Lung ultrasound score predicts outcomes in COVID-19 patients admitted to the emergency department

There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

Abstract

Background

During the COVID-19 pandemic, creating tools to assess disease severity is one of the most important aspects of reducing the burden on emergency departments. Lung ultrasound has a high accuracy for the diagnosis of pulmonary diseases; however, there are few prospective studies demonstrating that lung ultrasound can predict outcomes in COVID-19 patients. We hypothesized that lung ultrasound score (LUS) at hospital admission could predict outcomes of COVID-19 patients. This is a prospective cohort study conducted from 14 March through 6 May 2020 in the emergency department (ED) of an urban, academic, level I trauma center. Patients aged 18 years and older and admitted to the ED with confirmed COVID-19 were considered eligible. Emergency physicians performed lung ultrasounds and calculated LUS, which was tested for correlation with outcomes. This protocol was approved by the local Ethics Committee number 3.990.817 (CAAE: 30417520.0.0000.0068).

Results

The primary endpoint was death from any cause. The secondary endpoints were ICU admission and endotracheal intubation for respiratory failure. Among 180 patients with confirmed COVID-19 who were enrolled (mean age, 60 years; 105 male), the average LUS was 18.7 ± 6.8. LUS correlated with findings from chest CT and could predict the estimated extent of parenchymal involvement (mean LUS with < 50% involvement on chest CT, 15 ± 6.7 vs. 21 ± 6.0 with > 50% involvement, p < 0.001), death (AUC 0.72, OR 1.13, 95% CI 1.07 to 1.21; p < 0.001), endotracheal intubation (AUC 0.76, OR 1.17, 95% CI 1.09 to 1.26; p < 0.001), and ICU admission (AUC: 0.71, OR 1.14, 95% CI 1.07 to 1.21; p < 0.001).

Conclusions

In COVID-19 patients admitted in ED, LUS was a good predictor of death, ICU admission, and endotracheal intubation.

Related collections

Most cited references 23

Record: found
Abstract: found
Article: not found

Acute respiratory distress syndrome: the Berlin Definition.

V Ranieri, Gordon D Rubenfeld, B Taylor Thompson … (2013)

The acute respiratory distress syndrome (ARDS) was defined in 1994 by the American-European Consensus Conference (AECC); since then, issues regarding the reliability and validity of this definition have emerged. Using a consensus process, a panel of experts convened in 2011 (an initiative of the European Society of Intensive Care Medicine endorsed by the American Thoracic Society and the Society of Critical Care Medicine) developed the Berlin Definition, focusing on feasibility, reliability, validity, and objective evaluation of its performance. A draft definition proposed 3 mutually exclusive categories of ARDS based on degree of hypoxemia: mild (200 mm Hg < PaO2/FIO2 ≤ 300 mm Hg), moderate (100 mm Hg < PaO2/FIO2 ≤ 200 mm Hg), and severe (PaO2/FIO2 ≤ 100 mm Hg) and 4 ancillary variables for severe ARDS: radiographic severity, respiratory system compliance (≤40 mL/cm H2O), positive end-expiratory pressure (≥10 cm H2O), and corrected expired volume per minute (≥10 L/min). The draft Berlin Definition was empirically evaluated using patient-level meta-analysis of 4188 patients with ARDS from 4 multicenter clinical data sets and 269 patients with ARDS from 3 single-center data sets containing physiologic information. The 4 ancillary variables did not contribute to the predictive validity of severe ARDS for mortality and were removed from the definition. Using the Berlin Definition, stages of mild, moderate, and severe ARDS were associated with increased mortality (27%; 95% CI, 24%-30%; 32%; 95% CI, 29%-34%; and 45%; 95% CI, 42%-48%, respectively; P < .001) and increased median duration of mechanical ventilation in survivors (5 days; interquartile [IQR], 2-11; 7 days; IQR, 4-14; and 9 days; IQR, 5-17, respectively; P < .001). Compared with the AECC definition, the final Berlin Definition had better predictive validity for mortality, with an area under the receiver operating curve of 0.577 (95% CI, 0.561-0.593) vs 0.536 (95% CI, 0.520-0.553; P < .001). This updated and revised Berlin Definition for ARDS addresses a number of the limitations of the AECC definition. The approach of combining consensus discussions with empirical evaluation may serve as a model to create more accurate, evidence-based, critical illness syndrome definitions and to better inform clinical care, research, and health services planning.

0 comments Cited 2392 times – based on 0 reviews      Review now

Bookmark

Record: found
Abstract: found
Article: found

Is Open Access

Prediction models for diagnosis and prognosis of covid-19 infection: systematic review and critical appraisal

Laure Wynants, Ben Van Calster, Marc M. J. Bonten … (2020)

