Lung Ultrasound for COVID-19 Initial Triage and Monitoring

University Hospital, Toulouse25 enrolled

Overview

The QUICK study main aim is to assess the predictive value at Day 1, of a model built on lung ultrasound (LUS) and clinical data, both recorded at hospital admission of COVID-19 patients.

Initial triage assessment is the cornerstone of first-line medical management for COVID-19 patients. Only an accurate and fast evaluation of COVID-19 patients respiratory system integrity, can allow optimal treatment care and medical resources attribution. Despite its very large deployment, the use of thoracic Computed Tomography (CT scan) for COVID-19 patients severity assessment is currently debated. Actually CT-scan use in this setting: i) it is associated with risky in/out hospital patient's transport, both in terms of medical management of patient's critical conditions and risk of COVID-19 nosocomial transmission, ii) risks related to x-ray exposure iii) CT-scan is a snapshot of respiratory system integrity and does not provide data that might be used for patient's monitoring. LUS is a non-invasive, non-ionizing, fully bedside imaging tool. Investigators team has previously contributed to the development and validation of LUS for critically ill patient's management. To the extent of our knowledge, there is neither data regarding COVID-19 patient's LUS patterns, nor about the potential link between LUS data, patient's severity and outcome. The investigators hypothesize that the combined use of LUS and clinical data (Q-SOFA score, SpiO2/FiO2) recorded at COVID-19 patients hospital admission, will allow to accurately predict short-term outcome. The investigators expect to predict at patient's hospital admission, the patient's clinical status at 24h: favorable (spontaneous ventilation with O2 \< 6 l/min) or unfavorable (spontaneous ventilation with O2 \> 6 l/min or under mechanical ventilation).

Study Type

OBSERVATIONAL

Enrollment

Conditions

COVID-19 Acute Respiratory Failure Triage Lung Ultrasound CT Scan

Interventions

thoracic lung ultrasoundOTHER

Patients will be recruited the day of their hospital admission. All patients will be assessed by thoracic Computed Tomography scan then immediately before/after CT scan, patients will be clinically assessed (Q-SOFA, SpiO2/FiO2) and a lung ultrasound evaluation (mean time of evaluation 7 min +/- 3 min; fully respect of COVID-19 barrier measures) will be performed by an investigator. Patients clinical status and outcomes will be extracted from patient's medical file at day 1 and day 28 from patient's admission by investigators blinded from previously recorded lung ultrasound data.

Eligibility

Sex: ALLMin age: 17 Years

Medical Language ↔ Plain English

Inclusion criteria: * Adult (\> 18 years). * Proven COVID-19 (specific PCR from respiratory track sample) * CT scan prescribed by physician in charge, independently of research. * Patients consent (or surrogate decision maker's consent in case of need). Exclusion criteria: * Reduction or cessation of active treatment. * Patient under guardianship, tutelage measure or judicial protection * Patient deprived of liberty by judicial order * No French health insurance. * Pregnancy or nursing woman. * Enrolled in another trial evaluating thoracic imaging.

Locations (1)

University Hospital Toulouse

Toulouse, France

Outcomes

Primary Outcomes

Area Under the Curve (AUC) of a predictive model built on LUS and clinical (Q-SOFA, SpiO2/FiO2) data

Area Under the Curve (AUC) of a predictive model at 24h from hospital admission (Favorable vs Unfavorable), built on LUS (12 thoracic regions) and clinical (Q-SOFA, SpiO2/FiO2) data recorded at hospital admission.

Time frame: Day 1

Secondary Outcomes

Area Under the Curve (AUC) of a predictive model built on CT scan and clinical (Q-SOFA, SpiO2/FiO2) data

Area Under the Curve (AUC) of a predictive model at 24h from hospital admission (Favorable vs Unfavorable), built on CT scan and clinical (Q-SOFA, SpiO2/FiO2) data recorded at hospital admission.

Time frame: Day 1

mortality

Time frame: Day 28

Data from ClinicalTrials.gov

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