Rapid identification of SARS-CoV-2-infected patients at the emergency department using routine testing

Clinical Decision Support System in laboratory medicine

Clinical Decision Support Systems (CDSS) have been implemented in almost all healthcare settings. Laboratory medicine (LM), is one of the most important structured health data stores, but efforts are still needed to clarify the use and scope of these tools, especially in the laboratory setting. The aim is to clarify CDSS concept in LM, in the last decade. There is no consensus on the definition of CDSS in LM. A theoretical definition of CDSS in LM should capture the aim of driving significant improvements in LM mission, prevention, diagnosis, monitoring, and disease treatment. We identified the types, workflow and data sources of CDSS. The main applications of CDSS in LM were diagnostic support and clinical management, patient safety, workflow improvements, and cost containment. Laboratory professionals, with their expertise in quality improvement and quality assurance, have a chance to be leaders in CDSS.

A Modified Corona Score Using Lung Ultrasound to Identify COVID-19 Patients

BACKGROUND

COVID-19 continues to circulate around the world with multiple different strains being active at once. While diagnosis with antigen and molecular testing is more readily available, there is still room for alternative methods of diagnosis, particularly in out-of-hospital settings, e.g., home or nursing homes, and in low-medium income countries, where testing may not be readily available.

STUDY OBJECTIVES

To evaluate the performance of two modified corona score methods compared with a traditional corona score approach to identify patients with COVID-19.

METHODS

This was a retrospective multicenter study performed to compare the ability to predict SARS-CoV-2 test results on a nasopharyngeal swab between the corona scores and two novel corona scores (modified 1 corona score (M1CS) and modified 2 corona score (M2CS)). The M1CS included lung ultrasound (LUS) and chest X-ray (CXR) results, while the M2SC only utilized LUS findings without CXRs. Emergency physicians performed point-of-care LUS and a physical examination upon admission to the emergency department.

RESULTS

Subjects positive for SARS-CoV-2 were older and had higher ferritin levels and temperature and lower diastolic blood pressure and oxygen saturation. The two groups differed on corona score and modified corona scores (p < 0.001 for all). SARS-CoV-2-positive patients had fewer pleural line irregularities (p = 0.025) but presented more frequently with an interstitial pattern on CXRs (p < 0.001).

CONCLUSIONS

In our study, LUS alone provided a valuable contribution to the corona score and improved its performance more than when CXR results were included. These results suggest that resource-limited areas where CXRs may be unavailable or prohibitively expensive can utilize an ultrasound as the sole imaging modality without a loss of diagnostic performance for SARS-CoV-2 pneumonia diagnosis.

COVID-19 screening in low resource settings using artificial intelligence for chest radiographs and point-of-care blood tests

Artificial intelligence (AI) systems for detection of COVID-19 using chest X-Ray (CXR) imaging and point-of-care blood tests were applied to data from four low resource African settings. The performance of these systems to detect COVID-19 using various input data was analysed and compared with antigen-based rapid diagnostic tests. Participants were tested using the gold standard of RT-PCR test (nasopharyngeal swab) to determine whether they were infected with SARS-CoV-2. A total of 3737 (260 RT-PCR positive) participants were included. In our cohort, AI for CXR images was a poor predictor of COVID-19 (AUC = 0.60), since the majority of positive cases had mild symptoms and no visible pneumonia in the lungs. AI systems using differential white blood cell counts (WBC), or a combination of WBC and C-Reactive Protein (CRP) both achieved an AUC of 0.74 with a suggested optimal cut-off point at 83% sensitivity and 63% specificity. The antigen-RDT tests in this trial obtained 65% sensitivity at 98% specificity. This study is the first to validate AI tools for COVID-19 detection in an African setting. It demonstrates that screening for COVID-19 using AI with point-of-care blood tests is feasible and can operate at a higher sensitivity level than antigen testing.

Automatic Detection of Severely and Mildly Infected COVID-19 Patients with Supervised Machine Learning Models

Objectives

When the prognosis of COVID-19 disease can be detected early, the intense-pressure and loss of workforce in health-services can be partially reduced. The primary-purpose of this article is to determine the feature-dataset consisting of the routine-blood-values (RBV) and demographic-data that affect the prognosis of COVID-19. Second, by applying the feature-dataset to the supervised machine-learning (ML) models, it is to identify severely and mildly infected COVID-19 patients at the time of admission.

Material and methods

The sample of this study consists of severely (n = 192) and mildly (n = 4010) infected-patients hospitalized with the diagnosis of COVID-19 between March-September, 2021. The RBV-data measured at the time of admission and age-gender characteristics of these patients were analyzed retrospectively. For the selection of the features, the minimum-redundancy-maximum-relevance (MRMR) method, principal-components-analysis and forward-multiple-logistics-regression analyzes were used. The features set were statistically compared between mild and severe infected-patients. Then, the performances of various supervised-ML-models were compared in identifying severely and mildly infected-patients using the feature set.

Results

In this study, 28 RBV-parameters and age-variable were found as the feature-dataset. The effect of features on the prognosis of the disease has been clinically proven. The ML-models with the highest overall-accuracy in identifying patient-groups were found respectively, as follows: local-weighted-learning (LWL)-97.86%, K-star (K*)-96.31%, Naive-Bayes (NB)-95.36% and k-nearest-neighbor (KNN)-94.05%. Also, the most successful models with the highest area-under-the-receiver-operating-characteristic-curve (AUC) values in identifying patient groups were found respectively, as follows: LWL-0.95%, K*-0.91%, NB-0.85% and KNN-0.75%.

Conclusion

The findings in this article have significant a motivation for the healthcare professionals to detect at admission severely and mildly infected COVID-19 patients.

Predictivity of CRP, Albumin, and CRP to Albumin Ratio on the Development of Intensive Care Requirement, Mortality, and Disease Severity in COVID-19

BACKGROUND

The C-reactive protein (CRP) to albumin ratio (CAR) is a new index calculated by dividing CRP by the albumin level. It has been claimed to have predictive value in determining morbidity and mortality in many critical diseases Aim: In this research, we aimed to elucidate the importance of CRP, albumin, and CAR as parameters that can predict the clinical course in COVID-19 patients.

MATERIALS & METHOD

In this retrospective analysis, the clinical, laboratory, and radiological findings of patients over the age of 18 who were diagnosed with SARS-CoV-2 infection with a positive reverse transcription-polymerase chain reaction (RT-PCR) test were evaluated. Age, gender, laboratory examinations at admission, and CRP and albumin values at the time of diagnosis have been recorded. The relationship of these parameters with the requirement for intensive care, exitus, and serious illness in the clinical follow-up of the patients was investigated. The baseline hospitalization parameters of the patients were compared between the severe and non-severe groups.

RESULTS

Individuals with severe disease had a higher rate of additional disease than those with non-severe disease. It was observed that the mean laboratory values ​​of patients with severe disease had a statistically higher level of D-dimer, CRP, aspartate aminotransferase (AST), platelet distribution width (PDW), CRP-albumin ratio, and ferritin, compared to mild to moderate cases (p<0.05). The rate of additional disease in deceased patients was higher than in patients who were alive (p<0.05). The CAR value was found to be moderately predictive in our study revealing the severity of the disease, and the possibility that the severity of the disease might be higher in patients with a CAR value above 21.47.

CONCLUSION

The results of this study revealed that CAR is a potential parameter in distinguishing critically ill COVID-19 patients in need of intensive care. Therefore, one can say that CAR is an important biomarker in clinically determining COVID-19.

Habitat Imaging Biomarkers for Diagnosis and Prognosis in Cancer Patients Infected with COVID-19

OBJECTIVES

Cancer patients have worse outcomes from the COVID-19 infection and greater need for ventilator support and elevated mortality rates than the general population. However, previous artificial intelligence (AI) studies focused on patients without cancer to develop diagnosis and severity prediction models. Little is known about how the AI models perform in cancer patients. In this study, we aim to develop a computational framework for COVID-19 diagnosis and severity prediction particularly in a cancer population and further compare it head-to-head to a general population.

METHODS

We have enrolled multi-center international cohorts with 531 CT scans from 502 general patients and 420 CT scans from 414 cancer patients. In particular, the habitat imaging pipeline was developed to quantify the complex infection patterns by partitioning the whole lung regions into phenotypically different subregions. Subsequently, various machine learning models nested with feature selection were built for COVID-19 detection and severity prediction.

RESULTS

These models showed almost perfect performance in COVID-19 infection diagnosis and predicting its severity during cross validation. Our analysis revealed that models built separately on the cancer population performed significantly better than those built on the general population and locked to test on the cancer population. This may be because of the significant difference among the habitat features across the two different cohorts.

CONCLUSIONS

Taken together, our habitat imaging analysis as a proof-of-concept study has highlighted the unique radiologic features of cancer patients and demonstrated effectiveness of CT-based machine learning model in informing COVID-19 management in the cancer population.

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.

Development and validation of an early warning score to identify COVID-19 in the emergency department based on routine laboratory tests: a multicentre case-control study

OBJECTIVES

Identifying patients with a possible SARS-CoV-2 infection in the emergency department (ED) is challenging. Symptoms differ, incidence rates vary and test capacity may be limited. As PCR-testing all ED patients is neither feasible nor effective in most centres, a rapid, objective, low-cost early warning score to triage ED patients for a possible infection is developed.

