Hrev_master Abstract Lung ultrasound (LUS) is an essential tool for respiratory dis- ease differential diagnosis at Emergency Department (ED), due to easy applicability and safety. During Sars-Cov 2 pandemic, LUS was widely used in every setting. This study aims to demonstrate the prognostic role of LUS independently of other factors and the identification of an LUS score cut-off to be applied in the ED. A multi-center prospective study was conducted on 285 patients, 123 from Pisa University Hospital, 162 from S. Maria Misericordia Hospital of Perugia. All patients received LUS examination by expert sonographers within 48 hours of admission with the same methodology. Univariate logistic analysis demonstrated that LUS is a mortality predictor, OR 2.8 (CL1.5-5.1). Using LUS score cut- off 1.3, the OR was 6.7 (CL2.7-1.6). In multivariate logistic anal- ysis, LUS score significantly predicted death, independently of other factors. ROC curves comparison demonstrated that the intro- duction of LUS score <1.3 to a multifactorial model improved the association with mortality (AUC 0.76vs0.84, p=0.04). LUS com- bined with clinical, anamnestic, laboratory, and blood gas parame- ters, would allow an effective prognostic stratification in Sars- Cov2 patients at ED. Introduction The lung is the main target organ of the coronavirus disease identified in 2019 (COVID-19). The disease typically manifests as bilateral interstitial pneumonia with predominantly postero-inferi- or distribution, in which chest computed tomography (CT) is the gold standard for diagnosis.1,2 Timely recognition and prognostic stratification of the disease allow early therapeutic procedures implementation, to prevent the most fearsome complications such as respiratory failure up to Acute Respiratory Distress Syndrome (ARDS).3 Lung ultrasound (LUS) is a tool widely used in the Emergency Department (ED) due to its safety, manageability, and efficacy in the first evaluation of many respiratory diseases.4 During the worldwide Sars-Cov2 pandemic, LUS gained a fundamental role in diagnosis5 related to the ultrasound patterns characterizing COVID-19 pneumonia, such as patchy distributed B-lines, “light beam” or subpleural consolidations.6,7 The use of numerical scores and topographic evaluation techniques allows a quantitative analy- sis of lung involvement.8 This approach is also made available and appropriate for LUS to monitor both during hospitalization9 and follow-up.10 The evaluation of the patient with Sars-Cov 2 infection by ED physicians is based on anamnestic, clinical, laboratory, and blood gas analysis. Numerous studies identified factors that strongly pre- dict an unfavorable outcome, including male gender, comorbidi- ties, lymphopenia, increasing inflammation markers, and blood gas parameters, in particular arterial oxygen partial pressure/frac- tional inspired oxygen ratio (P/F).11,12 Emergency Care Journal 2024; volume 20:12268 [Emergency Care Journal 2024; 20:12268] [page 39] Prognostic value of lung ultrasound score performed in the Emergency Department in COVID-19 patients: a prospective multicenter study in central Italy Greta Barbieri,1,2 Stefano De Vuono,3 Luna Gargani,2 Sokol Berisha,3 Stefano Spinelli,1 Chiara Del Carlo,1 Chiara Deri,1 Gennaro D’Angelo,1 Paolo Groff,3 Lorenzo Ghiadoni1,4 1Emergency Medicine Department, Pisa University Hospital; 2Department of Surgical, Medical, Molecular and Critical Area Pathology, University of Pisa; 3Emergency Department, Santa Maria della Misericordia Hospital, Perugia; 4Department of Clinical and Experimental Medicine, University of Pisa, Italy Correspondence: Greta Barbieri, Department of Surgical, Medical, Molecular and Critical Area Pathology, University of Pisa, Via Savi, 10 - 56126 Pisa, Italy. Tel.: +39.3470104897 E-mail: greta.barbieri@phd.unipi.it Key words: COVID-19; pneumonia; ultrasound; emergency depart- ment; lung ultrasound. Conflict of interest: the authors declare no potential conflict of interest. Contributions: GB, SDV: conceptualization, investigation, formal analysis, writing, review, and editing; LG: conceptualization, methodology, review; SB, SS: investigation, data curation, review; CDC, CD, GDA: data curation, review; PG: conceptualization, methodology, review; LG: conceptualization, formal analysis, review, and editing. Ethics approval: the research followed the Declaration of Helsinki ethical principles and the international standards of Good Clinical Practice. The local Ethics Committee approved the protocol (proto- col number 17828). Written informed