key: cord-0708547-t1y5dk21 authors: Lieveld, Arthur W. E.; Kok, Bram; Azijli, Kaoutar; Schuit, Frederik H.; van de Ven, Peter M.; de Korte, Chris L.; Nijveldt, Robin; van den Heuvel, Frederik M. A.; Teunissen, Bernd P.; Hoefsloot, Wouter; Nanayakkara, Prabath W. B.; Bosch, Frank H. title: Assessing COVID‐19 pneumonia—Clinical extension and risk with point‐of‐care ultrasound: A multicenter, prospective, observational study date: 2021-05-01 journal: J Am Coll Emerg Physicians Open DOI: 10.1002/emp2.12429 sha: c2ebabcf770958e0076ea8da8eb1ff27b22f5dd2 doc_id: 708547 cord_uid: t1y5dk21 BACKGROUND: Assessing the extent of lung involvement is important for the triage and care of COVID‐19 pneumonia. We sought to determine the utility of point‐of‐care ultrasound (POCUS) for characterizing lung involvement and, thereby, clinical risk determination in COVID‐19 pneumonia. METHODS: This multicenter, prospective, observational study included patients with COVID‐19 who received 12‐zone lung ultrasound and chest computed tomography (CT) scanning in the emergency department (ED). We defined lung disease severity using the lung ultrasound score (LUS) and chest CT severity score (CTSS). We assessed the association between the LUS and poor outcome (ICU admission or 30‐day all‐cause mortality). We also assessed the association between the LUS and hospital length of stay. We examined the ability of the LUS to differentiate between disease severity groups. Lastly, we estimated the correlation between the LUS and CTSS and the interrater agreement for the LUS. We handled missing data by multiple imputation with chained equations and predictive mean matching. RESULTS: We included 114 patients treated between March 19, 2020, and May 4, 2020. An LUS ≥12 was associated with a poor outcome within 30 days (hazard ratio [HR], 5.59; 95% confidence interval [CI], 1.26–24.80; P = 0.02). Admission duration was shorter in patients with an LUS <12 (adjusted HR, 2.24; 95% CI, 1.47–3.40; P < 0.001). Mean LUS differed between disease severity groups: no admission, 6.3 (standard deviation [SD], 4.4); hospital/ward, 13.1 (SD, 6.4); and ICU, 18.0 (SD, 5.0). The LUS was able to discriminate between ED discharge and hospital admission excellently, with an area under the curve of 0.83 (95% CI, 0.75–0.91). Interrater agreement for the LUS was strong: κ = 0.88 (95% CI, 0.77–0.95). Correlation between the LUS and CTSS was strong: κ = 0.60 (95% CI, 0.48–0.71). CONCLUSIONS: We showed that baseline lung ultrasound ‐ is associated with poor outcomes, admission duration, and disease severity. The LUS also correlates well with CTSS. Point‐of‐care lung ultrasound may aid the risk stratification and triage of patients with COVID‐19 at the ED. The main cause of morbidity and mortality in COVID-19 is viral pneumonia, which can progress to acute respiratory distress syndrome. Proper evaluation of pulmonary involvement is critical for appropriate triage, risk stratification, and efficient allocation of medical resources. This is especially important since new, more contagious genetic variants are emerging on multiple continents-some with the ability to generate more reinfections-which could put additional stress on already overwhelmed acute care pathways. 1 Mounting evidence suggests that imaging studies such as computed tomography (CT) are helpful in the diagnosis of COVID-19 pneumonia. Findings on CT may precede clinical symptoms, and the degree of pulmonary involvement can help predict patient outcome. [2] [3] [4] [5] [6] [7] [8] However, CT is expensive and cumbersome. CT scans can be difficult to perform on unstable patients, operation of CT equipment requires extra personnel, and unavailability and high costs can be an issue, even in high-income countries. In addition, the risk of COVID-19 transmission necessitates stringent desinfection protocols, and cleaning of the radiology suite may lead to increased delays between uses. Point-of-care ultrasound (POCUS) of the lung might circumvent these issues. Lung ultrasound has diagnostic accuracy comparable to CT-and superior to chest radiography-in multiple aetiologies of respiratory failure, from pneumonia to acute respiratory distress syndrome. [9] [10] [11] [12] [13] [14] [15] Recent studies show that lung ultrasound also has better diagnostic accuracy than chest radiography in diagnosing COVID-19 pneumonia. 18, 19 Chest radiography is a poor diagnostic test in COVID-19 as it may miss up to 40% of confirmed cases. 20 Meanwhile, diagnostic accuracy of lung ultrasound approaches that of CT for COVID-19 pneumonia. 21, 22 Lung ultrasound can safely exclude clinically relevant COVID-19 pneumonia and may aid COVID-19 diagnosis in high-prevalence situations. 