DESIGN

Case-control study.

SETTING

Secondary and tertiary hospitals in the Netherlands.

PARTICIPANTS

The study included patients presenting to the ED with venous blood sampling from July 2019 to July 2020 (n=10 417, 279 SARS-CoV-2-positive). The temporal validation cohort covered the period from July 2020 to October 2021 (n=14 080, 1093 SARS-CoV-2-positive). The external validation cohort consisted of patients presenting to the ED of three hospitals in the Netherlands (n=12 061, 652 SARS-CoV-2-positive).

PRIMARY OUTCOME MEASURES

The primary outcome was one or more positive SARS-CoV-2 PCR test results within 1 day prior to or 1 week after ED presentation.

RESULTS

The resulting 'CoLab-score' consists of 10 routine laboratory measurements and age. The score showed good discriminative ability (AUC: 0.930, 95% CI 0.909 to 0.945). The lowest CoLab-score had high sensitivity for COVID-19 (0.984, 95% CI 0.970 to 0.991; specificity: 0.411, 95% CI 0.285 to 0.520). Conversely, the highest score had high specificity (0.978, 95% CI 0.973 to 0.983; sensitivity: 0.608, 95% CI 0.522 to 0.685). The results were confirmed in temporal and external validation.

CONCLUSIONS

The CoLab-score is based on routine laboratory measurements and is available within 1 hour after presentation. Depending on the prevalence, COVID-19 may be safely ruled out in over one-third of ED presentations. Highly suspect cases can be identified regardless of presenting symptoms. The CoLab-score is continuous, in contrast to the binary outcome of lateral flow testing, and can guide PCR testing and triage ED patients.

Sustainable solutions for indoor pollution abatement during COVID phase: A critical study on current technologies & challenges

The appearance of the contagious virus COVID-19, several revelations and environmental health experts punctually predicted the possibly disastrous public health complications of coexisting catching and airborne contamination-arbitrated disease. But much attention has been given on the outdoor-mediated interactions. Almost 3.8 million premature deaths occur every year globally due to the illness from indoor air pollution. Considering the human staying longer span indoors due to restricted human activities or work from home, the indoor air quality (IAQ) might show prominent role for individual health life. Currently, the Environmental Protection Agency (EPA) ensures no regulation of indoor airborne pollution. Herein, the paper underlines the common bases of indoor air pollution, poor IAQ, and impacts of the aerosolized airborne particles on the human health. In order to address these challenges and collective contagion events in indoor environment, several emerging control techniques and preventive sustainable solutions are suggested. By this, more innovations need to be investigated in future to measure the impact of indoor air pollution on individual health.

Rapid, point-of-care antigen tests for diagnosis of SARS-CoV-2 infection

BACKGROUND

Accurate rapid diagnostic tests for SARS-CoV-2 infection would be a useful tool to help manage the COVID-19 pandemic. Testing strategies that use rapid antigen tests to detect current infection have the potential to increase access to testing, speed detection of infection, and inform clinical and public health management decisions to reduce transmission. This is the second update of this review, which was first published in 2020.

OBJECTIVES

To assess the diagnostic accuracy of rapid, point-of-care antigen tests for diagnosis of SARS-CoV-2 infection. We consider accuracy separately in symptomatic and asymptomatic population groups. Sources of heterogeneity investigated included setting and indication for testing, assay format, sample site, viral load, age, timing of test, and study design.

SEARCH METHODS

We searched the COVID-19 Open Access Project living evidence database from the University of Bern (which includes daily updates from PubMed and Embase and preprints from medRxiv and bioRxiv) on 08 March 2021. We included independent evaluations from national reference laboratories, FIND and the Diagnostics Global Health website. We did not apply language restrictions.

SELECTION CRITERIA

We included studies of people with either suspected SARS-CoV-2 infection, known SARS-CoV-2 infection or known absence of infection, or those who were being screened for infection. We included test accuracy studies of any design that evaluated commercially produced, rapid antigen tests. We included evaluations of single applications of a test (one test result reported per person) and evaluations of serial testing (repeated antigen testing over time). Reference standards for presence or absence of infection were any laboratory-based molecular test (primarily reverse transcription polymerase chain reaction (RT-PCR)) or pre-pandemic respiratory sample.

DATA COLLECTION AND ANALYSIS

We used standard screening procedures with three people. Two people independently carried out quality assessment (using the QUADAS-2 tool) and extracted study results. Other study characteristics were extracted by one review author and checked by a second. We present sensitivity and specificity with 95% confidence intervals (CIs) for each test, and pooled data using the bivariate model. We investigated heterogeneity by including indicator variables in the random-effects logistic regression models. We tabulated results by test manufacturer and compliance with manufacturer instructions for use and according to symptom status.

MAIN RESULTS

We included 155 study cohorts (described in 166 study reports, with 24 as preprints). The main results relate to 152 evaluations of single test applications including 100,462 unique samples (16,822 with confirmed SARS-CoV-2). Studies were mainly conducted in Europe (101/152, 66%), and evaluated 49 different commercial antigen assays. Only 23 studies compared two or more brands of test. Risk of bias was high because of participant selection (40, 26%); interpretation of the index test (6, 4%); weaknesses in the reference standard for absence of infection (119, 78%); and participant flow and timing 41 (27%). Characteristics of participants (45, 30%) and index test delivery (47, 31%) differed from the way in which and in whom the test was intended to be used. Nearly all studies (91%) used a single RT-PCR result to define presence or absence of infection. The 152 studies of single test applications reported 228 evaluations of antigen tests. Estimates of sensitivity varied considerably between studies, with consistently high specificities. Average sensitivity was higher in symptomatic (73.0%, 95% CI 69.3% to 76.4%; 109 evaluations; 50,574 samples, 11,662 cases) compared to asymptomatic participants (54.7%, 95% CI 47.7% to 61.6%; 50 evaluations; 40,956 samples, 2641 cases). Average sensitivity was higher in the first week after symptom onset (80.9%, 95% CI 76.9% to 84.4%; 30 evaluations, 2408 cases) than in the second week of symptoms (53.8%, 95% CI 48.0% to 59.6%; 40 evaluations, 1119 cases). For those who were asymptomatic at the time of testing, sensitivity was higher when an epidemiological exposure to SARS-CoV-2 was suspected (64.3%, 95% CI 54.6% to 73.0%; 16 evaluations; 7677 samples, 703 cases) compared to where COVID-19 testing was reported to be widely available to anyone on presentation for testing (49.6%, 95% CI 42.1% to 57.1%; 26 evaluations; 31,904 samples, 1758 cases). Average specificity was similarly high for symptomatic (99.1%) or asymptomatic (99.7%) participants. We observed a steady decline in summary sensitivities as measures of sample viral load decreased. Sensitivity varied between brands. When tests were used according to manufacturer instructions, average sensitivities by brand ranged from 34.3% to 91.3% in symptomatic participants (20 assays with eligible data) and from 28.6% to 77.8% for asymptomatic participants (12 assays). For symptomatic participants, summary sensitivities for seven assays were 80% or more (meeting acceptable criteria set by the World Health Organization (WHO)). The WHO acceptable performance criterion of 97% specificity was met by 17 of 20 assays when tests were used according to manufacturer instructions, 12 of which demonstrated specificities above 99%. For asymptomatic participants the sensitivities of only two assays approached but did not meet WHO acceptable performance standards in one study each; specificities for asymptomatic participants were in a similar range to those observed for symptomatic people. At 5% prevalence using summary data in symptomatic people during the first week after symptom onset, the positive predictive value (PPV) of 89% means that 1 in 10 positive results will be a false positive, and around 1 in 5 cases will be missed. At 0.5% prevalence using summary data for asymptomatic people, where testing was widely available and where epidemiological exposure to COVID-19 was suspected, resulting PPVs would be 38% to 52%, meaning that between 2 in 5 and 1 in 2 positive results will be false positives, and between 1 in 2 and 1 in 3 cases will be missed.

AUTHORS' CONCLUSIONS

Antigen tests vary in sensitivity. In people with signs and symptoms of COVID-19, sensitivities are highest in the first week of illness when viral loads are higher. Assays that meet appropriate performance standards, such as those set by WHO, could replace laboratory-based RT-PCR when immediate decisions about patient care must be made, or where RT-PCR cannot be delivered in a timely manner. However, they are more suitable for use as triage to RT-PCR testing. The variable sensitivity of antigen tests means that people who test negative may still be infected. Many commercially available rapid antigen tests have not been evaluated in independent validation studies. Evidence for testing in asymptomatic cohorts has increased, however sensitivity is lower and there is a paucity of evidence for testing in different settings. Questions remain about the use of antigen test-based repeat testing strategies. Further research is needed to evaluate the effectiveness of screening programmes at reducing transmission of infection, whether mass screening or targeted approaches including schools, healthcare setting and traveller screening.

