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1.
Nutrients ; 14(15)2022 Jul 26.
Article in English | MEDLINE | ID: covidwho-1957404

ABSTRACT

Retrospective studies showed a relationship between vitamin D status and COVID-19 severity and mortality, with an inverse relation between SARS-CoV-2 positivity and circulating calcifediol levels. The objective of this pilot study was to investigate the effect of vitamin D supplementation on the length of hospital stay and clinical improvement in patients with vitamin D deficiency hospitalized with COVID-19. The study was randomized, double blind and placebo controlled. A total of 50 subjects were enrolled and received, in addition to the best available COVID therapy, either vitamin D (25,000 IU per day over 4 consecutive days, followed by 25,000 IU per week up to 6 weeks) or placebo. The length of hospital stay decreased significantly in the vitamin D group compared to the placebo group (4 days vs. 8 days; p = 0.003). At Day 7, a significantly lower percentage of patients were still hospitalized in the vitamin D group compared to the placebo group (19% vs. 54%; p = 0.0161), and none of the patients treated with vitamin D were hospitalized after 21 days compared to 14% of the patients treated with placebo. Vitamin D significantly reduced the duration of supplemental oxygen among the patients who needed it (4 days vs. 7 days in the placebo group; p = 0.012) and significantly improved the clinical recovery of the patients, as assessed by the WHO scale (p = 0.0048). In conclusion, this study demonstrated that the clinical outcome of COVID-19 patients requiring hospitalization was improved by administration of vitamin D.


Subject(s)
COVID-19 , Cholecalciferol/therapeutic use , Dietary Supplements , Double-Blind Method , Hospitalization , Humans , Pilot Projects , Retrospective Studies , SARS-CoV-2 , Vitamin D , Vitamins/therapeutic use
2.
J Clin Med ; 11(15)2022 Jul 25.
Article in English | MEDLINE | ID: covidwho-1957366

ABSTRACT

Exercise limitation in COVID-19 survivors is poorly explained. In this retrospective study, cardiopulmonary exercise testing (CPET) was coupled with an oxidative stress assessment in COVID-19 critically ill survivors (ICU group). Thirty-one patients were included in this group. At rest, their oxygen uptake (VO2) was elevated (8 [5.6-9.7] mL/min/kg). The maximum effort was reached at low values of workload and VO2 (66 [40.9-79.2]% and 74.5 [62.6-102.8]% of the respective predicted values). The ventilatory equivalent for carbon dioxide remained within normal ranges. Their metabolic efficiency was low: 15.2 [12.9-17.8]%. The 50% decrease in VO2 after maximum effort was delayed, at 130 [120-170] s, with a still-high respiratory exchange ratio (1.13 [1-1.2]). The blood myeloperoxidase was elevated (92 [75.5-106.5] ng/mL), and the OSS was altered. The CPET profile of the ICU group was compared with long COVID patients after mid-disease (MLC group) and obese patients (OB group). The MLC patients (n = 23) reached peak workload and predicted VO2 values, but their resting VO2, metabolic efficiency, and recovery profiles were similar to the ICU group to a lesser extent. In the OB group (n = 15), no hypermetabolism at rest was observed. In conclusion, the exercise limitation after a critical COVID-19 bout resulted from an altered metabolic profile in the context of persistent inflammation and oxidative stress. Altered exercise and metabolic profiles were also observed in the MLC group. The contribution of obesity on the physiopathology of exercise limitation after a critical bout of COVID-19 did not seem relevant.

