Lung Ultrasound to Assist ICU Admission Decision-Making Process of COVID-19 Patients With Acute Respiratory Failure

Abstract

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.

Keywords: COVID-19; acute respiratory distress syndrome; acute respiratory failure; intensive care unit admission decision-making; lung ultrasound; machine learning.

Figure 1.

Study flowchart. Longitudinal data from 140 consecutive patients were included in the study. Ultimately, the dataset was split into two time series to enable further analysis: a learning sample (first 70 patients), which was used to establish the best predictive model (10-fold cross-validation and 1,000 bootstrap permutations), and a validation sample (last 70 patients), which has not been used during the previous phase, were employed to test model’s generalization. ARF = acute respiratory failure, IMV = invasive mechanical ventilation, LUS = lung ultrasound.

Figure 2.

Lung ultrasound patterns. Each of the 12 lung regions assessed per patient was classified using predefined lung ultrasounds profiles (A, B1, B2, B3, C1, C2, TPL, and pleural effusion [PE]). Sonographic LUS signs are not specific of COVID-19 when considered alone. Normal lung sliding (magenta triangles indicate the pleural line) with reverberating horizontal lines (blue triangles) were described as a profile. Interstitial syndrome was defined as the presence of more than two vertical lines in a given lung sector (depicted between vertical blue lines). To allow a semiquantitative assessment, we defined three B-lines patterns: B1 profile (thin, multiple, and well-defined), B2 profile (large and coalescent), and B3 profile (“shining white lung”). As recently reported, we distinguished two patterns of alveolar consolidation (blue circles): subpleural nontranslobar (C1 profile) (27) and posterior translobar with occasional mobile air bronchograms (C2 profile). A thickening of the pleural line with pleural line irregularity was considered as abnormal (Thickening of the Pleural Line, TPL), and PE was defined as a hypoechoic collection limited by the diaphragm and the pleura (PE profile). For additional information regarding lung ultrasounds semiotics, please see the “Materials and Methods” section.

Figure 3.

Extent of lung lesions. The number of quadrants depicting the same lung ultrasound patterns (A, B1, B2, B3, C1, C2, TPL, and pleural effusion [PE]) was summed and is represented according patient’s outcome (critical respiratory illness at 24 hr from hospital admission). p value < 0.05 was considered as statistically significant (*). For additional information, please see Supplemental Digital Content 1, http://links.lww.com/CCX/B14 and Supplemental Digital Content 6, http://links.lww.com/CCX/B17.