consent was obtained from all the patients. Patient consent for publication: written informed consent was obtained from a legally authorized representative(s) for anonymized patient information to be published in this article. Received: 9 January 2024. Accepted: 13 February 2024. Early view: 4 March 2024. This work is licensed under a Creative Commons Attribution 4.0 License (by-nc 4.0). ©Copyright: the Author(s), 2024 Licensee PAGEPress, Italy Emergency Care Journal 2024; 20:12268 doi:10.4081/ecj.2024.12268 Publisher's note: all claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be Non -co mmerc ial us e o nly Unfortunately, some systematic reviews and meta-analyses showed that the currently proposed models, based on these generic and thus not disease-specific prognostic factors, are at elevated risk of bias and have low performance, mainly because of high hetero- geneity and lack of prospective design.13,14 An appropriate early assessment of the severity of disease with risk stratification allows identifying correct hospitalization settings and therapeutic strategies.15 Chest imaging, either with chest CT or with LUS, is a cornerstone of the prognostic evaluation of the patient with COVID-19 pneumonia, given that many studies have shown that greater lung involvement predicts the need for inten- sive care, complications, or death.16,17 Aim of the study The goal of our study was to verify whether the LUS score could predict mortality, independently of other known prognostic factors. Secondary objectives were to verify whether an early involvement of the anterior chest areas is a negative prognostic factor; to identify an effective LUS score cut-off for rapid risk stratification to be applied in the ED; to assess whether the inclusion of LUS in the context of clinical-laboratory evaluation can improve prognostic stratification. Materials and Methods Setting and study design We conducted a prospective study in two hospitals of central Italy, University Hospital of Pisa and S. Maria Misericordia Hospital of Perugia. The 2 centers belong to the same geographical area and have a similar catchment area, approximately 100,000 inhabitants. The research followed the Declaration of Helsinki ethical prin- ciples and the international standards of Good Clinical Practice. The local Ethics Committee approved the protocol (protocol num- ber 17828). Written informed consent was obtained from all the patients. Study population We enrolled a total of 285 patients (123 in Pisa and 162 in Perugia) admitted to the ED between March 2020 and March 2022. All the subjects presented acute respiratory symptoms consis- tent with COVID-19 (fever, cough, dyspnoea, cold) and were pos- itive for the nasopharyngeal molecular swab for Sars-CoV-2. Pneumonia was not an inclusion criterion for the study and enrol- ment was anterior to or concurrent with imaging. The exclusion criteria were chronic fibrosis, advanced lung cancer, and dialysis. For each patient, we collected demographic data (age, sex), comorbidities, clinical presentation and course of the COVID-19 disease, data registered during ED stay such as vitals, laboratory tests, arterial blood gas analysis, ventilatory strategy (high flow nasal cannula HFNC, non-invasive ventilation NIV, orotracheal intubation (OTI), hospitalization setting, outcomes. We performed standardized LUS with the topographic scheme and standardized report on all enrolled patients within 48 hours of admission, in the ED, or the first days of hospitalization. We excluded patients who, due to clinical needs, or technical or orga- nizational problems due to the pandemic, did not undergo LUS with adequate methods and reporting within the scheduled times. LUS topographic scheme and scoring We adopted a 16-area scanning scheme (8 scans for each hemi- thorax) reduced to 12 areas (6 areas for each hemi-thorax) in patients whose clinical conditions did not allow complete mobi- lization and exploration of more posterior areas (Figure 1). We used convex probes (frequency 2.5-5 MHz) along the intercostal spaces with the transverse approach. The focus was set on the pleural line and the progressive TGC (time gain compensation) was adjusted to optimize the image. Each area was evaluated independently, assigning a numerical value based on lung aeration (with increasing numerical scores as the lung aeration decreased). The score was derived by the one usually used in ARDS,18 with a score of 0 in case of normal aeration (only A-lines or less than 3 separated B-lines per area); and a score of 1 in case of 3 or