3.
Diagnostics (Basel) ; 12(7)2022 Jul 01.
Article in English | MEDLINE | ID: covidwho-1917364

ABSTRACT

Early diagnosis of COVID-19 is required to provide the best treatment to our patients, to prevent the epidemic from spreading in the community, and to reduce costs associated with the aggravation of the disease. We developed a decision tree model to evaluate the impact of using an artificial intelligence-based chest computed tomography (CT) analysis software (icolung, icometrix) to analyze CT scans for the detection and prognosis of COVID-19 cases. The model compared routine practice where patients receiving a chest CT scan were not screened for COVID-19, with a scenario where icolung was introduced to enable COVID-19 diagnosis. The primary outcome was to evaluate the impact of icolung on the transmission of COVID-19 infection, and the secondary outcome was the in-hospital length of stay. Using EUR 20000 as a willingness-to-pay threshold, icolung is cost-effective in reducing the risk of transmission, with a low prevalence of COVID-19 infections. Concerning the hospitalization cost, icolung is cost-effective at a higher value of COVID-19 prevalence and risk of hospitalization. This model provides a framework for the evaluation of AI-based tools for the early detection of COVID-19 cases. It allows for making decisions regarding their implementation in routine practice, considering both costs and effects.

4.
Diagnostics (Basel) ; 12(7)2022 Jun 28.
Article in English | MEDLINE | ID: covidwho-1911245

ABSTRACT

During the COVID-19 pandemic induced by the SARS-CoV-2, numerous chest scans were carried out in order to establish the diagnosis, quantify the extension of lesions but also identify the occurrence of potential pulmonary embolisms. In this perspective, the performed chest scans provided a varied database for a retrospective analysis of non-COVID-19 chest pathologies discovered de novo. The fortuitous discovery of de novo non-COVID-19 lesions was generally not detected by the automated systems for COVID-19 pneumonia developed in parallel during the pandemic and was thus identified on chest CT by the radiologist. The objective is to use the study of the occurrence of non-COVID-19-related chest abnormalities (known and unknown) in a large cohort of patients having suffered from confirmed COVID-19 infection and statistically correlate the clinical data and the occurrence of these abnormalities in order to assess the potential of increased early detection of lesions/alterations. This study was performed on a group of 362 COVID-19-positive patients who were prescribed a CT scan in order to diagnose and predict COVID-19-associated lung disease. Statistical analysis using mean, standard deviation (SD) or median and interquartile range (IQR), logistic regression models and linear regression models were used for data analysis. Results were considered significant at the 5% critical level (p < 0.05). These de novo non-COVID-19 thoracic lesions detected on chest CT showed a significant prevalence in cardiovascular pathologies, with calcifying atheromatous anomalies approaching nearly 35.4% in patients over 65 years of age. The detection of non-COVID-19 pathologies was mostly already known, except for suspicious nodule, thyroid goiter and the ascending thoracic aortic aneurysm. The presence of vertebral compression or signs of pulmonary fibrosis has shown a significant impact on inpatient length of stay. The characteristics of the patients in this sample, both from a demographic and a tomodensitometric point of view on non-COVID-19 pathologies, influenced the length of hospital stay as well as the risk of intra-hospital death. This retrospective study showed that the potential importance of the detection of these non-COVID-19 lesions by the radiologist was essential in the management and the intra-hospital course of the patients.

5.
ERJ Open Res ; 8(2)2022 Apr.
Article in English | MEDLINE | ID: covidwho-1833277

ABSTRACT

Purpose: In this study, we propose an artificial intelligence (AI) framework based on three-dimensional convolutional neural networks to classify computed tomography (CT) scans of patients with coronavirus disease 2019 (COVID-19), influenza/community-acquired pneumonia (CAP), and no infection, after automatic segmentation of the lungs and lung abnormalities. Methods: The AI classification model is based on inflated three-dimensional Inception architecture and was trained and validated on retrospective data of CT images of 667 adult patients (no infection n=188, COVID-19 n=230, influenza/CAP n=249) and 210 adult patients (no infection n=70, COVID-19 n=70, influenza/CAP n=70), respectively. The model's performance was independently evaluated on an internal test set of 273 adult patients (no infection n=55, COVID-19 n= 94, influenza/CAP n=124) and an external validation set from a different centre (305 adult patients: COVID-19 n=169, no infection n=76, influenza/CAP n=60). Results: The model showed excellent performance in the external validation set with area under the curve of 0.90, 0.92 and 0.92 for COVID-19, influenza/CAP and no infection, respectively. The selection of the input slices based on automatic segmentation of the abnormalities in the lung reduces analysis time (56 s per scan) and computational burden of the model. The Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) score of the proposed model is 47% (15 out of 32 TRIPOD items). Conclusion: This AI solution provides rapid and accurate diagnosis in patients suspected of COVID-19 infection and influenza.

