of present diagnostic techniques which are time‐consuming, labour‐intensive and non‐portable. In this scenario, biosensors can be considered as a means to outmatch customary techniques and deliver point‐of‐care diagnostics for many diseases in a much better way owing to their speed, cost‐effectiveness, accuracy, sensitivity and selectivity. Besides this, these biosensors have been aptly used to detect a wide spectrum of viruses thus facilitating timely delivery of correct therapy. The present review is an attempt to analyse such different kinds of biosensors that have been implemented for virus detection. Recently, the field of nanotechnology has given a great push to diagnostic techniques by the development of smart and miniaturised nanobiosensors which have enhanced the diagnostic procedure and taken it to a new level. The portability, hardiness and affordability of nanobiosensor make them an apt diagnostic agent for different kinds of viruses including SARS‐CoV‐2. The role of such novel nanobiosensors in the diagnosis of SARS‐CoV‐2 has also been addressed comprehensively in the present review. Along with this, the challenges and future position of developing such ultrasensitive nanobiosensors which should be taken into consideration before declaring these nano‐weapons as the ideal futuristic gold standard of diagnosis has also been accounted for here.

Assessment of the Diagnostic Ability of Four Detection Methods Using Three Sample Types of COVID-19 Patients

Background

Viral nucleic acid detection is considered the gold standard for the diagnosis of coronavirus disease 2019 (COVID-19), which is caused by SARS-CoV-2 infection. However, unsuitable sample types and laboratory detection kits/methods lead to misdiagnosis, which delays the prevention and control of the pandemic.

Methods

We compared four nucleic acid detection methods [two kinds of reverse transcription polymerase chain reactions (RT-PCR A: ORF1ab and N testing; RT-PCRB: only ORF1ab testing), reverse transcription recombinase aided amplification (RT-RAA) and droplet digital RT-PCR (dd-RT-PCR)] using 404 samples of 72 hospitalized COVID-19 patients, including oropharyngeal swab (OPS), nasopharyngeal swabs (NPS) and saliva after deep cough, to evaluate the best sample type and method for SARS-CoV-2 detection.

Results

Among the four methods, dd-RT-PCR exhibited the highest positivity rate (93.0%), followed by RT-PCR B (91.2%) and RT-RAA (91.2%), while the positivity rate of RT-PCR A was only 71.9%. The viral load in OPS [24.90 copies/test (IQR 15.58-129.85)] was significantly lower than that in saliva [292.30 copies/test (IQR 20.20-8628.55)] and NPS [274.40 copies/test (IQR 33.10-2836.45)]. In addition, if OPS samples were tested alone by RT-PCR A, only 21.4% of the COVID-19 patients would be considered positive. The accuracy of all methods reached nearly 100% when saliva and NPS samples from the same patient were tested simultaneously.

Conclusions

SARS-CoV-2 nucleic acid detection methods should be fully evaluated before use. High-positivity rate methods such as RT-RAA and dd-RT-PCR should be considered when possible. Furthermore, saliva after deep cough and NPS can greatly improve the accuracy of the diagnosis, and testing OPS alone is not recommended.

The ongoing impact of COVID-19 on adult cardiac surgery and suggestions for safe continuation throughout the pandemic: a review of expert opinions

Objectives:

To establish the impact of the COVID-19 pandemic on adult cardiac surgery by reviewing current data and use this to establish methods for safely continuing to carry out surgery.

Methods:

Conduction of a literature search via PubMed using the search terms: ‘(adult cardiac OR cardiothoracic OR surgery OR minimally invasive OR sternotomy OR hemi-sternotomy OR aortic valve OR mitral valve OR elective OR emergency) AND (COVID-19 or coronavirus OR SARS-CoV-2 OR 2019-nCoV OR 2019 novel coronavirus OR pandemic)’. Thirty-two articles were selected.

