Early proximal tubular injury in COVID-19 related ARDS: the URICOV study (preprint)

Background: During COVID-19, renal impairment is the most frequent after lung impairment and is associated with a poor prognosis particularly in intensive care unit (ICU). In this work we aimed to assess the existence and incidence of early renal dysfunction and its prognostic value in patients with COVID-19-related acute respiratory distress syndrome (ARDS) and to compare them with patients with non-COVID-19-related ARDS. Methods: This prospective multicenter study was conducted in 3 ICUs. Patients aged 18 years and older with invasive mechanical ventilation for ARDS were enrolled. Precise evaluation of renal dysfunction markers including urinary proteins electrophoresis (UPE) and quantification, was performed within 24 hours after mechanical ventilation onset. Results: From March 2020 to December 2021, 135 patients in ICU for ARDS were enrolled: 100 COVID-19 ARDS and 35 non-COVID-19 ARDS. UPE found more tubular dysfunction in COVID-19 patients (68% vs. 21.4%, p<0.0001) and more normal profiles in non-COVID-19 patients (65.0% vs. 11.2%, p=0.0003). COVID-19 patients significantly displayed early urinary leakage of tubular proteins like beta-2-microglobulin and free-light chains, tended to display more frequently acute kidney injury (AKI) (51.0% vs 34.3%, p=0.088), and had longer mechanical ventilation (20 vs. 9 days, p<0.0001) and longer ICU length of stay (26 vs. 15 days, p<0.0001). In COVID-19 ARDS, leakage of free lambda light chain was significantly associated with the onset of KDIGO ≥2 AKI (OR: 1.014, 95%CI [1.003-1.025], p=0.011). Conclusion: Patients admitted to the ICU for COVID-19-related ARDS display a proximal tubular dysfunction, prior to the onset of AKI, which predicts AKI. Proximal tubular damage seems an important mechanism of COVID-19-induced nephropathy. Analysis of urinary proteins is a reliable and non-invasive tool to assess proximal tubular dysfunction in the ICU. Trial Registration: Registered retrospectively with www.clinicaltrials.gov (NCT05699889) 26 January 2023.

How automated measurement of lung lesions can predict the course of patients hospitalized with SARS-Cov-2 pneumonia (preprint)

IntroductionThe aim of this study was to evaluate if an automated measurement of lung lesions, epicardial fat and pericardial volume during the days surrounding hospital admission for COVID-19 pneumonia may predict intubation or mortality. The second purpose of this study was to assess whether the association of these Computed Tomography (CT) measures with the SOFA (Sequential Organ Failure Assessment) score, could predict intubation and mortality better than the SOFA score alone.MethodsThis observational retrospective study was conducted in Timone university hospital in Marseille in France, between March 10th and May 10th 2020. All adult patients with COVID-19, admitted with respiratory symptoms and having performed a chest CT three days before to two days after admission were eligible for inclusion. All chest CTs were analyzed using a local automated CT measurement software. The primary outcome was invasive mechanical ventilation (IMV) or death during the 60-day follow-up. Wilcoxon-Mann-Whitney test was used for univariate analysis and logistic regression were calculated for multivariate analysis. Results176 patients were included in the study. 57 (32.4%) received IMV or died during the 60-day follow-up. After univariate analysis, all lung automated volumetric measures of ground-glass (p=0.015), consolidation (p<0.001) and all lesions to parenchymal volume ratio (p<0.001) were significantly higher for the patients who required IMV or who died. All pulmonary-lesion rate was tested in multivariate analysis and remained significantly higher in the IMV or death group (p=0.003), with an Odd Ratio of 3.52 (1.55-8.01, 95% CI) for patients who had more than 19.5% of pulmonary lesion. Pericardial volume and epicardial fat were not significantly associated with IMV or mortality. In this study, the association of the criterion “pulmonary lesion >20%” to the SOFA score improves its predictive value on IMV or mortality with a AUC of 0.82.ConclusionAutomated chest CT measures of COVID-19 patients with respiratory symptoms admitted to hospital showed a significantly higher rate of lung lesions (ground glass, consolidation, or both) for those who later died or required IMV. Furthermore, the association of these automated CT measures to the SOFA score could help select patients requiring ICU upon entering hospital. 

Artificial Intelligence helps triage and monitoring of COVID-19 Intensive Care patients (preprint)

BackgroundThe rapid spread of coronavirus disease COVID 19 calls for early screening and monitoring of these patients to distinguish those that are likely to worsen from stable patients that may be directed to intermediate care facilities. We designed a score for COVID-19 patients severity assessment, dynamic intubation and prolonged stay prediction using the Breathing Frequency (BF) and oxygen saturation (SPO2) signals.MethodsWe recorded BF, and SPO2 signals of confirmed COVID-19 patients admitted during the first and second outbreak of the pandemic in France (March to May 2020 and September 2020 to February 2021) in an ICU of a teaching hospital. We extracted four features from the signals that represent the four last hours before intubation for intubated patients and the mean of the four hours before the median intubation time for non-intubated patients. These data were used to train AI algorithms for intubation recognition. Algorithm robustness was checked on a validation set of patients. We selected the best algorithm that was applied every hour to predict intubation, thus a severity evaluation. We performed a 24h moving average of these predictions giving a S24 severity score that represent the patient's severity during the last 24 h. MS24, the maximum of S24 was confronted with the risk of intubation and prolonged ICU stay (>5 days).ResultsWe included 177 patients. Among the tested algorithms, the Logistic regression classifier had the best performance. The model had an accuracy of 88.9 % for intubation recognition (AUC=0.92). The accuracy on the validation set was 92.6 %. The S24 score of intubated patients was significantly higher than non-intubated patients 48h before intubation and increased 24 hours before intubation. MS24 score allows distinguishing three severity situations with an increased risk of intubation: green (3%), orange (30%) and red (76%). A MS24 score superior to 20 was highly predictive of an ICU stay greater than 5 day with an accuracy of 88.8% (AUC=0.95).ConclusionsThe score we designed uses simple signals and seems to be efficient to visualize the patient's respiratory situation and may help in decision-making. Real-time computation is easy to implement.