23 The advantages of lung ultrasound over CT include being fast to operate, simple to clean, low investment and operating costs, portable, and easily repeatable. With proper personal protective equipment, lung ultrasound can be safely performed at the bedside within 10 minutes. Because of its usefulness in diagnosis and follow-up, lung ultrasound has become common practice, and even standard practice, in acute and critical care. 16 ,17 The literature comparing the degree of pulmonary involvement between lung ultrasound and CT, along with literature detailing a lung ultrasound's ability to stratify patients and predict outcomes, is limited to only single-center and/or retrospective studies. 16, 17, 24, 25 In a recent meta-analysis, the World Health Organization (WHO) recommended that the value of lung ultrasound regarding both clinical outcomes and duration of hospital stay should be investigated further because of inconclusive evidence. 26 To address this knowledge gap, we prospectively assessed the value of lung ultrasound during initial emergency department (ED) presentation in COVID-19 risk stratification and prognostication. In addition, we also evaluated the correlation between lung ultrasound and CT results in quantifying lung involvement in patients with COVID-19. We performed a multicenter prospective, observational study. The study was registered with The Netherlands Trial Register and approved by the local medical ethical committees. We conducted the study in the following 3 large university hospitals in Patients were recruited between March 19, 2020, and May 4, 2020. Inclusion criteria were presentation at the ED for acute internal medicine, presence of a certified sonographer, confirmed COVID-19, and lung ultrasound and chest CT performed within 24 hours of presentation. Exclusion criteria were either no verbal consent and/or uninterpretable CT or lung ultrasound ( Figure 1 ). Per hospital procedure, all patients received a standard medical work-up (history, physical examination, common observations, and routine laboratory tests) and a SARS-CoV-2 polymerase chain reaction (PCR) test. Clinical criteria for ward admission were oxygen saturation <94% (need for conventional supplemental oxygen) and/or respiratory rate >20/minute. ICU admission criteria were either deterioration despite conventional respiratory support or need for mechanical ventilation. Lung ultrasound was performed or supervised by acute internal medicine physicians who are both certified in POCUS and had an Entrustable Professional Activity level of at least 4. The Entrustable Professional Activity concept has competency-based education targets to guarantee that all learners have a sufficient level of proficiency when they reach the required Entrustable Professional Activity level. Increasing levels of entrustment range from level 1 (not trusted to perform POCUS even under direct supervision) to level 4 (entrusted to use POCUS independently) and level 5 (engagement in POCUS education and research). 27 We predominantly used handheld systems, with settings amenable to the detection of B-line artefacts. 28 The scanning physician was blinded for the PCR and CT results, but not for the clinical picture. The lung ultrasound protocol consisted of a structured assessment of 6 zones of each hemithorax ( Figure 2 ). Each zone was scored accord- While correlated with the presence of pneumonia, the asso- ing to the same lung ultrasound score (LUS) classification system used in intensive care, acute respiratory distress syndrome, and recent COVID-19 literature. 15, 16, 21, 24, 29, 30 This system scores zones from 0 (well aerated) to 3 (consolidated) ( Figure S1 ). The total LUS sums the scores from all 12 zones, creating a final score range from 0 (all regions are well aerated) to 36 (all regions are consolidated). Local radiologists assessed the chest CTs using the COVID-19 Report- is suspected (ie, CO-RADS 3 and higher). 34 The CTSS is a visual assessment of the percentage of disease involvement in each lobe (Table S1 ). The total CTSS is the sum of the individual lobar scores and can range from 0 (no involvement) to 25 (maximum involvement). The radiologists were blinded for lung ultrasound and PCR results, but not for clinical information. The diagnosis of COVID-19 was established by a positive PCR test result (from a nasopharyngeal, oropharyngeal, sputum, or bronchoalveolar lavage sample) or by a clinician impression after alternative diagnoses was excluded. In patients with high clinical suspicion but negative PCR, a multidisciplinary team of hospital clinicians determined the presence of COVID-19 on the basis of clinical, laboratory, microbiological, and/or CT data and only after excluding alternative diagnoses. Use of clinician impression as a reference is in line with WHO recommendations because of the suboptimal sensitivity of the PCR test. 26 The