Multi-organ point-of-care ultrasound for COVID-19 (PoCUS4COVID): international expert consensus

COVID-19 has caused great devastation in the past year. Multi-organ point-of-care ultrasound (PoCUS) including lung ultrasound (LUS) and focused cardiac ultrasound (FoCUS) as a clinical adjunct has played a significant role in triaging, diagnosis and medical management of COVID-19 patients. The expert panel from 27 countries and 6 continents with considerable experience of direct application of PoCUS on COVID-19 patients presents evidence-based consensus using GRADE methodology for the quality of evidence and an expedited, modified-Delphi process for the strength of expert consensus. The use of ultrasound is suggested in many clinical situations related to respiratory, cardiovascular and thromboembolic aspects of COVID-19, comparing well with other imaging modalities. The limitations due to insufficient data are highlighted as opportunities for future research

Imaging algorithm for COVID-19: A practical approach

© 2020 Elsevier Inc. The global pandemic of COVID-19 pneumonia caused by the novel coronavirus (SARS-CoV-2) has strained healthcare resources across the world with emerging challenges of mass testing, resource allocation and management. While reverse transcriptase-polymerase chain reaction (RT-PCR) test is the most commonly utilized test and considered the current gold standard for diagnosis, the role of chest imaging has been highlighted by several studies demonstrating high sensitivity of computed tomography (CT). Many have suggested using CT chest as a first-line screening tool for the diagnosis of COVID-19. However, with advancement of laboratory testing and challenges in obtaining a CT scan without significant risk to healthcare providers, the role of imaging in diagnosis has been questioned. Several imaging societies have released consensus statements and guidelines on utilizing imaging resources and optimal reporting. In this review, we highlight the current evidence on various modalities in thoracic imaging for the diagnosis of COVID-19 and describe an algorithm on how to use these resources in an optimal fashion in accordance with the guidelines and statements released by major imaging societies

Multi-organ point-of-care ultrasound for COVID-19 (PoCUS4COVID): international expert consensus.

COVID-19 has caused great devastation in the past year. Multi-organ point-of-care ultrasound (PoCUS) including lung ultrasound (LUS) and focused cardiac ultrasound (FoCUS) as a clinical adjunct has played a significant role in triaging, diagnosis and medical management of COVID-19 patients. The expert panel from 27 countries and 6 continents with considerable experience of direct application of PoCUS on COVID-19 patients presents evidence-based consensus using GRADE methodology for the quality of evidence and an expedited, modified-Delphi process for the strength of expert consensus. The use of ultrasound is suggested in many clinical situations related to respiratory, cardiovascular and thromboembolic aspects of COVID-19, comparing well with other imaging modalities. The limitations due to insufficient data are highlighted as opportunities for future research.post-print2.282 K

Chest CT in COVID-19:What the Radiologist Needs to Know

Chest CT has a potential role in the diagnosis, detection of complications, and prognostication of coronavirus disease 2019 (COVID-19). Implementation of appropriate precautionary safety measures, chest CT protocol optimization, and a standardized reporting system based on the pulmonary findings in this disease will enhance the clinical utility of chest CT. However, chest CT examinations may lead to both false-negative and false-positive results. Furthermore, the added value of chest CT in diagnostic decision making is dependent on several dynamic variables, most notably available resources (real-time reverse hariscription-polymerase chain reaction [RT-PCR] tests, personal protective equipment, CT scanners, hospital and radiology personnel availability, and isolation room capacity) and the prevalence of both COVID-19 and other diseases with overlapping manifestations at chest CT. Chest CT is valuable to detect both alternative diagnoses and complications of COVID-19 (acute respiratory distress syndrome, pulmonary embolism, and heart failure), while its role for prognostication requires further investigation. The authors describe imaging and managing care of patients with COVID-19, with topics including (a) chest CT protocol, (b) chest CT findings of COVID-19 and its complications, (c) the diagnostic accuracy of chest CT and its role in diagnostic decision making and prognostication, and (d) reporting and communicating chest CT findings. The authors also review other specific topics, including the pathophysiology and clinical manifestations of COVID-19, the World Health Organization case definition, the value of performing RT-PCR tests, and the radiology department and personnel impact related to performing chest CT in COVID-19. (C) RSNA, 202