6.
EuropePMC; 2021.
Preprint in English | EuropePMC | ID: ppcovidwho-306701

ABSTRACT

Our motivating application is a real-world problem: COVID-19 classification from CT imaging, for which we present an explainable Deep Learning approach based on a semi-supervised classification pipeline that employs variational autoencoders to extract efficient feature embedding. We have optimized the architecture of two different networks for CT images: (i) a novel conditional variational autoencoder (CVAE) with a specific architecture that integrates the class labels inside the encoder layers and uses side information with shared attention layers for the encoder, which make the most of the contextual clues for representation learning, and (ii) a downstream convolutional neural network for supervised classification using the encoder structure of the CVAE. With the explainable classification results, the proposed diagnosis system is very effective for COVID-19 classification. Based on the promising results obtained qualitatively and quantitatively, we envisage a wide deployment of our developed technique in large-scale clinical studies.Code is available at https://git.etrovub.be/AVSP/ct-based-covid-19-diagnostic-tool.git.

7.
Sensors (Basel) ; 21(23)2021 Dec 05.
Article in English | MEDLINE | ID: covidwho-1555018

ABSTRACT

This study introduces machine learning predictive models to predict the future values of the monitored vital signs of COVID-19 ICU patients. The main vital sign predictors include heart rate, respiration rate, and oxygen saturation. We investigated the performances of the developed predictive models by considering different approaches. The first predictive model was developed by considering the following vital signs: heart rate, blood pressure (systolic, diastolic and mean arterial, pulse pressure), respiration rate, and oxygen saturation. Similar to the first approach, the second model was developed using the same vital signs, but it was trained and tested based on a leave-one-subject-out approach. The third predictive model was developed by considering three vital signs: heart rate (HR), respiration rate (RR), and oxygen saturation (SpO2). The fourth model was a leave-one-subject-out model for the three vital signs. Finally, the fifth predictive model was developed based on the same three vital signs, but with a five-minute observation rate, in contrast with the aforementioned four models, where the observation rate was hourly to bi-hourly. For the five models, the predicted measurements were those of the three upcoming observations (on average, three hours ahead). Based on the obtained results, we observed that by limiting the number of vital sign predictors (i.e., three vital signs), the prediction performance was still acceptable, with the average mean absolute percentage error (MAPE) being 12%,5%, and 21.4% for heart rate, oxygen saturation, and respiration rate, respectively. Moreover, increasing the observation rate could enhance the prediction performance to be, on average, 8%,4.8%, and 17.8% for heart rate, oxygen saturation, and respiration rate, respectively. It is envisioned that such models could be integrated with monitoring systems that could, using a limited number of vital signs, predict the health conditions of COVID-19 ICU patients in real-time.


Subject(s)
COVID-19 , Humans , Intensive Care Units , SARS-CoV-2 , Vital Signs
8.
J Belg Soc Radiol ; 105(1): 62, 2021.
Article in English | MEDLINE | ID: covidwho-1497693

ABSTRACT

Teaching point: The use of dual-energy instead of conventional single-energy computed tomography pulmonary angiogram can provide additional value concerning the diagnosis of COVID-19 and its complications, especially in the detection of small pulmonary embolism.

