Proteinuria in COVID-19: prevalence, characterization and prognostic role (preprint)

Background: Proteinuria has been commonly reported in patients with COVID-19, suggesting a renal involvement in this infection. However, only dipstick tests have been used thus far. Here, the quantification and characterization of proteinuria and hematuria are investigated. Their potential association with mortality was assessed. Methods: This retrospective, observational and monocentric study includes 153 patients hospitalized with COVID-19 between March 28th and April 30th 2020, in whom total proteinuria and urine α1-microglobulin (a marker of tubular injury) have been measured. Association with mortality was evaluated with a follow-up until May 7th 2020. Results: According to the Kidney Disease Improving Global Outcomes staging, 14% (n=21) had stage 1 proteinuria (<150 mg/g of urine creatinine), 42% (n=64) had stage 2 (between 150 and 500 mg/g) and 44% (n=68) had stage 3 (over 500 mg/g). Urine α1-microglobulin concentration was higher than 10 or 15 mg/g in 94% and 89% of patients, respectively. After a median follow-up of 27 [14;30] days, the mortality rate reached 18%. Total proteinuria and urine α1-microglobulin (as continuous and/or categorical variables) were associated with mortality in unadjusted and adjusted models. This association was even stronger in subgroups of patients with normal renal function or without urinary catheter. Conclusions: Proteinuria is frequent in patients with COVID-19. Its characterization suggests a tubular origin with increased urine α1-microglobulin. Tubular proteinuria seems associated with mortality in COVID-19.

Risk factors for mortality in a multicenter cohort of mechanically ventilated COVID-19 patients in Belgium. (preprint)

Background: Considering the high mortality rate of severe Covid-19 patients, it is necessary to identify prognostic factors and therapies which could be valuable in this setting.Methods: The method consisted in a multicentric retrospective analysis in all consecutive Covid-19 patients admitted to intensive care unit (ICU) and mechanically ventilated for more than 24 hours from March 1 to April 25, 2020.Admission date, age, sex, body mass index, underlying conditions, treatments, physiological values, use of vasopressors, renal replacement therapy and extracorporeal membrane oxygenation, duration of mechanical ventilation, length of ICU stay, ICU and ventilator-free days at day 42 were collected. Primary outcome was survival. Simple and multiple time-dependent Cox regression models were used to assess the effects of factors on survival. Results: Out of 2003 patients hospitalized for SARS-CoV-2, 361 were admitted to the participating ICUs, 257 were ventilated for more than 24 hours and 247 were included in the study. The length of stay in ICU was 21 (12-32) days and the mortality rate was 45%. Using multiple regression, risk factors for mortality were age, high serum creatinine value, low mean arterial pressure, low lymphocytes count on day 0 and the absence of corticosteroid therapy during the first week of mechanical ventilation. The mortality rate of the patients who received corticosteroids was 34% and 48% for patients who did not (p = 0.01).Conclusion: In this multicenter cohort, the mortality of patients with SARS-CoV-2 pneumonia treated with mechanical ventilation was high. The risk factors for mortality included age, renal and circulatory dysfunction, lymphopenia and the absence of corticosteroid therapy during the first week of mechanical ventilation. 

Development and validation of an automated radiomic CT signature for detecting COVID-19 (preprint)

Background : The coronavirus disease 2019 (COVID-19) outbreak has reached pandemic status. Drastic measures of social distancing are enforced in society and healthcare systems are being pushed to and over their limits. Objectives : To develop a fully automatic framework to detect COVID-19 by applying AI to chest CT and evaluate validation performance. Methods : In this retrospective multi-site study, a fully automated AI framework was developed to extract radiomics features from volumetric chest CT exams to learn the detection pattern of COVID-19 patients. We analysed the data from 181 RT-PCR confirmed COVID-19 patients as well as 1200 other non-COVID-19 control patients to build and assess the performance of the model. The datasets were collected from 2 different hospital sites of the CHU Liege, Belgium. Diagnostic performance was assessed by the area under the receiver operating characteristic curve (AUC), sensitivity and specificity. Results : 1381 patients were included in this study. The average age was 64.4 and 63.8 years with a gender balance of 56% and 52% male in the COVID-19 and control group, respectively. The final curated dataset used for model construction and validation consisted of chest CT scans of 892 patients. The model sensitivity and specificity for detecting COVID-19 in the test set (training 80% and test 20% of patients) were 78.94% and 91.09%, respectively, with an AUC of 0.9398 (95% CI: 0.875-1). The negative predictive value of the algorithm was found to be larger than 97%. Conclusions : Benchmarked against RT-PCR confirmed cases of COVID-19, our AI framework can accurately differentiate COVID-19 from routine clinical conditions in a fully automated fashion. Thus, providing rapid accurate diagnosis in patients suspected of COVID-19 infection, facilitating the timely implementation of isolation procedures and early intervention.