Effect of a Multiorgan Focused Clinical Ultrasonography on Length of Stay in Patients Admitted With a Cardiopulmonary Diagnosis

Cid-Serra, Ximena; Royse, Alistair; Canty, David; Johnson, Douglas; Maier, Andrea B.; Fazio, Timothy; El-Ansary, Doa; Royse, Colin

doi:10.1001/jamanetworkopen.2021.38228

Cited by 10 publications

(16 citation statements)

References 55 publications

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“…Recent publications have questioned the ability of broadly applied POCUS protocols to alter clinical outcomes in a meaningful way. 12 The value of POCUS lies in integrating findings into the larger clinical picture and incorporating POCUS findings with interventions that can meaningfully change outcomes. As evidenced in this case, POCUS findings could expedite care for this patient in a way that would not have occurred without POCUS.…”

Section: Discussionmentioning

confidence: 99%

Sinister Point of Care Ultrasound (POCUS) Finding in a Patient with New Heart Failure

Kolbenson¹,

Ma²,

Olszynski³

2022

Can Journ Gen Int Med

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Decompensated heart failure is a common clinical syndrome encountered by internal medicine physicians. Identifying the reason for decompensation is vital to the management of this condition, but may require several investigations over the course of a hospital admission. Herein, we describe a patient who presented with new presumed decompensated heart failure. Upon clinical deterioration, point of care ultrasound (POCUS) identified the presence of a mitral valve vegetation, and evidence of aortic and mitral regurgitation, supporting and expediting the presumptive diagnosis of infective endocarditis.

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Section: Discussionmentioning

confidence: 99%

Sinister Point of Care Ultrasound (POCUS) Finding in a Patient with New Heart Failure

Kolbenson¹,

Ma²,

Olszynski³

2022

Can Journ Gen Int Med

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“…This study was approved by The Melbourne Health Human Research Ethics Committee (Australia) (28/08/2018, AC-TRN12618001442291, HREC/66935/MH-2020) and was performed in accordance with the Declaration of Helsinki. LUS images used in this study were acquired from a previous study [15]. Written informed consent was obtained from all participating patients [15] (Melbourne Health Human Research Ethics Committee approval HREC/18/MH/269, trial registration: http://www.ANZCTR.org.au/ACTRN12618001442291.aspx).…”

Section: Methodsmentioning

confidence: 99%

“…LUS images used in this study were acquired from a previous study [15]. Written informed consent was obtained from all participating patients [15] (Melbourne Health Human Research Ethics Committee approval HREC/18/MH/269, trial registration: http://www.ANZCTR.org.au/ACTRN12618001442291.aspx).…”

Section: Methodsmentioning

confidence: 99%

“…All patients were admitted to the Royal Melbourne Hospital under an Internal Medicine unit with a cardiorespiratory-related presentation. The LUS examination was performed by an experienced physician trained (XC) in point-of-care US (POCUS) [15] using a Sonosite X-Porte portable US imaging system (Fujifilm, Bothell, WA, USA) with settings shown in Table 1. The examination followed a standardized iLungScan protocol (The University of Melbourne, Ultrasound Education Group [16]).…”

Section: Methodsmentioning

confidence: 99%

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Automatic Deep Learning-Based Pleural Effusion Segmentation in Lung Ultrasound Images

Vukovic

Wang

Antico

et al. 2023

Preprint

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Point-of-care lung ultrasound (LUS) allows real-time patient scanning to help diagnose pleural effusion (PE) and plan further investigation and treatment. LUS typically requires training and experience from the clinician to accurately interpret the images. To address this limitation, we previously demonstrated a deep-learning model capable of detecting the presence of PE on LUS at an accuracy greater than 90%, when compared to an experienced LUS operator. This follow-up study aimed to develop a deep-learning model to provide segmentations for PE in LUS. Three thousand and forty-one LUS images from twenty four patients diagnosed with PE were selected for this study. Two LUS experts provided the ground truth for training by reviewing and segmenting the images. The algorithm was then trained using ten-fold cross-validation. Once training was completed, the algorithm segmented a separate subset of patients. Comparing the segmentations, we demonstrated an average Dice Similarity Coefficient (DSC) of 0.70 between the algorithm and experts. In contrast, an average DSC of 0.61 was observed between the experts. In summary, we showed that the trained algorithm achieved a comparable average DSC at PE segmentation. This represents a promising step toward developing a computational tool for accurately augmenting PE diagnosis and treatment.

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“…In the Original Investigation titled “Effect of a Multiorgan Focused Clinical Ultrasonography on Length of Stay in Patients Admitted With a Cardiopulmonary Diagnosis: A Randomized Clinical Trial,” published December 21, 2021, there were errors in Figure 2, with the labels on the left-hand side appearing in the incorrect order. This article has been corrected …”

mentioning

confidence: 99%

Errors in Figure 2

2022

JAMA Netw Open

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Effect of a Multiorgan Focused Clinical Ultrasonography on Length of Stay in Patients Admitted With a Cardiopulmonary Diagnosis

Cited by 10 publications

References 55 publications

Sinister Point of Care Ultrasound (POCUS) Finding in a Patient with New Heart Failure

Sinister Point of Care Ultrasound (POCUS) Finding in a Patient with New Heart Failure

Automatic Deep Learning-Based Pleural Effusion Segmentation in Lung Ultrasound Images

Errors in Figure 2

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