Aims/hypothesis Coronavirus disease-2019 (COVID-19) is a life-threatening infection caused by the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) virus. Diabetes has rapidly emerged as a major comorbidity for COVID-19 severity. However, the phenotypic characteristics of diabetes in COVID-19 patients are unknown. Methods We conducted a nationwide multicentre observational study in people with diabetes hospitalised for COVID-19 in 53 French centres in the period 10-31 March 2020. The primary outcome combined tracheal intubation for mechanical ventilation and/or death within 7 days of admission. Age-and sex-adjusted multivariable logistic regressions were performed to assess the prognostic value of clinical and biological features with the endpoint. ORs are reported for a 1 SD increase after standardisation. Results The current analysis focused on 1317 participants: 64.9% men, mean age 69.8 ± 13.0 years, median BMI 28.4 (25th-75th percentile: 25.0-32.7) kg/m 2 ; with a predominance of type 2 diabetes (88.5%). Microvascular and macrovascular diabetic complications were found in 46.8% and 40.8% of cases, respectively. The primary outcome was encountered in 29.0% (95% CI 26.6, 31.5) of participants, while 10.6% (9.0, 12.4) died and 18.0% (16.0, 20.2) were discharged on day 7. In univariate analysis, characteristics prior to admission significantly associated with the primary outcome were sex, BMI and previous treatment with renin-angiotensin-aldosterone system (RAAS) blockers, but not age, type of diabetes, HbA 1c , diabetic complications or glucoselowering therapies. In multivariable analyses with covariates prior to admission, only BMI remained positively associated with the primary outcome (OR 1.28 [1.10, 1.47]). On admission, dyspnoea (OR 2.10 [1.31, 3.35]), as well as lymphocyte count (OR A complete list of the CORONADO trial investigators is provided in the Electronic supplementary material (ESM).
The authors regret a mistake in Table 1. Contrary to the statement in footnote a (applicable to age, BMI and HbA 1c), only BMI was standardised and thus the OR for HbA 1c and age is expressed per 1 unit, not 1 SD. The table and footnote are corrected here.
Objective:
Male sex is a determinant of severe coronavirus disease-2019 (COVID-19). We aimed to characterize sex differences in severe outcomes in adults with diabetes hospitalized for COVID-19.
Methods:
We performed a sex-stratified analysis of clinical and biological features and outcomes (i.e. invasive mechanical ventilation [IMV], death, intensive care unit [ICU] admission and home discharge at day 7 [D7] or day 28 [D28]) in 2,380 patients with diabetes hospitalized for COVID-19 and included in the nationwide CORONADO observational study (NCT04324736).
Results:
The study population was predominantly male (63.5%). After multiple adjustments, female sex was negatively associated with the primary outcome (IMV and/or death, OR 0.66 [0.49-0.88]), death (OR 0.49 [0.30-0.79]) and ICU admission (OR 0.57 [0.43-0.77]) at D7, but only with ICU admission (OR 0.58 [0.43-0.77]) at D28. Older age and a history of microvascular complications were predictors of death at D28 in both sexes, while chronic obstructive pulmonary disease (COPD) was predictive of death in women only. At admission, CRP, AST and eGFR predicted death in both sexes. Lymphocytopenia was an independent predictor of death in women only, while thrombocytopenia and elevated plasma glucose concentration were predictors of death in men only.
Conclusions:
In patients with diabetes admitted for COVID-19, female sex was associated with lower incidence of early severe outcomes, but did not influence the overall in-hospital mortality, suggesting that diabetes mitigates the female protection from COVID-19 severity. Sex-associated biological determinants may be useful to optimize COVID-19 prevention and management in women and men.
Background and study aims Computer-aided diagnostic tools using deep neural networks are efficient for detection of lesions in endoscopy but require a huge number of images. The impact of the quality of annotation has not been tested yet. Here we describe a multi-expert annotated dataset of images extracted from capsules from Crohn’s disease patients and the impact of the quality of annotations on the accuracy of a recurrent attention neural network.
Methods Images of capsule were annotated by a reader first and then reviewed by three experts in inflammatory bowel disease. Concordance analysis between experts was evaluated by Fleiss’ kappa and all the discordant images were, again, read by all the endoscopists to obtain a consensus annotation. A recurrent attention neural network developed for the study was tested before and after the consensus annotation. Available neural networks (ResNet and VGGNet) were also tested under the same conditions.
Results The final dataset included 3498 images with 2124 non-pathological (60.7 %), 1360 pathological (38.9 %), and 14 (0.4 %) inconclusive. Agreement of the experts was good for distinguishing pathological and non-pathological images with a kappa of 0.79 (P < 0.0001). The accuracy of our classifier and the available neural networks increased after the consensus annotation with a precision of 93.7 %, sensitivity of 93 %, and specificity of 95 %.
Conclusions The accuracy of the neural network increased with improved annotations, suggesting that the number of images needed for the development of these systems could be diminished using a well-designed dataset.
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