Use of an algorithm based on routine blood laboratory tests to exclude COVID-19 in a screening-setting of healthcare workers

Background

COVID-19 is an ongoing pandemic leading to exhaustion of the hospital care system. Our health care system has to deal with a high level of sick leave of health care workers (HCWs) with COVID-19 related complaints, in whom an infection with SARS-CoV-2 has to be ruled out before they can return back to work. The aim of the present study is to investigate if the recently described CoLab-algorithm can be used to exclude COVID-19 in a screening setting of HCWs.

Methods

In the period from January 2021 till March 2021, HCWs with COVID-19-related complaints were prospectively collected and included in this study. Next to the routinely performed SARS-CoV-2 RT-PCR, using a set of naso- and oropharyngeal swab samples, two blood tubes (one EDTA- and one heparin-tube) were drawn for analysing the 10 laboratory parameters required for running the CoLab-algorithm.

Results

In total, 726 HCWs with a complete CoLab-laboratory panel were included in this study. In this group, 684 HCWs were tested SARS-CoV-2 RT-PCR negative and 42 cases RT-PCR positive. ROC curve analysis showed an area under the curve (AUC) of 0.853 (95% CI: 0.801–0.904). At a safe cut-off value for excluding COVID-19 of -6.525, the sensitivity was 100% with a specificity of 34% (95% CI: 21 to 49%). No SARS-CoV-2 RT-PCR cases were missed with this cut-off and COVID-19 could be safely ruled out in more than one third of HCWs.

Conclusion

The CoLab-score is an easy and reliable algorithm that can be used for screening HCWs with COVID-19 related complaints. A major advantage of this approach is that the results of the score are available within 1 hour after collecting the samples. This results in a faster return to labour process of a large part of the COVID-19 negative HCWs (34%), next to a reduction in RT-PCR tests (reagents and labour costs) that can be saved.

Cytokeratin 18 cell death assays as biomarkers for quantification of apoptosis and necrosis in COVID-19: a prospective, observational study

BACKGROUND

The mechanism by which SARS-CoV-2 triggers cell damage and necrosis are yet to be fully elucidated. We sought to quantify epithelial cell death in patients with COVID-19, with an estimation of relative contributions of apoptosis and necrosis.

METHODS

Blood samples were collected prospectively from adult patients presenting to the emergency department. Circulating levels of caspase-cleaved (apoptosis) and total cytokeratin 18 (CK-18) (total cell death) were determined using M30 and M65 enzyme assays, respectively. Intact CK-18 (necrosis) was estimated by subtracting M30 levels from M65.

RESULTS

A total of 52 COVID-19 patients and 27 matched sick controls (with respiratory symptoms not due to COVID-19) were enrolled. Compared with sick controls, COVID-19 patients had higher levels of M65 (p = 0.046, total cell death) and M30 (p = 0.0079, apoptosis). Hospitalised COVID-19 patients had higher levels of M65 (p= 0.014) and intact CK-18 (p= 0.004, necrosis) than discharged patients. Intensive care unit (ICU)-admitted COVID-19 patients had higher levels of M65 (p= 0.004), M30 (p= 0.004) and intact CK-18 (p= 0.033) than hospitalised non-ICU admitted patients. In multivariable logistic regression, elevated levels of M65, M30 and intact CK-18 were associated with increased odds of ICU admission (OR=22.05, p=0.014, OR=19.71, p=0.012 and OR=14.12, p=0.016, respectively).

CONCLUSION

Necrosis appears to be the main driver of hospitalisation, whereas apoptosis and necrosis appear to drive ICU admission. Elevated levels CK-18 levels are independent predictors of severe disease, and could be useful for risk stratification of COVID-19 patients and in assessment of therapeutic efficacy in early-phase COVID-19 clinical trials.

Clinical prediction models for diagnosis of COVID-19 among adult patients: a validation and agreement study

BACKGROUND

Since the beginning of the pandemic, hospitals have been constantly overcrowded, with several observed waves of infected cases and hospitalisations. To avoid as much as possible this situation, efficient tools to facilitate the diagnosis of COVID-19 are needed.

OBJECTIVE

To evaluate and compare prediction models to diagnose COVID-19 identified in a systematic review published recently using performance indicators such as discrimination and calibration measures.

METHODS

A total of 1618 adult patients present at two Emergency Department triage centers and for whom qRT-PCR tests had been performed were included in this study. Six previously published models were reconstructed and assessed using diagnostic tests as sensitivity (Se) and negative predictive value (NPV), discrimination (Area Under the Roc Curve (AUROC)) and calibration measures. Agreement was also measured between them using Kappa's coefficient and IntraClass Correlation Coefficient (ICC). A sensitivity analysis has been conducted by waves of patients.

RESULTS

Among the 6 selected models, those based only on symptoms and/or risk exposure were found to be less efficient than those based on biological parameters and/or radiological examination with smallest AUROC values (< 0.80). However, all models showed good calibration and values above > 0.75 for Se and NPV but poor agreement (Kappa and ICC < 0.5) between them. The results of the first wave were similar to those of the second wave.

CONCLUSION

Although quite acceptable and similar results were found between all models, the importance of radiological examination was also emphasized, making it difficult to find an appropriate triage system to classify patients at risk for COVID-19.

Automated prediction of low ferritin concentrations using a machine learning algorithm

OBJECTIVES

Computational algorithms for the interpretation of laboratory test results can support physicians and specialists in laboratory medicine. The aim of this study was to develop, implement and evaluate a machine learning algorithm that automatically assesses the risk of low body iron storage, reflected by low ferritin plasma levels, in anemic primary care patients using a minimal set of basic laboratory tests, namely complete blood count and C-reactive protein (CRP).

METHODS

Laboratory measurements of anemic primary care patients were used to develop and validate a machine learning algorithm. The performance of the algorithm was compared to twelve specialists in laboratory medicine from three large teaching hospitals, who predicted if patients with anemia have low ferritin levels based on laboratory test reports (complete blood count and CRP). In a second round of assessments the algorithm outcome was provided to the specialists in laboratory medicine as a decision support tool.

RESULTS

Two separate algorithms to predict low ferritin concentrations were developed based on two different chemistry analyzers, with an area under the curve of the ROC of 0.92 (Siemens) and 0.90 (Roche). The specialists in laboratory medicine were less accurate in predicting low ferritin concentrations compared to the algorithms, even when knowing the output of the algorithms as support tool. Implementation of the algorithm in the laboratory system resulted in one new iron deficiency diagnosis on average per day.

CONCLUSIONS

Low ferritin levels in anemic patients can be accurately predicted using a machine learning algorithm based on routine laboratory test results. Moreover, implementation of the algorithm in the laboratory system reduces the number of otherwise unrecognized iron deficiencies.

Severe Acute Respiratory Syndrome Coronavirus 2 Diagnostic Tests for Border Screening During the Very Early Phase of Coronavirus Disease 2019 Pandemic: A Systematic Review and Meta-Analysis

Diagnosis of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) during border screening among returning residents and prioritized travelers during the early phase of a pandemic can reduce the risk of importation and transmission in the community. This study aimed to compare the accuracy of various SARS-CoV-2 diagnostics and assess their potential utility as border screening for infection and immunity. Systematic literature searches were conducted in six electronic databases for studies reporting SARS-CoV-2 diagnostics (up to April 30, 2020). Meta-analysis and methodological assessment were conducted for all included studies. The performance of the diagnostic tests was evaluated with pooled sensitivity, specificity, and their respective 95% confidence intervals. A total of 5,416 unique studies were identified and 95 studies (at least 29,785 patients/samples) were included. Nucleic acid amplification tests (NAAT) consistently outperformed all other diagnostic methods regardless of the selected viral genes with a pooled sensitivity of 98% and a pooled specificity of 99%. Point-of-care (POC) serology tests had moderately high pooled sensitivity (69%), albeit lower than laboratory-based serology tests (89%), but both had high pooled specificity (96-98%). Serology tests were more sensitive for sampling collected at ≥ 7 days than ≤ 7 days from the disease symptoms onset. POC NAAT and POC serology tests are suitable for detecting infection and immunity against the virus, respectively as border screening. Independent validation in each country is highly encouraged with the preferred choice of diagnostic tool/s.

The potential of volatile organic compounds-based breath analysis for COVID-19 screening: a systematic review & meta-analysis

COVID-19 is a major problem with an increasing incidence and mortality. The discovery of Volatile Organic Compounds (VOCs) based on breath analysis offers a reliable, rapid, and affordable screening method. This study examined VOC-based breath analysis diagnostic performance for SARS-COV-2 infection compared to RT-PCR. A systematic review was conducted in 8 scientific databases based on the PRISMA guideline. Original English studies evaluating human breaths for COVID-19 screening and mentioning sensitivity and specificity value compared to RT-PCR were included. Six studies were included with a total of 4093 samples from various settings. VOCs-based breath analysis had the cumulative sensitivity of 98.2% (97.5% CI 93.1%-99.6%) and specificity of 74.3% (97.5% CI 66.4%-80.9%). Subgroup analysis on chemical analysis (GC-MS) and pattern recognition (eNose) revealed higher sensitivity in the eNose group. VOC-based breath analysis shows high sensitivity and promising specificity for COVID-19 public screening.

