Severe Acute Respiratory Syndrome Coronavirus 2 detection in induced sputum of asthmatic patients using saliva sampling device

Since the beginning of the severe acute respiratory syndrome coronavirus 2 pandemic, the potential contamination of the induced sputum obtained from asthmatic patients in routine is a question of concern. The goal of this study was to assess this contamination using a saliva sample collection device. One hundred seventy-five sputum samples of asthmatic patients without fever were tested. We did not identify any positive PCR on sputum samples from asthmatic patients reporting chronic/episodic respiratory symptoms similar to what is seen in case of COVID-19. This technique was useful to evaluate the contamination of sputum samples generated during the pandemic.

Clinical experience in anti-synthetase syndrome: a monocentric retrospective analytical study.

OBJECTIVES: Anti-synthetase syndrome (ASS) is a rare autoimmune disorder combining autoantibodies and specific clinical manifestations. One of the particularities of ASS is the pleiomorphic radiological presentation seen at the initial work-up. Evaluating treatment response can also be challenging and requires specific clinical, functional, biological and radiological monitoring. For these reasons, it is fundamental to identify specific radiological and clinical features of ASS for improved diagnosis and therapeutic approaches.METHODS: We retrospectively studied all patients suffering from ASS in the CHU of Liège from 2008 to 2019. We analysed the clinical features, pulmonary function tests (PFTs), computed tomography (CT), and longitudinal evolution with regard to patient treatment.RESULTS: In the whole cohort of 30 patients, we identified 19 with anti-JO1 antibodies, 5 with anti-PL12 antibodies and 6 with anti-PL7 antibodies. The sex ratio was slightly in favour of males. Interestingly, PL-12 syndrome was more likely to be present in younger patients than those associated with other antibodies. Overall, 77% of the overall cohort exhibited specific pulmonary involvement without any significant difference with regard to the severity assessed by PFT at diagnosis. In contrast, the radiological presentation was pleomorphic for anti-JO1 syndrome, and anti-PL12 syndrome exhibited mainly ground-glass opacities (GGOs) and reticular abnormalities, while those with anti-PL7 antibodies showed reticulations and bronchiectasis. Longitudinal CT analysis mainly showed a reduction in consolidations and GGOs with specific therapies.CONCLUSION: In our single-centre retrospective study, we found different profiles for different autoantibodies according to age and radiological appearance.

Development of a Clinical Decision Support System for Severity Risk Prediction and Triage of COVID-19 Patients at Hospital Admission: an International Multicenter Study

Key pointsO_ST_ABSQuestionC_ST_ABSHow do nomograms and machine-learning algorithms of severity risk prediction and triage of COVID-19 patients at hospital admission perform? FindingsThis model was prospectively validated on six test datasets comprising of 426 patients and yielded AUCs ranging from 0.816 to 0.976, accuracies ranging from 70.8% to 93.8%, sensitivities ranging from 83.7% to 100%, and specificities ranging from 41.0% to 95.7%. The cut-off probability values for low, medium, and high-risk groups were 0.072 and 0.244. MeaningThe findings of this study suggest that our models performs well for the diagnosis and prediction of progression to severe or critical illness of COVID-19 patients and could be used for triage of COVID-19 patients at hospital admission. IMPORTANCEThe outbreak of the coronavirus disease 2019 (COVID-19) has globally strained medical resources and caused significant mortality for severely and critically ill patients. However, the availability of validated nomograms and the machine-learning model to predict severity risk and triage of affected patients is limited. OBJECTIVETo develop and validate nomograms and machine-learning models for severity risk assessment and triage for COVID-19 patients at hospital admission. DESIGN, SETTING, AND PARTICIPANTSA retrospective cohort of 299 consecutively hospitalized COVID-19 patients at The Central Hospital of Wuhan, China, from December 23, 2019, to February 13, 2020, was used to train and validate the models. Six cohorts with 426 patients from eight centers in China, Italy, and Belgium, from February 20, 2020, to March 21, 2020, were used to prospectively validate the models. MAIN OUTCOME AND MEASURESThe main outcome was the onset of severe or critical illness during hospitalization. Model performances were quantified using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity. RESULTSOf the 299 hospitalized COVID-19 patients in the retrospective cohort, the median age was 50 years ((interquartile range, 35.5-63.0; range, 20-94 years) and 137 (45.8%) were men. Of the 426 hospitalized COVID-19 patients in the prospective cohorts, the median age was 62.0 years ((interquartile range, 50.0-72.0; range, 19-94 years) and 236 (55.4%) were men. The model was prospectively validated on six cohorts yielding AUCs ranging from 0.816 to 0.976, with accuracies ranging from 70.8% to 93.8%, sensitivities ranging from 83.7% to 100%, and specificities ranging from 41.0% to 95.7%. The cut-off values of the low, medium, and high-risk probabilities were 0.072 and 0.244. The developed online calculators can be found at https://covid19risk.ai/. CONCLUSION AND RELEVANCEThe machine learning models, nomograms, and online calculators might be useful for the prediction of onset of severe and critical illness among COVID-19 patients and triage at hospital admission. Further prospective research and clinical feedback are necessary to evaluate the clinical usefulness of this model and to determine whether these models can help optimize medical resources and reduce mortality rates compared with current clinical practices.

Development and validation of an automated radiomic CT signature for detecting COVID-19

BackgroundThe coronavirus disease 2019 (COVID-19) outbreak has reached pandemic status. Drastic measures of social distancing are enforced in society and healthcare systems are being pushed to and over their limits. ObjectivesTo develop a fully automatic framework to detect COVID-19 by applying AI to chest CT and evaluate validation performance. MethodsIn this retrospective multi-site study, a fully automated AI framework was developed to extract radiomics features from volumetric chest CT exams to learn the detection pattern of COVID-19 patients. We analysed the data from 181 RT-PCR confirmed COVID-19 patients as well as 1200 other non-COVID-19 control patients to build and assess the performance of the model. The datasets were collected from 2 different hospital sites of the CHU Liege, Belgium. Diagnostic performance was assessed by the area under the receiver operating characteristic curve (AUC), sensitivity and specificity. Results1381 patients were included in this study. The average age was 64.4{+/-}15.8 and 63.8{+/-}14.4 years with a gender balance of 56% and 52% male in the COVID-19 and control group, respectively. The final curated dataset used for model construction and validation consisted of chest CT scans of 892 patients. The model sensitivity and specificity for detecting COVID-19 in the test set (training 80% and test 20% of patients) were 78.94% and 91.09%, respectively, with an AUC of 0.9398 (95% CI: 0.875-1). The negative predictive value of the algorithm was found to be larger than 97%. ConclusionsBenchmarked against RT-PCR confirmed cases of COVID-19, our AI framework can accurately differentiate COVID-19 from routine clinical conditions in a fully automated fashion. Thus, providing rapid accurate diagnosis in patients suspected of COVID-19 infection, facilitating the timely implementation of isolation procedures and early intervention.