Development and external validation of a prediction model for the transition from mild to moderate or severe form of COVID-19.

OBJECTIVES: COVID-19 pandemic seems to be under control. However, despite the vaccines, 5 to 10% of the patients with mild disease develop moderate to critical forms with potential lethal evolution. In addition to assess lung infection spread, chest CT helps to detect complications. Developing a prediction model to identify at-risk patients of worsening from mild COVID-19 combining simple clinical and biological parameters with qualitative or quantitative data using CT would be relevant to organizing optimal patient management. METHODS: Four French hospitals were used for model training and internal validation. External validation was conducted in two independent hospitals. We used easy-to-obtain clinical (age, gender, smoking, symptoms' onset, cardiovascular comorbidities, diabetes, chronic respiratory diseases, immunosuppression) and biological parameters (lymphocytes, CRP) with qualitative or quantitative data (including radiomics) from the initial CT in mild COVID-19 patients. RESULTS: Qualitative CT scan with clinical and biological parameters can predict which patients with an initial mild presentation would develop a moderate to critical form of COVID-19, with a c-index of 0.70 (95% CI 0.63; 0.77). CT scan quantification improved the performance of the prediction up to 0.73 (95% CI 0.67; 0.79) and radiomics up to 0.77 (95% CI 0.71; 0.83). Results were similar in both validation cohorts, considering CT scans with or without injection. CONCLUSION: Adding CT scan quantification or radiomics to simple clinical and biological parameters can better predict which patients with an initial mild COVID-19 would worsen than qualitative analyses alone. This tool could help to the fair use of healthcare resources and to screen patients for potential new drugs to prevent a pejorative evolution of COVID-19. CLINICAL TRIAL REGISTRATION: NCT04481620. CLINICAL RELEVANCE STATEMENT: CT scan quantification or radiomics analysis is superior to qualitative analysis, when used with simple clinical and biological parameters, to determine which patients with an initial mild presentation of COVID-19 would worsen to a moderate to critical form. KEY POINTS: • Qualitative CT scan analyses with simple clinical and biological parameters can predict which patients with an initial mild COVID-19 and respiratory symptoms would worsen with a c-index of 0.70. • Adding CT scan quantification improves the performance of the clinical prediction model to an AUC of 0.73. • Radiomics analyses slightly improve the performance of the model to a c-index of 0.77.

Comorbidities and COPD severity in a clinic-based cohort.

BACKGROUND: Chronic obstructive pulmonary disease (COPD) is an important cause of morbidity and mortality around the world. The aim of our study was to determine the association between specific comorbidities and COPD severity. METHODS: Pulmonologists included patients with COPD using a web-site questionnaire. Diagnosis of COPD was made using spirometry post-bronchodilator FEV1/FVC < 70%. The questionnaire included the following domains: demographic criteria, clinical symptoms, functional tests, comorbidities and therapeutic management. COPD severity was classified according to GOLD 2011. First we performed a principal component analysis and a non-hierarchical cluster analysis to describe the cluster of comorbidities. RESULTS: One thousand, five hundred and eighty-four patients were included in the cohort during the first 2 years. The distribution of COPD severity was: 27.4% in group A, 24.7% in group B, 11.2% in group C, and 36.6% in group D. The mean age was 66.5 (sd: 11), with 35% of women. Management of COPD differed according to the comorbidities, with the same level of severity. Only 28.4% of patients had no comorbidities associated with COPD. The proportion of patients with two comorbidities was significantly higher (p < 0.001) in GOLD B (50.4%) and D patients (53.1%) than in GOLD A (35.4%) and GOLD C ones (34.3%). The cluster analysis showed five phenotypes of comorbidities: cluster 1 included cardiac profile; cluster 2 included less comorbidities; cluster 3 included metabolic syndrome, apnea and anxiety-depression; cluster 4 included denutrition and osteoporosis and cluster 5 included bronchiectasis. The clusters were mostly significantly associated with symptomatic patients i.e. GOLD B and GOLD D. CONCLUSIONS: This study in a large real-life cohort shows that multimorbidity is common in patients with COPD.