An integrated framework for identifying clinical-laboratory indicators for novel pandemics: COVID-19 and MIS-C

During the critical early stages of an emerging pandemic, limited availability of pathogen-specific testing can severely inhibit individualized risk screening and pandemic tracking. Standard clinical laboratory tests offer a widely available complementary data source for first-line risk screening and pandemic surveillance. Here, we propose an integrated framework for developing clinical-laboratory indicators for novel pandemics that combines population-level and individual-level analyses. We apply this framework to 7,520,834 clinical laboratory tests recorded over five years and find clinical-lab-test combinations that are strongly associated with SARS-CoV-2 PCR test results and Multisystem Inflammatory Syndrome in Children (MIS-C) diagnoses: Interleukin-related tests (e.g. IL4, IL10) were most strongly associated with SARS-CoV-2 infection and MIS-C, while other more widely available tests (ferritin, D-dimer, fibrinogen, alanine transaminase, and C-reactive protein) also had strong associations. When novel pandemics emerge, this framework can be used to identify specific combinations of clinical laboratory tests for public health tracking and first-line individualized risk screening.

Coronavirus Disease 2019 (COVID-19) Diagnostic Clinical Decision Support: A Pre-Post Implementation Study of CORAL (COvid Risk cALculator)

BACKGROUND

Isolation of hospitalized persons under investigation (PUIs) for coronavirus disease 2019 (COVID-19) reduces nosocomial transmission risk. Efficient evaluation of PUIs is needed to preserve scarce healthcare resources. We describe the development, implementation, and outcomes of an inpatient diagnostic algorithm and clinical decision support system (CDSS) to evaluate PUIs.

METHODS

We conducted a pre-post study of CORAL (COvid Risk cALculator), a CDSS that guides frontline clinicians through a risk-stratified COVID-19 diagnostic workup, removes transmission-based precautions when workup is complete and negative, and triages complex cases to infectious diseases (ID) physician review. Before CORAL, ID physicians reviewed all PUI records to guide workup and precautions. After CORAL, frontline clinicians evaluated PUIs directly using CORAL. We compared pre- and post-CORAL frequency of repeated severe acute respiratory syndrome coronavirus 2 nucleic acid amplification tests (NAATs), time from NAAT result to PUI status discontinuation, total duration of PUI status, and ID physician work hours, using linear and logistic regression, adjusted for COVID-19 incidence.

RESULTS

Fewer PUIs underwent repeated testing after an initial negative NAAT after CORAL than before CORAL (54% vs 67%, respectively; adjusted odd ratio, 0.53 [95% confidence interval, .44-.63]; P < .01). CORAL significantly reduced average time to PUI status discontinuation (adjusted difference [standard error], -7.4 [0.8] hours per patient), total duration of PUI status (-19.5 [1.9] hours per patient), and average ID physician work-hours (-57.4 [2.0] hours per day) (all P < .01). No patients had a positive NAAT result within 7 days after discontinuation of precautions via CORAL.

CONCLUSIONS

CORAL is an efficient and effective CDSS to guide frontline clinicians through the diagnostic evaluation of PUIs and safe discontinuation of precautions.

Clinical Features of Early Stage COVID-19 in a Primary Care Setting

Background: The coronavirus disease 2019 (COVID-19) pandemic remains a global healthcare crisis. Nevertheless, the majority of COVID-19 cases involve mild to moderate symptoms in the early stages. The lack of information relating to these cases necessitates further investigation.

Methods: Patients visiting the outpatient clinic at the Kamagaya General Hospital were screened by interview and body temperature check. After initial screening, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection was suspected in 481 patients who then underwent blood tests and the loop-mediated isothermal amplification (LAMP) test for SARS-CoV-2. Clinical characteristics between positive and negative SARS-CoV-2 groups were compared. Further, the novel predictive value of routine blood test results for SARS-CoV-2 infection was evaluated using ROC analysis.

Results: A total of 15,560 patients visited our hospital during the study period. After exclusion and initial screening by interview, 481 patients underwent the LAMP test and routine blood tests. Of these patients, 69 (14.3%) were positive for SARS-CoV-2 and diagnosed with COVID-19 (positive group), and 412 (85.7%) were negative (negative group). The median period between the first onset of symptoms and visit to our hospital was 3.4 and 2.9 days in the negative and positive groups, respectively. Cough (p = 0.014), rhinorrhea (p = 0.039), and taste disorders (p < 0.001) were significantly more common in the positive group, while gastrointestinal symptoms in the negative group (p = 0.043). The white blood cell count (p < 0.001), neutrophil count (p < 0.001), and percentage of neutrophils (p < 0.001) were higher in the negative group. The percentage of monocytes (p < 0.001) and the levels of ferritin (p < 0.001) were higher in the positive group. As per the predictive values for COVID-19 using blood tests, the values for the area under the curve for the neutrophil-to-monocyte ratio (NMR), white blood cell-to-hemoglobin ratio (WHR), and the product of the two (NMWH) were 0.857, 0.837, and 0.887, respectively.

Conclusion: Symptoms in early stage COVID-19 patients were similar to those in previous reports. Some blood test results were not consistent with previous reports. NMR, WHR, and NMWH are novel diagnostic scores in early-stage mild-symptom COVID-19 patients in primary care settings.

CCEDRRN COVID-19 Infection Score (CCIS): development and validation in a Canadian cohort of a clinical risk score to predict SARS-CoV-2 infection in patients presenting to the emergency department with suspected COVID-19

OBJECTIVES

To develop and validate a clinical risk score that can accurately quantify the probability of SARS-CoV-2 infection in patients presenting to an emergency department without the need for laboratory testing.

DESIGN

Cohort study of participants in the Canadian COVID-19 Emergency Department Rapid Response Network (CCEDRRN) registry. Regression models were fitted to predict a positive SARS-CoV-2 test result using clinical and demographic predictors, as well as an indicator of local SARS-CoV-2 incidence.

SETTING

32 emergency departments in eight Canadian provinces.

PARTICIPANTS

27 665 consecutively enrolled patients who were tested for SARS-CoV-2 in participating emergency departments between 1 March and 30 October 2020.

MAIN OUTCOME MEASURES

Positive SARS-CoV-2 nucleic acid test result within 14 days of an index emergency department encounter for suspected COVID-19 disease.

RESULTS

We derived a 10-item CCEDRRN COVID-19 Infection Score using data from 21 743 patients. This score included variables from history and physical examination and an indicator of local disease incidence. The score had a c-statistic of 0.838 with excellent calibration. We externally validated the rule in 5295 patients. The score maintained excellent discrimination and calibration and had superior performance compared with another previously published risk score. Score cut-offs were identified that can rule-in or rule-out SARS-CoV-2 infection without the need for nucleic acid testing with 97.4% sensitivity (95% CI 96.4 to 98.3) and 95.9% specificity (95% CI 95.5 to 96.0).

CONCLUSIONS

The CCEDRRN COVID-19 Infection Score uses clinical characteristics and publicly available indicators of disease incidence to quantify a patient's probability of SARS-CoV-2 infection. The score can identify patients at sufficiently high risk of SARS-CoV-2 infection to warrant isolation and empirical therapy prior to test confirmation while also identifying patients at sufficiently low risk of infection that they may not need testing.

TRIAL REGISTRATION NUMBER

NCT04702945.

High immature platelet fraction with reduced platelet count on hospital admission. Can it be useful for COVID-19 diagnosis?

Introduction

Health professions are heavily engaged facing the current threat of SARS‐CoV‐2 (COVID‐19). Although there are many diagnostic tools, an accurate and rapid laboratory procedure for diagnosing COVID‐19 is recommended. We focused on platelet parameters as the additional biomarkers for clinical diagnosis in patients presenting to the emergency department (ED).

Materials and Methods

Five hundred and sixty‐one patients from February to April 2020 have been recruited. Patients were divided into three groups: (N = 50) COVID‐19 positive and (N = 21) COVID‐19 negative with molecular testing, (N = 490) as reference population without molecular testing. A Multiplex rRT‐PCR from samples collected by nasopharyngeal swabs was performed and the hematological data collected.

Results

We detected a mild anemia in COVID‐19 group and lymphopenia against reference population: hemoglobin (g/dL) 13.0 (11.5‐14.8) versus 13.9 (12.8‐15.0) (P = .0135); lymphocytes (10⁹/L) 1.24 (0.94‐1.73) versus 1.99 (1.49‐2.64) (P < .0001). In addition, abnormal platelet parameters as follows (COVID group vs reference population): PLT (×10⁹/L) 209 (160‐258) vs 236 (193‐279) (P = .0239). IPF (%) 4.05 (2.5‐5.9) versus 3.4 (2.2‐4.9) (P = .0576); H‐IPF (%) 1.25 (0.8‐2.2) versus 0.95 (0.6‐1.5) (P = .0171) were identified. In particular, COVID positive group had a high H‐IPF/IPF Ratio compared to reference population [0.32 (0.29‐0.36) versus 0.29 (0.26‐0.32), respectively, (P = .0003)]. Finally, a PLT difference of nearly 50 × 10⁹/L between pre/postCOVID‐19 sampling for each patient was found (N = 42) (P = .0194).

