Your browser doesn't support javascript.
Show: 20 | 50 | 100
Results 1 - 2 de 2
Filter
1.
Physiol Rep ; 9(4): e14748, 2021 02.
Article in English | MEDLINE | ID: covidwho-1100462

ABSTRACT

A decreased lung diffusing capacity for carbon monoxide (DLCO ) has been reported in a variable proportion of subjects over the first 3 months of recovery from severe coronavirus disease 2019 (COVID-19). In this study, we investigated whether measurement of lung diffusing capacity for nitric oxide (DLNO ) offers additional insights on the presence and mechanisms of gas transport abnormalities. In 94 subjects, recovering from mild-to-severe COVID-19 pneumonia, we measured DLNO and DLCO between 10 and 266 days after each patient was tested negative for severe acute respiratory syndrome coronavirus 2. In 38 subjects, a chest computed tomography (CT) was available for semiquantitative analysis at six axial levels and automatic quantitative analysis of entire lungs. DLNO was abnormal in 57% of subjects, independent of time of lung function testing and severity of COVID-19, whereas standard DLCO was reduced in only 20% and mostly within the first 3 months. These differences were not associated with changes of simultaneous DLNO /DLCO ratio, while DLCO /VA and DLNO /VA were within normal range or slightly decreased. DLCO but not DLNO positively correlated with recovery time and DLCO was within the normal range in about 90% of cases after 3 months, while DLNO was reduced in more than half of subjects. Both DLNO and DLCO inversely correlated with persisting CT ground glass opacities and mean lung attenuation, but these were more frequently associated with DLNO than DLCO decrease. These data show that an impairment of DLNO exceeding standard DLCO may be present during the recovery from COVID-19, possibly due to loss of alveolar units with alveolar membrane damage, but relatively preserved capillary volume. Alterations of gas transport may be present even in subjects who had mild COVID-19 pneumonia and no or minimal persisting CT abnormalities. TRIAL REGISTRY: ClinicalTrials.gov PRS: No.: NCT04610554 Unique Protocol ID: SARS-CoV-2_DLNO 2020.


Subject(s)
COVID-19/physiopathology , Carbon Monoxide/metabolism , Lung/physiopathology , Nitric Oxide/metabolism , Pulmonary Diffusing Capacity , COVID-19/complications , COVID-19/diagnostic imaging , Female , Humans , Lung/diagnostic imaging , Male , Middle Aged , Pulmonary Diffusing Capacity/methods , Pulmonary Diffusing Capacity/physiology , Radiography, Thoracic , Respiratory Function Tests , Severity of Illness Index
2.
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
SELECTION OF CITATIONS
SEARCH DETAIL