key: cord-0702703-8vhl9rg4 authors: Chardoli, Mojtaba; Sabbaghan Kermani, Shaghayegh; Abdollahzade Manqoutaei, Sanaz; Loesche, Michael A.; Duggan, Nicole M.; Schulwolf, Sara; Tofighi, Rojin; Yadegari, Sina; Shokoohi, Hamid title: Lung ultrasound in predicting COVID‐19 clinical outcomes: A prospective observational study date: 2021-11-01 journal: J Am Coll Emerg Physicians Open DOI: 10.1002/emp2.12575 sha: 4ed31375f98b288d74776e3bd197f46792d74460 doc_id: 702703 cord_uid: 8vhl9rg4 STUDY OBJECTIVE: We sought to determine the ability of lung point‐of‐care ultrasound (POCUS) to predict mechanical ventilation and in‐hospital mortality in patients with coronavirus disease 2019 (COVID‐19). METHODS: This was a prospective observational study of a convenience sample of patients with confirmed COVID‐19 presenting to 2 tertiary hospital emergency departments (EDs) in Iran between March and April 2020. An emergency physician attending sonographer performed a 12‐zone bilateral lung ultrasound in all patients. Research associates followed the patients on their clinical course. We determined the frequency of positive POCUS findings, the geographic distribution of lung involvement, and lung severity scores. We used multivariable logistic regression to associate lung POCUS findings with clinical outcomes. RESULTS: A total of 125 patients with COVID‐like symptoms were included, including 109 with confirmed COVID‐19. Among the included patients, 33 (30.3%) patients were intubated, and in‐hospital mortality was reported in 19 (17.4%). Lung POCUS findings included pleural thickening 95.4%, B‐lines 90.8%, subpleural consolidation 86.2%, consolidation 46.8%, effusions 19.3%, and atelectasis 18.3%. Multivariable logistic regression incorporating binary and scored POCUS findings were able to identify those at highest risk for need of mechanical ventilation (area under the curve 0.80) and in‐hospital mortality (area under the curve 0.87). In the binary model ultrasound (US) findings in the anterior lung fields were significantly associated with a need for intubation and mechanical ventilation (odds ratio [OR] 3.67; 0.62–21.6). There was an inverse relationship between mortality and posterior lung field involvement (OR 0.05; 0.01–0.23; and scored OR of 0.57; 0.40–0.82). Anterior lung field involvement was not associated with mortality. CONCLUSIONS: In patients with COVID‐19, the anatomic distribution of findings on lung ultrasound is associated with outcomes. Lung POCUS‐based models may help clinicians to identify those patients with COVID‐19 at risk for clinical deterioration. Key Words: COVID‐19; Lung Ultrasound; Mechanical ventilation; Prediction; ICU admission; Mortality; Clinical outcome; Risk stratification; Diagnostic accuracy As of mid-October 2020, the World Health Organization confirmed approximately 40 million cases and 1 million deaths attributable to coronavirus disease 2019 (COVID-19). 1, 2 Clinical progression of COVID-19 is variable and can range from patients remaining asymptomatic and able to recover at home to patients developing severe respiratory failure requiring prolonged hospitalization and intensive care. [3] [4] [5] [6] [7] Therefore, improving targeted care will require exploring strategies for rapid and accurate prognosis in patients with suspected COVID-19. 8 COVID-19 exhibits characteristic imaging findings including bilateral, patchy, reticular-nodular opacities, ground-glass opacities, or a "crazy-paving" pattern on CT scan. These findings are typically more prominent in a peripheral and basilar distribution. 9, 10 As imaging findings can precede clinical symptoms, imaging studies have been used to predict clinical outcomes and prognosticate disease course. 11 Multiple lung involvement severity scores have been proposed. Generally, data suggest more severe scores correlate with worse clinical outcomes including need for intensive care unit (ICU) admission, mechanical ventilation, and even patient death. [12] [13] [14] [15] [16] [17] [18] Existing imagingbased lung severity scores are almost exclusively created using chest x-ray (CXR) or computed tomography (CT) scan. The number of studies focusing on point-of-care ultrasound (POCUS) in this context is limited. The early identification of patients at risk of adverse clinical outcomes and those who need mechanical ventilation is of interest considering the variable progression pattern in COVID-19 and the need for critical care resources. POCUS is an important imaging modality in identifying lung pathology in many clinical settings. Well-established standardized protocols for cardiopulmonary POCUS aid emergency department and ICU physicians in assessing patients with acute undifferentiated respiratory distress. [19] [20] [21] Compared to chest radiography, POCUS demonstrates higher sensitivity and specificity for diagnosing pneumonia, pneumothorax, pleural effusion, and alveolar-interstitial syndrome. [22] [23] [24] [25] [26] Data supporting the use of POCUS in COVID-19 are promising. [27] [28] [29] [30] [31] POCUS-based triage algorithms, monitoring strategies, and scoring systems aimed at COVID-19 diagnosis and assessment have been proposed. [32] [33] [34] [35] Although existing data suggest the extent of pulmonary findings in POCUS correlate with disease severity, there are few studies formally assessing the predictive capabilities of POCUS in COVID-19. 