Conclusions

COVID‐19 group results highlighted higher IPF and H‐IPF values, with increased H‐IPF/IPF Ratio, associated to PLT count reduction. These findings shall be adopted for a timely diagnosis of patients upon hospital admission.

Assessing the capacity of symptom scores to predict COVID-19 positivity in Nigeria: a national derivation and validation cohort study

OBJECTIVES

This study aimed to develop and validate a symptom prediction tool for COVID-19 test positivity in Nigeria.

DESIGN

Predictive modelling study.

SETTING

All Nigeria States and the Federal Capital Territory.

PARTICIPANTS

A cohort of 43 221 individuals within the national COVID-19 surveillance dataset from 27 February to 27 August 2020. Complete dataset was randomly split into two equal halves: derivation and validation datasets. Using the derivation dataset (n=21 477), backward multivariable logistic regression approach was used to identify symptoms positively associated with COVID-19 positivity (by real-time PCR) in children (≤17 years), adults (18-64 years) and elderly (≥65 years) patients separately.

OUTCOME MEASURES

Weighted statistical and clinical scores based on beta regression coefficients and clinicians' judgements, respectively. Using the validation dataset (n=21 744), area under the receiver operating characteristic curve (AUROC) values were used to assess the predictive capacity of individual symptoms, unweighted score and the two weighted scores.

RESULTS

Overall, 27.6% of children (4415/15 988), 34.6% of adults (9154/26 441) and 40.0% of elderly (317/792) that had been tested were positive for COVID-19. Best individual symptom predictor of COVID-19 positivity was loss of smell in children (AUROC 0.56, 95% CI 0.55 to 0.56), either fever or cough in adults (AUROC 0.57, 95% CI 0.56 to 0.58) and difficulty in breathing in the elderly (AUROC 0.53, 95% CI 0.48 to 0.58) patients. In children, adults and the elderly patients, all scoring approaches showed similar predictive performance.

CONCLUSIONS

The predictive capacity of various symptom scores for COVID-19 positivity was poor overall. However, the findings could serve as an advocacy tool for more investments in resources for capacity strengthening of molecular testing for COVID-19 in Nigeria.

Deep learning with robustness to missing data: A novel approach to the detection of COVID-19

In the context of the current global pandemic and the limitations of the RT-PCR test, we propose a novel deep learning architecture, DFCN (Denoising Fully Connected Network). Since medical facilities around the world differ enormously in what laboratory tests or chest imaging may be available, DFCN is designed to be robust to missing input data. An ablation study extensively evaluates the performance benefits of the DFCN as well as its robustness to missing inputs. Data from 1088 patients with confirmed RT-PCR results are obtained from two independent medical facilities. The data includes results from 27 laboratory tests and a chest x-ray scored by a deep learning model. Training and test datasets are taken from different medical facilities. Data is made publicly available. The performance of DFCN in predicting the RT-PCR result is compared with 3 related architectures as well as a Random Forest baseline. All models are trained with varying levels of masked input data to encourage robustness to missing inputs. Missing data is simulated at test time by masking inputs randomly. DFCN outperforms all other models with statistical significance using random subsets of input data with 2-27 available inputs. When all 28 inputs are available DFCN obtains an AUC of 0.924, higher than any other model. Furthermore, with clinically meaningful subsets of parameters consisting of just 6 and 7 inputs respectively, DFCN achieves higher AUCs than any other model, with values of 0.909 and 0.919.

A cross sectional evaluation of the corona-score for swift identification of SARS-CoV-2 infection at a tertiary care hospital in Pakistan

Background

The Corona-Score is one of the first and most widely used predictive model for coronavirus 2 (SARS-CoV-2) infection. The purpose of this study was to validate the performance of Corona-Score in a cohort of Pakistani patients pursuing care for suspected infection.

Methods

After seeking institution's ethical committee exemption, results of serum lactate dehydrogenase (LDH), C-reactive protein (CRP), ferritin, absolute lymphocyte and neutrophil counts, chest x-ray findings and demographics of suspected COVID-19 cases with respiratory symptoms were recouped from electronic medical record. The pre-validated score as proposed by Kurstjens S et al., was calculated. The subjects were divided into SARS-CoV-2 positive and negative on the basis of reverse transcription-polymerase chain reaction (RT-PCR) findings. Median and interquartile range (IQR) was calculated for the score in the two groups and the difference was assessed using the independent sample median test. Receiver operating characteristics (ROC) curve analysis was plotted. Statistical analyses were carried out using SPSS 26, with statistical significance set at p value < 0.05.

Results

A total of sixty cases, 30 (50%) RT-PCR positive and 30 (50%) negative with a median Corona-Score of 3.5 (IQR: 0-6) and 1.5 (IQR: 0-4) respectively, were evaluated. A p-value of 0.61 showing no statistically significant between group differences was observed. The area under the curve of Corona-Score in our population of patients was 0.59 (95% CI: 0.45-0.74). Using the cut-off values of four originally identified by Kurstjens et al. the model displayed 43.3% sensitivity and 70% specificity with an overall accuracy of 56.67%.

Conclusion

Corona-Score displayed a lower diagnostic accuracy which may be attributable to the different genetic framework, viral strain and severity of the disease in Pakistanis compared to the population where this score was originally validated. However, large multi-center studies across the country are dire need of time to evaluate the score in overly exhausted health care setup and limited availability of PCR testing.

Inter-rater reliability and prospective validation of a clinical prediction rule for SARS-CoV-2 infection

Objectives

Accurate estimation of the risk of SARS‐CoV‐2 infection based on bedside data alone has importance to emergency department (ED) operations and throughput. The 13‐item CORC (COVID [or coronavirus] Rule‐out Criteria) rule had good overall diagnostic accuracy in retrospective derivation and validation. The objective of this study was to prospectively test the inter‐rater reliability and diagnostic accuracy of the CORC score and rule (score ≤ 0 negative, > 0 positive) and compare the CORC rule performance with physician gestalt.

Methods

This noninterventional study was conducted at an urban academic ED from February 2021 to March 2021. Two practitioners were approached by research coordinators and asked to independently complete a form capturing the CORC criteria for their shared patient and their gestalt binary prediction of the SARS‐CoV‐2 test result and confidence (0%–100%). The criterion standard for SARS‐CoV‐2 was from reverse transcriptase polymerase chain reaction performed on a nasopharyngeal swab. The primary analysis was from weighted Cohen's kappa and likelihood ratios (LRs).

Results

For 928 patients, agreement between observers was good for the total CORC score, κ = 0.613 (95% confidence interval [CI] = 0.579–0.646), and for the CORC rule, κ = 0.644 (95% CI = 0.591–0.697). The agreement for clinician gestalt binary determination of SARs‐CoV‐2 status was κ = 0.534 (95% CI = 0.437–0.632) with median confidence of 76% (first–third quartile = 66–88.5). For 425 patients who had the criterion standard, a negative CORC rule (both observers scored CORC < 0), the sensitivity was 88%, and specificity was 51%, with a negative LR (LR−) of 0.24 (95% CI = 0.10–0.50). Among patients with a mean CORC score of >4, the prevalence of a positive SARS‐CoV‐2 test was 58% (95% CI = 28%–85%) and positive LR was 13.1 (95% CI = 4.5–37.2). Clinician gestalt demonstrated a sensitivity of 51% and specificity of 86% with a LR− of 0.57 (95% CI = 0.39–0.74).

Conclusion

In this prospective study, the CORC score and rule demonstrated good inter‐rater reliability and reproducible diagnostic accuracy for estimating the pretest probability of SARs‐CoV‐2 infection.

A comprehensive review of imaging findings in COVID-19 - status in early 2021

Medical imaging methods are assuming a greater role in the workup of patients with COVID-19, mainly in relation to the primary manifestation of pulmonary disease and the tissue distribution of the angiotensin-converting-enzyme 2 (ACE 2) receptor. However, the field is so new that no consensus view has emerged guiding clinical decisions to employ imaging procedures such as radiography, computer tomography (CT), positron emission tomography (PET), and magnetic resonance imaging, and in what measure the risk of exposure of staff to possible infection could be justified by the knowledge gained. The insensitivity of current RT-PCR methods for positive diagnosis is part of the rationale for resorting to imaging procedures. While CT is more sensitive than genetic testing in hospitalized patients, positive findings of ground glass opacities depend on the disease stage. There is sparse reporting on PET/CT with [18F]-FDG in COVID-19, but available results are congruent with the earlier literature on viral pneumonias. There is a high incidence of cerebral findings in COVID-19, and likewise evidence of gastrointestinal involvement. Artificial intelligence, notably machine learning is emerging as an effective method for diagnostic image analysis, with performance in the discriminative diagnosis of diagnosis of COVID-19 pneumonia comparable to that of human practitioners.

A systematic review of prediction models to diagnose COVID-19 in adults admitted to healthcare centers

BACKGROUND

The COVID-19 pandemic is putting significant pressure on the hospital system. To help clinicians in the rapid triage of patients at high risk of COVID-19 while waiting for RT-PCR results, different diagnostic prediction models have been developed. Our objective is to identify, compare, and evaluate performances of prediction models for the diagnosis of COVID-19 in adult patients in a health care setting.