Results:

Cardiac surgery patients have an increased risk of complications from COVID-19 and require vital finite resources such as intensive care beds, also required by COVID-19 patients. Thus reducing their admission and potential hospital-acquired infection with COVID-19 is paramount. During the peak, only emergencies such as acute aortic dissections were treated, triaging patients according to surgical priority and cancelling all elective procedures. Screening and 2-week quarantine prior to admission were essential changes, alongside additional levels of PPE. Focus was on reducing length of stay and switching to day-cases to reduce post-operative transmission risk, whilst several hospitals adopted ‘hot’ and ‘cold’ operating theatres for covid-confirmed and covid-negative patients.

Conclusions:

This paper suggests a ‘CARDIO’ approach for reintroducing elective procedures: ‘Care, Assess, Re-Evaluate, Develop, Implement, Overcome’; prioritising the mental and physical health of the workforce, learning from and sharing experiences and objectively prioritising patients to improve case load.

Utility of Routine Laboratory Biomarkers to Detect COVID-19: A Systematic Review and Meta-Analysis

No routine laboratory biomarkers perform well enough in diagnosing COVID-19 in isolation for them to be used as a standalone diagnostic test or to help clinicians prioritize patients for treatment. Instead, other diagnostic tests are needed. The aim of this work was to statistically summarise routine laboratory biomarker measurements in COVID-19-positive and -negative patients to inform future work. A systematic literature review and meta-analysis were performed. The search included names of commonly used, routine laboratory tests in the UK NHS, and focused on research papers reporting laboratory results of patients diagnosed with COVID-19. A random effects meta-analysis of the standardized mean difference between COVID-19-positive and -negative groups was conducted for each biomarker. When comparing reported laboratory biomarker results, we identified decreased white blood cell, neutrophil, lymphocyte, eosinophil, and platelet counts; while lactate dehydrogenase, aspartate aminotransferase, and alanine aminotransferase were elevated in COVID-19-positive compared to COVID-19-negative patients. Differences were identified across a number of routine laboratory biomarkers between COVID-19-positive and -negative patients. Further research is required to identify whether routine laboratory biomarkers can be used in the development of a clinical scoring system to aid with triage of patients.

Sensitivity of SARS-CoV-2 RNA polymerase chain reaction using a clinical and radiological reference standard

OBJECTIVES

Diagnostic tests for SARS-CoV-2 are important for epidemiology, clinical management, and infection control. Limitations of oro-nasopharyngeal real-time PCR sensitivity have been described based on comparisons of single tests with repeated sampling. We assessed SARS-CoV-2 PCR clinical sensitivity using a clinical and radiological reference standard.

METHODS

Between March-May 2020, 2060 patients underwent thoracic imaging and SARS-CoV-2 PCR testing. Imaging was independently double- or triple-reported (if discordance) by blinded radiologists according to radiological criteria for COVID-19. We excluded asymptomatic patients and those with alternative diagnoses that could explain imaging findings. Associations with PCR-positivity were assessed with binomial logistic regression.

RESULTS

901 patients had possible/probable imaging features and clinical symptoms of COVID-19 and 429 patients met the clinical and radiological reference case definition. SARS-CoV-2 PCR sensitivity was 68% (95% confidence interval 64-73), was highest 7-8 days after symptom onset (78% (68-88)) and was lower among current smokers (adjusted odds ratio 0.23 (0.12-0.42) p < 0.001).

CONCLUSIONS

In patients with clinical and imaging features of COVID-19, PCR test sensitivity was 68%, and was lower among smokers; a finding that could explain observations of lower disease incidence and that warrants further validation. PCR tests should be interpreted considering imaging, symptom duration and smoking status.

Detection of SARS-CoV-2 Infection in Human Nasopharyngeal Samples by Combining MALDI-TOF MS and Artificial Intelligence

The high infectivity of SARS-CoV-2 makes it essential to develop a rapid and accurate diagnostic test so that carriers can be isolated at an early stage. Viral RNA in nasopharyngeal samples by RT-PCR is currently considered the reference method although it is not recognized as a strong gold standard due to certain drawbacks. Here we develop a methodology combining the analysis of from human nasopharyngeal (NP) samples by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) with the use of machine learning (ML). A total of 236 NP samples collected in two different viral transport media were analyzed with minimal sample preparation and the subsequent mass spectra data was used to build different ML models with two different techniques. The best model showed high performance in terms of accuracy, sensitivity and specificity, in all cases reaching values higher than 90%. Our results suggest that the analysis of NP samples by MALDI-TOF MS and ML is a simple, safe, fast and economic diagnostic test for COVID-19.