9.
Crit Care Explor ; 3(7): e0491, 2021 Jul.
Article in English | MEDLINE | ID: covidwho-1313894

ABSTRACT

To investigate exercise capacity at 3 and 6 months after a prolonged ICU stay. DESIGN: Observational monocentric study. SETTING: A post-ICU follow-up clinic in a tertiary university hospital in Liège, Belgium. PATIENTS: Patients surviving an ICU stay greater than or equal to 7 days for a severe coronavirus disease 2019 pneumonia and attending our post-ICU follow-up clinic. MEASUREMENTS AND MAIN RESULTS: Cardiopulmonary and metabolic variables provided by a cardiopulmonary exercise testing on a cycle ergometer were collected at rest, at peak exercise, and during recovery. Fourteen patients (10 males, 59 yr [52-62 yr], all obese with body mass index > 27 kg/m2) were included after a hospital stay of 40 days (35-53 d). At rest, respiratory quotient was abnormally high at both 3 and 6 months (0.9 [0.83-0.96] and 0.94 [0.86-0.97], respectively). Oxygen uptake was also abnormally increased at 3 months (8.24 mL/min/kg [5.38-10.54 mL/min/kg]) but significantly decreased at 6 months (p = 0.013). At 3 months, at the maximum workload (67% [55-89%] of predicted workload), oxygen uptake peaked at 81% (64-104%) of predicted maximum oxygen uptake, with oxygen pulse and heart rate reaching respectively 110% (76-140%) and 71% (64-81%) of predicted maximum values. Ventilatory equivalent for carbon dioxide remains within normal ranges. The 50% decrease in oxygen uptake after maximum effort was delayed, at 130 seconds (115-142 s). Recovery was incomplete with a persistent anaerobic metabolism. At 6 months, no significant improvement was observed, excepting an increase in heart rate reaching 79% (72-95%) (p = 0.008). CONCLUSIONS: Prolonged reduced exercise capacity was observed up to 6 months in critically ill coronavirus disease 2019 survivors. This disability did not result from residual pulmonary or cardiac dysfunction but rather from a metabolic disorder characterized by a sustained hypermetabolism and an impaired oxygen utilization.

10.
Int J Infect Dis ; 109: 209-216, 2021 Aug.
Article in English | MEDLINE | ID: covidwho-1309244

ABSTRACT

OBJECTIVES: Various symptoms and considerable organ dysfunction persist following infection with severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). Uncertainty remains about the potential mid- and long-term health sequelae. This prospective study of patients hospitalized with coronavirus disease 2019 (COVID-19) in Liège University Hospital, Belgium aimed to determine the persistent consequences of COVID-19. METHODS: Patients admitted to the University Hospital of Liège with moderate-to-severe confirmed COVID-19, discharged between 2 March and 1 October 2020, were recruited prospectively. Follow-up at 3 and 6 months after hospital discharge included demographic and clinical data, biological data, pulmonary function tests (PFTs) and high-resolution computed tomography (CT) scans of the chest. RESULTS: In total, 199 individuals were included in the analysis. Most patients received oxygen supplementation (80.4%). Six months after discharge, 47% and 32% of patients still had exertional dyspnoea and fatigue. PFTs at 3-month follow-up revealed a reduced diffusion capacity of carbon monoxide (mean 71.6 ± 18.6%), and this increased significantly at 6-month follow-up (P<0.0001). Chest CT scans showed a high prevalence (68.9% of the cohort) of persistent abnormalities, mainly ground glass opacities. Duration of hospitalization, intensive care unit admission and mechanical ventilation were not associated with the persistence of symptoms 3 months after discharge. CONCLUSION: The prevalence of persistent symptoms following hospitalization with COVID-19 is high and stable for up to 6 months after discharge. However, biological, functional and iconographic abnormalities improved significantly over time.


Subject(s)
COVID-19 , Cohort Studies , Follow-Up Studies , Humans , Prospective Studies , SARS-CoV-2
11.
Sci Rep ; 11(1): 13476, 2021 06 29.
Article in English | MEDLINE | ID: covidwho-1287817