METHODS

A search for relevant references has been conducted on the MEDLINE and Scopus databases. Rigorous eligibility criteria have been established (e.g., adult participants, suspicion of COVID-19, medical setting) and applied by two independent investigators to identify suitable studies at 2 different stages: (1) titles and abstracts screening and (2) full-texts screening. Risk of bias (RoB) has been assessed using the Prediction model study Risk of Bias Assessment Tool (PROBAST). Data synthesis has been presented according to a narrative report of findings.

RESULTS

Out of the 2334 references identified by the literature search, 13 articles have been included in our systematic review. The studies, carried out all over the world, were performed in 2020. The included articles proposed a model developed using different methods, namely, logistic regression, score, machine learning, XGBoost. All the included models performed well to discriminate adults at high risks of presenting COVID-19 (all area under the ROC curve (AUROC) > 0.500). The best AUROC was observed for the model of Kurstjens et al (AUROC = 0.940 (0.910-0.960), which was also the model that achieved the highest sensitivity (98%). RoB was evaluated as low in general.

CONCLUSION

Thirteen models have been developed since the start of the pandemic in order to diagnose COVID-19 in suspected patients from health care centers. All these models are effective, to varying degrees, in identifying whether patients were at high risk of having COVID-19.

COVID-19-Lessons Learned and Questions Remaining

In this article, the editors of Clinical Infectious Diseases review some of the most important lessons they have learned about the epidemiology, clinical features, diagnosis, treatment and prevention of SARS-CoV-2 infection and identify essential questions about COVID-19 that remain to be answered.

Applications of machine learning in the chemical pathology laboratory

Machine learning (ML) is an area of artificial intelligence that provides computer programmes with the capacity to autodidact and learn new skills from experience, without continued human programming. ML algorithms can analyse large data sets quickly and accurately, by supervised and unsupervised learning techniques, to provide classification and prediction value outputs. The application of ML to chemical pathology can potentially enhance efficiency at all phases of the laboratory's total testing process. Our review will broadly discuss the theoretical foundation of ML in laboratory medicine. Furthermore, we will explore the current applications of ML to diverse chemical pathology laboratory processes, for example, clinical decision support, error detection in the preanalytical phase, and ML applications in gel-based image analysis and biomarker discovery. ML currently demonstrates exploratory applications in chemical pathology with promising advancements, which have the potential to improve all phases of the chemical pathology total testing pathway.

Characteristics and laboratory findings on admission to the emergency department among 2873 hospitalized patients with COVID-19: the impact of adjusted laboratory tests in multicenter studies. A multicenter study in Spain (BIOCOVID-Spain study)

Identification of predictors for severe disease progression is key for risk stratification in COVID-19 patients. We aimed to describe the main characteristics and identify the early predictors for severe outcomes among hospitalized patients with COVID-19 in Spain. This was an observational, retrospective cohort study (BIOCOVID-Spain study) including COVID-19 patients admitted to 32 Spanish hospitals. Demographics, comorbidities and laboratory tests were collected. Outcome was in-hospital mortality. For analysis, laboratory tests values were previously adjusted to assure the comparability of results among participants. Cox regression was performed to identify predictors. Study population included 2873 hospitalized COVID-19 patients. Nine variables were independent predictors for in-hospital mortality, including creatinine (Hazard ratio [HR]:1.327; 95% Confidence Interval [CI]: 1.040-1.695, p = .023), troponin (HR: 2.150; 95% CI: 1.155-4.001; p = .016), platelet count (HR: 0.994; 95% CI: 0.989-0.998; p = .004) and C-reactive protein (HR: 1.037; 95% CI: 1.006-1.068; p = .019). This is the first multicenter study in which an effort was carried out to adjust the results of laboratory tests measured with different methodologies to guarantee their comparability. We reported a comprehensive information about characteristics in a large cohort of hospitalized COVID-19 patients, focusing on the analytical features. Our findings may help to identify patients early at a higher risk for an adverse outcome.

Diagnostic prediction of COVID-19 based on clinical and radiological findings in a relatively low COVID-19 prevalence area

Background

Distinguishing coronavirus disease 2019 (COVID-19) pneumonia from other lung diseases is often difficult, especially in a highly comorbid patient population in a low prevalence region. We aimed to distinguish clinical data and computed tomography (CT) images between COVID-19 and other lung diseases in an advanced care hospital.

Methods

We assessed clinical characteristics, laboratory data, and chest CT images of patients with COVID-19 and non-COVID-19 patients who were suspected of having COVID-19 between February 20 and May 21, 2020, at the University of Tokyo Hospital.

Results

Typical appearance for COVID-19 on CT images were found in 24 of 29 COVID-19 cases and 21 of 168 non-COVID-19 cases, according to the Radiological Society of North America Expert Consensus Statement (for predicting COVID-19, sensitivity 0.828, specificity 0.875, positive predictive value 0.533, negative predictive value 0.967). When we focused on cases with typical CT images, loss of taste or smell, and close contact with COVID-19 patients were exclusive characteristics for the COVID-19 cases. Among laboratory data, high fibrinogen (P < 0.01) and low white blood cell count (P < 0.01) were good predictors for COVID-19 with typical CT images in multivariate analysis.

Conclusions

In a relatively low prevalence region, CT screening has high sensitivity to COVID-19 in patients with suspected symptoms. When chest CT findings are typical for COVID-19, close contact, loss of taste or smell, lower white blood cell count, and higher fibrinogen are good predictors for COVID-19.

Automated Assessment of COVID-19 Reporting and Data System and Chest CT Severity Scores in Patients Suspected of Having COVID-19 Using Artificial Intelligence

Background The coronavirus disease 2019 (COVID-19) pandemic has spread across the globe with alarming speed, morbidity, and mortality. Immediate triage of patients with chest infections suspected to be caused by COVID-19 using chest CT may be of assistance when results from definitive viral testing are delayed. Purpose To develop and validate an artificial intelligence (AI) system to score the likelihood and extent of pulmonary COVID-19 on chest CT scans using the COVID-19 Reporting and Data System (CO-RADS) and CT severity scoring systems. Materials and Methods The CO-RADS AI system consists of three deep-learning algorithms that automatically segment the five pulmonary lobes, assign a CO-RADS score for the suspicion of COVID-19, and assign a CT severity score for the degree of parenchymal involvement per lobe. This study retrospectively included patients who underwent a nonenhanced chest CT examination because of clinical suspicion of COVID-19 at two medical centers. The system was trained, validated, and tested with data from one of the centers. Data from the second center served as an external test set. Diagnostic performance and agreement with scores assigned by eight independent observers were measured using receiver operating characteristic analysis, linearly weighted κ values, and classification accuracy. Results A total of 105 patients (mean age, 62 years ± 16 [standard deviation]; 61 men) and 262 patients (mean age, 64 years ± 16; 154 men) were evaluated in the internal and external test sets, respectively. The system discriminated between patients with COVID-19 and those without COVID-19, with areas under the receiver operating characteristic curve of 0.95 (95% CI: 0.91, 0.98) and 0.88 (95% CI: 0.84, 0.93), for the internal and external test sets, respectively. Agreement with the eight human observers was moderate to substantial, with mean linearly weighted κ values of 0.60 ± 0.01 for CO-RADS scores and 0.54 ± 0.01 for CT severity scores. Conclusion With high diagnostic performance, the CO-RADS AI system correctly identified patients with COVID-19 using chest CT scans and assigned standardized CO-RADS and CT severity scores that demonstrated good agreement with findings from eight independent observers and generalized well to external data. © RSNA, 2020 Supplemental material is available for this article.

A simple tool to help ruling-out Covid-19 in the emergency department: derivation and validation of the LDH-CRP-Lymphocyte (LCL) score

After the outbreak of the Covid-19 pandemic, cases of SARSCoV- 2 infections may gradually decrease in the next months. Given the reduced prevalence of the disease, Emergency Departments (ED) are starting to receive more and more non- Covid19 patients. Thus, a way to quickly discriminate ED patients with potential Covid-19 infection from non-Covid19 patients is needed in order to keep potentially contagious patients isolated while awaiting second-level testing. In this paper, we present the derivation and validation of a simple, practical, and cheap score that could be helpful to rule out Covid-19 among ED patients with suspicious symptoms (fever and/or dyspnoea). The LCL score was derived from a cohort of 335 patients coming to the ED of our hospital from March 16th to April 1st, 2020. It was then retrospectively validated in a similar cohort of 173 patients admitted to our ED during April. The score is based on blood values of lactate dehydrogenase, C-reactive protein, and lymphocyte count. The LCL score performed well both in the derivation and in the validation cohort, with an AUC respectively of 0.81 (95% CI: 0.77 – 0.86) and of 0.71 (95% CI: 0.63 – 0.78), given the difference in Covid- 19 prevalence between the two cohorts (57% vs 41% respectively). An LCL score equal to 0 had a negative predictive value of 0.92 in the derivation cohort and of 0.81 in the validation cohort, with a negative likelihood ratio respectively of 0.08 and 0.36 for Covid- 19 exclusion. This score could, therefore, constitute a useful tool to help physicians manage patients in the ED.