Predictive models for COVID-19 detection using routine blood tests and machine learning

The problem of accurate, fast, and inexpensive COVID-19 tests has been urgent till now. Standard COVID-19 tests need high-cost reagents and specialized laboratories with high safety requirements, are time-consuming. Data of routine blood tests as a base of SARS-CoV-2 invasion detection allows using the most practical medicine facilities. But blood tests give general information about a patient’s state, which is not directly associated with COVID-19. COVID-19-specific features should be selected from the list of standard blood characteristics, and decision-making software based on appropriate clinical data should be created. This review describes the abilities to develop predictive models for COVID-19 detection using routine blood tests and machine learning

An Ensemble Learning Model for COVID-19 Detection from Blood Test Samples

Current research endeavors in the application of artificial intelligence (AI) methods in the diagnosis of the COVID-19 disease has proven indispensable with very promising results. Despite these promising results, there are still limitations in real-time detection of COVID-19 using reverse transcription polymerase chain reaction (RT-PCR) test data, such as limited datasets, imbalance classes, a high misclassification rate of models, and the need for specialized research in identifying the best features and thus improving prediction rates. This study aims to investigate and apply the ensemble learning approach to develop prediction models for effective detection of COVID-19 using routine laboratory blood test results. Hence, an ensemble machine learning-based COVID-19 detection system is presented, aiming to aid clinicians to diagnose this virus effectively. The experiment was conducted using custom convolutional neural network (CNN) models as a first-stage classifier and 15 supervised machine learning algorithms as a second-stage classifier: K-Nearest Neighbors, Support Vector Machine (Linear and RBF), Naive Bayes, Decision Tree, Random Forest, MultiLayer Perceptron, AdaBoost, ExtraTrees, Logistic Regression, Linear and Quadratic Discriminant Analysis (LDA/QDA), Passive, Ridge, and Stochastic Gradient Descent Classifier. Our findings show that an ensemble learning model based on DNN and ExtraTrees achieved a mean accuracy of 99.28% and area under curve (AUC) of 99.4%, while AdaBoost gave a mean accuracy of 99.28% and AUC of 98.8% on the San Raffaele Hospital dataset, respectively. The comparison of the proposed COVID-19 detection approach with other state-of-the-art approaches using the same dataset shows that the proposed method outperforms several other COVID-19 diagnostics methods.publishedVersio

Comparison of machine learning techniques to handle imbalanced COVID-19 CBC datasets

The Coronavirus pandemic caused by the novel SARS-CoV-2 has significantly impacted human health and the economy, especially in countries struggling with financial resources for medical testing and treatment, such as Brazil’s case, the third most affected country by the pandemic. In this scenario, machine learning techniques have been heavily employed to analyze different types of medical data, and aid decision making, offering a low-cost alternative. Due to the urgency to fight the pandemic, a massive amount of works are applying machine learning approaches to clinical data, including complete blood count (CBC) tests, which are among the most widely available medical tests. In this work, we review the most employed machine learning classifiers for CBC data, together with popular sampling methods to deal with the class imbalance. Additionally, we describe and critically analyze three publicly available Brazilian COVID-19 CBC datasets and evaluate the performance of eight classifiers and five sampling techniques on the selected datasets. Our work provides a panorama of which classifier and sampling methods provide the best results for different relevant metrics and discuss their impact on future analyses. The metrics and algorithms are introduced in a way to aid newcomers to the field. Finally, the panorama discussed here can significantly benefit the comparison of the results of new ML algorithms

