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A proposed lung ultrasound and phenotypic algorithm for the care of COVID-19 patients with acute respiratory failure.

ABSTRACT: Pulmonary complications are the most common clinical manifestations of coronavirus disease (COVID-19). From recent clinical observation, two phenotypes have emerged: a low elastance or L-type and a high elastance or H-type. Clinical presentation, pathophysiology, pulmonary mechanics, radiological and ultrasound findings of these two phenotypes are different. Consequently, the therapeutic approach also varies between the two. We propose a management algorithm that combines the respiratory rate and oxygenation index with bedside lung ultrasound examination and monitoring that could help determine earlier the requirement for intubation and other surveillance of COVID-19 patients with respiratory failure.

SUBMITTER: Denault AY

PROVIDER: S-EPMC7241588 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature

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A proposed lung ultrasound and phenotypic algorithm for the care of COVID-19 patients with acute respiratory failure.

Denault André Y AY Delisle Stéphane S Canty David D Royse Alistair A Royse Colin C Serra Ximena Cid XC Gebhard Caroline E CE Couture Étienne J ÉJ Girard Martin M Cavayas Yiorgos Alexandros YA Peschanski Nicolas N Langevin Stéphan S Ouellet Paul P

Canadian journal of anaesthesia = Journal canadien d'anesthesie 20200521 10

Pulmonary complications are the most common clinical manifestations of coronavirus disease (COVID-19). From recent clinical observation, two phenotypes have emerged: a low elastance or L-type and a high elastance or H-type. Clinical presentation, pathophysiology, pulmonary mechanics, radiological and ultrasound findings of these two phenotypes are different. Consequently, the therapeutic approach also varies between the two. We propose a management algorithm that combines the respiratory rate an ...[more]

PMID: 32440906

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Project description: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.DesignProspective cohort study.SettingCritical Care Unit from University Hospital Purpan (Toulouse, France) between July 2020 and March 2021.PatientsAdult 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.InterventionLUS assessment.Measurements and main resultsOne 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).ConclusionsLUS 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.

Project description:BackgroundAwake prone positioning (APP) reduces the intubation rate in COVID-19 patients treated by high-flow nasal cannula (HFNC). However, the lung aeration response to APP has not been addressed. We aimed to explore the lung aeration response to APP by lung ultrasound (LUS).MethodsThis two-center, prospective, observational study enrolled patients with COVID-19-induced acute hypoxemic respiratory failure treated by HFNC and APP. LUS score was recorded 5-10 min before, 1 h after APP, and 5-10 min after supine in the first APP session within the first three days. The primary outcome was LUS score changes in the first three days. Secondary outcomes included changes in SpO2/FiO2 ratio, respiratory rate and ROX index (SpO2/FiO2/respiratory rate) related to APP, and the rate of treatment success (patients who avoided intubation).ResultsSeventy-one patients were enrolled. LUS score decreased from 20 (interquartile range [IQR] 19-24) to 19 (18-21) (p < 0.001) after the first APP session, and to 19 (18-21) (p < 0.001) after three days. Compared to patients with treatment failure (n = 20, 28%), LUS score reduction after the first three days in patients with treatment success (n = 51) was greater (- 2.6 [95% confidence intervals - 3.1 to - 2.0] vs 0 [- 1.2 to 1.2], p = 0.001). A decrease in dorsal LUS score > 1 after the first APP session was associated with decreased risk for intubation (Relative risk 0.25 [0.09-0.69]). APP daily duration was correlated with LUS score reduction in patients with treatment success, especially in dorsal lung zones (r = - 0.76; p < 0.001).ConclusionsIn patients with acute hypoxemic respiratory failure due to COVID-19 and treated by HFNC, APP reduced LUS score. The reduction in dorsal LUS scores after APP was associated with treatment success. The longer duration on APP was correlated with greater lung aeration. Trial registration This study was prospectively registered on clinicaltrials.gov on April 22, 2021. Identification number NCT04855162 .

Dataset Information

A proposed lung ultrasound and phenotypic algorithm for the care of COVID-19 patients with acute respiratory failure.

Publications

A proposed lung ultrasound and phenotypic algorithm for the care of COVID-19 patients with acute respiratory failure.

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