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1.
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
2.
Biosci Trends ; 14(4): 290-296, 2020 Sep 21.
Article in English | MEDLINE | ID: covidwho-609824

ABSTRACT

This study aimed to determine the clinical significance of Krebs von den Lungen-6 (KL-6) in patients with COVID-19, so as to find a marker with high sensitivity, specificity and easy detection to evaluate the lung injury and inflammation of COVID-19. Sixty-three COVID-19 patients and 43 non-COVID-19 patients with similar clinical phenotypes and/or imaging findings were enrolled to test the levels of KL-6 using chemiluminescent immunoassay. In addition, the blood gas, imaging and lymphocyte factors tests were collected from all participants. The data was finally analyzed using multivariate statistical analysis. The results showed KL-6 levels in COVID-19 patients were higher than those in non-COVID-19 patients (P < 0.001). Moreover, the KL-6 levels in severe and critically severe patients were significantly upregulated compared with patients with mild and common type (P < 0.05). Meanwhile, the imaging evaluation showed a significant correlation between KL-6 and pulmonary lesion area (P < 0.05). KL-6 was also found to be significantly correlated with oxygenation index and oxygen partial pressure difference of alveolar artery (PA-aDO2) (Both P < 0.01). In conclusion, KL-6 could be an indicator to evaluate the progression of COVID-19, which is parallel to the level of lung injury and inflammation in patients. Moreover, it can also reflect the pulmonary ventilation function.


Subject(s)
Coronavirus Infections/blood , Lung/diagnostic imaging , Mucin-1/blood , Pneumonia, Viral/blood , Adult , Aged , Betacoronavirus , Blood Gas Analysis , COVID-19 , Case-Control Studies , Coronavirus Infections/diagnostic imaging , Coronavirus Infections/immunology , Female , Humans , Male , Middle Aged , Pandemics , Pneumonia, Viral/diagnostic imaging , Pneumonia, Viral/immunology , SARS-CoV-2
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