Precision targeting of preventative therapy for tuberculosis

Background: Scale-up of preventative treatment for tuberculosis (TB) represents a cornerstone of global control efforts. I examined a range of approaches to enable more precise targeting of preventative treatment to people at highest risk. Methods: I evaluated whether prognostic tests for TB (tuberculin skin test (TST), QuantiFERON Gold-in-tube (QFT-GIT) and T-SPOT.TB) may be optimised by implementing higher thresholds, or by a newer generation assay (QuantiFERON-TB Gold Plus; QFT-Plus). Next, I conducted a systematic review and individual participant data meta-analysis (IPD-MA) to examine TB risk among people tested for latent infection (LTBI) in settings with low TB transmission and to develop a multivariable prognostic model for incident TB. Finally, I performed a systematic review and IPD-MA of whole-blood RNA sequencing data to evaluate blood transcriptomic signatures as next-generation biomarkers. Results: In a UK cohort of 9,610 adults, higher TST, QFT-GIT and T-SPOT.TB results were associated with increased incident TB risk. Implementing higher cut-offs led to a marginal improvement in positive predictive value, but at the cost of a marked loss in sensitivity. The newer generation QFT-Plus had similar predictive ability. In a pooled dataset of >80,000 participants from 18 cohort studies, TB risk was heterogeneous among people with LTBI, even after stratification by indication for testing. I developed and validated a multivariable prognostic model, which incorporates quantitative LTBI test results and clinical covariates, and demonstrated strong potential for clinical utility to inform provision of preventative treatment. Among 1,126 whole-blood RNA sequencing samples, eight transcriptomic signatures (comprising 1-25 transcripts) performed similarly for predicting incident TB, but only met global accuracy benchmarks over a 3-6 month time-horizon. Conclusions: Personalised risk estimates integrating quantitative LTBI test results and clinical covariates may facilitate more precise targeting of preventative treatment. Blood transcriptomic biomarkers show promise, but only represent short-term TB risk. Future research priorities are highlighted