more B-lines or coalescent B-lines occupying ≤ 50% of the screen; score 2 for coalescent B-lines occupying > 50% of the screen; and score 3 for consolidation. A final LUS score was indexed since it was obtained from the sum of all single values divided by the number of all explored areas. Anterior thoracic involvement was defined by the presence of LUS scores > 1 in at least 3 of the 4 anterior lung areas (R1 to R4 and L1 to L4). Each exam was reviewed by expert sonographers (GB, SD) to verify the methodology and scoring assignment. All sonographers had undergone and successfully passed a LUS training on B-lines and a dedicated LUS training on COVID-19 findings. The B-lines inter-observer variability was examined by intraclass correlation coefficient (ICC) on 50 previously acquired LUS videos evaluated by an expert reader (LG). Statistical analysis Data are expressed as mean ± standard deviation, median, and interquartile range (IQR) for continuous numeric variables and as percentages for categorical variables. Differences between groups were analyzed with a parametric test (Student’s T test) for normal- ly distributed variables and a non-parametric test (Mann-Whitney U test) for non-normally distributed variables. χ2 test was used for comparisons between variables expressed in the form of frequen- cies. Receiving Operator Curves (ROC) were used to identify the best cut-off values of the LUS aeration score and their diagnostic accuracy in mortality prediction. Regression coefficient (β) and odds ratio (OR) with the corresponding 95% confidence interval Article Figure 1. Topographic scheme adopted in the study. [page 40] [Emergency Care Journal 2024; 20:12268] Non -co mmerc ial us e o nly (CI) were assumed as outputs of the logistic regression models. A p-value of 0.05 was considered statistically significant. Comparisons between ROC curves derived from difference evalu- ation models were made by De Long’s test. Results We analyzed 285 patients (123 of Pisa, 162 of Perugia) with a mean age of 62.5 ± 14.9 years, of which 182 (63.9%) were male, and 103 (36.1%) were female. The most frequent clinical presen- tations upon admission to the ED were fever (n=205, 71.9%), cough (n=138, 48,4%), and dyspnoea (n=165, 58%). The days between the onset of symptoms and hospital admission were on average 7.4±5.4. Table 1 shows the baseline characteristics, comorbidities, and outcomes of the two study center populations compared. Data showed no significant differences in the two subgroups, except for the mean age, which was higher in the Pisa group. Univariate logistic analysis showed that the indexed LUS score was a significant predictor of death with an Odds Ratio (OR) of 2.8. A cut-off of 1.3 for the LUS score was identified through the ROC curve, with the best combination of sensitivity, equal to 70%, and specificity, equal to 73%. Anterior thoracic involvement, defined as a LUS score ≥ 1 for at least 3 of the 4 anterior areas, at LUS examination performed within 6 days of symptom onset, was also predictive of death. The univariate logistic regression analyses of the LUS score are shown in Table 2. We also evaluated the correlation between the LUS score indexed with P/F, demonstrating an inverse relationship between the 2 parameters (R-squared 0.09, R-0.31, p>0.001). This relation- ship is confirmed by subdividing the population based on the P/F cut-off value of 200, demonstrating higher mean indexed LUS score values in the group with altered P/F (16.77±9.6 vs 12.39±8.4, p-value 0.008). Subsequently, we verified mortality predictive power of clinical, laboratory blood gas indicators, already validat- ed in literature.19-21 The results of these analyses, performed as uni- variate logistic regression, are shown in Table 3. Finally, we used the variables probed in previous tests in dif- ferent multivariate logistic regression models (Table 4). Among clinical-laboratory parameters, the most impacting variable was the P/F<200, with an OR of 6.8. Our results showed that the LUS score, as introduced in multivariate regression models, maintained the ability to significantly predict death (Table 4). Finally, we verified the accuracy in predicting death in three models, through a comparison of ROC curves. Figure 2 shows a significant improvement in the accuracy thanks to the LUS score introduction, with a corresponding increase in the area under the curve (AUC). The best evaluation model was the one that included clinical- laboratory parameters, LUS, and age, with AUC= 0.92. Discussion LUS provided a key tool in the