multidisciplinary team is composed of specialists in infectious disease, respiratory disease, and microbiology and discusses all admitted The primary outcome was poor COVID-19 outcome, defined as the composite endpoint of 30-day all-cause mortality or ICU admission. Secondary outcomes included hospital length of stay (days) and disease severity groups (ED discharge, hospital ward admission, ICU admission). We used Kaplan-Meier curves and Cox proportional hazards regression to assess the relationship between the LUS and time to poor outcome (a composite endpoint of 30-day all-cause mortality or ICU admission) and admission duration. We dichotomized the LUS using the median. We tested the proportional hazards assumption by checking that the Kaplan-Meier curves do not cross and the log minus log curves run parallel. We assessed linearity for continuous variables by dividing them in tertiles and checking whether β coefficients systematically increase or decrease with increasing categories. We reported hazard ratios (HRs; adjusted for any confounders if applicable) as effect size for the dichotomized LUS used in the Kaplan-Meier curves as well as for each per-point increase in the LUS separately. We considered the following variables as potential confounders for a relation between the LUS and outcome: duration of symptoms, age, sex, and comorbidities (chronic obstructive pulmonary disease, cardiovascular disease, hypertension, and diabetes mellitus). 2 We examined confounders separately to keep within the rule of 5-10 events per variable. We used a 10% change in the LUS β coefficient as confounder threshold. 35 We estimated a multivariate model, when allowed by the events-per-variable rule, in which we adjusted for all confounders identified. We compared differences between the following 3 disease severity grades: (1) mild, no admission/ED discharge; (2) moderate, hospital/ward admission; and (3) severe, ICU admission. We also compared means and proportions between these groups by 1-way analysis of We determined the ability of the LUS to discriminate between disease severity by comparing receiver operating characteristic (ROC) curves. We determined the optimal cutoff with the Youden index. We also reported sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, and diagnostic odds ratio. [36] [37] [38] A diagnostic odds ratio of >10 is considered good. 39 We also used a "gray zone" approach. 40 This method allows the binary constraint of a "black or white" decision to be avoided, which is often inappropriate for clinical or screening practice. Instead, our approach constructs a 3-zone division for quantitative tests and purposefully includes a gray zone of inconclusive test results. In this case, we used a negative likelihood ratio of 0.1 and 0.2 and a positive likelihood ratio of 10 and 5 for the cutoffs of the gray zone. At these likelihood ratios, the approximate posttest change in probability is ∼45% (large change) or ∼30% (moderate change). 41 We employed multiple imputation by chained equations with predictive mean matching to account for any missing data, assuming a missing-at-random pattern. We pooled effect estimates from the imputation data sets using Rubin's rules. We generated m = 10 imputed data sets, because of ≈5%-10% missing data. 42 We used all patient characteristics, laboratory values, and radiological information mentioned in Table 1 and Table S2 with ≤30% missing data for imputation. We A total of 114 patients were included in the study, and data were collected between March 19, 2020, and May 4, 2020 ( Figure 1 ). After initial intake at the ED, 24 patients were discharged home, 79 were admitted to the ward, and 12 required ICU admission (see Table 1 There was a statistically significant difference in mean LUS between the disease severity grades (mild, no admission; moderate, ward admission; and severe, ICU admission) as determined by 1-way ANOVA (P < 0.001) (see end of Table 1 ). The mean LUS data were 6. The AUC of the ROC curve that depicts the ability of the LUS to discriminate between hospital admission (ward and ICU combined) We report crude and adjusted HR. Only duration of symptoms was a significant confounder. CI, confidence interval; HR, hazard ratio. a Lung ultrasound score ≥12 is the reference category. and no admission was 0.83 (95% CI, 0.75-0.91), which was almost identical to the CTSS ( Figure 5 , Figure S1 ). The optimal cutoff was 12, We found a strong positive association between the LUS and CTSS, with a Pearson correlation coefficient of 0.60 (95% CI, 0.48-0.71). The interrater agreement for the LUS was excellent, with an ICC of 0.88 (95% CI, 0.77-0.95). Our study has several limitations. First, although we included consecutive patients when a certified sonographer was present, we could not include