Klinisch-biochemische Biomarker der COVID-19 Erkrankung

Die neuartige COVID-19 Erkrankung, die durch SARS-CoV-2 ausgelöst wird, ist seit November 2019 im wissenschaftlichen Fokus. Während Maßnahmen zur Eindämmung der im März 2020 ausgerufenen Pandemie erfolgten, fokussiert sich das wissenschaftliche Interesse auf das verbesserte Verständnis der Pathogenität des Virus, sowie Diagnostik und Therapie der Erkrankung. Dabei erscheint der Goldstandard der Diagnose via RT-PCR zu Beginn der Pandemie mit einer hohen falsch-negativen Rate ausbaufähig. Das Ziel der vorliegenden Studie war es, eine Unterstützung der RT-PCR anhand klinisch-biochemischer Laborparameter bei Aufnahme zu ermöglichen, sowie eine Risikostratifizierung für Patienten, die eine intensivmedizinische Behandlung benötigen oder versterben. Insgesamt wurden 46 Patienten in einem Zeitraum vom 10.04.2020 bis 30.06.2020 an der Uniklinik Marburg rekrutiert und klinisch-biochemische Laborparameter bei Aufnahme ins Krankenhaus erhoben. Bei Patienten mit Verdacht auf eine COVID-19-Infektion wurde eine RT-PCR aus dem Nasen-Rachen-Bereich durchgeführt. Anschließend erfolgte die Einteilung in eine SARS-CoV-2-positive Kohorte sowie eine Kontroll-gruppe mit negativer PCR. Für die prognostischen Analysen von COVID-19-Patienten erfolgte eine zusätzliche Unterteilung in Patienten auf Intensiv- und Normalstation, so-wie Verstorbene und Überlebende. Die statistische Auswertung erfolgte mittels Mann-Whitney-U-Test und bei dichotomen Parametern mittels Fisher-exact-Test. Unter-schiedliche Schweregrade wie mild, moderat, schwer und kritisch, wurden anhand der NIH und WHO klassifiziert und mittels ANOVA verglichen. Die Auswertung des Patientenkollektivs ergab führende Symptome wie Husten, Dyspnoe und Fieber. Während sich COVID-19 Positive durch längere Aufenthaltsdauer und häufigere invasive Beatmung kennzeichneten, waren die Gruppen ansonsten homogen in demografischen Daten, Vitalparameter bei Aufnahme, Behandlungsstrategien und Komplikationen. Als diagnostische Biomarker konnten in unserer Studie ALT, Amyl, AST, CK, HDL, LDH und LIP quantifiziert werden, die im Literaturvergleich mit ähnlichen Ergebnissen einhergehen. Anhand der Berechnung von Cut-off Werten konnten mittels ALT, Amyl, AST, LDH und LIP mit hoher Sensitivität und Spezifität eine Infektion mit SARS-CoV-2 vorhergesagt werden. Während die beschriebenen Parameter sich in COVID-19 Positiven erhöht darstellten, war einzig HDL, vermutlich aufgrund seiner Funktion als negatives Akute-Phase-Protein, erniedrigt aufzufinden. Ätiologisch ursächlich scheint eine Schädigung des hepatischen und pankreatischen Systems bei COVID-19-Patienten, während eine kardiale Schädigung allein anhand von einer erhöhten CK, sowie eine Dyslipidämie anhand von reduzierten HDL-Spiegeln, nicht erklärt werden kann. Der dahinter liegende Pathomechanismus ist möglicherweise eine erhöhte Anzahl an ACE2-Rezeptoren in den Organen, die durch das SARS-CoV-2-Virus infiltriert werden und dadurch frühzeitig geschädigt werden können. Die vorgestellten Routinelaborparameter können bei anfänglicher Ressourcenknappheit von RT-PCR-Tests oder Schnelltests eine Einschätzung über das COVID-19-Erkrankungsrisiko unterstützen, diese aber nicht ersetzen. Als prognostische Biomarker für einen schweren Verlauf mit Aufnahme auf Intensivstation konnten AST, Harnstoff und BUN, hs-TNI, NT-pro-BNP festgelegt werden. Die Vorhersage mittels ROC gelang bei allen Parametern mit hoher Sensitivität und Spezifität, dabei erscheint NT-pro-BNP am sensitivsten und das kardiale System als eines der ersten, das einen schweren Verlauf anzeigt. Außerdem zeigten Patienten in unter-schiedlichen Schweregraden der Erkrankung Besonderheiten im Lipidprofil und der tP. Schwererkrankte hatten demnach signifikant höhere Cholesterin-, HDL-, LDL-, sowie tP-Werte als kritisch Erkrankte, während es sich andersherum in den Triglyceridleveln verhielt. Als Biomarker für ein Versterben an einer COVID-19 Erkrankung konnte in der vorliegenden Studie tBili und tP dienen, wobei mittels tBili mit guter Sensitivität und Spezifität ein Versterben vorhergesagt werden konnte. Eine Vorhersage des Versterbens mittels tP gelang nicht aufgrund niedriger Sensitivität und Spezifität. Mögliche Ursachen für veränderte Laborparameter bei schweren Verläufen sind erhöhte Zytokinspiegel bei COVID-19 Patienten, die hepatische, nephrologische, kardiale, so-wie Schädigungen im Lipidprofil bedingen können. Weiterhin können aufgrund hypoxischer Zustände die Ausschüttung pro-inflammatorischer Zytokine erhöht er-scheinen und zusätzliche Entzündungsreaktionen auslösen. Im Literaturvergleich konnten weitere Parameter als prognostische Parameter für einen schweren Verlauf sowie für ein Versterben von Bedeutung sein, die aufgrund reduzierter Teilnehmerzahl in der vorliegenden Studie nicht signifikant erschienen. Durch diese Arbeit kann ein besonderes Augenmerk auf das Monitoring dieser Parameter gelenkt werden, die eine Risikostratifizierung ermöglichen und, wenn notwendig, eine Triagierung der schwererkrankten Fälle. Eine Optimierung sowie individuell patientenabgestimmte Behandlungen können die Mortalität der Erkrankung reduzieren