management, diagnosis, and monitoring of patients during the Sars-Cov2 pandemic.8 The present study demonstrated an association of the standard- ized LUS score with mortality in patients hospitalized for COVID- 19, both when indexed LUS was considered as continuous value or with the cut-off of 1.3 (OR of respectively 2.8 and 6.7). Multivariate logistic regression analysis demonstrated that the LUS score is an independent predictor of death in COVID-19 Article Table 1. Populations’ features in the two subgroups. Tot; n=285 (%) Perugia; n= 162 (%) Pisa; n=123 (%) p Age 62.5 ± 14.9 60.8 ± 12.7 64.6 ± 17.2 0.006 Male sex 182 (63.9) 100 (61.7) 82 (66.7) 0.39 Smoke 50 (17.7) 25 (15.4) 25 (20.8) 0.24 Asthma 15 (5.3) 6 (3.7) 9 (7.3) 0.18 COPB 19 (6.7) 7 (4.3) 12 (9.8) 0.07 Hypertension 134 (47) 80 (49.4) 54 (43.9) 0.35 Chronic cardiac disease 27 (9.5) 12 (7.4) 15 (12.2) 0.17 Diabetes 49 (17.2) 29 (17.9) 20 (16.3) 0.72 Dyslipidaemia 52 (18.3) 31 (19.1) 21 (17.1) 0.65 Obesity 33 (14.5) 21 (13.0) 12 (18.5) 0.29 ICU 22 (7.8) 12 (7.4) 10 (8.4) 0.75 ETI 16 (5.6) 12 (7.4) 4 (3.3) 0.13 Death 27 (9.7) 18 (11.1) 9 (7.7) 034 COPD, chronic obstructive pulmonary disease; ICU, Intensive care unit; ETI, Endotracheal intubation. Table 2. LUS univariate regression logistic analysis. Odds ratio CL Beta p Indexed LUS score 2.8 1.5-5.1 1 0.001 LUS cut-off=1,3 6.7 2.7-16 1.9 <0.001 Early anterior lung involvement 3.4 1.1-10 1.2 0.03 CL, confidence limits. [Emergency Care Journal 2024; 20:12268] [page 41] Non -co mmerc ial us e o nly patients, compared to other powerful prognostic factors. Its signif- icance is maintained in different models, even in the presence of age. Importantly, the ROC curves demonstrated that the introduc- tion of LUS into the prognostic model results in a significant improvement in the accuracy of the evaluation (p value=0.04). Several authors showed that lung ultrasound has a central role in the diagnosis and monitoring of COVID-19 patients. Some authors suggest that lung ultrasound with scoring may have a role in identifying the patient’s prognosis, alone or associated with other indicators.17,22-25 Sacco et al. demonstrated, in a single-center population with a similar size, a correlation between LUS score and P/F, also con- firmed by our results. Furthermore, the LUS score proved to effec- tively predict death, with sensitivity and specificity values like those of our study, although independence from other factors was not demonstrated.22 A meta-analysis and a systematic review of the prognostic role of the LUS score have been published on the subject.26,27 In May 2021 Song G et al., in their meta-analysis including 16 studies for a total of 1541 covid patients, showed that the LUS score was high- er in non-survivors than in survivors.26 However, the studies included in this meta-analysis were highly heterogeneous, includ- ing small populations, did not describe when lung ultrasounds were performed during the hospital route, and above all these stud- ies did not adjust for possible confounding factors. In 2022 Gil- Rodríguez et al. published a systematic review showing that a high LUS score is associated with developing unfavorable outcomes, including 66 studies for a total of 4687 patients.27 Only 7 of the studies included in this systematic review evaluated the role of LUS score in the prediction of mortality. Particularly in three of them,28-30 the authors compared LUS scores between survivors and non-survivors, and in four the authors directly correlated LUS scores with death.16,31-33 Only two of them adjusted for possible confounding factors: Garcia de Alencar et al.34 adjusted for age and Wangüemert Pèrez et al.35 for sex and age-adjusted Charlson index. Numerous studies have demonstrated that the typical distri- bution of COVID-19 pneumonia is mantle, patchy, and prevalent in the lung posterior fields, with a typical evolution over time in Article Table 4. Multivariate regression logistic models compared. Odds ratio CL Beta p Model 1: C-L p Female sex 0.3 0.1-1.2 -1.16 0.08 L < 20% 1.4 0.3-6.8 0.3 0.68 CRP > 6.5 1.9 0.6-6.6 0.6 0.31 P/F < 200 6.8 2.4-19.4 1.9 <0.001 Lat > 1.3 2.3 0.8-6.4 0.8 0,11 Model 2: C-L p + LUS Female sex 0.2 0.1-0.9 0.7 0.03 L < 20% 1.4 0.3-7.3 0.9 0.7 CRP > 6.5 2 0.6-7.1 0.6 0.3 P/F < 200 5.4 1.8-16.4 0.6 0.002 Lat > 1.3 2,5 0.9-7.5 0.6 0.09 LUS > 1.3 6.7 2.2-20.3 0.6 <0.001 Model 3: C-L p + LUS + age Female sex 0.2 0.04-0.8 3.1 0.03 L < 20% 1 0.2-6.1 0.03 0.9 CRp > 6.5 2.2 0.5-9.3 0.8 0.3 P/F < 200 7.3 1.9-28.6 1.9 0.004 Lat > 1.3 2.1 0.6-7.6 0.7 0.3 LUS > 1.3 