Hemocytometric characteristics of COVID-19 patients with and without cytokine Storm syndrome on the Sysmex XN-10 hematology analyzer

OBJECTIVES

COVID-19 is an ongoing global pandemic. There is an urgent need for identification and understanding of clinical and laboratory parameters related to progression towards a severe and fatal form of this illness, often preceded by a so-called cytokine-storm syndrome (CSS). Therefore, we explored the hemocytometric characteristics of COVID-19 patients in relation to the deteriorating clinical condition CSS, using the Sysmex XN-10 hematology analyzer.

METHODS

From March 1st till May 16th, 2020, all patients admitted to our hospital with respiratory complaints and suspected for COVID-19 were included (n=1,140 of whom n=533 COVID-19 positive). The hemocytometric parameters of immunocompetent cells in peripheral blood (neutrophils [NE], lymphocytes [LY] and monocytes [MO]) obtained upon admission to the emergency department (ED) of COVID-19 positive patients were compared with those of the COVID-19 negative ones. Moreover, patients with CSS (n=169) were compared with COVID-19 positive patients without CSS, as well as with COVID-19 negative ones.

RESULTS

In addition to a significant reduction in leukocytes, thrombocytes and absolute neutrophils, it appeared that lymphocytes-forward scatter (LY-FSC), and reactive lymphocytes (RE-LYMPHO)/leukocytes were higher in COVID-19-positive than negative patients. At the moment of presentation, COVID-19 positive patients with CSS had different neutrophils-side fluorescence (NE-SFL), neutrophils-forward scatter (NE-FSC), LY-FSC, RE-LYMPHO/lymphocytes, antibody-synthesizing (AS)-LYMPHOs, high fluorescence lymphocytes (HFLC), MO-SSC, MO-SFL, and Reactive (RE)-MONOs. Finally, absolute eosinophils, basophils, lymphocytes, monocytes and MO-FSC were lower in patients with CSS.

CONCLUSIONS

Hemocytometric parameters indicative of changes in immunocompetent peripheral blood cells and measured at admission to the ED were associated with COVID-19 with and without CSS.

Routine laboratory testing to determine if a patient has COVID-19

BACKGROUND

Specific diagnostic tests to detect severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and resulting COVID-19 disease are not always available and take time to obtain results. Routine laboratory markers such as white blood cell count, measures of anticoagulation, C-reactive protein (CRP) and procalcitonin, are used to assess the clinical status of a patient. These laboratory tests may be useful for the triage of people with potential COVID-19 to prioritize them for different levels of treatment, especially in situations where time and resources are limited.

OBJECTIVES

To assess the diagnostic accuracy of routine laboratory testing as a triage test to determine if a person has COVID-19.

SEARCH METHODS

On 4 May 2020 we undertook electronic searches in the Cochrane COVID-19 Study Register and the COVID-19 Living Evidence Database from the University of Bern, which is updated daily with published articles from PubMed and Embase and with preprints from medRxiv and bioRxiv. In addition, we checked repositories of COVID-19 publications. We did not apply any language restrictions.

SELECTION CRITERIA

We included both case-control designs and consecutive series of patients that assessed the diagnostic accuracy of routine laboratory testing as a triage test to determine if a person has COVID-19. The reference standard could be reverse transcriptase polymerase chain reaction (RT-PCR) alone; RT-PCR plus clinical expertise or and imaging; repeated RT-PCR several days apart or from different samples; WHO and other case definitions; and any other reference standard used by the study authors.

DATA COLLECTION AND ANALYSIS

Two review authors independently extracted data from each included study. They also assessed the methodological quality of the studies, using QUADAS-2. We used the 'NLMIXED' procedure in SAS 9.4 for the hierarchical summary receiver operating characteristic (HSROC) meta-analyses of tests for which we included four or more studies. To facilitate interpretation of results, for each meta-analysis we estimated summary sensitivity at the points on the SROC curve that corresponded to the median and interquartile range boundaries of specificities in the included studies.

MAIN RESULTS

We included 21 studies in this review, including 14,126 COVID-19 patients and 56,585 non-COVID-19 patients in total. Studies evaluated a total of 67 different laboratory tests. Although we were interested in the diagnotic accuracy of routine tests for COVID-19, the included studies used detection of SARS-CoV-2 infection through RT-PCR as reference standard. There was considerable heterogeneity between tests, threshold values and the settings in which they were applied. For some tests a positive result was defined as a decrease compared to normal vaues, for other tests a positive result was defined as an increase, and for some tests both increase and decrease may have indicated test positivity. None of the studies had either low risk of bias on all domains or low concerns for applicability for all domains. Only three of the tests evaluated had a summary sensitivity and specificity over 50%. These were: increase in interleukin-6, increase in C-reactive protein and lymphocyte count decrease. Blood count Eleven studies evaluated a decrease in white blood cell count, with a median specificity of 93% and a summary sensitivity of 25% (95% CI 8.0% to 27%; very low-certainty evidence). The 15 studies that evaluated an increase in white blood cell count had a lower median specificity and a lower corresponding sensitivity. Four studies evaluated a decrease in neutrophil count. Their median specificity was 93%, corresponding to a summary sensitivity of 10% (95% CI 1.0% to 56%; low-certainty evidence). The 11 studies that evaluated an increase in neutrophil count had a lower median specificity and a lower corresponding sensitivity. The summary sensitivity of an increase in neutrophil percentage (4 studies) was 59% (95% CI 1.0% to 100%) at median specificity (38%; very low-certainty evidence). The summary sensitivity of an increase in monocyte count (4 studies) was 13% (95% CI 6.0% to 26%) at median specificity (73%; very low-certainty evidence). The summary sensitivity of a decrease in lymphocyte count (13 studies) was 64% (95% CI 28% to 89%) at median specificity (53%; low-certainty evidence). Four studies that evaluated a decrease in lymphocyte percentage showed a lower median specificity and lower corresponding sensitivity. The summary sensitivity of a decrease in platelets (4 studies) was 19% (95% CI 10% to 32%) at median specificity (88%; low-certainty evidence). Liver function tests The summary sensitivity of an increase in alanine aminotransferase (9 studies) was 12% (95% CI 3% to 34%) at median specificity (92%; low-certainty evidence). The summary sensitivity of an increase in aspartate aminotransferase (7 studies) was 29% (95% CI 17% to 45%) at median specificity (81%) (low-certainty evidence). The summary sensitivity of a decrease in albumin (4 studies) was 21% (95% CI 3% to 67%) at median specificity (66%; low-certainty evidence). The summary sensitivity of an increase in total bilirubin (4 studies) was 12% (95% CI 3.0% to 34%) at median specificity (92%; very low-certainty evidence). Markers of inflammation The summary sensitivity of an increase in CRP (14 studies) was 66% (95% CI 55% to 75%) at median specificity (44%; very low-certainty evidence). The summary sensitivity of an increase in procalcitonin (6 studies) was 3% (95% CI 1% to 19%) at median specificity (86%; very low-certainty evidence). The summary sensitivity of an increase in IL-6 (four studies) was 73% (95% CI 36% to 93%) at median specificity (58%) (very low-certainty evidence). Other biomarkers The summary sensitivity of an increase in creatine kinase (5 studies) was 11% (95% CI 6% to 19%) at median specificity (94%) (low-certainty evidence). The summary sensitivity of an increase in serum creatinine (four studies) was 7% (95% CI 1% to 37%) at median specificity (91%; low-certainty evidence). The summary sensitivity of an increase in lactate dehydrogenase (4 studies) was 25% (95% CI 15% to 38%) at median specificity (72%; very low-certainty evidence).

AUTHORS' CONCLUSIONS

Although these tests give an indication about the general health status of patients and some tests may be specific indicators for inflammatory processes, none of the tests we investigated are useful for accurately ruling in or ruling out COVID-19 on their own. Studies were done in specific hospitalized populations, and future studies should consider non-hospital settings to evaluate how these tests would perform in people with milder symptoms.

Laboratory markers included in the Corona Score can identify false negative results on COVID-19 RT-PCR in the emergency room

After December 2019 outbreak in China, the novel Coronavirus infection (COVID-19) has very quickly overflowed worldwide. Infection causes a clinical syndrome encompassing a wide range of clinical features, from asymptomatic or oligosymptomatic course to acute respiratory distress and death. In a very recent work we preliminarily observed that several laboratory tests have been shown as characteristically altered in COVID-19. We aimed to use the Corona score, a validated point-based algorithm to predict the likelihood of COVID-19 infection in patients presenting at the Emergency rooms. This approach combines chest images-relative score and several laboratory parameters to classify emergency room patients. Corona score accuracy was satisfactory, increasing the detection of positive patients' rate.