Higher Accuracy of Lung Ultrasound over Chest X-ray for Early Diagnosis of COVID-19 Pneumonia

BACKGROUND

The COVID-19 pandemic rapidly strained healthcare systems worldwide. The reference standard for diagnosis is a positive reverse transcription polymerase chain reaction (RT-PCR) test, but results are not immediate and sensibility is variable.

AIM

To evaluate the diagnostic accuracy of lung ultrasound compared to chest X-ray for COVID-19 pneumonia.

DESIGN AND SETTING

A retrospective analysis of symptomatic patients admitted into one primary care centre in Spain between March and September 2020.

METHOD

Patients' chest X-rays and lung ultrasounds were categorized as normal or pathologic. RT-PCR confirmed COVID-19 infection. Pathologic lung ultrasound images were further categorized as showing either local or diffuse interstitial disease. McNemar and Fisher tests were used to compare diagnostic accuracy.

RESULTS

Most of the 212 patients presented fever at admission, either as a standalone symptom (37.74% of patients) or together with others (72.17% of patients). The positive predictive value of the lung ultrasound was 90% for the diffuse interstitial pattern and 46.92% for local pattern. The lung ultrasound had a significantly higher sensitivity (82.75%) (p < 0.001), but lower specificity (71%) than the chest X-ray (54.02% and 86%, respectively) (p = 0.008) for identifying interstitial lung disease. Moreover, sensitivity of the lung ultrasound for severe interstitial disease was 100%, and was significantly higher than the chest X-ray (58.33%) (p = 0.002).

CONCLUSION

The lung ultrasound is more accurate than the chest X-ray for identifying patients with COVID-19 pneumonia and it is especially useful for those presenting diffuse interstitial disease.

A Software Tool for Calculating the Uncertainty of Diagnostic Accuracy Measures

Screening and diagnostic tests are applied for the classification of people into diseased and non-diseased populations. Although diagnostic accuracy measures are used to evaluate the correctness of classification in clinical research and practice, there has been limited research on their uncertainty. The objective for this work was to develop a tool for calculating the uncertainty of diagnostic accuracy measures, as diagnostic accuracy is fundamental to clinical decision-making. For this reason, the freely available interactive program Diagnostic Uncertainty has been developed in the Wolfram language. The program provides six modules with nine submodules for calculating and plotting the standard measurement, sampling and combined uncertainty and the resultant confidence intervals of various diagnostic accuracy measures of screening or diagnostic tests, which measure a normally distributed measurand, applied at a single point in time in samples of non-diseased and diseased populations. This is done for differing sample sizes, mean and standard deviation of the measurand, diagnostic threshold and standard measurement uncertainty of the test. The application of the program is demonstrated with an illustrative example of glucose measurements in samples of diabetic and non-diabetic populations, that shows the calculation of the uncertainty of diagnostic accuracy measures. The presented interactive program is user-friendly and can be used as a flexible educational and research tool in medical decision-making, to calculate and explore the uncertainty of diagnostic accuracy measures.

Diagnostic value of using a combination of nucleic acid and specific antibody tests for SARS-CoV-2 in coronavirus disease 2019

Coronavirus disease 2019 (COVID-19) is a newly emerged disease with various clinical manifestations and imaging features. The diagnosis of COVID-19 depends on a positive nucleic acid amplification test by real-time reverse transcription-polymerase chain reaction (RT-PCR) for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). However, the clinical manifestations and imaging features of COVID-19 are non-specific, and nucleic acid test for SARS-CoV-2 can have false-negative results. It is presently believed that detection of specific antibodies to SARS-CoV-2 is an effective screening and diagnostic indicator for SARS-CoV-2 infection. Thus, a combination of nucleic acid and specific antibody tests for SARS-CoV-2 will be more effective to diagnose COVID-19, especially to exclude suspected cases.