8.5 2.3-30.8 2.1 0.001 Age > 65 21.7 4-116.7 3.08 <0.001 C-L p, clinical-laboratory parameters; LUS, lung ultrasound; CRP, C-reactive protein. [page 42] [Emergency Care Journal 2024; 20:12268] Table 3. Clinical-laboratory parameters univariate regression logistic analysis. Odds ratio CL Beta p Age > 65 18.7 4.3-8.0 2.9 <0.001 Male sex 5.1 1.5-17.3 1.6 <0.001 Neutrophils (%) 1.2 1.06-1.3 0.2 0.002 Lymphocytes (%) 0.9 0.8-1 -0.1 0.002 L < 20% 4.7 1.08-20.4 1.6 0.03 CRP 1 0.99-1.007 0.0003 0.93 CRP > 6.5 2.7 1.1-6.4 1 0.02 P/F > 200 8.8 3.6-21.3 2.2 <0.001 Lactate 1.9 1.2-2.8 0.6 0.002 Lactate <1.3 3.6 1.4-9 1.3 0.005 L, lymphocytes; CRP, C-reactive protein. Non -co mmerc ial us e o nly terms of extension and consolidation.6 Our study shows that pul- monary involvement extended to lung anterior areas from the early stages, is a strong predictor of mortality, as already suggested by Castelao et al.36 This confirms the observation relating to the clin- ical and imaging evolution observed in Sars-Cov 2 disease, in which the implementation of therapeutic actions in the early stages of the disease is crucial for the good outcome of the patient. The main strength of our study is that we showed that the LUS score can predict mortality also after adjusting for the main vari- ables currently considered as severity disease predictors, whether they are patient pre-existing characteristics (age, gender), laborato- ry indicators (inflammation index, lactate) or respiratory parame- ters (P/F ratio). Also, most of the studies in both the meta-analysis and the systematic review included a population’s sample size smaller than ours. Our population consisted of two numerically similar groups from hospitals in the same geographical and catchment area. The patients were homogeneous in terms of anamnestic characteristics, management, hospitalization setting, and ventilatory strategy. The methodology was rigorous through topographic evalua- tion and numerical reporting scheme by trained operators, super- vised by expert sonographers, despite the urgency conditions in which LUS examinations were performed. Furthermore, the choice of the indexed score allowed an accurate examination of all patients including critically ill patients, in which the chest could not be fully evaluated. Limitations US findings, despite a standardized methodology, are some- times nonspecific. They could be attributable to pathological phe- nomena other than Sars-Cov2 pneumonia, such as lung congestion of cardiogenic origin, which can also be correlated to a negative prognosis. For reasons related to the pandemic situation, which did not allow a complete evaluation in all cases, the subjects are not consecutive. Another limitation of the study was the partial discrepancy of the methodology applied, given that 12 quadrants were explored in some subjects and 16 in others. However, this choice considered the emergency needs and allowed even very critical patients to be included in the study, in which LUS necessarily had to be per- formed quickly and without mobilization. Conclusions Our study demonstrates that LUS applied with a topographic standardized scanning scheme and numerical reporting predicts mortality in patients hospitalized for COVID-19. The combination of the indexed LUS score with a cut-off of 1.3 to other clinical, anamnestic, laboratory, and blood gas analytical parameters, guar- antees an accurate prognostic stratification in the patient affected by Sars-Cov2 disease. Testing for anterior thoracic involvement early in the disease history is also useful in predicting outcomes. Thus, LUS should be an indispensable element of diagnostic, mon- itoring, and prognostic evaluation in low-resource care settings. References 1. Li Y, Xia L. Coronavirus Disease 2019 (COVID-19): role of chest CT in diagnosis and management. AJR Am J Roentgenol 2020;214:1280-6. 2. Islam N, Ebrahimzadeh S, Salameh JP, et al. Thoracic imaging tests for the diagnosis of COVID-19. Cochrane Database Syst Rev 2021;3:CD013639. 3. Xu Z, Shi L, Wang Y, et al. 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Wangüemert Pérez AL, Figueira Gonçalves JM, Hernández Pérez JM, et al. Prognostic value of lung ultrasound and its link with inflammatory biomarkers in patients with SARS-CoV-2 infection. Respir Med Res 2021;79:100809. 36. Castelao J, Graziani D, Soriano JB, Izquierdo JL. Findings and prognostic value of lung ultrasound in COVID-19 pneumonia. J Ultrasound Med 2021;40:1315-24. Article [page 44] [Emergency Care Journal 2024; 20:12268] Non -co mmerc ial us e o nly