Emergency Department Overcrowding: A Retrospective Spatial Analysis and the Geocoding of Accesses. A Pilot Study in Rome

The overcrowding of first aid facilities creates considerable hardship and problems which have repercussions on patients’ wellbeing, the time needed for a diagnosis, and on the quality of the assistance. The basic objective of this contribution, based on the data collected by the Hospital Policlinico Umberto I in Rome (Lazio region, Italy), is to carry out a territorial screening of the municipality using GIS applications and spatial analyses aimed at reducing—in terms of triage—code white (inappropriate) attendances, after having identified the areas of greatest provenance of improperly used emergency room access. Working in a GIS environment and using functions for geocoding, we have tested an experimental model aimed at giving a close-up geographical-sanitary look at the situation: recognizing the territorial sectors in Rome which contribute to amplifying the Policlinico Umberto I emergency room overcrowding; leading up to an improvement of the situation; promoting greater awareness and knowledge of the services available on the territory, a closer relationship between patient and regular doctor (general practitioner, GP) or Local Healthcare Unit and a more efficient functioning of the emergency room. In particular, we have elaborated a “source” map from which derive all the others and it is a dot map on which all the codes white have been geolocalized on a satellite image through geocoding. We have produced three sets made up of three digital cartographic elaborations each, constructed on the census sections, the census areas and the sub-municipal areas, according to data aggregation, for absolute and relative values, and using different templates. Finally, following the same methodology and steps, we elaborated another dot map about all the codes red to provide another kind of information and input for social utility. In the near future, this system could be tested on a platform that spatially analyzes the emergency department (ED) accesses in near-real-time in order to facilitate the identification of critical territorial issues and intervene in a shorter time to regulate the influx of patients to the ED.

Updates on laboratory investigations in coronavirus disease 2019 (COVID-19)

The coronavirus disease 2019 (COVID-19) pandemic is still spreading worldwide, affecting several million people. Unlike the previous two coronavirus outbreaks, COVID-19 has caused several thousand deaths for respiratory and multiple organ failure. As specifically concerns this latest infectious pathology, laboratory medicine can provide a substantial contribution to diagnosing an acute severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection through molecular testing, establishing the presence and extent of an immune response against the virus, mostly through serological testing. However, it can also help to predict the risk of unfavorable disease progression by measuring some conventional laboratory tests and, last but not least, can provide reliable therapeutic guidance. This article is hence aimed at offering recent updates on the important role and value of laboratory investigations in COVID-19, also providing information on some hot topics such as virus RNA detection in different biological samples, causes of recurrent positivity of reverse-transcription polymerase chain reaction (RT-PCR), potential strategies for enhancing the throughput of molecular testing (i.e., pre-test probability assessment, sample pooling, use of rapid tests), as well as pragmatic indications for enhancing the quality and value of serological testing and laboratory-based monitoring.

Antibody tests for identification of current and past infection with SARS-CoV-2

BACKGROUND

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus and resulting COVID-19 pandemic present important diagnostic challenges. Several diagnostic strategies are available to identify current infection, rule out infection, identify people in need of care escalation, or to test for past infection and immune response. Serology tests to detect the presence of antibodies to SARS-CoV-2 aim to identify previous SARS-CoV-2 infection, and may help to confirm the presence of current infection.

OBJECTIVES

To assess the diagnostic accuracy of antibody tests to determine if a person presenting in the community or in primary or secondary care has SARS-CoV-2 infection, or has previously had SARS-CoV-2 infection, and the accuracy of antibody tests for use in seroprevalence surveys.

SEARCH METHODS

We undertook electronic searches in the Cochrane COVID-19 Study Register and the COVID-19 Living Evidence Database from the University of Bern, which is updated daily with published articles from PubMed and Embase and with preprints from medRxiv and bioRxiv. In addition, we checked repositories of COVID-19 publications. We did not apply any language restrictions. We conducted searches for this review iteration up to 27 April 2020.

SELECTION CRITERIA

We included test accuracy studies of any design that evaluated antibody tests (including enzyme-linked immunosorbent assays, chemiluminescence immunoassays, and lateral flow assays) in people suspected of current or previous SARS-CoV-2 infection, or where tests were used to screen for infection. We also included studies of people either known to have, or not to have SARS-CoV-2 infection. We included all reference standards to define the presence or absence of SARS-CoV-2 (including reverse transcription polymerase chain reaction tests (RT-PCR) and clinical diagnostic criteria).

DATA COLLECTION AND ANALYSIS

We assessed possible bias and applicability of the studies using the QUADAS-2 tool. We extracted 2x2 contingency table data and present sensitivity and specificity for each antibody (or combination of antibodies) using paired forest plots. We pooled data using random-effects logistic regression where appropriate, stratifying by time since post-symptom onset. We tabulated available data by test manufacturer. We have presented uncertainty in estimates of sensitivity and specificity using 95% confidence intervals (CIs).

MAIN RESULTS

We included 57 publications reporting on a total of 54 study cohorts with 15,976 samples, of which 8526 were from cases of SARS-CoV-2 infection. Studies were conducted in Asia (n = 38), Europe (n = 15), and the USA and China (n = 1). We identified data from 25 commercial tests and numerous in-house assays, a small fraction of the 279 antibody assays listed by the Foundation for Innovative Diagnostics. More than half (n = 28) of the studies included were only available as preprints. We had concerns about risk of bias and applicability. Common issues were use of multi-group designs (n = 29), inclusion of only COVID-19 cases (n = 19), lack of blinding of the index test (n = 49) and reference standard (n = 29), differential verification (n = 22), and the lack of clarity about participant numbers, characteristics and study exclusions (n = 47). Most studies (n = 44) only included people hospitalised due to suspected or confirmed COVID-19 infection. There were no studies exclusively in asymptomatic participants. Two-thirds of the studies (n = 33) defined COVID-19 cases based on RT-PCR results alone, ignoring the potential for false-negative RT-PCR results. We observed evidence of selective publication of study findings through omission of the identity of tests (n = 5). We observed substantial heterogeneity in sensitivities of IgA, IgM and IgG antibodies, or combinations thereof, for results aggregated across different time periods post-symptom onset (range 0% to 100% for all target antibodies). We thus based the main results of the review on the 38 studies that stratified results by time since symptom onset. The numbers of individuals contributing data within each study each week are small and are usually not based on tracking the same groups of patients over time. Pooled results for IgG, IgM, IgA, total antibodies and IgG/IgM all showed low sensitivity during the first week since onset of symptoms (all less than 30.1%), rising in the second week and reaching their highest values in the third week. The combination of IgG/IgM had a sensitivity of 30.1% (95% CI 21.4 to 40.7) for 1 to 7 days, 72.2% (95% CI 63.5 to 79.5) for 8 to 14 days, 91.4% (95% CI 87.0 to 94.4) for 15 to 21 days. Estimates of accuracy beyond three weeks are based on smaller sample sizes and fewer studies. For 21 to 35 days, pooled sensitivities for IgG/IgM were 96.0% (95% CI 90.6 to 98.3). There are insufficient studies to estimate sensitivity of tests beyond 35 days post-symptom onset. Summary specificities (provided in 35 studies) exceeded 98% for all target antibodies with confidence intervals no more than 2 percentage points wide. False-positive results were more common where COVID-19 had been suspected and ruled out, but numbers were small and the difference was within the range expected by chance. Assuming a prevalence of 50%, a value considered possible in healthcare workers who have suffered respiratory symptoms, we would anticipate that 43 (28 to 65) would be missed and 7 (3 to 14) would be falsely positive in 1000 people undergoing IgG/IgM testing at days 15 to 21 post-symptom onset. At a prevalence of 20%, a likely value in surveys in high-risk settings, 17 (11 to 26) would be missed per 1000 people tested and 10 (5 to 22) would be falsely positive. At a lower prevalence of 5%, a likely value in national surveys, 4 (3 to 7) would be missed per 1000 tested, and 12 (6 to 27) would be falsely positive. Analyses showed small differences in sensitivity between assay type, but methodological concerns and sparse data prevent comparisons between test brands.

AUTHORS' CONCLUSIONS

The sensitivity of antibody tests is too low in the first week since symptom onset to have a primary role for the diagnosis of COVID-19, but they may still have a role complementing other testing in individuals presenting later, when RT-PCR tests are negative, or are not done. Antibody tests are likely to have a useful role for detecting previous SARS-CoV-2 infection if used 15 or more days after the onset of symptoms. However, the duration of antibody rises is currently unknown, and we found very little data beyond 35 days post-symptom onset. We are therefore uncertain about the utility of these tests for seroprevalence surveys for public health management purposes. Concerns about high risk of bias and applicability make it likely that the accuracy of tests when used in clinical care will be lower than reported in the included studies. Sensitivity has mainly been evaluated in hospitalised patients, so it is unclear whether the tests are able to detect lower antibody levels likely seen with milder and asymptomatic COVID-19 disease. The design, execution and reporting of studies of the accuracy of COVID-19 tests requires considerable improvement. Studies must report data on sensitivity disaggregated by time since onset of symptoms. COVID-19-positive cases who are RT-PCR-negative should be included as well as those confirmed RT-PCR, in accordance with the World Health Organization (WHO) and China National Health Commission of the People's Republic of China (CDC) case definitions. We were only able to obtain data from a small proportion of available tests, and action is needed to ensure that all results of test evaluations are available in the public domain to prevent selective reporting. This is a fast-moving field and we plan ongoing updates of this living systematic review.