ABSTRACT

Face masks and personal respirators are used to curb the transmission of SARS-CoV-2 in respiratory droplets; filters embedded in some personal protective equipment could be used as a non-invasive sample source for applications, including at-home testing, but information is needed about whether filters are suited to capture viral particles for SARS-CoV-2 detection. In this study, we generated inactivated virus-laden aerosols of 0.3-2 microns in diameter (0.9 µm mean diameter by mass) and dispersed the aerosolized viral particles onto electrostatic face mask filters. The limit of detection for inactivated coronaviruses SARS-CoV-2 and HCoV-NL63 extracted from filters was between 10 to 100 copies/filter for both viruses. Testing for SARS-CoV-2, using face mask filters and nasopharyngeal swabs collected from hospitalized COVID-19-patients, showed that filter samples offered reduced sensitivity (8.5% compared to nasopharyngeal swabs). The low concordance of SARS-CoV-2 detection between filters and nasopharyngeal swabs indicated that number of viral particles collected on the face mask filter was below the limit of detection for all patients but those with the highest viral loads. This indicated face masks are unsuitable to replace diagnostic nasopharyngeal swabs in COVID-19 diagnosis. The ability to detect nucleic acids on face mask filters may, however, find other uses worth future investigation.


Subject(s)
COVID-19/pathology , Masks/virology , Nasopharynx/virology , SARS-CoV-2/isolation & purification , Adult , Aerosols , Aged , COVID-19/virology , Female , Hospitalization , Humans , Limit of Detection , Male , Middle Aged , Particle Size , RNA, Viral/analysis , Real-Time Polymerase Chain Reaction , SARS-CoV-2/physiology , Static Electricity , Viral Load , Young Adult
12.
PLoS One ; 16(4): e0249920, 2021.
Article in English | MEDLINE | ID: covidwho-1186609

ABSTRACT

OBJECTIVE: To establish whether one can build a mortality prediction model for COVID-19 patients based solely on demographics and comorbidity data that outperforms age alone. Such a model could be a precursor to implementing smart lockdowns and vaccine distribution strategies. METHODS: The training cohort comprised 2337 COVID-19 inpatients from nine hospitals in The Netherlands. The clinical outcome was death within 21 days of being discharged. The features were derived from electronic health records collected during admission. Three feature selection methods were used: LASSO, univariate using a novel metric, and pairwise (age being half of each pair). 478 patients from Belgium were used to test the model. All modeling attempts were compared against an age-only model. RESULTS: In the training cohort, the mortality group's median age was 77 years (interquartile range = 70-83), higher than the non-mortality group (median = 65, IQR = 55-75). The incidence of former/active smokers, male gender, hypertension, diabetes, dementia, cancer, chronic obstructive pulmonary disease, chronic cardiac disease, chronic neurological disease, and chronic kidney disease was higher in the mortality group. All stated differences were statistically significant after Bonferroni correction. LASSO selected eight features, novel univariate chose five, and pairwise chose none. No model was able to surpass an age-only model in the external validation set, where age had an AUC of 0.85 and a balanced accuracy of 0.77. CONCLUSION: When applied to an external validation set, we found that an age-only mortality model outperformed all modeling attempts (curated on www.covid19risk.ai) using three feature selection methods on 22 demographic and comorbid features.


Subject(s)
COVID-19/mortality , Age Factors , Aged , Aged, 80 and over , Belgium/epidemiology , COVID-19/diagnosis , COVID-19/epidemiology , Cohort Studies , Communicable Disease Control , Comorbidity , Electronic Health Records , Female , Hospitalization , Humans , Male , Middle Aged , Netherlands/epidemiology , Prognosis , Risk Assessment , Risk Factors , SARS-CoV-2/isolation & purification
13.
Front Mol Biosci ; 8: 600881, 2021.
Article in English | MEDLINE | ID: covidwho-1170100

ABSTRACT

The severity of coronavirus disease 2019 (COVID-19) varies significantly with cases spanning from asymptomatic to lethal with a subset of individuals developing Severe Acute Respiratory Syndrome (SARS) and death from respiratory failure. To determine whether global nucleosome and citrullinated nucleosome levels were elevated in COVID-19 patients, we tested two independent cohorts of COVID-19 positive patients with quantitative nucleosome immunoassays and found that nucleosomes were highly elevated in plasma of COVID-19 patients with a severe course of the disease relative to healthy controls and that both histone 3.1 variant and citrullinated nucleosomes increase with disease severity. Elevated citrullination of circulating nucleosomes is indicative of neutrophil extracellular trap formation, neutrophil activation and NETosis in severely affected individuals. Importantly, using hospital setting (outpatient, inpatient or ICU) as a proxy for disease severity, nucleosome levels increased with disease severity and may serve as a guiding biomarker for treatment. Owing to the limited availability of mechanical ventilators and extracorporal membrane oxygenation (ECMO) equipment, there is an urgent need for effective tools to rapidly assess disease severity and guide treatment selection. Based on our studies of two independent cohorts of COVID-19 patients from Belgium and Germany, we suggest further investigation of circulating nucleosomes and citrullination as biomarkers for clinical triage, treatment allocation and clinical drug discovery.