every patient who tested positive for COVID-19. However, we tried to enroll every eligible patient when a certified sonographer was present, therefore minimizing any possible selection bias. We therefore feel that these omissions have not influenced our results. We look forward to seeing these results corroborated in different settings. To our knowledge, this is the first multicenter study to prospectively compare the correlation between the LUS and CTSS in COVID-19 and assess the ability of the LUS to discriminate COVID-19 pneumonia disease severity and its association with prognosis. Our results show that COVID-19 pulmonary involvement quantified by the semiquantitative LUS is strongly correlated with the pulmonary involvement assessed by CT. An increasing LUS is also positively associated with disease severity. This is consistent with other findings on viral pneumonias and acute respiratory distress syndrome 15, 30 as well as recent retrospective data in COVID-19. 16, 24, 43 The WHO and Fleischner Society agree that imaging studies should be considered in the triage and management of patients, especially in resource-constrained environments (eg, no immediate PCR results) and in patients with at least moderate disease. 26, 44 Although CT is considered the gold standard to assess the degree of pulmonary involvement, our findings indicate that lung ultrasound could be a viable alternative to CT in the initial assessment of lung involvement in COVID-19. Initially, it was believed that lung ultrasound would be less reliable than CT because of its perceived inability to detect central lesions. 44 However, the peripheral distribution of COVID-19 makes lung ultrasound ideal for the detection of these abnormalities. 21, 45, 46 In fact, lung ultrasound also serves as a reliable test in viral pneumonias that produce more central abnormalities, such as influenza. [47] [48] [49] Incorporating lung ultrasound into routine COVID-19 diagnostics offers the potential of reducing stress on conventional radiological resources, decreasing the need of transporting patients who are ill and unstable to CT and lowering the amount of healthcare personnel exposed to patients who are potentially contagious. 24, 50 Furthermore, the LUS is able to discriminate well between patients who require admission and those who do not. Interestingly, the LUS could do so just as well as the CTSS. Furthermore, the gray zone analysis indicates that the LUS changes the posttest probability of admission moderately (∼30%) in more than two-thirds of patients and largely (∼45%) in almost half of the patients, which is also comparable with the CTSS. 2 but might still benefit from a higher level of monitoring based on a high LUS. One could also speculate that serial point-of-care lung ultrasound might be used to monitor pulmonary involvement, track the disease course, guide management, and determine response to treatment without any radiation exposure. The LUS might also be a welcome addition to laboratory markers given the speed with which the results of POCUS are available. Lung ultrasound results can be obtained within 5-10 minutes, which is much faster than traditional laboratory or imaging results. Fortunately, POCUS is simple to learn for (para)medical personnel. 56, 57 Furthermore, as an affordable and easy-to-use tool, POCUS could reduce obstacles to proper care, which is of particular importance given the ethnoracial, cultural, and socioeconomic disparities COVID-19 has laid bare worldwide. 58 In summary, we demonstrate that the LUS correlates well with the CTSS. Moreover, COVID-19 pulmonary involvement measured by lung ultrasound is significantly associated with poor outcome, disease severity, and admission duration. Lung ultrasound may, therefore, help the triage, risk stratification, and management of patients with COVID-19 at the ED. New coronavirus variants could cause more reinfections, require updated vaccines Chest CT in COVID-19 at the ED: validation of the COVID-19 Reporting and Data System (CO-RADS) and CT severity score Time course of lung changes at chest CT during recovery from coronavirus disease 2019 (COVID-19) The clinical and chest CT Features associated with severe and critical COVID-19 pneumonia CT image visual quantitative evaluation and clinical classification of coronavirus disease (COVID-19) Frequency and distribution of chest radiographic findings in COVID-19 positive patients Clinical and high-resolution CT features of the COVID-19 infection: comparison of the initial and follow-up changes Association between initial chest CT or clinical features and clinical course in patients with coronavirus disease 2019 pneumonia Accuracy of lung ultrasonography versus chest radiography for the diagnosis of adult community-acquired pneumonia: review of the