A New Scoring Model to Diagnose COVID-19 Using Lung Ultrasound in the Emergency Department

Mortality in COVID-19 patients is significantly correlated with age, fever duration, cardiac history, and B-profile and areas of consolidation in LUS. However, it is negatively correlated with initial O2 saturation and ejection fraction. This study was aiming to design a new scoring model to diagnose COVID-19 using bedside lung ultrasound (LUS) in the emergency department (ED).

Patients:

The study recruited all patient with pulmonary symptoms and presented to ED between 27th March 2020 and 17th May 2020. Exclusion criteria were (i) patients with congestive heart failure (n= 7), (ii) patients with known interstitial lung fibrosis (n= 4), and (iii) patients with poor echo-window (n= 4). Patients were seen first at ED where they underwent the required investigations and then classified. Patients were questioned about symptoms suspecting COVID-19 infection. Those who met the suspected clinical and investigational criteria were given a standard mask and were rapidly transferred safely to a separate waiting and isolation area with available infrastructure and tools for hand and respiratory hygiene practice. If the patient was proved to be positive for COVID-19, he was sent to quarantine. For negative patients, they were admitted to intermediate or ICU according to their clinical status. All recruited patients underwent the followings: complete blood count (CBC), arterial blood gas (ABGs), RT-PCR assay to detect COVID-19, chest X-ray, chest CT, LUS, and echocardiography (according to its availability, with precautions for the operators and the probe similar to those exerted to LUS).

LUS examination:

Two trained medical personnel, one ICU physician and one ICU nurse, entered the isolation room respecting all the preventive measures for respiratory, droplet, and contact isolation provided by the world health organization for the COVID-19 outbreak. The ultrasound probe and the tablet were put in two different sterile, plastic probe and tablet covers. Imaging was performed using a curvilinear probe (2-5 MHz) with different devices according to the availability in each centre. Six-point LUS (three in each hemithorax) was performed as described in the bedside lung ultrasonography in emergency (BLUE) protocol \[13\].

Statistical analysis:

Statistical analysis included comparing different parameters between COVID-19 positive-patients and COVID-19 negative-patients, using independent t-test for numerical variables and chi-square for categorical variables. All significantly different variables were entered in a forward stepwise binary logistic regression analysis to select the best model. After selecting the best model. The variable chosen in the last step was weighed using the odds ratios (ORs) calculated from the regression coefficient (β) for each variable, the ORs were multiplied by 0.125 to calculate a score for each variable and the number was rounded to the nearest integer giving of scoring system of 10 points. All study patients were scored. The cutoff point of the score was calculated using ROC analysis, and calculation of sensitivity and specificity was performed. Also, variables associated with mortality in COVID-19 positive were entered in a forward binary logistic regression, which selected the best model and the ORs were calculated for each variable using the regression coefficient (β). Before adding the variables in the regression analysis, the determination of the proper cutoff values of different contentious variables was done using ROC analysis. Patient Data were entered, checked, and analyzed using SPSS for Windows version 16 (SPSS, Inc. Chicago, IL, USA). For all the above mentioned statistical tests, the threshold of significance is fixed at a 5% level (p \< 0.05).

Eligibility Criteria:
Inclusion Criteria:

covid19 patients at emergency room.

Exclusion Criteria:

(i) patients with congestive heart failure (n= 7), (ii) patients with known interstitial lung fibrosis (n= 4), and (iii) patients with poor echo-window (n= 4).