15.
Diagnostics (Basel) ; 11(1)2020 Dec 30.
Article in English | MEDLINE | ID: covidwho-1006985

ABSTRACT

The 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 beyond their limits. To help in the fight against this threat on human health, a fully automated AI framework was developed to extract radiomics features from volumetric chest computed tomography (CT) exams. The detection model was developed on a dataset of 1381 patients (181 COVID-19 patients plus 1200 non COVID control patients). A second, independent dataset of 197 RT-PCR confirmed COVID-19 patients and 500 control patients was used to assess the performance of the model. Diagnostic performance was assessed by the area under the receiver operating characteristic curve (AUC). The model had an AUC of 0.882 (95% CI: 0.851-0.913) in the independent test dataset (641 patients). The optimal decision threshold, considering the cost of false negatives twice as high as the cost of false positives, resulted in an accuracy of 85.18%, a sensitivity of 69.52%, a specificity of 91.63%, a negative predictive value (NPV) of 94.46% and a positive predictive value (PPV) of 59.44%. Benchmarked 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.

16.
J Allergy Clin Immunol Pract ; 9(1): 160-169, 2021 01.
Article in English | MEDLINE | ID: covidwho-899066

ABSTRACT

BACKGROUND: Asthmatics and patients with chronic obstructive pulmonary disease (COPD) have more severe outcomes with viral infections than people without obstructive disease. OBJECTIVE: To evaluate if obstructive diseases are risk factors for intensive care unit (ICU) stay and death due to coronavirus disease 2019 (COVID19). METHODS: We collected data from the electronic medical record from 596 adult patients hospitalized in University Hospital of Liege between March 18 and April 17, 2020, for severe acute respiratory syndrome coronavirus 2 (SARS-CoV2) infection. We classified patients into 3 groups according to the underlying respiratory disease, present before the COVID19 pandemic. RESULTS: Among patients requiring hospitalization for COVID19, asthma and COPD accounted for 9.6% and 7.7%, respectively. The proportions of asthmatics, patients with COPD, and patients without obstructive airway disease hospitalized in the ICU were 17.5%, 19.6%, and 14%, respectively. One-third of patients with COPD died during hospitalization, whereas only 7.0% of asthmatics and 13.6% of patients without airway obstruction died due to SARS-CoV2. The multivariate analysis showed that asthma, COPD, inhaled corticosteroid treatment, and oral corticosteroid treatment were not independent risk factors for ICU admission or death. Male gender (odds ratio [OR]: 1.9; 95% confidence interval [CI]: 1.1-3.2) and obesity (OR: 8.5; 95% CI: 5.1-14.1) were predictors of ICU admission, whereas male gender (OR 1.9; 95% CI: 1.1-3.2), older age (OR: 1.9; 95% CI: 1.6-2.3), cardiopathy (OR: 1.8; 95% CI: 1.1-3.1), and immunosuppressive diseases (OR: 3.6; 95% CI: 1.5-8.4) were independent predictors of death. CONCLUSION: Asthma and COPD are not risk factors for ICU admission and death related to SARS-CoV2 infection.