literature and meta-analysis Lung ultrasound in diagnosing pneumonia in the emergency department: a systematic review and metaanalysis Lung ultrasound for the emergency diagnosis of pneumonia, acute heart failure, and exacerbations of chronic obstructive pulmonary disease/asthma in adults: a systematic review and meta-analysis Point-of-care ultrasonography in patients admitted with respiratory symptoms: a single-blind, randomised controlled trial Lung ultrasound integrated with clinical assessment for the diagnosis of acute decompensated heart failure in the emergency department: a randomized controlled trial BLUE-protocol and FALLS-protocol Lung ultrasound for critically ill patients on behalf of the AZUREA Network. Comparative study of lung ultrasound and chest computed tomography scan in the assessment of severity of confirmed COVID-19 pneumonia Pointof-care lung ultrasound in the assessment of suspected COVID-19: a retrospective service evaluation with a severity score 6 lung ultrasound versus chest x-ray for the diagnosis of COVID-19 pneumonia Descriptive analysis of a comparison between lung ultrasound and chest radiography in patients suspected of COVID-19 Combatting COVID-19: is ultrasound an important piece in the diagnostic puzzle Diagnosing COVID-19 pneumonia in a pandemic setting: lung ultrasound versus CT (LUVCT) A multi-centre, prospective, observational study Correlation between chest computed tomography and lung ultrasonography in patients with coronavirus disease 2019 (COVID-19) Lung ultrasound for the diagnosis of SARS-CoV-2 pneumonia in the emergency department Lung ultrasound predicts clinical course and outcomes in COVID-19 patients Lung ultrasonography for risk stratification in patients with COVID-19: a prospective observational cohort study Use of chest imaging in COVID-19 Point-of-care ultrasound (PoCUS) for the internist in acute medicine: a uniform curriculum COVID-19 Lung Ultrasound Guideline -British Medical Ultrasound Society Bedside ultrasound assessment of positive end-expiratory pressureinduced lung recruitment The diagnostic accuracy for ARDS of global versus regional lung ultrasound scores-a post hoc analysis of an observational study in invasively ventilated ICU patients Working Group of the Dutch Radiological Society. CO-RADS-a categorical CT assessment scheme for patients with suspected COVID-19: definition and evaluation. Radiology Pulmonary sequelae in convalescent patients after severe acute respiratory syndrome: evaluation with thin-section CT Inleiding in De Toegepaste Biostatistiek Systematic reviews of evaluations of diagnostic and screening tests A readers' guide to the interpretation of diagnostic test properties: clinical example of sepsis The diagnostic odds ratio: a single indicator of test performance Odds ratio not prevalence independent A grey zone for quantitative diagnostic and screening tests Simplifying likelihood ratios Flexible Imputation of Missing Data Semiquantitative lung ultrasound scores in the evaluation and follow-up of critically ill patients with COVID-19: a single-center study The role of chest imaging in patient management during the covid-19 pandemic: a multinational consensus statement from the Fleischner Society Coronavirus disease 2019 (COVID-19): a systematic review of imaging findings in 919 patients Chinese Critical Care Ultrasound Study Group (CCUSG). Findings of lung ultrasonography of novel corona virus pneumonia during the 2019-2020 epidemic Performance of radiologists in differentiating COVID-19 from non-COVID-19 viral pneumonia at chest CT Early recognition of the 2009 pandemic influenza A (H1N1) pneumonia by chest ultrasound Characteristic CT findings distinguishing 2019 novel coronavirus disease (COVID-19) from influenza pneumonia Lung ultrasound in patients with acute respiratory failure reduces conventional imaging and health care provider exposure to COVID-19 Prognostic value of bedside lung ultrasound score in patients with COVID-19 Radiomics analysis of computed tomography helps predict poor prognostic outcome in COVID-19 Chest CT score in COVID-19 patients: correlation with disease severity and short-term prognosis International evidencebased recommendations for point-of-care lung ultrasound Point-of-care ultrasound (POCUS): unnecessary gadgetry or evidence-based medicine? UltraNurse: teaching pointof-care ultrasound to intensive care nurses Lung ultrasound in emergency and critically ill patients: number of supervised exams to reach basic competence Access to lifesaving medical resources for African countries: cOVID-19 testing and response, ethics, and politics Is there a role for lung ultrasound during the COVID-19 pandemic? Clinical letters Thoracic ultrasound and SARS-COVID-19: a pictorial essay The authors declare no conflict of interest.