Alberto Goffi1,2,3,4, Richelle Kruisselbrink5,6, Giovanni Volpicelli7. 1. Interdepartmental Division of Critical Care Medicine, University of Toronto, Toronto, ON, Canada. alberto.goffi@uhn.ca. 2. Division of Respirology (Critical Care), Department of Medicine, University Health Network, Toronto, ON, Canada. alberto.goffi@uhn.ca. 3. Department of Medicine, University of Toronto, Toronto, ON, Canada. alberto.goffi@uhn.ca. 4. Toronto Western Hospital, 399 Bathurst Street, 2nd Floor McLaughlin Rm 411-H, Toronto, ON, M5T 2S8, Canada. alberto.goffi@uhn.ca. 5. Department of Anesthesia, University Health Network, Toronto, ON, Canada. 6. Department of Anesthesia, University of Toronto, Toronto, ON, Canada. 7. Department of Emergency Medicine, San Luigi Gonzaga University Hospital, Orbassano, Turin, Italy.
Abstract
PURPOSE: Lung ultrasound (LUS) has emerged as an effective and accurate goal-directed diagnostic tool that can be applied in real time for the bedside assessment of patients with respiratory symptoms and signs. Lung ultrasound has definite and easily recognized findings and has been shown to outperform physical examination and chest radiography for the diagnosis and monitoring of many pulmonary and pleural conditions. In this article, we review the principles of LUS image acquisition and interpretation, summarizing key terms and sonographic findings. PRINCIPAL FINDINGS: Although LUS is easy to learn, adequate training and performance in an organized fashion are crucial to its clinical effectiveness and to prevent harm. Therefore, we review normal LUS findings and propose step-wise approaches to the most common LUS diagnoses, such as pneumothorax, pleural effusion, interstitial syndrome, and lung consolidation. We highlight potential pitfalls to avoid and review a recently published practical algorithm for LUS use in clinical practice. CONCLUSIONS: Because of the unique physical properties of the lungs, only a careful and systematic analysis of both artifacts and anatomical images allows accurate interpretation of sonographic findings. Future studies exploring the use of software for automatic interpretation, quantitative methods for the assessment of interstitial syndrome, and continuous monitoring devices may further simplify and expand the use of this technique at the bedside in acute medicine and the perioperative setting.
PURPOSE: Lung ultrasound (LUS) has emerged as an effective and accurate goal-directed diagnostic tool that can be applied in real time for the bedside assessment of patients with respiratory symptoms and signs. Lung ultrasound has definite and easily recognized findings and has been shown to outperform physical examination and chest radiography for the diagnosis and monitoring of many pulmonary and pleural conditions. In this article, we review the principles of LUS image acquisition and interpretation, summarizing key terms and sonographic findings. PRINCIPAL FINDINGS: Although LUS is easy to learn, adequate training and performance in an organized fashion are crucial to its clinical effectiveness and to prevent harm. Therefore, we review normal LUS findings and propose step-wise approaches to the most common LUS diagnoses, such as pneumothorax, pleural effusion, interstitial syndrome, and lung consolidation. We highlight potential pitfalls to avoid and review a recently published practical algorithm for LUS use in clinical practice. CONCLUSIONS: Because of the unique physical properties of the lungs, only a careful and systematic analysis of both artifacts and anatomical images allows accurate interpretation of sonographic findings. Future studies exploring the use of software for automatic interpretation, quantitative methods for the assessment of interstitial syndrome, and continuous monitoring devices may further simplify and expand the use of this technique at the bedside in acute medicine and the perioperative setting.
Authors: Maurizio Cereda; Yi Xin; Alberto Goffi; Jacob Herrmann; David W Kaczka; Brian P Kavanagh; Gaetano Perchiazzi; Takeshi Yoshida; Rahim R Rizi Journal: Anesthesiology Date: 2019-09 Impact factor: 7.892
Authors: Renata Aparecida de Almeida Monteiro; Amaro Nunes Duarte-Neto; Luiz Fernando Ferraz da Silva; Ellen Pierre de Oliveira; Ellen Caroline Toledo do Nascimento; Thais Mauad; Paulo Hilário do Nascimento Saldiva; Marisa Dolhnikoff Journal: Intensive Care Med Date: 2021-01-03 Impact factor: 17.440