Subject(s)
Asthma/epidemiology , COVID-19/mortality , Intensive Care Units/statistics & numerical data , Pulmonary Disease, Chronic Obstructive/epidemiology , Belgium/epidemiology , Comorbidity , Critical Illness , Female , Humans , Length of Stay/statistics & numerical data , Male , Middle Aged , Risk Factors , SARS-CoV-2
17.
J Exp Med ; 217(12)2020 12 07.
Article in English | MEDLINE | ID: covidwho-759875

ABSTRACT

Infection with SARS-CoV-2 is causing a deadly and pandemic disease called coronavirus disease-19 (COVID-19). While SARS-CoV-2-triggered hyperinflammatory tissue-damaging and immunothrombotic responses are thought to be major causes of respiratory failure and death, how they relate to lung immunopathological changes remains unclear. Neutrophil extracellular traps (NETs) can contribute to inflammation-associated lung damage, thrombosis, and fibrosis. However, whether NETs infiltrate particular compartments in severe COVID-19 lungs remains to be clarified. Here we analyzed postmortem lung specimens from four patients who succumbed to COVID-19 and four patients who died from a COVID-19-unrelated cause. We report the presence of NETs in the lungs of each COVID-19 patient. NETs were found in the airway compartment and neutrophil-rich inflammatory areas of the interstitium, while NET-prone primed neutrophils were present in arteriolar microthrombi. Our results support the hypothesis that NETs may represent drivers of severe pulmonary complications of COVID-19 and suggest that NET-targeting approaches could be considered for the treatment of uncontrolled tissue-damaging and thrombotic responses in COVID-19.


Subject(s)
Betacoronavirus/physiology , Coronavirus Infections/immunology , Coronavirus Infections/virology , Extracellular Traps/physiology , Lung/blood supply , Lung/virology , Pneumonia, Viral/immunology , Pneumonia, Viral/virology , Aged , COVID-19 , Coronavirus Infections/pathology , Female , Humans , Lung/pathology , Male , Middle Aged , Pandemics , Pneumonia, Viral/pathology , SARS-CoV-2
18.
Eur Respir J ; 56(2)2020 08.
Article in English | MEDLINE | ID: covidwho-744960

ABSTRACT

BACKGROUND: The outbreak of coronavirus disease 2019 (COVID-19) has globally strained medical resources and caused significant mortality. OBJECTIVE: To develop and validate a machine-learning model based on clinical features for severity risk assessment and triage for COVID-19 patients at hospital admission. METHOD: 725 patients were used to train and validate the model. This included a retrospective cohort from Wuhan, China of 299 hospitalised COVID-19 patients from 23 December 2019 to 13 February 2020, and five cohorts with 426 patients from eight centres in China, Italy and Belgium from 20 February 2020 to 21 March 2020. The main outcome was the onset of severe or critical illness during hospitalisation. Model performances were quantified using the area under the receiver operating characteristic curve (AUC) and metrics derived from the confusion matrix. RESULTS: In the retrospective cohort, the median age was 50 years and 137 (45.8%) were male. In the five test cohorts, the median age was 62 years and 236 (55.4%) were male. The model was prospectively validated on five cohorts yielding AUCs ranging from 0.84 to 0.93, with accuracies ranging from 74.4% to 87.5%, sensitivities ranging from 75.0% to 96.9%, and specificities ranging from 55.0% to 88.0%, most of which performed better than the pneumonia severity index. The cut-off values of the low-, medium- and high-risk probabilities were 0.21 and 0.80. The online calculators can be found at www.covid19risk.ai. CONCLUSION: The machine-learning model, nomogram and online calculator might be useful to access the onset of severe and critical illness among COVID-19 patients and triage at hospital admission.


Subject(s)
Coronavirus Infections/diagnosis , Hospital Mortality/trends , Machine Learning , Pneumonia, Viral/diagnosis , Triage/methods , Adult , Age Factors , Aged , Area Under Curve , Belgium , COVID-19 , COVID-19 Testing , China , Clinical Laboratory Techniques , Cohort Studies , Coronavirus Infections/epidemiology , Decision Support Systems, Clinical , Female , Hospitalization/statistics & numerical data , Humans , Internationality , Italy , Male , Middle Aged , Pandemics/statistics & numerical data , Pneumonia, Viral/epidemiology , Predictive Value of Tests , ROC Curve , Reproducibility of Results , Retrospective Studies , Risk Assessment , Severity of Illness Index , Sex Factors , Survival Analysis
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