36,37 Defining the prognostic capabilities of POCUS in COVID-19 may have significant implications for triage and overall management of patients with this global disease. We sought to assess the ability of ED lung POCUS to predict mechanical ventilation and in-hospital mortality in patients with COVID-19. This prospective, observational study took place at the Firouzgar Gen- An emergency physician with >10 years of emergency ultrasound Six-second clips of each view were recorded electronically and were reviewed by 2 attending emergency physicians with fellowship training in emergency ultrasound for quality control. For lung POCUS, using a linear transducer a 12-zone bilateral scanning protocol (6 zones per hemithorax) was performed. 35 With patients in supine, semisupine, or lateral decubitus positions, the scanning points on each hemithorax were included: The primary end point was in-hospital mortality. Outcomes included a need for intubation and mechanical ventilation. We also reported the frequency of ICU admission and ward admission >72 hours, noting that there were no guidelines for ICU or ward admissions. All patients were followed until death or discharge. A research associate trained in data abstraction and blinded to the ultrasound results documented the occurrence of clinical outcomes on their day-by day follow-up and after the completion of hospital course or in-hospital death. All statistical analyses were performed in the R statistical programming environment. 38 Descriptive statistics are reported as medians with the 25th and 75th percentile for continuous variables and percentages for categorical variables. Comparisons between groups were made with the chi-square test with Yates' continuity corrections for categorical variables, respectively. Correction for multiple testing was performed by controlling the false discovery rate with a q-value threshold of 0.1 by the Benjamini and Hochberg method. Each outcome was assessed independently of the others. As described previously, ultrasound evaluation was performed at 6 sites bilaterally and assessed for 6 possible lung pathologies for a com- Test performances of the final models were then calculated using the original data set. We did not have enough subjects for a validation set. For each model, a response operator characteristic curve, area under the curve (AUC), sensitivity, and specificity were calculated. Calibration curves (not shown) and Hosmer-Lemeshow goodness-of-fit testing was then implemented to ensure all models were properly calibrated. Demographic data for enrolled patients are reported in Table 1 In We compared the relative frequencies of the US findings and their eventual disposition outcome. There were no differences in the frequency of US findings between outcomes (Figure 2A) . We then hypothesized that the geographic distribution of findings may be associated with subject outcomes. We performed a similar analysis as previously with the 6 lung sites. The AS lung field was involved in 28.0% of cases who were discharged, but this increased to 60.0% (dif- We performed simple univariate logistic regressions to test the association between sonographic results and outcomes of intubation and death. Each predictor was tested twice: as a binary (whether the predictor was involved or present) and as a scored metric (sum of the number of times the predictor was involved or present). Tables 2 and 3 show the results of the binary and scored univariate regressions of both intubation and death, respectively (Table 2) . Similar patterns were found between the 2 outcomes and predictor types (Table 3 ). We generated multivariable models of both outcomes with binary and scored predictors that were found to be significant in the univariate models. The results of these models are summarized in Figure 3 . Limitations to this study include the possibility of selection bias from enrolling a convenience sample of patients with COVID-19 based on Reported is the OR (95% CI), and the P value. Abbreviations: AI, anterior inferior; AS, anterior superior; Ax, axillary; CI, confidence interval; OR, odds ratio; PI, posterior inferior; PLAPS, posterolateral alveolar and/or pleural syndrome; PS, posterior superior. the availability of physician sonographer. The potential exclusion of the lower acuity patients and those who were discharged from the ED may explain the high prevalence of CT and POCUS findings in our study. However, this potential bias may be less relevant in this cohort as we intentionally were looking at the adverse outcome in those who had established COVID-19 and had higher severity of the disease. The relatively small number of subjects significantly limits the power of the study to detect more nuanced findings. This is compounded by the relatively large number of possible ultrasound findings and their combinations. Thus, for this study, we simplified the search space but recognize that a larger data set may yield even more interesting findings. The high frequency of involvement of certain pathologies and lung sites means there was relatively little variance in the data set, whereas other pathologies were particularly rare. Both can easily lead The ultrasound examinations were performed by an attending physician with extensive experience in POCUS, which may assist with a high intrarater reliability but perhaps limit the generalizability of the study. We also included patients who may clinically needed proning position, but we did not collect data on the duration and frequency of this practice. Expanding