Abstract Objective To review and critically appraise published and preprint reports of prediction models for diagnosing coronavirus disease 2019 (covid-19) in patients with suspected infection, for prognosis of patients with covid-19, and for detecting people in the general population at risk of being admitted to hospital for covid-19 pneumonia. Design Rapid systematic review and critical appraisal. Data sources PubMed and Embase through Ovid, Arxiv, medRxiv, and bioRxiv up to 24 March 2020. Study selection Studies that developed or validated a multivariable covid-19 related prediction model. Data extraction At least two authors independently extracted data using the CHARMS (critical appraisal and data extraction for systematic reviews of prediction modelling studies) checklist; risk of bias was assessed using PROBAST (prediction model risk of bias assessment tool). Results 2696 titles were screened, and 27 studies describing 31 prediction models were included. Three models were identified for predicting hospital admission from pneumonia and other events (as proxy outcomes for covid-19 pneumonia) in the general population; 18 diagnostic models for detecting covid-19 infection (13 were machine learning based on computed tomography scans); and 10 prognostic models for predicting mortality risk, progression to severe disease, or length of hospital stay. Only one study used patient data from outside of China. The most reported predictors of presence of covid-19 in patients with suspected disease included age, body temperature, and signs and symptoms. The most reported predictors of severe prognosis in patients with covid-19 included age, sex, features derived from computed tomography scans, C reactive protein, lactic dehydrogenase, and lymphocyte count. C index estimates ranged from 0.73 to 0.81 in prediction models for the general population (reported for all three models), from 0.81 to more than 0.99 in diagnostic models (reported for 13 of the 18 models), and from 0.85 to 0.98 in prognostic models (reported for six of the 10 models). All studies were rated at high risk of bias, mostly because of non-representative selection of control patients, exclusion of patients who had not experienced the event of interest by the end of the study, and high risk of model overfitting. Reporting quality varied substantially between studies. Most reports did not include a description of the study population or intended use of the models, and calibration of predictions was rarely assessed. Conclusion Prediction models for covid-19 are quickly entering the academic literature to support medical decision making at a time when they are urgently needed. This review indicates that proposed models are poorly reported, at high risk of bias, and their reported performance is probably optimistic. Immediate sharing of well documented individual participant data from covid-19 studies is needed for collaborative efforts to develop more rigorous prediction models and validate existing ones. The predictors identified in included studies could be considered as candidate predictors for new models. Methodological guidance should be followed because unreliable predictions could cause more harm than benefit in guiding clinical decisions. Finally, studies should adhere to the TRIPOD (transparent reporting of a multivariable prediction model for individual prognosis or diagnosis) reporting guideline. Systematic review registration Protocol https://osf.io/ehc47/, registration https://osf.io/wy245.

0 comments Cited 1241 times – based on 0 reviews      Review now

Bookmark

Record: found
Abstract: found
Article: not found

Chest CT Findings in Coronavirus Disease-19 (COVID-19): Relationship to Duration of Infection

Adam Bernheim, Xueyan Mei, Mingqian Huang … (2020)

Abstract In this retrospective study, chest CTs of 121 symptomatic patients infected with coronavirus disease-19 (COVID-19) from four centers in China from January 18, 2020 to February 2, 2020 were reviewed for common CT findings in relationship to the time between symptom onset and the initial CT scan (i.e. early, 0-2 days (36 patients), intermediate 3-5 days (33 patients), late 6-12 days (25 patients)). The hallmarks of COVID-19 infection on imaging were bilateral and peripheral ground-glass and consolidative pulmonary opacities. Notably, 20/36 (56%) of early patients had a normal CT. With a longer time after the onset of symptoms, CT findings were more frequent, including consolidation, bilateral and peripheral disease, greater total lung involvement, linear opacities, “crazy-paving” pattern and the “reverse halo” sign. Bilateral lung involvement was observed in 10/36 early patients (28%), 25/33 intermediate patients (76%), and 22/25 late patients (88%).

0 comments Cited 1134 times – based on 0 reviews      Review now

Bookmark

All references

Author and article information

Contributors

Julio Cesar Garcia de Alencar:

ORCID: http://orcid.org/0000-0001-5859-6060

julio.alencar@hc.fm.usp.br

Journal

Journal ID (nlm-ta): Ann Intensive Care

Journal ID (iso-abbrev): Ann Intensive Care

Title: Annals of Intensive Care

Publisher: Springer International Publishing (Cham )

ISSN (Electronic): 2110-5820

Publication date (Electronic): 11 January 2021

Publication date PMC-release: 11 January 2021

Publication date Collection: 2021

Volume: 11

Electronic Location Identifier: 6

Affiliations

[1 ]GRID grid.411074.7, ISNI 0000 0001 2297 2036, Emergency Department, , Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo, ; 255, Dr. Enéas de Carvalho Aguiar st., São Paulo, SP Brazil

[2 ]GRID grid.11899.38, ISNI 0000 0004 1937 0722, Medical student, , Faculdade de Medicina da Universidade de São Paulo, ; São Paulo, Brazil

Author information

Julio Cesar Garcia de Alencar http://orcid.org/0000-0001-5859-6060

Article

Publisher ID: 799

DOI: 10.1186/s13613-020-00799-w

PMC ID: 7797883

PubMed ID: 33427998

SO-VID: 52b29c76-f922-42ec-a808-eee95f3bcff0

License:

Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

History

Date received : 18 September 2020

Date accepted : 31 December 2020

Funding

Funded by: FundRef http://dx.doi.org/10.13039/501100001807, Fundação de Amparo à Pesquisa do Estado de São Paulo;

Award ID: 2020/04.738-8

Award ID: 2016/14.566-4

Award Recipient : Heraldo Possolo Souza

Custom metadata

ScienceOpen disciplines: Emergency medicine & Trauma

Keywords: covid-19,severe acute respiratory syndrome coronavirus 2,ultrasonography,critical care,emergency medicine

Data availability:

ScienceOpen disciplines: Emergency medicine & Trauma

Keywords: covid-19, severe acute respiratory syndrome coronavirus 2, ultrasonography, critical care, emergency medicine

Lung ultrasound score predicts outcomes in COVID-19 patients admitted to the emergency department

Read this article at

Abstract

Background

Results

Conclusions

Related collections

Novel Coronavirus Disease COVID-19

Most cited references 23

Acute respiratory distress syndrome: the Berlin Definition.

Prediction models for diagnosis and prognosis of covid-19 infection: systematic review and critical appraisal

Chest CT Findings in Coronavirus Disease-19 (COVID-19): Relationship to Duration of Infection

Author and article information

Contributors

Journal

Affiliations

Author information

Article

History

Funding

Categories

Custom metadata

Comments

Comment on this article

Similar content 175

Cited by 42

Most referenced authors 737