Prevalence of COVID-Associated Renal Injury in ICU and Prognosis of Proximal Tubular Dysfunction in Acute Respiratory Distress Syndrome

BACKGROUND AND AIMS During COVID-19, the renal impairment is the most frequent after lung impairment and is associated of poor prognosis particularly in the intensive care unit (ICU). In this work, we aim to assess the incidence of acute kidney injury (AKI) in COVID-19-related acute respiratory distress syndrome (ARDS) patients, the existence of an early renal dysfunction and its prognosis, and its specificity compared with patients with non-COVID ARDS. METHOD This a prospective and multicentric study led in four ICUs. Patients of 18 years and older in ICU with invasive mechanical ventilation for ARDS were enrolled. Precise evaluation of renal dysfunction markers, including urinary protein electrophoresis, was performed within 24 h after the onset of mechanical ventilation. RESULTS From March 2020 to September 2021, 131 patients in ICU for ARDS were enrolled, 98 COVID-19 ARDS and 33 ARDS from other causes. There was more tubular profile in COVID-19 patients (68% versus 24%;P = .001) and a more mixed, tubular and glomerular profile in non-COVID-19 patients (29% versus 14%;P = .001). COVID-19 patients displayed an important tubular proteinuria, tended to display more AKI (49% versus 31%;P = .07), and had a longer duration of mechanical ventilation (18 versus 10 days;P = .002) and longer ICU length of stay (23 versus 15 days;P = .013). In COVID-19 patients, tubular proteinuria was associated with poor renal prognosis with a significant association with the onset of KDIGO ≥ 2 AKI. CONCLUSION COVID-19 ARDS patients had a specific renal impairment with tubular dysfunction, which appeared to be of poor prognosis on kidney and disease evolution.

Triage and monitoring of COVID-19 patients in intensive care using unsupervised machine learning.

BACKGROUND: We designed an algorithm to assess COVID-19 patients severity and dynamic intubation needs and predict their length of stay using the breathing frequency (BF) and oxygen saturation (SpO2) signals. METHODS: We recorded the BF and SpO2 signals for confirmed COVID-19 patients admitted to the ICU of a teaching hospital during both the first and subsequent outbreaks of the pandemic in France. An unsupervised machine-learning algorithm (the Gaussian mixture model) was applied to the patients' data for clustering. The algorithm's robustness was ensured by comparing its results against actual intubation rates. We predicted intubation rates using the algorithm every hour, thus conducting a severity evaluation. We designed a S24 severity score that represented the patient's severity over the previous 24 h; the validity of MS24, the maximum S24 score, was checked against rates of intubation risk and prolonged ICU stay. RESULTS: Our sample included 279 patients. . The unsupervised clustering had an accuracy rate of 87.8% for intubation recognition (AUC = 0.94, True Positive Rate 86.5%, true Negative Rate 90.9%). The S24 score of intubated patients was significantly higher than that of non-intubated patients at 48 h before intubation. The MS24 score allowed for the distinguishing between three severity levels with an increased risk of intubation: green (3.4%), orange (37%), and red (77%). A MS24 score over 40 was highly predictive of an ICU stay greater than 5 days at an accuracy rate of 81.0% (AUC = 0.87). CONCLUSIONS: Our algorithm uses simple signals and seems to efficiently visualize the patients' respiratory situations, meaning that it has the potential to assist staffs' in decision-making. Additionally, real-time computation is easy to implement.

Factors Associated with 90-Day Mortality in Invasively Ventilated Patients with COVID-19 in Marseille, France.

OBJECTIVES: To describe clinical characteristics and management of intensive care units (ICU) patients with laboratory-confirmed COVID-19 and to determine 90-day mortality after ICU admission and associated risk factors. METHODS: This observational retrospective study was conducted in six intensive care units (ICUs) in three university hospitals in Marseille, France. Between 10 March and 10 May 2020, all adult patients admitted in ICU with laboratory-confirmed SARS-CoV-2 and respiratory failure were eligible for inclusion. The statistical analysis was focused on the mechanically ventilated patients. The primary outcome was the 90-day mortality after ICU admission. RESULTS: Included in the study were 172 patients with COVID-19 related respiratory failure, 117 of whom (67%) received invasive mechanical ventilation. 90-day mortality of the invasively ventilated patients was 27.4%. Median duration of ventilation and median length of stay in ICU for these patients were 20 (9-33) days and 29 (17-46) days. Mortality increased with the severity of ARDS at ICU admission. After multivariable analysis was carried out, risk factors associated with 90-day mortality were age, elevated Charlson comorbidity index, chronic statins intake and occurrence of an arterial thrombosis. CONCLUSION: In this cohort, age and number of comorbidities were the main predictors of mortality in invasively ventilated patients. The only modifiable factor associated with mortality in multivariate analysis was arterial thrombosis.

Predict Score: A New Biological and Clinical Tool to Help Predict Risk of Intensive Care Transfer for COVID-19 Patients.

BACKGROUND: The COVID-19 crisis has strained world health care systems. This study aimed to develop an innovative prediction score using clinical and biological parameters (PREDICT score) to anticipate the need of intensive care of COVID-19 patients already hospitalized in standard medical units. METHODS: PREDICT score was based on a training cohort and a validation cohort retrospectively recruited in 2020 in the Marseille University Hospital. Multivariate analyses were performed, including clinical, and biological parameters, comparing a baseline group composed of COVID-19 patients exclusively treated in standard medical units to COVID-19 patients that needed intensive care during their hospitalization. RESULTS: Independent variables included in the PREDICT score were: age, Body Mass Index, Respiratory Rate, oxygen saturation, C-reactive protein, neutrophil-lymphocyte ratio and lactate dehydrogenase. The PREDICT score was able to correctly identify more than 83% of patients that needed intensive care after at least 1 day of standard medical hospitalization. CONCLUSIONS: The PREDICT score is a powerful tool for anticipating the intensive care need for COVID-19 patients already hospitalized in a standard medical unit. It shows limitations for patients who immediately need intensive care, but it draws attention to patients who have an important risk of needing intensive care after at least one day of hospitalization.