this work to include additional institutions and sonographers with variable levels of experience may improve the generalizability of our work. In the present study, we aimed to define the prognostic capabilities of POCUS in patients with COVID-19 for clinical outcomes, such as hospital admission, ICU admission, intubation, and patient death. Although there were no differences in the frequency of ultrasound findings and patient outcomes, geographical distribution of findings demonstrated interesting trends with clinical course. We generated logistic regression models that were able to identify subjects at highest risk of needing mechanical ventilation and highest risk of death. These trends suggest lung POCUS may be a useful tool in assisting emergency physicians in risk-stratifying patients at the point of disposition from the ED. The most frequently identified lung pathologies on POCUS were pleural thickening, B-lines, and subpleural consolidations, which were found in many subjects with COVID-19. All of these were found at similar levels regardless of disease severity. Thus, these are sensitive markers for disease but not useful for risk stratification given their ubiquitous presence. Interestingly, consolidation, found in nearly half of subjects, was found to be inversely related to the disease severity. This may reflect the presence of an otherwise asymptomatic or mild case of COVID-19 with a concomitant pneumonia, which is easier to treat with antibiotics. Alternatively, consolidation may represent a different, milder phenotype of COVID-19. Atelectasis and effusions were relatively uncommon and did not associate with outcomes. The largest source of variation was the anatomic distribution of findings. The most common sites of involvement were the axillary sites and anterior sites, with posterior being the least common. All sites had similar distributions of lung pathology. In general, those who were admitted had higher involvement of anterior and axillary lung findings. The exception proved to be the posterior lung fields, in particular the PI site, which has most frequently involved in the discharged groups. The posterior lung fields were found to be inversely associated with disease severity, a somewhat unexpected finding. Although not significant, anterior lung findings increased in frequency with worsening severity and were found to be a significant predictor of intubation in our models. Axillary findings were so common they were not useful for risk stratification. The models were able to accurately risk stratify those who would eventually be intubated or expire during their course. We were able to generate both scored and binary models that performed similarly. Although the scored models have an overall higher accuracy, the advantage of the binary models is their simplicity. The binary models would allow the sonographer to stop scanning once the first pathology was identified in the posterior and anterior lung fields. Thus, a 12-point lung scan may on occasion be able to be reduced to just 2 points, dramatically decreasing the time required to perform such a scan. The scanning protocol we used may have affected our findings. We used a 12-zone lung POCUS protocol modified from previously proposed POCUS protocols in COVID-19. 35 Although it is possible including the additional 2 zones may have improved the quality of our data, we find this unlikely. Six-zone bilateral lung POCUS protocols have been proposed for diagnosis of COVID-19. 25 Scanning 12-or 14-zones requires a considerable amount of time the sonographer must spend in the room with a potentially COVID-19-positive patient, thus increasing the infection risk. In one of the few existing studies assessing the utility of lung POCUS in predicting clinical outcomes in patients with COVID-19, a lung ultrasound score determined by completing a bilateral 12-zone POCUS within 24 hours of patient presentation was associated with severe illness at the time of admission. 36 Patients who clinically deteriorated underwent a second complete POCUS exam at which point the majority of patients demonstrated worsening scores often secondary to loss of aeration in the anterior lung. This is like our findings in that anterior lung findings associated with more severe clinical outcomes. In another recent study by Lieveld et al., they assessed the association between the lung ultrasound and poor outcome including ICU admission and 30day all-cause mortality. They concluded that the extend of pulmonary involvement detected by lung ultrasound were associated with poor outcome, admission duration, and disease severity. 37 In a retrospective observational study assessing the association of findings on serial chest CT scans with clinical outcomes in patients with COVID-19, higher lung involvement scores were also found to be associated with higher patient mortality. 39 Further studies are needed to assess for scanning protocols that can offer definitive diagnostic and prognostic capabilities. The authors declare no conflict of interest. MC-study concept and design, acquisition of data, analysis and interpretation, critical revision of the manuscript. SSK and SAM-study design, acquisition of data, analysis and interpretation, critical revision of the manuscript. ML-analysis and interpretation of data, developing prediction models, critical revision of the manuscript, statistical expertise. 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