A Risk Model of Admitting Patients With Silent SARS-CoV-2 Infection to Surgery and Development of Severe Postoperative Outcomes and Death: Projections Over 24 Months for 5 Geographical Regions

Objective:

To model the risk of admitting silent COVID-19-infected patients to surgery with subsequent risk of severe pulmonary complications and mortality.

Summary Background Data:

With millions of operations cancelled during the COVID-19 pandemic, pressure is mounting to reopen and increase surgical activity. The risk of admitting patients who have silent SARS-Cov-2 infection to surgery is not well investigated, but surgery on patients with COVID-19 is associated with poor outcomes. We aimed to model the risk of operating on nonsymptomatic infected individuals and associated risk of perioperative adverse outcomes and death.

Methods:

We developed 2 sets of models to evaluate the risk of admitting silent COVID-19-infected patients to surgery. A static model let the underlying infection rate (R rate) and the gross population-rate of surgery vary. In a stochastic model, the dynamics of the COVID-19 prevalence and a fixed population-rate of surgery was considered. We generated uncertainty intervals (UIs) for our estimates by running low and high scenarios using the lower and upper 90% uncertainty limits. The modelling was applied for high-income regions (eg, United Kingdom (UK), USA (US) and European Union without UK (EU27), and for the World (WORLD) based on the WHO standard population.

Results:

Both models provided concerning rates of perioperative risk over a 24-months period. For the US, the modelled rates were 92,000 (UI 68,000–124,000) pulmonary complications and almost 30,000 deaths (UI 22,000–40·000), respectively; for Europe, some 131,000 patients (UI 97,000–178,000) with pulmonary complications and close to 47,000 deaths (UI 34,000–63,000) were modelled. For the UK, the model suggested a median daily number of operations on silently infected ranging between 25 and 90, accumulating about 18,700 (UI 13,700–25,300) perioperative pulmonary complications and 6400 (UI 4600–8600) deaths. In high-income regions combined, we estimated around 259,000 (UI 191,000–351,000) pulmonary complications and 89,000 deaths (UI 65,000–120,000). For the WORLD, even low surgery rates estimated a global number of 1.2 million pulmonary complications and 350,000 deaths.

Conclusions:

The model highlights a considerable risk of admitting patients with silent COVID-19 to surgery with an associated risk for adverse perioperative outcomes and deaths. Strategies to avoid excessive complications and deaths after surgery during the pandemic are needed.

The Role of Anticoagulation in Post-COVID-19 Concomitant Stroke, Myocardial Infarction, and Left Ventricular Thrombus: A Case Report

Patient: Male, 65-year-old

Final Diagnosis: Heart failure • myocardial infarction • stroke

Symptoms: Right sided weakness

Medication:—

Clinical Procedure: CT scan • echocardiography

Specialty: Cardiology • Infectious Diseases • Medicine, General and Internal • Neurology

Deep learning based detection of COVID-19 from chest X-ray images

The whole world is facing a health crisis, that is unique in its kind, due to the COVID-19 pandemic. As the coronavirus continues spreading, researchers are concerned by providing or help provide solutions to save lives and to stop the pandemic outbreak. Among others, artificial intelligence (AI) has been adapted to address the challenges caused by pandemic. In this article, we design a deep learning system to extract features and detect COVID-19 from chest X-ray images. Three powerful networks, namely ResNet50, InceptionV3, and VGG16, have been fine-tuned on an enhanced dataset, which was constructed by collecting COVID-19 and normal chest X-ray images from different public databases. We applied data augmentation techniques to artificially generate a large number of chest X-ray images: Random Rotation with an angle between - 10 and 10 degrees, random noise, and horizontal flips. Experimental results are encouraging: the proposed models reached an accuracy of 97.20 % for Resnet50, 98.10 % for InceptionV3, and 98.30 % for VGG16 in classifying chest X-ray images as Normal or COVID-19. The results show that transfer learning is proven to be effective, showing strong performance and easy-to-deploy COVID-19 detection methods. This enables automatizing the process of analyzing X-ray images with high accuracy and it can also be used in cases where the materials and RT-PCR tests are limited.

