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In Critical care explorations

** : There is only low-certainty evidence on the use of predictive models to assist COVID-19 patient's ICU admission decision-making process. Accumulative evidence suggests that lung ultrasound (LUS) assessment of COVID-19 patients allows accurate bedside evaluation of lung integrity, with the added advantage of repeatability, absence of radiation exposure, reduced risk of virus dissemination, and low cost. Our goal is to assess the performance of a quantified indicator resulting from LUS data compared with standard clinical practice model to predict critical respiratory illness in the 24 hours following hospital admission.

DESIGN : Prospective cohort study.

SETTING : Critical Care Unit from University Hospital Purpan (Toulouse, France) between July 2020 and March 2021.

PATIENTS : Adult patients for COVID-19 who were in acute respiratory failure (ARF), defined as blood oxygen saturation as measured by pulse oximetry less than 90% while breathing room air or respiratory rate greater than or equal to 30 breaths/min at hospital admission. Linear multivariate models were used to identify factors associated with critical respiratory illness, defined as death or mild/severe acute respiratory distress syndrome (Pao2/Fio2 < 200) in the 24 hours after patient's hospital admission.

INTERVENTION : LUS assessment.

MEASUREMENTS AND MAIN RESULTS : One hundred and forty COVID-19 patients with ARF were studied. This cohort was split into two independent groups: learning sample (first 70 patients) and validation sample (last 70 patients). Interstitial lung water, thickening of the pleural line, and alveolar consolidation detection were strongly associated with patient's outcome. The LUS model predicted more accurately patient's outcomes than the standard clinical practice model (DeLong test: Testing: z score = 2.50, p value = 0.01; Validation: z score = 2.11, p value = 0.03).

CONCLUSIONS : LUS assessment of COVID-19 patients with ARF at hospital admission allows a more accurate prediction of the risk of critical respiratory illness than standard clinical practice. These results hold the promise of improving ICU resource allocation process, particularly in the case of massive influx of patients or limited resources, both now and in future anticipated pandemics.

Aguersif Amazigh, Sarton Benjamine, Bouharaoua Sihem, Gaillard Lucien, Standarovski Denis, Faucoz Orphée, Martin Blondel Guillaume, Khallel Hatem, Thalamas Claire, Sommet Agnes, Riu Béatrice, Morand Eric, Bataille Benoit, Silva Stein


COVID-19, acute respiratory distress syndrome, acute respiratory failure, intensive care unit admission decision-making, lung ultrasound, machine learning