Publications of Anne-Noëlle Frix
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See detailAsthma and COPD Are Not Risk Factors for ICU Stay and Death in Case of SARS-CoV2 Infection
CALMES, Doriane ULiege; Graff, Sophie ULiege; MAES, Nathalie ULiege et al

in Journal of Allergy and Clinical Immunology: In Practice (2020)

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See detailDevelopment of a Clinical Decision Support System for Severity Risk Prediction and Triage of COVID-19 Patients at Hospital Admission: an International Multicenter Study.
Wu, Guangyao; Yang, Pei; Xie, Yuanliang et al

in European Respiratory Journal (2020)

BACKGROUND: The outbreak of the coronavirus disease 2019 (COVID-19) has globally strained medical resources and caused significant mortality. OBJECTIVE: To develop and validate machine-learning model ... [more ▼]

BACKGROUND: The outbreak of the coronavirus disease 2019 (COVID-19) has globally strained medical resources and caused significant mortality. OBJECTIVE: To develop and validate 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 including a retrospective cohort of 299 hospitalised COVID-19 patients at Wuhan, China, from December 23, 2019, to February 13, 2020, and five cohorts with 426 patients from eight centers in China, Italy, and Belgium, from February 20, 2020, to March 21, 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: The median age was 50.0 years and 137 (45.8%) were men in the retrospective cohort. The median age was 62.0 years and 236 (55.4%) were men in five cohorts. The model was prospectively validated on five cohorts yielding AUCs ranging from 0.84 to 0.89, with accuracies ranging from 74.4% to 87.5%, sensitivities ranging from 75.0% to 96.9%, and specificities ranging from 57.5% to 88.0%, all 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. [less ▲]

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See detailThermoplastie bronchique dans le traitement de l’asthme sévère. Analyse rétrospective de 10 cas traités au CHU de Liège
Frix, Anne-Noëlle ULiege; HEINEN, Vincent ULiege; SCHLEICH, FLorence ULiege et al

in Revue Médicale de Liège (2019), 74(2), 74-81

Summary: As treating severe forms of asthma represents a medical and economical challenge, research for new therapies in this area is extensive and expansive. Recently, bronchial thermoplasty (BT) – ie ... [more ▼]

Summary: As treating severe forms of asthma represents a medical and economical challenge, research for new therapies in this area is extensive and expansive. Recently, bronchial thermoplasty (BT) – ie. bronchoscopic procedure delivering a thermic form of energy through radiofrequency to the bronchi, in order to interfere with the components of the smooth muscle layer – arose as a promising technique. Our study followed the path of 10 patients from CHU Liège (University Hospital), who underwent this procedure in a context of severe asthma. We compared clinical and spirometric and treatment data in patients at 0 – 6 and 12 months post-procedural intervals, in order to determine whether thermoplasty had been improving asthma. Overall, we observed a stabilization and possibly a clinical improvement while reducing the total amount of exacerbation rate, and the burden of maintenance oral corticoids. © 2019 Revue Medicale de Liege. All Rights Reserved. [less ▲]

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