Sensitivity of the Molecular Test in Saliva for Detection of COVID-19 in Pediatric Patients With Concurrent Conditions

Background: The reference standard for the molecular diagnostic testing for COVID-19 is the use of nasopharyngeal or combined nasopharyngeal and oropharyngeal (NP/OP) swabs. Saliva has been proposed as a minimally invasive specimen whose collection reduces the risks for health care personnel. Objective: To assess the suitability of saliva for COVID-19 diagnosis as a replacement of the reference standard NP/OP swab in the setting of a tertiary care pediatric unit. Study design: A paired study based in the prospective cohort design in patients suspected of having COVID-19. Methods: RT-PCR was used to detect SARS-CoV-2 in paired samples of saliva and NP/OP swab collected from May through August 2020 from 156 pediatric participants, of whom 128 has at least one comorbidity and 91 showed clinical symptoms related to SARS-CoV-2 infection. Additionally, we studied a group of 326 members of the hospital staff, of whom 271 had symptoms related to SARS-CoV-2 infection. Results: In the group of pediatric participants the sensitivity of the diagnostic test in saliva was 82.3% (95% CI 56.6-96.2) and the specificity 95.6% (95% CI 90.8-98.4). The prevalence of COVID-19 was 10.9% (17/156). In 6 of the 23 participants who tested positive for SARS-CoV-2 in at least one specimen type, the virus was detected in saliva but not in NP/OP swab, while in 3 participants the NP/OP swab was positive and saliva negative. In the group of adults, the sensitivity of the test in saliva was 77.8% (95% CI 67.2-86.3) and prevalence 24.8% (81/326). Discordant results between the two types of specimens showed a significant association with low viral load in the pharynx of adults but not of pediatric participants. Interpretation: In the context of a pediatric tertiary care hospital, the sensibility of the test in saliva is not high enough to replace the use of NP/OP swab for COVID-19 diagnosis. Neither NP/OP swab nor saliva could detect all the participants infected with SARS-CoV-2.

Development and External Validation of a Machine Learning Tool to Rule Out COVID-19 Among Adults in the Emergency Department Using Routine Blood Tests: A Large, Multicenter, Real-World Study

Background

Conventional diagnosis of COVID-19 with reverse transcription polymerase chain reaction (RT-PCR) testing (hereafter, PCR) is associated with prolonged time to diagnosis and significant costs to run the test. The SARS-CoV-2 virus might lead to characteristic patterns in the results of widely available, routine blood tests that could be identified with machine learning methodologies. Machine learning modalities integrating findings from these common laboratory test results might accelerate ruling out COVID-19 in emergency department patients.

Objective

We sought to develop (ie, train and internally validate with cross-validation techniques) and externally validate a machine learning model to rule out COVID 19 using only routine blood tests among adults in emergency departments.

Methods

Using clinical data from emergency departments (EDs) from 66 US hospitals before the pandemic (before the end of December 2019) or during the pandemic (March-July 2020), we included patients aged ≥20 years in the study time frame. We excluded those with missing laboratory results. Model training used 2183 PCR-confirmed cases from 43 hospitals during the pandemic; negative controls were 10,000 prepandemic patients from the same hospitals. External validation used 23 hospitals with 1020 PCR-confirmed cases and 171,734 prepandemic negative controls. The main outcome was COVID 19 status predicted using same-day routine laboratory results. Model performance was assessed with area under the receiver operating characteristic (AUROC) curve as well as sensitivity, specificity, and negative predictive value (NPV).

Results

Of 192,779 patients included in the training, external validation, and sensitivity data sets (median age decile 50 [IQR 30-60] years, 40.5% male [78,249/192,779]), AUROC for training and external validation was 0.91 (95% CI 0.90-0.92). Using a risk score cutoff of 1.0 (out of 100) in the external validation data set, the model achieved sensitivity of 95.9% and specificity of 41.7%; with a cutoff of 2.0, sensitivity was 92.6% and specificity was 59.9%. At the cutoff of 2.0, the NPVs at a prevalence of 1%, 10%, and 20% were 99.9%, 98.6%, and 97%, respectively.

Conclusions

A machine learning model developed with multicenter clinical data integrating commonly collected ED laboratory data demonstrated high rule-out accuracy for COVID-19 status, and might inform selective use of PCR-based testing.